796 results about "Multilayer perceptron" patented technology

The invention provides a remote sensing image target detection method based on an improved FCOS, and relates to the technical field of target detection, and the method comprises the following steps: carrying out the convolution and pooling operation of a remote sensing target image based on a pre-trained convolutional neural network, generating a preliminary feature, and carrying out the enhancement processing of the preliminary feature through a twin multilayer perceptron; carrying out fusion processing on different levels of feature information in the multi-level feature map through a feature map splicing mode and a feature map enhancement mode; and determining the position of the remote sensing image target according to the analysis result to obtain a target detection result. According to the method, the feature pyramid network is combined with an innovative coordinate attention mechanism, fusion of high-level semantic information and low-level detail information is realized, an enhanced multi-scale feature map is generated to cope with diversity of target scales in a remote sensing image, multi-scale fusion and an anchor-frame-free design are combined, false detection is effectively reduced, and the detection accuracy is improved. Therefore, the method shows efficient, accurate and wide application potential in the field of remote sensing image target detection.

The invention relates to an industrial Internet of Things anomaly detection method based on time sequence and text joint modeling, and belongs to the technical field of industrial Internet of Things anomaly detection. The method comprises the following steps: constructing text prompt information based on collected industrial Internet of Things time sequence data, and respectively taking the text prompt information as inputs of a time sequence channel and a text prompt channel; a sensor association graph is constructed by using a multi-hop GCN, and on the basis of the association graph, time feature modeling from local to global is completed by using multi-scale expansion convolution and combining a differential attention mechanism; performing word segmentation processing on the text prompt information through a word segmentation device, and encoding the text prompt information into vector representation; and calculating attention weight between time sequence embedding and text prompt embedding, fusing to obtain joint embedding representation, enhancing the joint embedding representation, inputting the enhanced joint embedding representation into MLP for reconstruction, calculating an abnormal score through a reconstruction error, and carrying out industrial Internet of Things anomaly detection according to the abnormal score. The method is high in anomaly detection accuracy, and can improve the equipment anomaly perception and risk early warning capability.

The invention discloses a knowledge cross validation question-answering method and system for reducing hallusion of a large language model, and belongs to the technical field of artificial intelligence, and the method is implemented by the following steps: generating results through multiple times of sampling: when a user puts forward a question, controlling the large model to perform multiple times of sampling, and generating a specified number of results; calculating hidden state related indexes: extracting the hidden state of the last token of the middle layer of the large model corresponding to the result, and calculating covariance matrixes and answer discrete feature values of the hidden states; mLP model prediction: inputting the discrete feature value of the answer and the length of the answer into a multilayer perceptron MLP, and outputting a hallucination-free probability; querying and summarizing a knowledge graph; and calculating a final illusion-free score and outputting a result. According to the method, the answer quality and credibility of a large language model can be remarkably improved, and the method is particularly suitable for application scenes with extremely high requirements on the accuracy of single-mode text generation contents.

The invention relates to a point cloud static denoising method based on geometric constraint and deep learning, belongs to the technical field of point cloud processing, and solves the problem of poor point cloud denoising effect caused by poor geometric fidelity and weak generalization ability for complex noise in the prior art. Comprising the following steps: after preprocessing a to-be-denoised point cloud, dividing the to-be-denoised point cloud into a plurality of point cloud blocks with overlapping regions; for each point cloud block, performing multi-round iterative optimization on the coordinates of each point cloud by using the trained denoising network, and optimizing the predicted coordinates of each point cloud output by the denoising network in each round of iteration by using normal guided filtering and incremental updating to obtain the coordinates of each point cloud for the next round of iteration; the denoising network sequentially comprises a dynamic graph convolutional network, a first multi-layer perceptron and a second multi-layer perceptron; and after the optimization results of the point cloud blocks are fused, carrying out post-processing filtering to obtain a denoised point cloud. The point cloud denoising precision and the detail retention capability are improved.

The invention discloses a network intrusion detection method and device for a gating multilayer perceptron based on spatio-temporal feature fusion, and the method comprises the steps: carrying out the preprocessing of the network flow of an Internet of Things system, capturing the time features of network behaviors through a bidirectional LSTM network through designing a new spatio-temporal feature extraction and learning method, and carrying out the detection of the network intrusion of the gating multilayer perceptron. And capturing a network behavior spatial relationship through a full connection layer network with a gating unit, and finally obtaining a space-time fusion feature association relationship through a gMLP network of a feature fusion module. And finally, based on a classification sub-network of a full connection layer, accurately classifying network attack behaviors according to the space-time fusion feature vector. The gMLP is used for mining the space-time relation between network behaviors, so that the network intrusion detection capability is improved. A simple full connection layer is adopted as a classifier, so that malicious behaviors can be effectively detected according to the spatial-temporal correlation of the acquired data.

The invention discloses a recommendation system and method for keeping semantic integrity based on a large language model. According to the method, user-article interaction data and text information are fused, a prompt template of a user and an article is constructed, a configuration file with rich semantics is generated by utilizing a large language model, and initial embedded representation is extracted. Then, two-stage dimensionality reduction transformation is carried out through principal component analysis and a multi-layer perceptron, semantic information is reserved, and low-dimensional embedding is generated; on the basis of the embedding, cosine similarity is calculated, a user-user and article-article similar graph is constructed, and final embedding representation is generated through graph convolutional network coding. Meanwhile, collaborative filtering is combined to capture an interaction relationship and optimize a joint learning target, including recommendation loss, cross-modal comparison loss and regularization terms, so as to align semantics and collaborative filtering embedding, and finally generate a high-precision personalized recommendation result. The method effectively improves the semantic comprehension ability and recommendation accuracy of a recommendation system, and is suitable for various recommendation scenes.

The invention discloses a state evaluation method, system and device and a medium, and belongs to the field of machine learning, and the method comprises the steps: obtaining multi-modal sensor data, carrying out the preprocessing and fusion, and obtaining a fusion feature vector; detecting the fusion feature vector based on a mixed architecture model constructed by a spline enhanced multi-layer sensing network, a convolutional neural network and a Transform attention mechanism to obtain an anomaly type and confidence; when the detection result is abnormal, generating an abnormal event according to an event driving mechanism; responding and acquiring time sequence data from historical sensor data, and performing simulation prediction according to the physical information neural network model to obtain a prediction result; the prediction result is fed back to the detection model to update the confidence degree, the target detection result is obtained, the state evaluation result is obtained in combination with the prediction result, and therefore the state evaluation accuracy and reliability can be improved.

The invention relates to the technical field of computing power resource allocation, and particularly discloses a computing power resource allocation method and system based on multi-dimensional dynamic evaluation and a product. The method comprises the steps of obtaining evaluation index data of a to-be-scheduled task in multiple dimensions; dynamically configuring a weight value of each evaluation index according to a task attribute and a system state; the evaluation index data is fuzzified by using a preset membership function, a fuzzy evaluation matrix is constructed in combination with the weight value, and comprehensive calculation is performed through a fuzzy inference rule to obtain fuzzy comprehensive evaluation data of the task, so that an accurate evaluation result is generated; modeling a task execution process by adopting a multi-layer perceptron model, and predicting the execution performance of the task; and based on the evaluation result and the prediction result, dynamically selecting a proper strategy from a plurality of predefined task scheduling strategies, and scheduling the tasks to optimize computing power resource allocation. According to the method, the resource utilization efficiency is remarkably improved through multi-dimensional evaluation and a dynamic scheduling mechanism.

The invention relates to the technical field of water treatment, and discloses an intelligent control method and system for a water treatment agent, and the method comprises the steps: collecting water quality parameters and flow data in real time, dynamically constructing a time sequence characteristic space, and integrating a current parameter value, a historical lagging parameter value and interaction characteristics; preprocessing the data, extracting key interaction features, and constructing a multi-dimensional feature space; the processed data are input into a learning prediction model integrated by three basic learners including a multilayer perceptron, a random forest and support vector regression, the learning prediction model is fused through a dynamic weighting strategy, the weight is adaptively adjusted according to prediction consistency, and a prediction value is output; and adding is controlled according to the predicted value, dynamic correction is carried out in combination with historical deviation, and meanwhile, adding amount range limitation and change rate constraint per minute are implemented. The problems of slow response, high error rate and weak compatibility in the prior art are solved, and the purposes of high control precision, low cost, strong anti-interference capability, high automation level and high response speed are achieved.

The invention provides a logic synthesis cutting delay prediction method based on a graph neural network, which comprises the following steps of: firstly, constructing a cutting (Cut) delay prediction data set of a logic synthesis circuit, then extracting structural and functional features of nodes in a cutting sub-graph, constructing a heterogeneous cutting level graph taking a cutting super node as a center, and finally, carrying out segmentation on the heterogeneous cutting level graph. And packaging into a graph neural network input format. Then, a delay prediction model is constructed, the delay prediction model comprises a graph neural network module and a multi-layer perceptron module, the graph neural network module extracts circuit structure features and pointer attention mechanism dynamic association standard cell library features through SAGE convolution, and enhanced cutting representation is formed; and finally, carrying out delay prediction on cutting in a new circuit by utilizing the trained model. According to the method, the cutting structure modeling capability and the delay prediction precision are effectively improved, the method can be used for optimizing a cutting selection strategy in a synthesizer, and the logic comprehensive quality is improved.

The invention relates to the technical field of ship design optimization, and discloses a hull shape optimization method based on neural network modeling. In the data acquisition stage of the method, initial appearance parameters and hydrodynamic performance data of a ship body are obtained, the appearance parameters comprise geometric dimensions and shape features, and the performance data comprise resistance coefficients and wave-making resistance values. In the neural network construction stage, a neural network model with a multi-layer perceptron structure is trained by using collected data, weights are updated through a back propagation algorithm, and a nonlinear mapping relation between appearance parameters and hydrodynamic performance indexes is established. In the shape optimization stage, the trained neural network model is used for carrying out iterative adjustment on the shape of the ship body, fluid dynamic performance indexes are recalculated through the model after each adjustment until preset convergence conditions are met, and finally optimized ship body shape data are output. According to the method, partial complex calculation is replaced by the neural network, and intelligent optimization of the hull appearance is realized.

The invention discloses a visibility regression prediction method based on multi-modal transfer learning and time coding, and relates to the technical field of artificial intelligence. The method comprises the following steps: S1, dividing a data set in different periods according to illumination characteristics, and splitting each period into a training set, a verification set and a test set; s2, preprocessing the data set; s3, constructing an initial model containing a pre-training deep learning network, a time coding module and a multi-layer perceptron regression head; s4, extracting image visual features and time feature vectors; s5, fusing the features and inputting the features into a regression head for prediction; s6, carrying out scheduling training by using layered parameter freezing, an AdamW optimizer and a dual learning rate, and combining with a mixed early stop strategy until convergence; and S7, evaluating the test set to determine a final model. According to the method, complementarity of image and time information is mined, high-precision prediction is realized, generalization is good under different illumination conditions, a layering strategy and an optimization mechanism guarantee stable and efficient training, a multilayer perceptron combination technology enhances expression, and overfitting is effectively prevented.

The embodiment of the invention discloses a heterogeneous many-core-based large-scale hybrid expert language model reasoning method and architecture. The method comprises the steps of obtaining the number of computing devices in a heterogeneous many-core architecture and the number of cores in each computing device; according to the number of the computing devices and the number of cores in each computing device, segmenting the input tensor of the expert multilayer perceptron layer to obtain a plurality of tensor segmentation results; each tensor segmentation result is distributed to each core of each computing device, parallel expert multi-layer perceptron layer computing is carried out in each core of each computing device according to the distributed tensor segmentation result, and an expert weighting result is obtained; specialist weighting results in the cores of the computing devices are stipulated and aggregated, and a large-scale mixed expert language model reasoning result based on the heterogeneous many-cores is obtained. According to the method, tensor segmentation is carried out, calculation is placed on a proper calculation equipment core, a plurality of highly optimized parallel operators can be realized, and end-to-end efficient reasoning is completed.

The invention discloses a TR assembly gold wire bonding process parameter prediction method based on a multilayer perceptron neural network, and belongs to the technical field of microwave device intelligent manufacturing. According to the method, an intelligent mapping model of gold wire bonding geometric parameters and radio frequency performance is constructed by fusing a multi-layer perceptron neural network and parameterized electromagnetic simulation. The method specifically comprises the following steps: generating 45 groups of samples in a process parameter space by adopting Latin hypercube sampling; obtaining an S parameter data set through batch processing electromagnetic simulation; box-Cox conversion and normalization preprocessing are carried out on the data; the method comprises the following steps: constructing an MLP neural network model of a 3-32-16-2 structure, and determining hyper-parameters by using Bayesian optimization; and after training is completed, rapid reverse mapping from target performance to process parameters is realized. According to the method, the number of traditional tests is reduced from more than 200 to 45, the predicted root-mean-square error of S21 is smaller than or equal to 0.12 dB, the determination coefficient is larger than or equal to 0.96, and the parameter backstepping time lt is obtained; according to the method, full-process automation from simulation, training, optimization to production and issuing is realized, and the development efficiency of the TR component is remarkably improved.

The invention discloses a laser radar positioning and three-dimensional mapping method based on an implicit neural field, and belongs to the technical field of positioning and mapping. The method comprises the following steps: for first frame data in radar point cloud data, converting an input sampling point from a sensor coordinate system to a world coordinate system by using a unit matrix or a constant matrix to initialize a map; the hierarchical potential features are optimized in a self-supervision mode, an octree data structure is used for storing the optimized potential features to generate a potential feature set, and features in the potential feature set are decoded into symbol distance values through a shallow multilayer perceptron (MLP); starting from a second frame of data of the radar point cloud data, obtaining a predicted pose through a matching mode from the frame to the implicit map every time a frame is input; converting a new input point into a world coordinate system by using the obtained predicted pose, and determining whether a new potential feature needs to be added or not according to a Morton value of the new input point; and completing the construction of the NSDF by repeating the operations of predicting the pose and expanding the NSDF.

A system and method of creating 3D field data of at least one specimen of an engineering component includes obtaining a first set of 3D field data, defining at least one geometry parameter, training a variational autoencoder model (VAE), splitting the VAE into an encoder model and a decoder model, connecting a multilayer perceptron network model (MLP) to an input layer of the decoder model of the VAE to form a Hybrid Multilayer Perceptron-Variational Autoencoder model (MLP-VAE), training the MLP-VAE to map values, defining to at least partially define geometry data of at least one additional specimen of the engineering component, using the trained MLP-VAE to predict 3D field data related to the at least one additional specimen by directly mapping the respective at least one value of the at least one geometry parameter to respective predicted result data of the at least one specimen.

The invention relates to a coronary heart disease risk prediction method based on multi-source data processing. The coronary heart disease risk prediction method comprises the following steps: extracting structured clinical data by using a multi-layer perceptron to obtain a first feature vector; processing the physiological signal time sequence data by using a one-dimensional convolutional neural network to obtain a second feature vector; splicing the first feature vector and the second feature vector to obtain a fused feature vector; training a coronary heart disease risk prediction model based on the fused feature vector, and combining decision trees generated by all iteration rounds to obtain the coronary heart disease risk prediction model; and processing the fusion feature vector of the target object by using the trained coronary heart disease risk prediction model, and outputting a coronary heart disease risk prediction value.

The invention discloses a CXL heterogeneous memory hot page method and system based on MLP six-dimensional feature modeling, and the technical scheme is that the method comprises the steps: collecting an NUMA memory access track when a process runs, and constructing a six-dimensional system feature vector composed of the total number of pages, the average access amount of two nodes, the access range, the height difference page number and the imbalance ratio; standardizing the features, inputting the standardized features into a multi-layer perceptron, and predicting a hot page judgment threshold value, a maximum migration page number and a migration period in a linkage manner; screening candidate pages according to prediction parameters, migrating the candidate pages between DRAM and CXL nodes, and recording bandwidth and time delay for evaluation and batch retraining. According to the method, self-adaptive linkage of the threshold value, the scale and the rhythm is achieved, reciprocating migration and resource waste are reduced, the hot page judgment sensitivity and specificity are improved, the error migration rate is reduced, the migration scale and the rhythm are matched in a self-adaptive mode along with loads, and the system performance tends to be more stable.

The invention discloses an AI-based gene data analysis system. The system comprises a plurality of omics data matrixes; local association pattern mining is performed on the multi-omics data matrix through a 1D-CNN one-dimensional convolutional neural network, a topological structure of a gene network is identified through continuous coherence analysis, dynamic weights are allocated to sequence features and a topological feature matrix by using a dynamic attention mechanism, and weighted multi-scale feature vectors are output; establishing a multi-modal fusion model based on a Transform architecture to fuse the multi-scale feature vectors, performing fine adjustment on the adaptive disease data set by using the general genome feature of a pre-training model, and outputting a fused feature vector; and inputting the fusion feature vector into an MLP multilayer perceptron for disease risk prediction, generating a disease risk prediction index in combination with an SHAP algorithm, and generating an auxiliary decision scheme according to the prediction index. And the accuracy and generalization ability of disease risk classification are effectively improved.

The invention relates to the technical field of artificial intelligence application, and discloses an enzyme EC number prediction method, and the method comprises the steps: obtaining the sample sequence characteristics of a to-be-predicted sample containing a substrate SMILES sequence and a product SMILES sequence through a target BERT model; constructing a molecular object and feature coding based on atom mapping, atom truncation and sequence analysis, constructing a reaction graph of a to-be-predicted sample, inputting the reaction graph into a target graph isomorphic neural network, and constructing molecular graph features of the to-be-predicted sample based on a recursive neighborhood aggregation mechanism; and fusing the sample sequence features of the to-be-predicted sample with the molecular map features by using a bidirectional cross attention mechanism to obtain multi-modal features, inputting the multi-modal features into the multi-layer perceptron, and obtaining the prediction probability of the enzyme EC number of the to-be-predicted sample. According to the method, efficient and accurate end-to-end prediction of enzyme EC numbering is realized through the multi-dimensional chemical spatial characteristics of the collaborative modeling reaction.

The invention discloses a time prediction method and system based on double-domain feature fusion. The method and system adapt to long and short term time series prediction requirements of multiple scenes such as weather forecast, energy scheduling, traffic flow and financial exchange rate. The method comprises the steps that a multi-field data set is obtained and preprocessed, and instance normalization is carried out; performing double-domain multi-scale characteristic decomposition by adopting down-sampling and discrete wavelet transform to obtain continuous trend and high-frequency mutation details; a local unit is obtained through patch cutting and embedding, local time sequence association is mined through depth separable convolution, cross-patch global interaction is achieved in combination with a multi-layer perceptron, and local-to-global progressive fusion is completed; and constructing bidirectional attention flow enhanced cross-domain and cross-scale collaboration, and combining with standardized data training to obtain a prediction model. According to the invention, the method can improve the depiction capability of non-stable and non-linear complex time sequence data containing abrupt change and multi-period superposition, gives consideration to the adaptability of long and short term prediction, remarkably improves the accuracy of multi-field time sequence prediction, and promotes the application of the prediction technology in multiple scenes.

The invention discloses a background lamina reconstruction method for a three-dimensional transient flow field based on four-dimensional tensor decomposition, and the method comprises the steps: S1, placing a background plate and a camera at two sides of a to-be-measured flow field, and shooting a distortion image of the background plate through the to-be-measured flow field; s2, representing a flow field to be measured by adopting a four-dimensional space-time grid, and decomposing the four-dimensional space-time grid to obtain a tensor decomposition module; the tensor decomposition module is used for mapping the space-time point into a feature vector, the feature vector serves as input of the multi-layer perceptron, and output of the multi-layer perceptron is the refractive index and the refractive index gradient value of the space-time point; and S3, performing Ntotal round iteration, performing light reverse tracking in each round of iteration, calculating a total loss function according to a light reverse tracking result and the distortion image, and updating the tensor decomposition module and the multilayer perceptron. The method effectively solves the problems that an existing inversion method is high in calculation complexity and poor in reconstruction timeliness, and the reconstruction speed and calculation efficiency are greatly improved.

The invention discloses a machine learning-based Bellamya piriformis gender prediction method and application, and belongs to the technical field of aquatic animal gender determination. According to the method, shell shape character data of the Bellamya piriformis is taken as a training sample, and a multilayer perceptron model (MLP), a principal component analysis dimensionality reduction model (PCA) and a random forest model are respectively constructed to extract gender difference characteristics of the Bellamya piriformis. The performance and prediction accuracy of the constructed MLP model are optimal, the prediction accuracy reaches 99.1%, and the recall rate and the F1 score both exceed 95%. The method breaks through the limitation of the existing bellamya piriformis sex identification method, can quickly and accurately predict the sex of the bellamya piriformis, and provides convenience for breeding of new varieties of the bellamya piriformis and screening of male and female parents in artificial propagation.

The invention relates to a method and device for predicting online open course learner satisfaction and electronic equipment, and the method comprises the steps: predicting the online open course satisfaction of students through an MLP and RBF neural network model by using a virtual learning environment of a large-scale online teaching and learning platform and combining learning behavior data in a learning management system (LMS); the model comprises a data acquisition and processing module, a multilayer perceptron (MLP) module, a radial basis function (RBF) neural network module, a classification tree module and a control block, the data acquisition and processing module is used for generating a training and testing data set, and the MLP and RBF neural network model predicts the satisfaction degree of a learner. The MLP model carries out feature extraction through a multi-layer perceptron structure and different activation functions, the RBF model measures the distance between input data and a center by using a radial basis function to realize feature extraction, the classification tree is used for judging a prediction model to which a data point belongs, the control block integrates features from the MLP and RBF neural network models, and the RBF model is used for determining a prediction model to which the data point belongs. Experimental results show that the prediction accuracy of low-satisfaction-degree learners and high-satisfaction-degree learners can be improved at the same time through the combination scheme of the MLP and the RBF, the method can be applied to learner satisfaction degree prediction of various online open courses, an educational institution is helped to know the satisfaction degree condition of students in time, course design and teaching strategies are optimized, and the teaching efficiency is improved. And important support is provided for teaching reform and optimization in the field of online education.

The invention discloses a multi-axial fatigue life prediction method based on physical information, aiming at the problem of multi-axial fatigue damage of a material, an MLP-Attention model provided by the invention adopts a multi-layer sensor MLP to process a complex nonlinear relationship, and meanwhile, aiming at the characteristics of complex data and strong nonlinearity of a multi-axial fatigue experiment, the multi-axial fatigue life prediction method based on the physical information can be used for predicting the multi-axial fatigue life of the material. A self-attention mechanism Attention is introduced, and the accuracy of a model prediction result is improved through adaptive feature weighting; for the difficulty that the multi-axial fatigue life has large discreteness, Gaussian process regression is selected and fused to perform uncertainty analysis on the prediction result so as to predict the fatigue life interval, the practicability and prediction precision of the prediction model are greatly improved, and the problem that uncertainty cannot be quantified in the traditional technology is solved.

The invention belongs to the technical field of crossing of high polymer materials and artificial intelligence, and discloses a method for optimizing the proportion of a nano-composite flame-retardant master batch in engineering plastic based on machine learning. The method is used for solving the problems that a traditional experience trial and error method is low in efficiency in multi-target performance collaborative optimization, high-dimensional nonlinear parameter space search is difficult, and microscopic coupling effect modeling is missing. According to the method, a multi-dimensional input feature set containing material composition and process parameters is constructed, six key performance indexes are combined to form training data, a mixed model formed by cascading a multi-layer perceptron and Gaussian process regression is trained, an improved non-dominated sorting genetic algorithm (NSGA-II) is adopted for multi-target global optimization, and an optimal ratio meeting preset constraints is obtained. The method is an industrial internet technology system service, and solves the technical problem that multi-target performance is difficult to collaboratively optimize due to the fact that a traditional experience trial-and-error method cannot perform modeling and searching on a high-dimensional nonlinear parameter space of a nano-composite flame-retardant system.

The invention provides a network unknown behavior detection method, device and equipment based on flow feature analysis, relates to the technical field of network security, and aims to solve the problems that related technologies for network behavior detection are difficult to adapt to a complex encrypted network environment and highly depend on artificial tags. The method comprises the following steps: performing byte coding processing on original byte data of network traffic to be detected to obtain target traffic in a single-channel grayscale image form; performing feature extraction on the target traffic according to a traffic feature extractor based on an unsupervised training task; clustering and classifying the feature vectors of the target traffic through a clustering algorithm group and an incremental updating type classifier group; inputting the feature vector, the clustering result and the classification result into an MLP multilayer perceptron model for training, and converting the feature vector of the target flow according to a trained metric learning model; and performing simple clustering division on the converted feature vectors to obtain a final detection result.

The invention discloses a dynamic routing optimization transmission method based on a neural network. The method comprises the following steps: S1, collecting original state data in a network environment; s2, preprocessing the original state data; s3, constructing a causal graph based on the historical abnormal event log, and performing vectorization representation on the causal graph; s4, modeling is carried out on the state features and the topological features, state and structure fusion processing is carried out, and a performance prediction tensor is output through a multi-layer perceptron; s5, constructing a joint optimization target based on the embedded vector and the performance prediction tensor, and screening an optimal path set; and S6, generating a routing control instruction according to the optimal path set, and issuing the routing control instruction to the network forwarding equipment. According to the method, causal graph embedding and neural network modeling are fused, dynamic path optimization is realized, and the method has the advantages of high intelligence, high stability and high adaptability.

The invention relates to the technical field of networking equipment abnormity identification, in particular to an Internet of Things equipment abnormal behavior identification method and system based on artificial intelligence. The method comprises the following steps: collecting operation state data of target equipment through multi-source sensing, segmenting a multi-dimensional time sequence feature matrix based on a sliding time window, extracting a time sequence behavior feature vector representing equipment state evolution, and constructing a behavior feature set; performing nested coding on the behavior feature set by the feature representation model to obtain a discriminative nested feature vector, taking the discriminative nested feature vector as input of an anomaly recognition model, and outputting an anomaly score value for each equipment behavior track; and comparing the abnormal score value with a discrimination threshold value to generate a corresponding state label. A multi-layer perceptron and a lightweight multi-layer perceptron are utilized to jointly generate a discriminative feature vector, a high anomaly score is generated in an unseen structural disturbance sample, and the recognition capability of an unknown abnormal mode is improved.

The invention provides a temperature compensation error elimination method in a full-automatic transformer transformation ratio test, and belongs to the technical field of transformers. A heat conduction coefficient matrix, a convective heat transfer coefficient matrix and a radiation form factor matrix are utilized to construct a temperature field dynamic evolution equation set, and a neural network compensation model combining a multi-layer perceptron architecture and an attention mechanism is adopted. A hierarchical fusion weight is dynamically adjusted based on a thermal gain stability matrix eigenvalue, a temperature gradient vector modulus length and a thermal capacity matrix condition number through a gating weight function, compensation parameters are adjusted in advance by using a temperature field prediction algorithm, and continuous improvement of measurement precision is realized through a temperature compensation effect evaluation mechanism and a dynamic parameter optimization strategy. The technical problems that the measurement precision is reduced and accurate temperature compensation cannot be realized due to temperature change in the transformation ratio test process of the transformer are solved.