14results about How to "Quantitative uncertainty" patented technology

The invention relates to a distribution network maintenance topology identification system based on multi-source data fusion, and the system achieves the effective integration and precise processing of multi-source power distribution network data through the cooperative work of a multi-source data collection module, a data preprocessing module and a measurement generation module, and remarkably improves the temporal-spatial resolution of the state quantity of a distribution network. According to the method, a confidence evaluation system is constructed, and an algorithm confidence evaluation system is used for carrying out confidence scoring on an output identification structure, so that the uncertainty of an identification result can be quantified, and the availability and reliability of the system in an actual complex environment are greatly improved; through seamless connection and data interaction between a database and a marketing system and a scheduling system, the applicability and effectiveness of the system in an actual maintenance scene are ensured, accurate and reliable topology identification support can be provided for power distribution network maintenance, and the maintenance efficiency and the power supply reliability are improved; the method has the advantages of being high in self-adaption, accurate and reliable.

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 present application relates to geological exploration and three-dimensional modeling technical field, disclose a kind of fluorite ore three-dimensional exploration modeling and analysis system, the system includes: data preprocessing and metallogenic period definition module, space-time geological structure tensor field construction and dynamic updating module, dynamic geodesic distance field calculation module, and multi-stage feedback driven level set modeling module.The present application is evolved by executing multi-stage time sequence, the model boundary of previous geological period evolution convergence is used as the starting boundary of current period, and the inheritance type mineralization is simulated.In evolution, the system also performs dynamic feedback: narrow band area near the current evolution surface is identified, the tensor field module is instructed to update locally in the narrow band, and the geodesic distance field is recalculated to guide the next evolution.The present application can also generate a three-dimensional boundary probability volume by applying a disturbance to the input parameters and repeating the modeling to quantify the uncertainty of the model.

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.

This application discloses a method, apparatus, and electronic device for predicting user electricity usage data. The method, applied in the field of data processing, includes: collecting target electricity data from a target user; inputting the target electricity data into a target prediction model to calculate the target probability distribution of the target user's electricity usage data after responding to a power dispatch request; and predicting the target user's electricity usage data based on the target probability distribution. This application solves the problem in related technologies where, due to high sample acquisition costs and limited training sample capacity, prediction accuracy is low when using neural network models to predict a user's ability to regulate electricity in response to a power dispatch request.

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.

The method comprises the following steps: in the device layer, the action prediction of a single distributed energy device is performed with a confidence interval, the action prediction is constrained in a physically executable range through a feasibility mapping function, and a device prediction result is formed; in the cluster layer, the distributed energy devices are divided into multiple clusters according to an electrical topology by using a graph neural network, and the power adjustable interval and the stability boundary of the clusters are extracted as cluster characteristics; in the system layer, the device prediction result and the cluster characteristics are jointly input into a physical information neural network, and the running data of the power distribution system is output through simulation deduction; a virtual energy pheromone network is constructed based on the topology of the power distribution system, the node pheromone and the edge pheromone in the sampling field are sensed and sampled to obtain a sensing value, the action prediction of each distributed energy device in the cluster with a confidence interval is spliced, and a coordination strategy of each cluster is formed, so that the anti-disturbance ability under uncertainty is improved.

The invention discloses a diffusion model radar track information probability prediction method based on conditional prior. The method comprises the following steps: acquiring historical track information of a target; inputting the historical track information into the trained condition prediction network to obtain predicted track information of the target; wherein the condition prediction network is used for extracting non-stationary factors and potential variables when the target moves to obtain non-stationary features and potential variable information, and predicting a future track of the target according to the non-stationary features and the potential variable information; the predicted track information is input into the trained diffusion model to generate a plurality of possible predicted track sequences through random sampling and gradual denoising processes, predicted track distribution of the target is obtained, and the predicted track distribution further comprises uncertainty information of each predicted track sequence. The prediction information provided by the invention is more and comprises the quantitative information of the uncertainty of each prediction track, and reliable probability reference and firm decision support can be provided for subsequent decision tasks.

The invention relates to the technical field of grain storage monitoring, and particularly discloses a processing characteristic monitoring and analyzing system for early indica rice for rice noodles in a storage process, which comprises the following steps of: generating a three-dimensional quality deterioration risk map according to storage environment data, and performing layered space sampling according to the three-dimensional quality deterioration risk map to obtain a sample set with coordinate identification; performing differential scanning calorimetry analysis on the sample to obtain gelatinization enthalpy value data, and integrating the gelatinization enthalpy value data with space coordinates and risk levels of the sample into a structured database; by taking the space coordinate as a position variable, the gelatinization enthalpy value as a target variable and the risk value as an auxiliary variable, processing by adopting a covariant Kriging algorithm, and generating a continuous aging degree prediction curved surface covering the whole storage space and corresponding uncertainty evaluation data; and finally, constructing a three-dimensional visual distribution map based on a prediction result, calculating a whole warehouse statistical index, identifying a high-confidence local deterioration region, and forming a quality decision report. The problem of monitoring distortion of a traditional method is effectively solved.

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.