10 results about "Conditional probability" patented technology

The invention discloses a shipping accident analysis method. The method comprises the following steps: constructing a shipping accident knowledge graph; inputting questions, retrieving the shipping accident knowledge graph by adopting a retrieval enhancement generation method, and generating answers; wherein the retrieval enhancement generation method is adopted to retrieve the shipping accident knowledge graph and generate the answer, and the method comprises the following steps of: analyzing a question by adopting a large language model to generate a query statement; retrieving in the shipping accident knowledge graph according to the query statement, outputting nodes and edges matched with the query statement from the shipping accident knowledge graph, and generating a Bayesian network according to the matched nodes and edges; generating a query result according to the conditional probability of a target node and a father node thereof and / or the posterior probability of a child node thereof in the Bayesian network, and inputting the query result into the large language model; and the large language model generates an answer according to the query result. The invention further discloses a shipping accident analysis device, computer equipment and a storage medium. The method comprises the following steps: extracting and storing shipping accident data by adopting a large language model in combination with a knowledge graph, and analyzing the shipping accident data in combination with a Bayesian network analysis model to obtain an occurrence probability of a complete causal chain from causes to accident results in an accident, so that objective and accurate accident causals are sorted out for complex shipping accidents, and the probability of occurrence of the complete causal chain is improved. And reliable conclusions and suggestions are provided for subsequent shipping businesses.

The invention discloses a personalized aided teaching content dynamic generation and pushing method based on an AI large model, and belongs to the technical field of data processing, and the method comprises the following steps: collecting the conditional probability distribution of an output layer of the AI large model, and constructing a reference probability distribution vector; obtaining a generation probability distribution feature vector in a subsequent generation round and carrying out distribution similarity analysis to judge a generation probability distribution drift state; and performing controlled adjustment on the generated parameter set in a drift state, and constructing a stable window and dynamically updating the reference probability distribution vector in a continuous non-drift state. Teaching content is generated based on the adjusted generation parameter set and pushed in batches, generation stability monitoring is executed in the pushing process, pushing constraint adjustment is conducted according to the oscillation state and the convergence state, and finally the converged generation parameter set is stored as an initial generation strategy of the corresponding teaching task. The problem of recessive degradation of teaching content generation quality in the prior art is solved.

The application discloses a density prior guided unsupervised point cloud depth denoising method, and has the following advantages compared with the prior art: firstly, a density prior is designed, and the probability of each point being located on a real underlying surface can be described through the distribution of a noise point cloud; when discussing the conditional probability distribution of a clean point, not only the distance between a neighborhood point and the current point is considered, but also the probability of the neighborhood point being located on the underlying sampling surface is considered, thereby effectively avoiding the problems existing in the prior art, improving the quality of the predicted true value, and reducing the influence of outliers; secondly, multi-scale features of points in the noise point cloud are extracted, a score of the points is learned through an MLP, and low-noise points are obtained; the low-noise points are pre-filtered; finally, the pre-filtered point set is up-sampled to obtain a denoised point cloud, and the denoised point cloud is constrained by the density prior, thereby effectively improving the unsupervised point cloud denoising quality.

The application discloses a shipping accident analysis method, comprising the following steps: constructing a shipping accident knowledge graph; inputting a question, searching the shipping accident knowledge graph by using a search enhancement generation method, and generating an answer; wherein the search enhancement generation method comprises the following steps: analyzing the question by using a large language model, generating a query statement, searching the shipping accident knowledge graph according to the query statement, outputting nodes and edges matched with the query statement from the shipping accident knowledge graph, generating a Bayesian network according to the matched nodes and edges, generating a query result according to the conditional probability of a target node and its parent nodes and / or the posterior probability of the target node and its child nodes in the Bayesian network, inputting the query result into the large language model, and generating an answer according to the query result by the large language model. The application further discloses a shipping accident analysis device, a computer device and a storage medium. By using a large language model combined with a knowledge graph to extract and store shipping accident data, and by using a Bayesian network analysis model to analyze the shipping accident data, the occurrence probability of a complete cause-and-effect chain from causes to results of an accident can be obtained, so that objective and accurate causes and effects of a complex shipping accident can be sorted out, and reliable conclusion opinions can be provided for subsequent shipping business.

The invention discloses a power grid poisoning failure data recovery method and system based on attribute combination, and relates to the technical field of power grid data processing and information security, and the method comprises the steps: firstly marking a to-be-recovered data record and a failure attribute position after poisoning or failure data is detected; extracting various combinations of non-failure attributes in the record, and constructing a voucher group for estimating a failure value; counting all possible values and prior probabilities of failure attributes in the total data, and calculating the occurrence frequency of each voucher combination and the co-occurrence frequency of each voucher combination and candidate values; based on a conditional probability formula, the sum of the credibility of all the candidate values under all the voucher conditions is obtained, and the candidate value with the highest total credibility is selected to recover the failure position; and finally, writing the recovery value back to the data set to complete data recovery. High recovery accuracy can still be kept for high-failure-rate data, and key measurement data can be quickly recovered after a power system is subjected to data poisoning attack.

The invention discloses a reservoir fusion analysis method and system based on multiple sensors, belongs to the technical field of artificial intelligence, and aims to solve the technical problem of how to realize accurate sensing and comprehensive evaluation of a reservoir operation state and timely discover potential safety hazards. Comprising the following steps: collecting reservoir monitoring data, meteorological data and human activity data as original time sequence data, and constructing a data set based on the original time sequence data and a corresponding reservoir safety level; performing data preprocessing on the data set; screening out features with high mutual information and large information difference as target features, and constructing a sample set based on the target features and corresponding reservoir safety levels; constructing a reservoir monitoring model based on an RNN network, a C NN network, an attention mechanism and a conditional probability distribution layer; and inputting the preprocessed to-be-measured time series data into the trained reservoir monitoring model, and predicting and outputting the reservoir safety level and the corresponding probability value of the target reservoir through the reservoir monitoring model.

The invention discloses a generation power prediction method and device, electronic equipment and a storage medium, and belongs to the field of artificial intelligence. The method comprises the steps of obtaining background meteorological conditions of weather data prediction; inputting the background meteorological conditions into a trained conditional probability model to obtain conditional probability distribution output by the conditional probability model; disturbing the weather forecast data based on the conditional probability distribution to obtain disturbed weather forecast data; and inputting the disturbed weather forecast data into a preset power prediction model, and determining a power generation power prediction value according to an output result of the power prediction model. According to the embodiment of the invention, the conditional probability distribution of the weather forecast data space-time displacement error is predicted according to the background meteorological condition, the weather forecast data is disturbed based on the prediction result, and the power generation power is predicted according to the disturbed weather forecast data, so that the influence of the weather forecast data space-time displacement error on power generation power prediction is reduced; and the accuracy of power generation power prediction is improved.

The invention provides a machine generated text detection method and system based on direct differential learning, and relates to the technical field of artificial intelligence safety. Comprising the following steps of: regenerating or polishing and rewriting a human text by using a large language model, and constructing machine-generated text data so as to obtain a data pair of the human written text and the machine-generated text; based on the constructed data pair, the scoring model is optimized by adopting direct difference learning, and the optimization target is to maximize the conditional probability difference of the machine-generated text and minimize the conditional probability difference of the human text at the same time; obtaining a to-be-detected text, and calculating a conditional probability difference of the to-be-detected text by using the optimized scoring model; and based on the conditional probability difference, estimating the probability that the to-be-detected text belongs to the machine-generated text by adopting a reference clustering method. According to the method, a task target of machine generation text detection is explicitly modeled through direct differential learning, and the method has remarkable advantages in the aspects of detection robustness, cross-domain generalization ability and calculation efficiency.

The invention relates to the technical field of education intelligent evaluation systems, in particular to a civil education development evaluation method and system based on artificial intelligence, in the civil education development evaluation method and system based on artificial intelligence, through hierarchical aggregation calculation of a graph convolutional network, multi-dimensional features among nodes are dynamically integrated in a propagation path, the influence range of high-weight nodes is expanded, and the evaluation efficiency is improved. Low-weight nodes are replaced and reconstructed in an iteration process to form a resource linkage network with continuity and adaptive characteristics, a Bayesian network is adopted to carry out ratio reasoning on a resource proportion, resource adjustment is realized under budget and compilation constraint conditions, and through conditional probability updating of relatively high and relatively low nodes, the resource linkage network with continuity and adaptive characteristics is formed. According to the method, a distribution matrix with a self-balancing characteristic is established, so that resource input can be optimally adjusted according to a prior probability, node joint distribution calculation is used for capturing teacher stability and a course fluctuation trend, an observable risk area set is formed through statistical aggregation of fluctuation abnormal nodes, and an unstable structure is identified.

Embodiments of the present application provide a training method and an inference method of a flow matching generation model and related devices, which are used to improve the efficiency of the training process of the flow matching generation model. The method of the embodiments of the present application comprises: obtaining noise, a time t, a first modality object collected from the noise, and environmental features of the first modality object; inputting the noise, the time t, the first modality object, and the environmental features into an initialized flow matching generation model to obtain a predicted velocity field vector of a conditional probability path at a time t+1 output by the initialized flow matching generation model; calculating a loss between the predicted velocity field vector and a real velocity field vector by using a preset loss function, wherein the preset loss function comprises at least one of a first loss function and a second loss function, and a third loss function; and training the initialized flow matching generation model by using the loss and a back propagation algorithm until the flow matching generation model converges, to obtain a trained flow matching generation model.