10 results about "Probabilistic classification" patented technology

The invention discloses an enterprise supplier alternative scheme evaluation method and device based on allowable interval approximate estimation, equipment and a medium, and relates to the technical field of computers, and the method comprises the steps: building an initial decision matrix based on the evaluation value of each supplier alternative scheme of an enterprise under each evaluation attribute, performing approximate estimation by using an allowable interval approximate estimation method and the evaluation values to determine a first tolerance coefficient, and determining an evaluation value reference scheme based on the first tolerance coefficient and the mean value and the standard deviation of the evaluation values; performing category allocation on the supplier alternative schemes by using a target probability classification method, and determining a boundary contour based on an allocation result; and determining a first close degree of the supplier alternative scheme and a second close degree of the boundary contour based on the boundary contour, the initial decision matrix and the evaluation value reference scheme, and evaluating the supplier alternative scheme by using a preset evaluation rule to obtain an evaluation result. Enterprise vendor objects are efficiently classified and rank inversion present in TOPSIS is prevented.

The invention relates to a comprehensive quality evaluation method and system for cross-platform learning behaviors and a medium. The method comprises the following steps: preprocessing heterogeneous original data corresponding to each learning platform to obtain a learning behavior event set; creating a dynamic multi-modal event atlas based on the learning behavior event set; inputting the dynamic multi-modal event atlas into a heterogeneous graph neural network to obtain an atlas-level learning behavior embedding vector; a learning intention probability classification result and a cognitive state recognition result are obtained based on the atlas-level learning behavior embedding vector, and a preset probability graph model is triggered to conduct reasoning and conflict resolution on the dynamic multi-mode event atlas to obtain an updated dynamic multi-mode event atlas; and pre-defined comprehensive quality dimension features are extracted from the updated dynamic multi-modal event atlas, and dimension evaluation is carried out to obtain a comprehensive quality evaluation report. By adopting the method, comprehensive quality evaluation can be upgraded to a deep cognitive diagnosis level from surface behavior description.

A multi-input call panel for controlling an operation of an elevator system, is disclosed. The multi-input call panel includes a touchable interface associated with a plurality of touchable inputs arranged at different locations on the multi-input call panel; a touchless interface including a processor configured to receive readings of a sensor detecting motion in proximity to the touchable interface and executing a probabilistic classifier trained to output a probability of correspondence of the received readings with an intention to touch one or multiple touchable inputs from the plurality of touchable inputs; and a controller configured to control the operation of the elevator system according to a control command associated with a touchable input of the plurality of touchable inputs when the touchable input is touched on the touchable interface, the classifier outputs the probability of the intention to touch the touchable input above a threshold or both.

The invention provides an unmanned aerial vehicle inspection terminal data anomaly detection method and system based on behavior coding cooperating with Transform-RVM, and relates to the technical field of artificial intelligence, the method comprises the following steps: preprocessing sensor original data of an unmanned aerial vehicle inspection terminal, and generating a time sequence sample set; the time sequence samples are converted into behavior coding vectors, and space-time fusion features are generated by fusing equipment space topology information through a graph convolutional network; inputting the space-time fusion features into a Transform editor, and extracting deep features through a multi-head self-attention mechanism, residual connection, layer normalization and a feedforward network; constructing a probability classification model by adopting a relevance vector machine RVM, modeling a parameter optimization process by utilizing a neural differential equation, and performing efficient training in combination with a dynamic sparsity control and adjoint sensitivity method to obtain a highly sparse RVM model; and performing anomaly detection on the unmanned aerial vehicle inspection terminal data acquired in real time by using the highly sparse RVM model. According to the scheme, the anomaly detection precision of the unmanned aerial vehicle inspection terminal data can be improved.

Embodiments described herein provide a probabilistic approach using a probabilistic classifier to detect anomalous categorical data, e.g., data points in non-numerical data fields. Specifically, the probabilistic classifier may generate a conditional probability distribution of a data attribute, such as the “country of origin” field in a bond data record, conditioned on various other data attributes of the bond data record obtained from different databases. The generated conditional probability corresponding to the data attribute of the store non-numerical value, e.g., the “country of origin” equals a certain country code as stored, may indicate whether a data anomaly exists with the stored value for this particular data attribute. When an anomaly is detected with a data attribute, the categorical value associated with the highest conditional probability corresponding to the data attribute may be provided as the suggested value for the data attribute.

The invention discloses a Bayesian update-based industrial equipment residual life prediction method and system, and the method comprises the steps: building a prior model of an equipment degradation process through historical failure data, and obtaining an initial parameter through maximum likelihood estimation; collecting state monitoring data of the equipment, and performing feature extraction and normalization processing to form a degradation amount observation value; carrying out recursive updating on posterior distribution of degradation model parameters by utilizing a particle filtering algorithm and a Bayesian theorem; through a Markov chain Monte Carlo sampling technology, outputting a point estimation curve, a confidence interval curve and a failure probability density curve of the residual life; an equipment maintenance plan is dynamically adjusted according to the prediction result, and early warning is triggered when the failure probability exceeds a preset threshold value, so that decision support is provided for predictive maintenance; the system correspondingly comprises a data acquisition and preprocessing module, a prior model construction module and the like. According to the method, through fusion of probability classification and statistical reasoning, prediction accuracy is improved, and a reliable decision basis is provided for predictive maintenance of equipment.

The application discloses a kind of industrial equipment residual life prediction method and system based on bayesian updating, the prior model of equipment degradation process is established by historical failure data, and initial parameters are obtained using maximum likelihood estimation;Collect the condition monitoring data of equipment, after feature extraction and normalization processing, form the observation value of degradation amount;Using particle filtering algorithm and bayes theorem, the posterior distribution of degradation model parameters is recursively updated;Through Markov chain Monte Carlo sampling technology, the point estimate, confidence interval and failure probability density curve of residual life are output;According to the prediction result, dynamically adjust equipment maintenance plan, and trigger early warning when failure probability exceeds preset threshold, provide decision support for predictive maintenance;The system corresponds and contains data acquisition and pretreatment, prior model construction and the like module.The application is fused by probability classification and statistical inference, improves prediction accuracy, and provides reliable decision basis for equipment predictive maintenance.

This invention discloses a multi-feature fusion fault diagnosis method for vibration signals of a vehicle axle assembly, belonging to the field of mechanical fault diagnosis technology. The method includes acquiring and preprocessing the raw vibration signals of the vehicle axle assembly. Time-domain, frequency-domain, and time-frequency-domain features are extracted from the preprocessed signals to form a primary feature set. A subset of sensitive features is selected through redundancy analysis and importance ranking. A kernel function is used to map the sensitive features to a high-dimensional space and perform linear reconstruction oriented towards fault categories, resulting in a more discriminative fusion feature vector. This fusion feature is input into a pre-trained probabilistic classification model to calculate the posterior probability of belonging to each fault mode. The fault mode is determined based on the probability, and a diagnostic result is generated. This invention enhances the discriminative ability of fault features through kernel space feature reconstruction and provides a reliability assessment of the diagnostic results using probability output, thereby improving the accuracy of fault diagnosis and the level of decision support.

The application discloses a photovoltaic power generation power prediction method, which comprises data preprocessing, weather type probability classification, similar day synthesis and multi-view interactive parallel prediction; the method constructs a probability classification model to divide the sample data by weather types, and uses the uncertain information of weather division as a confidence weight to calibrate the model input features; the design uses physical priori to guide feature enhancement, solves the problem that the existing method is prone to feature distortion under extreme weather conditions and the model generalization ability is limited; in addition, the introduction of physical priori to guide feature enhancement enriches the information of photovoltaic data, suppresses the interference of noise or fuzzy samples, significantly improves the utilization efficiency of information, guides the prediction model to pay attention to samples with high confidence, and enhances the generalization ability of the model.