717 results about "Back propagation algorithm" patented technology

The invention provides an AI intelligent diagnosis method and system based on an intelligent system, and the method comprises the steps: collecting a building electromechanical equipment operation data flow containing multi-source heterogeneous data, such as a vibration spectrum, a thermal imaging characteristic and an energy consumption fluctuation parameter, through an edge calculation node, and carrying out the state time sequence coding to generate a coding sequence; and inputting the sequence into a fault prediction neural network to obtain a multi-dimensional fault feature distribution matrix. And then obtaining a standard diagnosis report of the expert knowledge base and an emergency diagnosis report of the field operation and maintenance record. And then a composite training objective function is constructed based on the result and equipment operation characteristic hidden space mapping, a fault prediction neural network parameter space is optimized through a back propagation algorithm, and a target fault prediction neural network is generated. And finally, fault prediction and diagnosis are carried out on the operation data flow of the building electromechanical equipment by using the network, so that comprehensive, accurate and intelligent fault diagnosis is realized.

The invention provides a textile defect positioning method, which comprises the following steps: according to an extracted defect feature map, fusing feature maps of different scales, extracting multi-scale defect features, and comprehensively depicting space distribution and combination information of defects; aiming at the condition that multiple flaws exist at the same time, a multi-task learning model is constructed, flaw detection, classification and positioning tasks are carried out at the same time, and a back propagation algorithm is adopted to optimize the multi-task learning model; positioning the defect area by adopting an image segmentation algorithm, obtaining the contour of the defect area according to the positioning result of the defect area and by adopting a Canny edge detection algorithm, and calculating the area and position information of the defect; and based on an evaluation result, a dynamic threshold adjustment algorithm is adopted, a defect judgment threshold is adjusted according to a production batch and a product type, and a threshold parameter is dynamically updated by analyzing historical data and a current production condition.

The invention discloses a solid-state battery performance testing method and system based on data analysis, and relates to the field of battery performance testing, and the method comprises the steps: building a multi-physics field coupled digital twinborn model, collecting a strain signal and temperature field distribution of a failure region according to a three-dimensional physics field state map of the failure region, and when abnormally abruptly changed, carrying out the testing of the performance of a solid-state battery. Triggering multi-stage early warning, matching physical failure evidences with a historical database, extracting an optimal test parameter combination through a meta-learning framework, dynamically adjusting a charging and discharging strategy and monitoring frequency, generating a real-time data stream, and updating parameters of a multi-physics field coupled digital twin model through a back propagation algorithm. According to the method, the test parameters are dynamically optimized through the meta-learning framework, the performance of the solid-state battery is monitored and predicted, the safety and the reliability of the solid-state battery are improved, and a charging and discharging strategy is optimized to prolong the service life of the battery.

The invention discloses a dam body water seepage amount prediction method based on a multi-scale convolutional neural network and a bidirectional long-short-term memory neural network, and the method comprises the steps: 1, carrying out the data collection and preprocessing: 2, building an MS-CNN neural network, and extracting the features of spatial data in monitoring data; step 3, establishing a Bi-LSTM neural network, and extracting features of time data in the monitoring data; 4, model fusion and training: taking the output of the MS-CNN neural network and the time sequence data as the input of a Bi-LSTM neural network, and realizing the lap joint of the two neural networks to obtain a complete water seepage prediction model; training the model through processed dam body water seepage monitoring data, optimizing parameters of the model through a back propagation algorithm, and finely adjusting hyper-parameters to improve the recognition capability of the model; the method improves the accuracy and robustness of the whole prediction model, achieves the high-precision prediction of the water seepage amount of the dam body and the early warning of the abnormal water seepage amount, and provides powerful support for the safety monitoring and maintenance of the dam.

The invention discloses a lithium battery health state prediction method and system. The method comprises the following steps: acquiring lithium battery operation data; inputting the lithium battery operation data into a lithium battery health state prediction model for health state prediction to obtain a prediction result; wherein the lithium battery health state prediction model is obtained by collecting historical battery operation data as a sample set to train a basic model, a forward propagation algorithm, a back propagation algorithm and hyper-parameter tuning are adopted in the training process, and the basic model is a model formed by connecting an encoder of a Transform layer, a multi-layer KAN network as a decoder and an unscented Kalman filter; and outputting the prediction result. By implementing the method provided by the invention, the accuracy and stability of SoH prediction can be improved.

The invention discloses a multi-task blood vessel segmentation model construction method based on a hybrid encoder. The encoder is a Swin Transform path multi-layer encoder based on a Mamform module, and the Mamform multi-layer encoder carries out data processing in combination with a W-MSA and an improved Mamba module; the decoder is a multi-task decoder and consists of three independent decoding paths which are respectively used for blood vessel segmentation, contour edge segmentation and blood vessel connectivity segmentation, and each decoding path adopts Swin Transformblock as a construction block and is used for generating feature mapping with the same size as an input image; an attention mechanism is shared, learned features are shared among decoders, the number of model parameters can be reduced, and overfitting is prevented; in the training process, a total loss function is formed through a loss function of a vascular connectivity decoding path, a self-distillation loss function and the like, the total loss function is calculated, model parameters are updated by using a back propagation algorithm, the segmentation performance of the model is gradually optimized, and after training is completed, the model can carry out precision on a vascular image.

The invention is suitable for the field of spatio-temporal data prediction, and provides a CNS model soil humidity prediction method fusing dynamic spatio-temporal pruning and a WOA algorithm, and the method comprises the steps: building an initial CNS model based on a multiple convolution module, a BiLSTM module and an attention mechanism module; taking the soil time sequence data as the input of an initial CNS model, and performing preliminary training on the initial CNS model; performing dynamic pruning on each module of the preliminarily trained model by adopting a dynamic space-time pruning strategy; model parameters are updated through a back propagation algorithm, and the CNS model used for soil humidity prediction is obtained. The CNS model integrates a convolutional neural network CNN, a space-time diagram convolutional network STGCN, a bidirectional long short-term memory network BiLSTM and an attention mechanism layer Attention, multi-modal data from different data sources can be comprehensively processed and analyzed, and the CNS model shows higher precision and robustness in a soil humidity prediction task; through combination of dynamic pruning and WOA algorithm optimization, the model can maintain stable prediction performance.

The present disclosure relates to an operating method for a cross-language machine reading comprehension task, and a natural language processing system. The method comprises: acquiring target language training data corresponding to source language training data; constructing a parallel language pair for training; feeding the parallel language pair to a two-column model constructed on the basis of a pre-trained language model, and using a reverse propagation algorithm for training, the two-column model comprising a source column and a target column, and by means of finding an answer fragment semantically similar to a source language output answer outputted by the source column from the context information given by the target column, calibrating the answer fragment outputted by the target column. The present disclosure uses an attention mechanism to skillfully transfer an answer fragment of a source language to an answer output space of a target language; and a cross-attention block is refined by means of gradient decoupling knowledge sharing technology. In addition, transportability across languages is improved by means of enabling the model to learn semantic alignment from multiple granularities and calibrating the model output under the guidance of a teacher.

The invention relates to a D2NNs-based terahertz phased array device and an implementation method thereof. The phased array device comprises an active modulation layer and a two-layer metasurface layer structure. The active modulation layer mainly comprises a patterned micro-nano metal electrode and an active functional material, and the optical property of the active functional material is adjusted through external voltage, so that light beam output with specific phase distribution is realized in a terahertz wave band. The metasurface layers are composed of silicon column structure arrays located on the surface of the substrate, the two metasurface layers can finely control complex electric field distribution of a specific diffraction plane, and an optical diffraction neural network architecture is constructed. The phase distribution of the metasurface layer is optimized by using gradient descent and back propagation algorithms in the neural network for each required regulation and control mode, and active regulation and control in a specific light beam direction can be realized under the driving of different voltages. According to the invention, the problems of the terahertz phased array technology in the aspects of regulation and control precision, continuity and the like are solved, and a more efficient and flexible beam regulation and control method is provided.

The invention discloses a cross-system fault diagnosis method and system combined with multi-dimensional anomaly detection, and relates to the technical field of fault diagnos.The method comprises the steps that a graph neural network with a liquid time constant network unit as a node is constructed through a dynamic topology dependency relationship and a multi-dimensional key performance index flow; each unit describes state evolution through a coupled ordinary differential equation system, and a liquid state time constant can be adaptively adjusted. A time back propagation algorithm is adopted to train a model to learn a normal behavior track contour reference, and anomaly is detected through a dynamic time warping distance. And determining a fault propagation path and a root cause through anti-fact intervention and forward integral solution. And generating an optimal diagnosis action sequence in a liquid graph neural network simulation environment, and calculating a reward value based on execution efficiency, accuracy and a repair effect to carry out strategy optimization. The abnormal detection accuracy and the root cause positioning precision are improved, the fault repair time is shortened, the operation and maintenance cost is reduced, and an intelligent fault diagnosis solution is provided for a complex information technology system.

The invention relates to the technical field of diagnosis and treatment path planning, in particular to an intelligent diagnosis and treatment path dynamic planning system based on symptom ontology reasoning, which comprises a symptom specialized mapping module, a holographic panoramic navigation module, a path optimization core module and a real-time feedback module, wherein the symptom specialized mapping module generates a dynamic medical resource recommendation parameter set by fusing a symptom ontology feature vector, medical institution treatment efficiency data and a specialized association rule base, and dynamic medical resource recommendation parameters comprise a multi-dimensional matching degree tensor and a treatment efficiency quantification matrix; the holographic panoramic navigation module receives the multi-dimensional matching degree tensor and constructs a three-dimensional medical resource topological graph, wherein the three-dimensional medical resource topological graph comprises hospital nodes and department connecting lines; the path optimization core module analyzes hospital nodes in the three-dimensional medical resource topological graph, and generates a path with optimal cost performance based on a multi-objective optimization algorithm of a treatment efficiency quantification matrix; and the real-time feedback module acquires and updates the treatment efficiency quantization matrix through a back propagation algorithm to form a closed-loop optimization system.

The invention provides a lightweight visible light ship target detection method based on edge feature guidance, and relates to the technical field of ship detection image data processing, and the method comprises the steps: collecting remote sensing satellite images, and carrying out the random distribution of the images after screening and marking, and obtaining a training set and a verification set; the backbone network module comprises a plurality of Conv modules and C3k2 modules which are mutually stacked; the neck module comprises a detail-enhanced convolution module and a hierarchical pyramid module based on dynamic feature aggregation; in the head module, after the features of all detection layers are subjected to independent convolution processing, feature transformation is carried out through a multi-branch detail enhancement convolution module; performing data enhancement on the training set; and obtaining a trained ship target detection model through a back propagation algorithm and a gradient descent optimization method. According to the invention, the lightweight and precision improvement of the detection head are realized, the robustness of the model to the illumination change is enhanced, and the global semantic information and the local detail features are fused to balance the detection of the small target and the large target.

The invention provides an image segmentation method and device based on an SAM (Segmentation All Model), and the method comprises the steps: obtaining a to-be-segmented image and first segmentation prompt information; inputting the to-be-segmented image and the first segmentation prompt information into a segmentation cutting model SAM obtained by pre-training to output a first image segmentation result; wherein the SAM obtained through pre-training is obtained by taking a mask decoder as a learnable parameter of model training and updating the weight of the learnable parameter through a back propagation algorithm. Therefore, according to the embodiment of the invention, the mask decoder of the SAM is set as the learnable parameter and the mask decoder is finely adjusted through the back propagation algorithm, and the whole SAM does not need to be adjusted, so that the fine adjustment efficiency of the model is ensured, the accuracy of the model is improved, and the accuracy and efficiency of image segmentation are improved.

The invention belongs to the technical field of computer vision, relates to a time sequence alignment and semantic enhancement-based multi-modal video abstract generation method, and designs a time sequence alignment and semantic enhancement-based multi-modal video abstract generation model, and outputs a corresponding video abstract according to video data; the model comprises a feature extraction module, a dual-scale attention module and a video abstract generation module, the feature extraction module is used for extracting visual features and text features, and the dual-scale attention module comprises a conditional self-attention network and a cooperative interaction cross-modal attention network. The visual features and the text features are aligned and fused on the local scale and the global scale respectively; the model is trained by using a back propagation algorithm, and classification loss, dynamic time warping loss based on different scales and semantic enhancement hinge loss are introduced into a loss function. Compared with the prior art, the generation effect of the video abstract can be further improved.

The invention discloses a neural network-based Wollaston series nonlinear compensation method, and the method specifically comprises the steps: enabling a Wollaston integral kernel to be used for initializing the weight of a neural network, and achieving the accurate and efficient nonlinear compensation through training; firstly, a transmission experiment is carried out based on an optical fiber transmission experiment platform, and experiment data are collected from a real-time oscilloscope to construct a training set; and then calculating a Wollaston integral kernel according to the link parameters, applying the Wollaston integral kernel to weight initialization of the neural network, and finally performing iterative updating on the weight parameters of the neural network by using the training data so as to improve the nonlinear compensation performance. Compared with a digital back propagation algorithm, a large number of iteration steps are omitted, single-step compensation is achieved, and the calculation efficiency is greatly improved. In a 12,000 km transmission experiment, the compensation performance superior to that of a digital back propagation algorithm is realized with lower calculation complexity and calculation time delay.

The invention relates to the field of artificial intelligence technology and medical treatment, and discloses an emotion recognition method, device and equipment based on electroencephalogram signals and a medium. The method comprises the steps that the electroencephalogram signals in a training data set are input into a feature encoder used for representing emotions and subjected to discretization processing; performing masking processing on the obtained target electroencephalogram feature sequence, and inputting the obtained mask electroencephalogram feature sequence into an electroencephalogram feature reconstruction model to obtain a predicted electroencephalogram feature sequence; constructing a target loss function based on the predicted and target electroencephalogram feature sequence, and training and updating the feature reconstruction model through a back propagation algorithm; and inputting a to-be-classified electroencephalogram signal into the trained feature reconstruction model, and then inputting an obtained electroencephalogram feature sequence into a pre-trained emotion classifier to obtain an emotion classification tag. The problem that in the prior art, the prediction result is not accurate enough when the emotion is predicted through electroencephalogram signals is solved.

The invention relates to the field of cold spraying, in particular to a cold spraying particle critical speed prediction method, electronic equipment and a medium. The cold spraying particle critical speed prediction method comprises the steps of inputting cold spraying particle sample data into a gradient lifting regression tree model and a correlation analysis model to obtain target features; the cold spraying particle sample data comprises cold spraying particle material parameters and critical speed; the target features are cold spraying particle material parameters input into the prediction model; inputting the target characteristics of the cold spraying particles to be measured into a critical speed prediction model to obtain the critical speed of the cold spraying particles; the critical speed prediction model is a neural network model optimized by adopting target features in cold spraying particle sample data, a genetic algorithm and a Levenberg-Marquardt back propagation algorithm. According to the method, the cold spraying ion critical speed prediction efficiency and reliability can be improved.

The disclosure provides a transformer fault detection model training method, a fault diagnosis method, and a related device. The method comprises: obtaining an initial voiceprint signal of a transformer, and a fault type corresponding to the initial voiceprint signal; preprocessing the initial voiceprint signal by using a wavelet packet analysis method, so as to obtain an input signal, and according to the input signal and the fault type, establishing an input signal data set; according to a preset feature extraction algorithm, performing feature extraction on a first input signal in a training data set to obtain a first voiceprint feature; using the first voiceprint feature and a first fault type corresponding to the first input signal to train an initial detection model, so as to obtain a first training result; according to the first training result and the fault type, determining a loss function; on the basis of the loss function, using a back propagation algorithm to iteratively adjust weight values of the initial detection model, until the loss function converges, so as to obtain a fault detection model. In the present disclosure, accurate transformer fault detection is realized by utilizing the obtained fault detection model.

The invention discloses an automatic driving federal reinforcement learning method based on meta learning and empirical vectors. The method comprises the following steps: constructing an automatic driving heterogeneous environment; constructing an Actor network, and updating Actor network parameters through a deterministic strategy gradient; constructing a Critic network, updating Critic network parameters through a meta learning algorithm and an error back propagation algorithm, adding experience vector integration in a training process, and updating learning experience of each automatic driving vehicle so as to optimize performance of a global strategy; in the training process, all automatic driving vehicles regularly send local Critic network parameters, Actor network parameters and local experience vectors to the central server for aggregation. According to the method, the adaptive capacity of the automatic driving vehicle to the environmental heterogeneity is improved, the problems that in existing federal reinforcement learning, the convergence speed is reduced and the strategy generalization capacity is reduced due to the dynamic change of the environment and the fluctuation of the strategy performance are solved, and therefore the training efficiency and the strategy robustness are improved.

The invention provides an effluent COD (Chemical Oxygen Demand) prediction method based on a mixed attention mechanism and a codec, which comprises the following steps of: acquiring an inflow parameter, an operation parameter and an effluent COD value of a sewage treatment plant, and carrying out preprocessing such as data cleaning, normalization and data set division on data; then constructing an effluent COD prediction model comprising a coder-decoder structure, a mixed attention mechanism and a regression module; taking the water inlet parameters and the operation parameters as input data, taking the effluent COD as output data, and training a model based on a back propagation algorithm; and calling the trained model to predict the effluent COD. According to the method, a coder-decoder structure is utilized to capture a complex mapping relation of a multivariate long-time sequence in the sewage treatment process, a mixed attention mechanism is designed to adaptively extract key information in time and space dimensions, the model can flexibly adapt to various working condition change conditions of a sewage treatment plant, and the prediction accuracy is improved.

The invention discloses an alloy component reverse design method and system based on knowledge distillation, and relates to the field of material gene and alloy reverse design, and the method comprises the steps: collecting alloy component data and corresponding performance data, carrying out the arrangement to obtain a data set, and calculating the atomic characteristics of each alloy in the data set; constructing and training a supervised learning model based on the atomic characteristics and the performance data, and establishing a mapping relation from the atomic characteristics of the alloy to the performance; a deep neural network model is trained based on the component data and the performance data, and a mapping relation from alloy components to performance is constructed; in the deep neural network model training process, a back propagation algorithm is adopted to adjust network parameters, and the prediction difference between the deep neural network model and the supervised learning model is minimized; and sampling optimization is carried out, component optimization is carried out through gradient information of the deep neural network model, and the optimal alloy component is searched. According to the method, the components and atomic characteristics of the alloy are combined, the components of the high-performance alloy are analyzed, and customized design and optimization of the material are achieved.

The invention provides a PCB processing method, device and equipment based on technological parameter optimization, and the method comprises the steps: carrying out the feature extraction of PCB design data, and obtaining feature data; matching an adaptive initial process parameter combination according to the feature data; performing quality detection on the PCB which is processed for the first time based on the initial process parameter combination, and analyzing detection data by using a defect identification algorithm to obtain a quality evaluation result; based on a quality evaluation result, optimizing the initial process parameter combination by using an error back propagation algorithm to obtain an optimized process parameter combination; and controlling secondary processing of the PCB based on the optimized process parameter combination. According to the method, the initial process parameter combination is optimized according to the quality evaluation result of the circuit board, so that the defect that an existing processing method cannot perform self-adaptive optimization according to the actual condition of the circuit board is overcome.

The invention relates to the technical field of intelligent communication, and discloses an end-to-end optimization method, a signal constellation geometry and probability joint shaping optimization method in an end-to-end intelligent communication system, and a communication device based on a neural network. According to the method, a transmitter is constructed by using a neural network, and a received signal passes through a two-stage equalization processing system to form an end-to-end optimization framework without assistance of a channel model. The transmitter adopts a modular design, can realize joint optimization of constellation probability shaping (PS) and geometric shaping (GS), and performs joint updating on all trainable parameters of the system based on a total loss function containing an equilibrium error and a decoding error through a back propagation algorithm. According to the invention, computing resources are effectively saved; the two-stage equalization system can accurately compensate channel damage, the realized PS and GS joint optimization can significantly improve the performance of the communication system and reduce the bit error rate, and the method has good performance advantages and practical value in an actual optical fiber communication system.

The invention provides a federated learning method and system based on a hybrid expert model, and relates to the technical field of machine learning, and the method comprises the steps: carrying out the initialization of a local gating network of a current client through employing a global gating network, obtaining an initial local gating network # imgabs0 #, carrying out the feature extraction of a local data set, and obtaining a local data set; k target expert models Sk with the highest adaptation degree with the data features are screened out from the multiple expert models or a required target expert model # imgabs1 # is downloaded from a server side by using a Top-K-based adaptive expert selection mechanism to ensure that all the selected expert models can be used locally; and the client uses local data to perform forward calculation on the target expert model, obtains training feedback parameters of the target expert based on a prediction result and a real label, and optimizes the target expert model through a back propagation algorithm # imgabs2, and the client performs weighted summation on calculation results of the target expert models to obtain a comprehensive prediction result. And the generalization ability and performance of the model are improved.

The invention discloses an oil pump state detection technology, and aims to provide an oil pump state detection system based on deep learning and a detection method thereof, and the key points of the technical scheme are that the oil pump state detection system comprises the following modules: a data collection module used for collecting operation data of an oil pump in real time; the data preprocessing module is used for processing the collected original operation data; the deep learning model training module is used for training the preprocessed data through a back propagation algorithm by adopting a convolutional neural network architecture, so that the model can learn the characteristics of the normal state and the fault state of the oil pump; the state detection module is used for classifying the data by utilizing the trained deep learning model when monitoring the operation data of the oil pump in real time, outputting the current state of the oil pump and judging whether an abnormity or a potential fault exists or not; when the fault early warning module detects that the oil pump is abnormal or has a potential fault, an early warning signal is sent out in time; the method is suitable for the technical field of oil pump state detection.

The invention discloses a trajectory prediction method and system based on a CNN-LSTM-Attention neural network, belongs to the technical field of machine learning and data analysis, and solves the problem that the precision and reliability of trajectory prediction are reduced due to noise and accumulative errors existing in IMU (Inertial Measurement Unit) data. The method comprises the steps of collecting sensor data, performing preprocessing, dividing the sensor data into a training set and a test set, and performing standardization processing on input features and target output; constructing a hybrid neural network architecture, setting structures and parameters of each layer in the hybrid neural network architecture, and defining a loss function and an optimizer; training set data is adopted to train the hybrid neural network architecture, and a back propagation algorithm is adopted to optimize model parameters; calculating an error index between the predicted trajectory and the real trajectory, and evaluating the prediction performance of the hybrid neural network architecture according to the error index; and selecting the neural network with the best prediction performance to carry out real-time trajectory prediction. The method is suitable for application scenes such as intelligent traffic systems, unmanned driving and route planning.

The invention provides an in-orbit integrated circuit aging fault diagnosis method and diagnosis system, and relates to the technical field of integrated circuit design, and the method comprises the steps: ground training: collecting characteristic parameters, carrying out the characteristic parameter processing based on an SPI protocol algorithm, a digital signal recovery fixed-point decimal algorithm, a wavelet transform filtering algorithm and a deviation standardization algorithm, and carrying out the fault diagnosis; constructing an aging failure fault data set, training through a back propagation algorithm to obtain feed-forward neural network parameters, and transplanting the feed-forward neural network parameters to a feed-forward neural network constructed based on Pytorch and a feed-forward neural network constructed based on FPGA for joint testing; and in-orbit testing: data enters a data buffer area, training data is provided for a learning training module, feedforward neural network parameters are periodically updated by using an exponential weighted moving average algorithm, and the feedforward neural network parameters are transplanted to a feedforward neural network established based on Pytorch and a feedforward neural network established by an FPGA for joint testing. Continuous learning of on-orbit data is realized, and the method adapts to irregular periodic electromagnetic influence of a space environment.

A bidirectional autoencoder learns or approximates an identity mapping as it trains a single network with a version of the new bidirectional backpropagation algorithm. Ordinary unidirectional autoencoders find many uses in image processing and in large language models. But they use separate networks for encoding and decoding. Bidirectional autoencoders use the same synaptic weights for encoding and decoding. The forward pass encodes while the backward pass decodes. Bidirectional autoencoders improved network performance and significantly reduced memory usage and used fewer parameters. Simulations compared unidirectional with bidirectional autoencoders for image compression and denoising. The models trained on the MNIST handwritten-digit and CIFAR-10 image datasets. The performance measures were the peak signal-to-noise ratio and the index of structural similarity. Bidirectional autoencoders outperformed unidirectional autoencoders and still reduced the number of trainable synaptic parameters by about 50%.

The invention provides a PCB manufacturability intelligent analysis and early warning method and system based on artificial intelligence, and the method comprises the steps: collecting and marking the multi-source time sequence process parameter data in the PCB design and manufacturing process under working conditions, building a dynamic causal graph model with direction and time lag marks through a sliding window and standardization processing by applying a causal discovery algorithm, and carrying out the calculation of the dynamic causal graph model. Dynamic expression of causal relationships among process variables is realized; when manufacturing abnormity is detected, abnormity attribution is carried out by combining a Bayesian back propagation algorithm, high-contribution-degree root dependent variables are screened, the effectiveness of root causes is verified through virtual intervention simulation and statistical test, and finally verification results and a causal mode are stored in a knowledge base to support subsequent rapid matching and reasoning. According to the method, the accuracy, efficiency and interpretability of PCB manufacturing abnormity attribution are improved, and process optimization and preventive intervention are facilitated.

The invention discloses a generative defense method and system for resisting attacks, relates to the technical field of network security, and aims to solve the problems that an existing defense scheme is high in calculation overhead and poor in real-time performance, static defense is easy to bypass, and robustness and accuracy are difficult to balance. The method comprises the following steps: constructing an adversarial network model by taking a pre-trained target model as a discriminator and a generative model as a generator; constructing a total loss function by combining a defensive loss function and an accuracy loss function, generating a defensive benign sample by adding defensive disturbance into a benign sample, generating a defensive confrontation sample by adding defensive disturbance after generating a confrontation sample based on the benign sample, and inputting the three types of samples into a target model to obtain total loss; and training the generative model to convergence by using a back propagation algorithm to obtain a trained adversarial network model for classification of defense disturbance samples. Defense generation network training is completed in the training stage, only defense disturbance needs to be overlaid in the reasoning stage, the real-time requirement is met, the robustness and accuracy of the model can be balanced, and the method is suitable for various attack scenes.