8results about How to "Quantitative uncertainty" patented technology

The application discloses a power distribution network topology fast adaptive inference method and device based on meta learning, and belongs to the technical field of smart grids. The method comprises the following steps: acquiring a first data set; the first data set comprises a plurality of groups of first topology data and first measurement data of the power distribution network; the first topology data represents the connection relationship between nodes in the power distribution network, and the first measurement data represents the operating state of each node; the first topology data is taken as the label of the first measurement data, and a meta initial model is trained; second measurement data and topology label data of a target power distribution network are acquired; the topology label data represents the connection relationship between part of nodes in the target power distribution network; the topology label data is taken as the label of the second measurement data, the meta initial model is trained, and a target topology inference model is obtained; real-time measurement data of the target power distribution network is acquired, the real-time measurement data is input into the target topology inference model, and target topology data is obtained. The application can improve the efficiency of power distribution network topology inference.

The invention discloses an offshore wind power project soil space variability analysis method. The method comprises the steps that drilling data and CPT data of an offshore wind power project are collected and preprocessed; a mathematical model is established, a correlation function of drilling and CPT is constructed, and distribution characteristics of model parameters are obtained; taking the obtained distribution as initial estimation, and for any to-be-predicted point, obtaining an optimal predicted value of the point in a spatial domain according to the known data points and spatial distribution characteristics of the to-be-predicted point; the prediction result is verified and corrected by using actually measured data obtained in the actual construction process; and a high-precision soil body spatial variability prediction model is obtained through repeated iterative optimization and is used for guiding fan foundation construction scheme formulation, optimization and engineering risk assessment. The method provides data support for the whole wind power plant.

The application discloses a kind of based on artificial intelligence's consumer goods harmful substance intelligent screening method, it is related to artificial intelligence technical field, it is difficult to effectively identify and analyze the technical problem that a large number of natural language, commercial name or commonly known as etc. non-standardized description exists in ingredient list, it is also difficult to accurately convert these fuzzy text information into the standardized terminology that can be accurately matched by regulation database;Including the following steps: the fuzzy ingredient list containing natural language or commercial name description is preprocessed by multi-task text regularization model;Then, utilize embedding semantic similarity model and the multi-evidence fusion presumption unit of improved attention mechanism, fuzzy component is intelligently analyzed and matched, and standard substance identification and confidence are output;Finally, through multi-agent risk decision framework combines real-time regulation database, dynamic compliance screening and risk assessment are carried out;The application improves the intelligent level of consumer goods safety compliance management.

The present application relates to a pathological whole slice image classification method based on multi-branch independent mask and Dirichlet evidence fusion, belonging to the cross field of biological information and artificial intelligence. In view of the defects of traditional multi-instance learning method in weakly supervised classification task of pathological whole slice image, such as excessive attention concentration and static fusion, the present application sets dynamic mask parameters through multi-branch independent setting, forces different branches to pay attention to different pathological regions, and solves the problem of insufficient feature diversity caused by attention concentration; combining the confidence and uncertainty of Dirichlet distribution quantization branch prediction, the branch fusion weight is dynamically adjusted based on evidence theory, and the fusion robustness of multi-branch prediction result is improved. The experiment is verified on the public pathological data set such as CAMELYON-16, compared with the MIL method, the present application improves the AUC index by 1.1-2.4%, and significantly enhances the accuracy and generalization ability of pathological WSI classification.

The application provides a spatio-temporal joint planning and decision-making method and system based on uncertainty perception, which obtains a detection result of detecting a moving target in a preset range of an autonomous vehicle, and static map data in the preset range of the autonomous vehicle, generates a spatio-temporal probability occupation heat map of the moving target, fuses the static map data and the spatio-temporal probability occupation heat map, updates an occupation probability distribution at a current time based on a probability propagation mechanism, constructs a three-dimensional probability occupation grid, maps a value in the three-dimensional probability occupation grid and a collision time to a risk field, performs opportunity-risk analysis on a preset candidate strategy, determines an output decision, determines a planning space, and determines an optimal perception trajectory in the planning space; the application can avoid blind passing in a high-uncertainty condition, effectively transfer perception uncertainty to a planning layer, and significantly improve the robustness and safety of an autonomous driving system in a long-tail scenario.

This invention provides a method and system for adaptive elastic scaling of ruggedized computers based on a domestically developed platform, relating to the field of computer resource management technology. The method includes: collecting computing unit operating state parameters to construct a multi-dimensional state tensor; identifying key driving characteristics through tensor decomposition and causal inference analysis; establishing a mapping relationship between load characteristics and resource requirements; quantifying uncertainty to obtain capacity demand prediction results and confidence intervals; and finally determining the execution strategy for scaling decisions. This invention improves the accuracy of resource allocation and the reliability of scaling decisions, reduces resource waste, and enhances system stability.

The present application relates to the technical field of engine life prediction, in particular to an engine life prediction method based on multi-head attention, which first normalizes the original data of the aero-engine sensor and eliminates redundant features; then realizes time-space double-dimension adaptive weighting through the multi-head attention mechanism, and strengthens the key degradation information; then inputs the weighted data into the long short-term memory network to learn the long-time dependence relationship, and completes the preliminary prediction of the remaining useful life; finally, combining Monte Carlo regularization and kernel density estimation, multiple prediction values are generated and the uncertainty is quantified, and the mean value is taken as the final prediction result. Experimental verification shows that this method effectively highlights the key degradation features, reduces the influence of uncertainty, and significantly improves the prediction performance under single working condition and multiple fault modes, providing reliable data support for preventive maintenance of aero-engines.

A multi-model power system inertia probabilistic prediction method based on porcupine optimization and adaptive kernel density estimation includes the following steps: acquiring power system inertia-related characteristic variables, constructing a data sample set for inertia prediction, and building a CNN-BiLSTM-MHAM deep learning model based on the data sample set; optimizing key hyperparameters of the model using the porcupine optimization algorithm based on the constructed CNN-BiLSTM-MHAM deep learning model to obtain optimized model structure parameters and inertia prediction results; constructing an error database based on the error between the inertia prediction results and actual values; and using the constructed error database, probabilistically modeling the prediction error using the adaptive bandwidth kernel density estimation method, and generating the probability interval for inertia prediction by combining the Bootstrap resampling method, thereby realizing the quantification and probabilistic expression of the uncertainty of the inertia prediction results. This method not only significantly improves the accuracy of power system inertia prediction but also more effectively characterizes the uncertainty and probability distribution characteristics of inertia fluctuations.