70 results about "Negative log likelihood" patented technology

The invention discloses a circuit board defect detection method and system based on multi-band impedance matching, and relates to the technical field of circuit board quality inspection, and the method comprises the steps: determining corresponding test bias voltages of a reconfigurable impedance matching network at a plurality of test frequency points according to a band bias voltage relation table; for each test frequency point, test bias voltage matched with the test frequency point is applied to each adjustable element, and a radio frequency signal is injected into the circuit board to be tested, so that a corresponding reflection coefficient is acquired through a test probe; based on the multi-frequency sparse dictionary and a compressed sensing algorithm, reconstructing a three-dimensional scattering image in the circuit board; and calculating negative logarithm likelihood distribution corresponding to the three-dimensional scattering image, and respectively solving a hierarchy anomaly score of the corresponding circuit physical layer by combining the spatial information of each circuit physical layer so as to screen at least one abnormal circuit physical layer. Therefore, signal feature reconstruction is used for replacing high-precision physical imaging, and hierarchical space identification of circuit board defects is realized.

The present application relates to the technical field of data processing, and particularly relates to a power monitoring method and system for remote electric energy meter, the method comprising: constructing a time series factor graph model with voltage and current phasor as hidden variables and original electric parameter data as observation nodes; performing synchronous compression wavelet transform on current phasors in the electric energy state sequence to generate a time-frequency energy distribution graph and obtain a monitoring feature vector sequence; inputting the monitoring feature vector sequence into a deep auto-encoder pre-trained on normal power consumption working condition data to calculate a reconstruction error, simultaneously calculating a Lyapunov index of the sequence within a preset time window, and obtaining a negative log-likelihood probability according to a pre-established Gaussian mixture model describing the distribution of the index under normal working condition; and weighting and fusing the reconstruction error and the negative log-likelihood probability to generate a comprehensive abnormality index for judging power consumption events. The present application can realize high-precision and low-false-alarm-rate detection of power consumption events.

The invention discloses an interpretable cancer survival prediction method based on multi-modal data fusion, and the method comprises the following steps: carrying out the preprocessing of a full-section pathological image, and obtaining an image feature matrix; preprocessing a pathological report text corresponding to the pathological image, and constructing a text feature matrix; preprocessing the high-dimensional gene expression data of the patient to generate a plurality of survival-related gene modules and feature vectors thereof; performing dynamic fusion on the obtained multi-modal features through a self-adaptive multi-modal expert hybrid module to obtain final fusion feature representation; and training a deep learning model by using fusion feature representation in combination with a negative log-likelihood loss function and a Cox proportional risk model, and performing prognosis analysis on the cancer patient. According to the explainable cancer survival prediction method based on multi-modal data fusion, the prediction accuracy is improved, good model interpretability is achieved, and biomarkers closely related to cancer prognosis can be automatically recognized.

The invention discloses a pedestrian trajectory prediction method and device based on pedestrian scene interactive modeling, and the method comprises the steps: extracting the trajectory feature space of each pedestrian through employing a multi-feature map, obtaining the feature maps of the relative position, speed and acceleration, and carrying out the convolution of each feature map, superposing to obtain pedestrian interaction features; encoding the scene semantic segmentation map by using a SwinTransform, mapping the pedestrian coordinate sequence into a position map, and aligning and fusing the position map with the semantic features to obtain space-time scene representation; cross-modal fusion of pedestrian interaction features and scene features is completed through a bidirectional self-attention mechanism; predicting a bivariate Gaussian distribution parameter of a future trajectory by using a time extrapolation convolutional network, and performing end-to-end training by using negative log-likelihood loss; according to the method, pedestrian interaction behaviors and scene constraints are comprehensively considered, and the future trajectory of the pedestrian can be more accurately predicted in a crowded and complex scene.

A chemical fertilizer recommendation interpretation generation method based on a large language model comprises the steps of obtaining historical interaction data and comment texts of a user for chemical fertilizer commodities, and extracting explicit preferences and implicit preferences of the user; encoding the commodity text information and the user behavior sequence by using a pre-training language model, and constructing commodity embedding and user interest representation; inputting the representation into an expert hybrid model for multi-modal feature alignment, and generating collaborative semantic embedding; the embedded information is injected into each layer of attention mechanism of a large language model; adopting a multi-stage generation strategy to sequentially generate abstract description, demand alignment, detail supplementation and feasible action suggestions, and outputting a structured recommendation explanation text; and the training language model is optimized by combining a character-level negative logarithm likelihood loss function and a low-rank self-adaptive fine tuning method in a training process. The method improves the individuation, readability and scientificity of recommendation explanation, has good deployment efficiency and practical application value, and is suitable for an intelligent agricultural recommendation system.

The invention relates to the technical field of data processing, in particular to an electric energy monitoring method and system for a remote electric energy meter, and the method comprises the steps: constructing a time sequence factor graph model with voltage and current phasors as hidden variables and original electric parameter data as observation nodes; performing synchronous compression wavelet transform on current phasors in the electric energy state sequence, generating a time-frequency energy distribution diagram, and obtaining a monitoring feature vector sequence; inputting the monitoring feature vector sequence into a depth auto-encoder pre-trained on normal power utilization condition data to calculate a reconstruction error, calculating a Lyapunov index of the sequence in a preset time window, and calculating the reconstruction error according to a pre-established Gaussian mixture model for describing index distribution under a normal condition. Obtaining a negative logarithm likelihood probability; and carrying out weighted fusion on the reconstruction error and the negative logarithm likelihood probability to generate a comprehensive abnormal index which is used for judging a power utilization event. According to the invention, high-precision and low-false-alarm-rate detection of power consumption events can be realized.

The invention provides a key point detection method based on deep learning, and the method comprises the steps: designing a lightweight network structure which comprises a feature encoder, a multistage feature fusion module, a reliability detector, a repeatability detector and a descriptor extractor, and outputting a reliability score graph, a repeatability score graph and dense descriptors; a non-maximum suppression sampling strategy is designed and fused, coordinates of a limited number of key points are sampled, bidirectional negative logarithm likelihood loss and bidirectional L1 distance loss are proposed to carry out coupling training on descriptors and reliability of the key points, and local similarity loss and local peak loss are proposed to optimize the positions of the key points. Compared with the prior art, the method not only achieves the performance equivalent to that of the most advanced SOTA method in feature matching, but also greatly shortens the reasoning time.

The invention discloses a quantitative rainfall estimation method based on a Hurdle-IMDL framework. The method comprises the following steps: acquiring historical rainfall measurement data and meteorological satellite observation data; constructing a biased quantitative rainfall estimation probability model based on a Hurdle model, correcting a biased rainfall estimation probability sub-model according to an IMDL method, substituting into empirical distribution to construct an empirical quantitative rainfall estimation probability model, and deriving a negative logarithm likelihood function of the empirical quantitative rainfall estimation probability model; constructing an AI model, and optimizing the AI model by taking a negative log-likelihood function as a loss function; and inputting meteorological satellite observation data of a to-be-inverted region into the trained AI model to obtain an estimated value of a parameter of the empirical quantitative precipitation estimation probability model, and estimating the precipitation according to conditional expectation. According to the invention, the Hurdle model is used to solve the zero expansion problem, the IMDL learning method is used to cope with the long tail problem, and the inversion accuracy of extreme rainfall is improved.

The invention discloses a time sequence neural network tire force estimation method and system under an unstructured road surface, and belongs to the technical field of vehicle dynamics control. The method comprises the steps of collecting vehicle state information under a non-structural road surface by constructing a vehicle information collection platform; carrying out cleaning, interpolation, normalization, time sequence completion and mask preprocessing on the vehicle state information; an LSTM model fused with the attention mechanism is constructed, and an output layer of the model outputs an estimated mean value and an estimated variance of tire force at the same time to quantify estimation uncertainty; performing network training by adopting a negative log-likelihood loss function; and automatically optimizing the network hyper-parameters by using a Bayesian optimization method. The method effectively solves the estimation problem caused by strong time sequence dependence, obvious nonlinearity and obvious hysteresis effect of the tire force under the unstructured road surface, does not need to additionally install a tire internal force sensor, is low in cost, high in precision and strong in generalization ability, and remarkably improves the performance of a vehicle chassis control system under the complex road condition.

The invention discloses a Bayesian optimization deep learning network and Gaussian likelihood coupled salinity prediction method. The method comprises the following steps: acquiring a salinity observation data set; preprocessing the data in the salinity observation data set; inputting the preprocessed salinity observation data set into a salinity prediction model to obtain an initial salinity prediction result; training the salinity prediction model based on a Bayesian optimization parameter optimization method to obtain a trained salinity prediction model, and obtaining an optimized salinity prediction result based on the trained salinity prediction model; and performing likelihood estimation on the optimized salinity prediction result based on a Gaussian likelihood method, constructing a negative log-likelihood loss function, obtaining a likelihood estimation result and a corresponding probability prediction interval, and taking the optimized salinity prediction result and the corresponding probability prediction interval as a final prediction result. According to the method, the high efficiency, the robustness and the real-time performance of salinity prediction can be improved.

The invention provides a lake permanganate index inversion method based on a mixed density network. The lake permanganate index inversion method comprises the following steps: constructing a water body apparent reflectance data set according to sampling time of actually measured water quality parameters and geographic coordinates of sampling points; constructing a convolutional neural network comprising a residual module, replacing an output layer part with a mixed probability model, designing a negative log-likelihood loss function, defining a sampling function, and outputting a permanganate finger simulation value; the optimal hyper-parameter of the network is determined through Bayesian optimization, and based on the optimal hyper-parameter, a five-fold cross validation method is used for model training and verification; and extracting a water body boundary of the target lake image, reading and processing pixel values of all wave bands pixel by pixel to form a target vector, inputting the vector into the trained mixed density network to obtain simulation values of all pixel points, and processing the simulation values into an image with geographic coordinates. According to the method, efficient inversion of the lake permanganate index based on the multispectral satellite data can be realized.

The invention relates to a pedestrian trajectory prediction technology, in particular to a multi-modal pedestrian trajectory prediction method and system based on mixed skewed distribution. The method comprises the following steps: introducing group trajectory standardization, calculating an average trajectory starting point and an average displacement of a group, and unifying coordinate representations of trajectories of different groups; time sequence dependence and space dependence of individual trajectories are modeled through a space-time encoder, and mixed skewed distribution of modeling trajectory end point uncertainty is output; sampling a plurality of trajectory end points from the mixed skewed distribution, performing linear interpolation on the trajectory end points and the trajectory starting points to obtain a preliminary prediction trajectory, and correcting a prediction result through a correction network; and through mean square loss of a prediction result and negative logarithm likelihood optimization model parameters of mixed skewed distribution, a model with a minimum objective function value on a verification set is used as a final model, and a future trajectory of a group is predicted. According to the method, mixed skewed distribution is introduced to model position distribution of individuals, and asymmetric advancing trends and different advancing intentions of the individuals are effectively modeled.

The application discloses a rotating machinery fault diagnosis method and system based on a Gaussian boundary constraint network, relates to the technical field of fault diagnosis, and obtains vibration signal data of rotating machinery under a single source domain working condition to form a source domain training set with labels; based on the source domain training set, a Gaussian boundary constraint network is trained to obtain a trained Gaussian boundary constraint network; the Gaussian boundary constraint network comprises a feature extractor, a feature decoupling module, a Gaussian boundary constraint module and a known category prediction head, the Gaussian boundary constraint module comprises a Gaussian classifier, and the Gaussian classifier trains the input first features through a negative log-likelihood loss and a confidence-aware boundary loss; vibration signal data of the rotating machinery is obtained, input into the trained Gaussian constraint network for fault diagnosis, and a fault diagnosis result is output. The application can process various label inconsistency scenarios, and realizes accurate identification of shared categories and robust rejection of unknown categories.

The invention provides a large language model staged pre-training method and system. The method comprises the following steps: training a Transform model by using a basic data set, optimizing a negative logarithm likelihood target, and adopting an AdamW optimizer and a cosine attenuation learning rate; continuing training by using the universal knowledge data set based on the first-stage parameters; and weighting the professional data in the training field by adopting an oversampling strategy. By structuring a training target and a data type, the model can efficiently learn language basis, general knowledge and professional skills in stages. Experiments show that according to the method, the training efficiency of the model in the basic stage is improved by 40%, the overall training time is shortened by 30%, and meanwhile the accuracy rate of tasks in the professional field is 15%-20% higher than that of traditional end-to-end training. Finally, model parameters are evaluated through professional ability, and both universal language understanding and domain specialty are achieved.

A system includes a controller configured to receive, at a trained transformer model, one or more run-time logs indicating information associated with interaction between a unique device and a server, output a user-score associated with the unique device and the one or more run-time logs in response to determining a negative log-likelihood of the one or more run-time logs in a next-log prediction probability distribution modeled by the trained transformer model, output a server-score utilizing at least normal-cluster centers associated with the trained transformer model and the one or more run-time logs, and in response to a sum of the user-score and server-score exceeding a threshold, outputting an indication of a cyber-threat associated with the one or more run-time logs.

This invention discloses a spacecraft pose estimation and uncertainty modeling method based on intrinsic space, belonging to the field of spacecraft pose estimation technology. The method involves directly constructing a hierarchical Bayesian probability model on the rotating manifold SO(3) and translation space, using Fisher and Gaussian distributions respectively, and introducing conjugate priors to achieve the fundamental decomposition and quantification of accidental and cognitive uncertainties. An end-to-end multi-task neural network is used to jointly learn the pose probability model parameters, key points, and segmentation information, and an iterative optimization module is employed to improve estimation accuracy. During the training phase, marginal negative log-likelihood and evidence regularization are jointly optimized; during the inference phase, the probability distribution of pose prediction is obtained through analytical marginalization, and the two types of uncertainty are distinguished. This invention improves pose estimation accuracy while outputting well-calibrated uncertainties, providing a reliable basis for the autonomous and safe operation of spacecraft in orbit.

The invention discloses a single cell chemical perturbation transcription response prediction method based on a generative neural network and application thereof, the generative neural network of a prediction model constructs a learnable conditional mapping function, and multi-modal embedding is constructed during prediction model training. A training result is optimized by adopting a zero-expansion Gaussian negative logarithm likelihood loss function; the multi-modal embedding is the key input of a conditional mapping function and comprises gene expression semantic features, multi-source biological priori knowledge and drug molecular structure semantic features, so that the learning ability of the conditional function on the true disturbance law of the known drug structure on the gene expression of the known cell type can be remarkably improved; the trained prediction model can break through the limitation that traditional drug reaction research is highly dependent on experimental conditions, low in flux and high in cost, and prediction single cell perturbation transcription response spectrums without drugs are obtained according to structural semantics of new drugs or expression semantics of new cells in a zero sample scene.

The present invention belongs to the fields of artificial intelligence and neurobiology, and particularly relates to a method for learning the constraints of a deep neural network topology. First, the rs-fMRI data of the subject is collected and preprocessed, the correlation coefficients between different brain regions are calculated to obtain the biological brain topology matrix, and a deep neural network model is constructed; then, the learning process of the neural network is constrained by the biological brain topology to train the model, and the backpropagation algorithm is used to update the neural network parameters. The loss function of the backpropagation algorithm simultaneously includes the negative log-likelihood loss and the topology matrix similarity loss. Therefore, after the model is trained, the topology matrix of the neural network will tend to be the biological topology matrix, thereby realizing the technology of directly integrating neurophysiological recordings into artificial neural networks. The present invention fills the technical gap in directly converting neurophysiological recordings into improvements in artificial neural networks, thereby improving the engineering performance of neural networks.

This invention discloses a method and system for assessing tumor content in liquid biopsies. The method performs quality control on high-throughput sequencing data from body fluid samples, filtering germline variations, clonal hematopoietic-related variations, and sequencing errors to obtain copy number variation (CNV) information and somatic mutation characteristic information. A weighted mean squared error loss function is constructed based on the CNV information, and the tumor content is solved using a constrained gradient descent method. When the CNV signal does not meet preset judgment conditions, the system switches to the mutation characteristic module, constructing a negative log-likelihood function based on a binomial distribution based on the principal clonal cluster, and solving for the tumor content through iterative optimization. In extreme cases, a backoff mechanism is triggered. This invention combines the stability of CNV at high tumor content with the sensitivity of mutational VAF at low content, achieving adaptive assessment from low to high tumor content.

The invention provides a spacecraft three-dimensional reconstruction method based on uncertain two-dimensional Gaussian sputtering, and the method comprises the steps: collecting simulation data; firstly, based on a constructed darkroom simulation environment, an image of a target model is captured through a camera; carrying out uncertain two-dimensional Gaussian training; introducing an uncertainty index into a two-dimensional Gaussian element, and training an uncertain two-dimensional Gaussian element by using the obtained image so as to carry out three-dimensional reconstruction and uncertainty estimation; filtering discrete points; the two-dimensional Gaussian which does not meet the volume and distance requirements is deleted, so that the three-dimensional reconstruction quality is improved; and performing three-dimensional reconstruction and uncertain estimation result output by using the filtered two-dimensional Gaussian. The invention provides a method for improving two-dimensional Gaussian sputtering surface reconstruction capability and measuring uncertainty under a limited visual angle. According to the method, uncertainty is introduced into two-dimensional Gaussian and a negative log-likelihood loss function is designed, so that redundant objects are suppressed through discrete two-dimensional Gaussian filtering, and the reconstruction quality of the three-dimensional surface grid is further improved.

The invention discloses a peptide de novo sequencing model based on non-autoregressive Transform and dual learning. Comprising the following steps: step 1, performing segmentation of different resolutions on original mass spectrum data through multi-scale processing; step 2, utilizing a self-attention mechanism of a Transform encoder to extract potential characteristics of the original mass spectrum; 3, based on the potential characteristics of the original mass spectrum and peptide precursor information, deducing a peptide sequence in parallel by using a non-autoregressive Transformer decoder; and 4, reconstructing mass spectrum data according to the generated peptide sequence by using a paradigm of dual learning through a Transform encoder. The training target of the model is to minimize the negative logarithm likelihood loss of the peptide sequence and the mass spectrum reconstruction loss. Compared with the prior art, the method has the advantages that global and local features in mass spectrum data can be better captured, the prediction accuracy from the mass spectrum to the peptide sequence is remarkably improved, and meanwhile, the reasoning speed of the model is increased. The model is suitable for large-scale mass spectrum data analysis, and has efficient calculation and good generalization performance.

This application discloses a method and system for detecting circuit board defects based on multi-band impedance matching, relating to the field of circuit board quality inspection technology. The method includes: determining the corresponding test bias voltage of a reconfigurable impedance matching network at multiple test frequencies based on a frequency bias voltage relationship table; applying a test bias voltage matching the test frequency to each adjustable element for each test frequency, and injecting an RF signal into the circuit board under test to acquire the corresponding reflection coefficient through a test probe; reconstructing a three-dimensional scattering image inside the circuit board based on a multi-frequency sparse dictionary and compressed sensing algorithm; calculating the negative log-likelihood distribution corresponding to the three-dimensional scattering image, and combining the spatial information of each circuit physical layer to obtain the hierarchical anomaly score of the corresponding circuit physical layer, thereby screening at least one abnormal circuit physical layer. Thus, by using signal feature reconstruction instead of high-precision physical imaging, hierarchical spatial identification of circuit board defects is achieved.

The invention discloses a rotating machine fault diagnosis method and system based on a Gaussian boundary constraint network, and relates to the technical field of fault diagnosis, and the method comprises the steps: obtaining vibration signal data of a rotating machine under a single source domain working condition, and forming a marked source domain training set; training the Gaussian boundary constraint network based on the source domain training set to obtain a trained Gaussian boundary constraint network; the Gaussian boundary constraint network comprises a feature extractor, a feature decoupling module, a Gaussian boundary constraint module and a known category prediction head, the Gaussian boundary constraint module comprises a Gaussian classifier, and the Gaussian classifier trains an input first feature through negative logarithm likelihood loss and confidence perception boundary loss; and acquiring vibration signal data of the rotating machine, inputting the vibration signal data into the trained Gaussian constraint network for fault diagnosis, and outputting a fault diagnosis result. The method can deal with various label inconsistent scenes, and realizes accurate identification of shared categories and robust rejection of unknown categories.

A computer-implemented multispectral imaging method for use in analysis of a sample comprising a plurality of types of fluorescent label, each of the plurality of types of fluorescent label having a respective emission spectrum is disclosed. The method comprises: receiving multi-channel image data, each channel in the multi-channel image data comprising image data derived from an unfiltered image of the sample and having a respective spectral content; for each channel: i) forming a vector of measured quantum particle counts from the image data for the channel, the vector having an entry for each pixel in the image; ii) iteratively generating, for each pixel in the image, a vector of possible values having entries for the contribution made by each of the plurality of types of fluorescent label to the unfiltered image, and for each iteration, calculating a vector of expected quantum particle counts, having an entry for each pixel in the image, by multiplying the vector of possible values for each pixel by a mixing matrix defining the relationship between the unfiltered image and the multi-channel image data; and iii) selecting the vectors of possible values for which a negative log-likelihood function describing the probability of a vector of measured quantum particle counts being generated given a corresponding vector of expected quantum particle counts is a minimum; and for each of the plurality of types of fluorescent label in the sample, constructing a corresponding data structure comprising image data in which, for each pixel, the data structure includes the entry for the contribution made by the type of fluorescent label from the vector of possible values for the pixel, each data structure thereby being useable to reconstruct an image of the sample with a spectral content corresponding to the respective emission spectrum of the type of fluorescent label for which the data structure was constructed.

The application discloses an anti-reward hacker recommendation system optimization method and an anti-reward hacker recommendation system, and the method comprises the following steps: a dynamic interval mechanism is constructed, a dynamic interval item is calculated based on a user historical sequence and a positive and negative sample pair, and the dynamic interval item is used for quantifying the preference difference intensity between the positive and negative samples; the dynamic interval item is subjected to cross-sample statistical normalization to obtain a normalized interval item; the dynamic interval is decoupled into an optimization constraint item through a stop gradient operation, model training is carried out in combination with a preference optimization loss; a negative log-likelihood loss is introduced, and the generation probability of the positive sample is directly maximized; the preference optimization loss and the negative log-likelihood loss are jointly optimized to obtain a total loss function, which is used for inhibiting the reward hacker behavior in the recommendation system. The application dynamically adjusts the interval threshold based on the user behavior intensity, ensures that the model differentiates modeling of the fine-grained preference and the coarse-grained demand of the final positive and negative samples in the recommendation sequence, and alleviates the recommendation deviation caused by data sparseness and diversity.

Disclosed is a computer-executed multi-spectral imaging method for use in the analysis of a sample comprising a plurality of types of fluorescent labels, each of the plurality of types of fluorescent labels having its own emission spectrum. The method includes receiving multi-channel image data, where each channel in the multi-channel image data is derived from an unfiltered image of the sample and includes image data having respective spectral components; for each channel, i) forming a vector of photon counts measured from the image data for the channel, the vector having an entry for each pixel in the image; ii) repeatedly generating a vector of possible values having an entry for the contribution made by each of the plurality of types of fluorescent labels to the unfiltered image for each pixel in the image, and for each iteration, calculating a vector of expected photon counts having an entry for each pixel in the image by multiplying a mixing matrix that defines the relationship between the unfiltered image and the multi-channel image data by the vector of possible values for each pixel; iii) selecting a vector of possible values for which the negative log-likelihood function describes the probability of generating the measured vector of photon counts assuming that the corresponding vector of expected photon counts is minimized; and constructing a corresponding data structure containing the image data for each of the plurality of types of fluorescent labels in the sample, where in the image data, for each pixel, the data structure includes an entry for the contribution made by that type of fluorescent label from the vector of possible values for the pixel, whereby each data structure can be used to reconstruct an image of the sample using the spectral components corresponding to the respective emission spectra of the type of fluorescent label for which the data structure was constructed.

The invention belongs to the technical field of image defect detection, and discloses a self-supervised multi-category industrial product image defect detection method and device based on a twin flow model. The method comprises the following steps: firstly, generating a simulated artificial defect sample by randomly sampling a normal sample and combining Poisson editing; then, a pre-trained feature extractor is used for extracting deep and shallow features, and the deep and shallow features are respectively input into a twin flow model for distribution estimation; fusing multi-scale feature output, and generating a feature distribution diagram of the normal sample; and finally, high-precision defect detection is realized through confrontation optimization of negative logarithm likelihood loss and self-supervised segmentation loss. Through the lightweight twin flow model architecture, the consumption of calculation and storage resources is remarkably reduced, and meanwhile, the accuracy and efficiency of multi-class defect detection are improved in combination with a self-supervision method. The method is suitable for real-time defect detection in an industrial assembly line.

The invention discloses a trajectory prediction system and method in a heterogeneous traffic environment based on a double-layer LSTM and a 4D graph, and belongs to the field of vehicle automatic driving, and the method comprises the following steps: S1, collecting the driving information of heterogeneous traffic participants, and carrying out the preprocessing of the information, and obtaining a data set which defines the instance information, category information and time-space interaction relation of the traffic participants; s2, constructing a 4D graph based on the data set in the step S1, wherein the 4D graph is used for capturing space-time interaction and category generality information of traffic participants; s3, constructing a double-layer LSTM model based on the 4D graph constructed in the step S2, and performing training by using the data set obtained in the step S1; s4, track prediction is carried out by using the trained double-layer LSTM model containing the model parameters, and the model parameters are updated; the model outputs bivariate Gaussian distribution parameters (a mean value, a standard deviation and a correlation coefficient) of future positions of traffic participants through cooperation of an instance layer and a category layer, and prediction of future trajectories in heterogeneous traffic scenes is realized based on optimization of a negative log-likelihood loss function.

The invention relates to the technical field of equipment management, and discloses an equipment reliability evaluation method and system, equipment and a medium, and the method comprises the steps: carrying out the feature extraction of an obtained target real-time time sequence data set which can reflect the state of to-be-detected equipment through a depth self-encoder and a self-attention mechanism encoder; correspondingly obtaining potential key features and time sequence features; reconstructing a first fusion feature obtained based on fusion of the potential key feature and the time sequence feature by using a decoder to obtain a reconstructed sample, calculating a reconstruction error of the reconstructed sample, fusing the reconstruction error and the first fusion feature to obtain a second fusion feature, and inputting the second fusion feature into a Gaussian mixture model to obtain a Gaussian mixture model; and comparing the obtained real-time negative logarithm likelihood value with a preset threshold value to obtain a deviation value, and performing evaluation by adopting a preset reliability evaluation algorithm according to the deviation value to obtain a reliability evaluation result of the to-be-detected equipment. According to the method, the accuracy of equipment reliability evaluation is effectively improved.

The application discloses an estimation method for an attacker's estimation of patrol strategy switching time in an anti-environment, and belongs to the field of unmanned system patrol strategy analysis. The method comprises the following steps: the attacker continuously observes a patrol area, and records a state observation sequence of a patroller; time points in the sequence satisfying a sliding window condition are taken as candidate change point time points, observation subsequences before and after the window are extracted, and a local state transition matrix is estimated; a difference value of the matrix before and after each candidate change point time point is calculated, a difference signal sequence is constructed, the sequence presents a peak value at a state transition matrix switching position; an interval cost function based on a negative log-likelihood is defined; a global optimization objective function with a penalty term is introduced, an optimal change point time point set is solved by using a pruning accurate linear time dynamic programming framework; and intervals are divided according to the optimal change point set, and state transition matrices of each section are independently estimated. The application can automatically determine the position of the switching time point only by relying on the observation sequence, and provides support for the attacker to identify a patrol blind area.