17 results about "Evidence reasoning" patented technology

The invention discloses a structure monitoring data fusion probability abnormity early warning method based on streaming super-threshold and evidence reasoning. The method comprises the following steps: establishing a historical data set of each sensor as sample data; performing temperature effect separation on the sample data, and extracting response components after temperature effect removal as an initial data set of anomaly detection; determining an initial threshold value; constructing a super-threshold data set; fitting the super-threshold data set, and obtaining posterior probability distribution of shape parameters and scale parameters through Bayesian inference; calculating probability distribution of the early warning trigger value; calculating an anomaly certainty degree index; dynamically updating the probability distribution of the early warning trigger value through a streaming threshold exceeding method; and performing synthesis through an evidence fusion method to obtain a fusion anomaly certainty degree index. According to the method, the fusion anomaly certainty degree can be formed, conflicts between indexes can be better processed, early warning misinformation caused by faults of a single sensor is avoided, and certain data support is provided for maintenance of the structure.

The invention discloses an investment risk decision support method and system based on evidence reasoning and deep learning, and the method comprises the steps: obtaining investment risk data, and determining a risk tag; constructing a Widamp based on evidence reasoning; the Deep risk early warning model comprises a Wide component, a Deep component, a full connection layer and an output layer; the Wide component comprises a plurality of basic precaution devices, investment risk data output by each basic precaution device is classified into confidence of each risk label, and basic probability distribution is calculated by combining the weight of each basic precaution device; combining the basic probability distribution by using an evidence reasoning combination rule to obtain a first classification probability that the investment risk data is classified into each risk tag; the Deep component comprises a deep neural network model, and outputs a second classification probability that the investment risk data is classified into each risk tag; the two classification probabilities are connected with the output layer through the full connection layer, and the final classification probability is output. According to the method, investment risk prediction can be carried out, and the result has interpretability.

The invention relates to a credible evidence reasoning method, device and equipment for multi-source data fusion, a multi-source decision information system is constructed, in the multi-source decision information system, a support matrix is designed to quantify correlation among multi-source data attributes, and a relationship is evaluated by combining concepts of similarity and distance. Firstly, multi-source data of different scales are converted into confidence distribution, probability significance is given to the confidence distribution, and therefore generated evidence can be applied to multi-scale data fusion. Then, a credible evidence reasoning rule is constructed, and in the rule, the credibility of the evidence is determined by the weight and the reliability of the evidence; by integrating multi-source data samples of different scales, confidence distribution of results is derived. Therefore, the problem of difficulty in determining confidence distribution of evidences is solved.

The invention provides a novel power system comprehensive evaluation method based on hierarchical evidence reasoning. The novel power system comprehensive evaluation method comprises the following steps of S1, selecting an index of an lth layer; s2, constructing an index pairwise comparison matrix C and an index data matrix X at the same time; s3, respectively calculating a subjective weight W1 and an objective weight W2 of the index by adopting an analytic hierarchy process and a CRITIC method; s4, calculating the comprehensive weight W of the index by using Bayesian network reasoning; s5, selecting index data of the kth region; s6, calculating a utility average value uavg (Il + 1k) of the (l + 1) th layer of indexes by adopting an evidence reasoning method; s7, judging whether the region is the last region or not; and S8, judging whether the index is the last layer index or not. By applying the technical scheme, subjective judgment and objective analysis can be integrated to realize multi-layer fusion of evidences in an uncertain environment, a novel electric power system statistical index system with a multi-layer structure can be accurately evaluated, and the comprehensive evaluation score of the novel electric power system in each region can be obtained.

The invention discloses an intention recognition method based on multi-source evidence fusion reasoning, and belongs to the technical field of artificial intelligence, and the method comprises the following steps: S1, obtaining a multi-source heterogeneous evidence at least comprising a text, a behavior sequence and external knowledge data, converting the multi-source heterogeneous evidence into a standardized evidence unit comprising evidence content, a source reliability score and intention correlation degree, and storing the standardized evidence unit in a database; constructing an initial evidence set based on the evidence reasoning graph model; s2, inputting the initial evidence set into an inference engine based on a neural network, and executing multi-hop inference through multiple rounds of active query: analyzing evidence consistency in the first hop and generating a preliminary intention hypothesis and a question asking problem, and dynamically generating a new problem in the subsequent hop based on new evidence to directionally obtain supplementary information until a termination condition is met; s3, carrying out dynamic evidence fusion calculation; and S4, outputting a highest confidence degree intention conclusion according to a global fusion result, and generating an interpretable evidence chain report containing a complete multi-hop reasoning path and key evidence support.

The present invention discloses an interpretable false information detection method and device for credible evidence reasoning and spatio-temporal feature aggregation. The method includes: from two perspectives of the user's own authority and information semantic interaction, using the TrustRank random walk idea to calculate the credibility index of comments during the information dissemination process, taking the comments as evidence, and discovering high-quality evidence; combining the credibility index of the evidence, through the long short-term memory network and the graph attention network, aggregating the temporal neighborhood and spatial neighborhood information of the evidence, and selecting and combining the spatio-temporal structural features of the information to enrich the node semantic representation and enhance the reliable representation of the evidence; through the multi-head attention mechanism, modeling the semantic interaction between the evidence and the source information, capturing the false part of the source information, and at the same time modeling the implicit stance of the evidence capsule in a transparent manner through the dynamic routing mechanism. The present invention not only enhances the interpretability of the false information detection result, but also improves the discrimination performance.

The present invention discloses a complex system risk assessment method based on relevant evidence reasoning rules, which is used to deal with complex system risk assessment under uncertainty. The present invention constructs a universal risk assessment indicator system, and models and describes the risk indicators as evidence under the framework of identification (FoD). The evidence is unified by a transformation matrix, and the reliability and correlation coefficient of the evidence are clearly measured. The relevant evidence reasoning rule (ERR-DE) model constructs a multi-source information fusion framework, in which multiple pieces of evidence with different weights, reliabilities and correlation coefficients can be aggregated, and the relationship between risk indicators and system risks is established. A parameter optimization model is constructed to optimize all subjective evidence parameters through the idea of ​​expectation maximization.

The invention discloses a public opinion management method based on evidence reasoning, a product, a medium and equipment, and relates to the technical field of data mining and analysis, and the method comprises the steps: obtaining original public opinion data of a plurality of data sources; preprocessing the original public opinion data to obtain preprocessed public opinion data; performing emotional tendency scoring on the preprocessed public opinion data by adopting an emotional analysis model and a theme generation model to obtain an emotional score; based on the emotion score, a Dempster-Shafer evidence theory is adopted to obtain a comprehensive emotion score; and performing public opinion state analysis and response strategy formulation according to the comprehensive emotion score to complete public opinion management. The public opinion management efficiency and accuracy can be effectively improved.

The application discloses a video safety monitoring method and device based on evidence reasoning and a storage medium, and relates to the technical field of video reasoning. The method inputs video data and a monitoring question into a preset reasoning model, obtains a reasoning result output by the reasoning model, takes the reasoning result as an input parameter of a preset comprehensive evaluation function, determines a comprehensive evidence score of the reasoning result through the comprehensive evaluation function, determines a normalized answer corresponding to each candidate answer according to a preset mapping function, takes a key frame index set corresponding to the candidate answer as a reasoning branch index set of the normalized answer if the normalized answers corresponding to the candidate answers are the same, determines an evidence weighted total score corresponding to the normalized answer according to each reasoning branch index set corresponding to the normalized answer and each corresponding comprehensive evidence score, and finally determines a monitoring result in the normalized answer according to the evidence weighted total score, thereby improving the accuracy of the monitoring result.

The present application relates to a credible evidence reasoning method, apparatus and equipment for multi-source data fusion, which constructs a multi-source decision information system. In this multi-source decision information system, a support matrix is designed to quantify the correlation between multi-source data attributes, and the concepts of similarity and distance are combined to evaluate the relationship. First, multi-source data of different scales are converted into confidence distributions and given probabilistic meanings, so that the generated evidence can be applied to multi-scale data fusion. Then, a credible evidence reasoning rule is constructed, in which the credibility of the evidence is determined by its weight and reliability. By integrating multi-source data samples of different scales, the confidence distribution of the results is derived. This solves the problem of difficulty in determining the confidence distribution of evidence.

The invention discloses an aviation plug health assessment method based on evidence reasoning, and relates to the technical field of equipment health assessment, and the method comprises the steps: determining assessment indexes reflecting the health state of an aviation plug, and obtaining the data of the aviation plug under each assessment index; obtaining a reference value corresponding to each health level in each evaluation index, converting data of the aviation plug under each evaluation index into confidence distribution of the reference value based on the reference value, and forming evidence of each evaluation index; for each health level, fusing the evidence of each evaluation index by using an evidence reasoning algorithm to obtain the evidence of each input evaluation index, and mapping the evidence into the confidence of output relative to each health level; and calculating a final utility value for representing the health state of the aviation plug according to the utility value of each health level and the confidence coefficient. According to the method, information generated in the electrical performance testing process and the mechanical performance testing process is comprehensively utilized, the uncertainty in the evaluation process is reduced, and the credibility of the evaluation result is improved.

The application discloses an intention recognition method based on multi-source evidence fusion reasoning, and belongs to the technical field of artificial intelligence, and comprises the following steps: step S1: acquiring multi-source heterogeneous evidence including at least text, behavior sequence and external knowledge data, converting into standardized evidence units containing evidence content, source reliability score and intention correlation degree, and constructing an initial evidence set based on an evidence reasoning graph model; step S2: inputting the initial evidence set into a reasoning engine based on a neural network, performing multi-hop reasoning through multiple rounds of active query: the first hop analyzes evidence consistency and generates a preliminary intention hypothesis and a follow-up question, subsequent hops dynamically generate new questions based on new evidence to acquire supplementary information in a directional manner, and the process is continued until a termination condition is met; step S3: dynamic evidence fusion calculation; and step S4: outputting an intention conclusion with the highest confidence according to a global fusion result, and generating an explainable evidence chain report containing a complete multi-hop reasoning path and key evidence support.

The application discloses a network performance evaluation method based on dynamic evidence reasoning rules, which comprises the following steps: dynamically adjusting the reference value of a test index based on the change of a network state; using the adjusted reference value to convert monitoring data into a confidence distribution form, converting the test index into a corresponding evidence form according to the mapping relationship of IF-Then; fusing the evidences corresponding to multiple indexes based on the evidence reasoning rules to obtain an evaluation result; and optimizing the model parameters by using an optimization algorithm through setting the constraint conditions and optimization targets of parameters. The application further discloses a network performance evaluation device based on dynamic evidence reasoning rules. The network performance evaluation method based on dynamic evidence reasoning rules can more accurately reflect the actual performance of a network in different scenarios according to the change of the network state, and meets the evaluation requirements of different network environments and business types.

The invention relates to the technical field of natural language processing, in particular to a document-level relation extraction method based on collaborative evidence reasoning, which comprises the following steps of: preprocessing and uniformly coding an input document to obtain semantic representations of entities and sentences in the document; constructing a collaborative heterogeneous graph between the entity pairs and the sentences, updating node representation through a graph attention network, and calculating a prediction probability of taking each entity pair and each sentence as evidence; an evidence sentence set is retrieved from the document for each entity pair, a pseudo document is constructed based on the evidence sentence set, the pseudo document is a new document formed by splicing evidence sentences according to the sequence of the original document, entity marks are reserved, and the evidence sentences only reserve sentences with the evidence prediction probability larger than or equal to 0.5 in the collaborative evidence layer; and taking the original document and the pseudo document as double-source input, and reasoning through a self-adaptive fusion function to predict a final relationship between the entity pairs. The method has the advantages of being high in relation extraction precision, high in multi-hop reasoning capacity and good in generalization.

The present application relates to the field of fact checking, and provides a fact checking method and system based on debiased reasoning enhancement and joint relationship aggregation, which comprises the following steps: performing semantic analysis on a statement text to obtain a tendency label; obtaining an initial input text from the statement text and an original document; inputting the initial input text into a checking model to obtain evidence text; performing complex plane projection to obtain fusion bias of the evidence text, generating a category preference prompt word, and performing debiased reasoning enhancement on the evidence text to obtain debiased reasoning enhancement text; obtaining a debiased evidence vector and a debiased reasoning vector from the debiased reasoning enhancement text; expanding the debiased evidence vector and the debiased reasoning vector through a binary Taylor series to obtain evidence reasoning features; expanding the debiased reasoning vector through a binary Taylor series to obtain reasoning aggregation features; and inputting the evidence reasoning features and the reasoning aggregation features into a multilayer perception machine classifier to obtain fact checking output. The present application is used for completing fact checking.

The invention discloses a network performance evaluation method based on a dynamic evidence reasoning rule, and the method comprises the steps: dynamically adjusting a reference value of a test index based on the change of a network state; the adjusted reference value is used for converting the monitoring data into a confidence distribution form, and the test index is converted into a corresponding evidence form according to an IF-Then mapping relation; fusing the evidences corresponding to the plurality of indexes based on an evidence reasoning rule to obtain an evaluation result; and by setting constraint conditions and optimization targets of the parameters, optimizing the model parameters by adopting an optimization algorithm. The invention further discloses a network performance evaluation device based on the dynamic evidence reasoning rule. According to the network performance evaluation method based on the dynamic evidence reasoning rule, the actual performance of the network in different scenes can be reflected more accurately according to the change of the network state, and the evaluation requirements of different network environments and service types are met.

This invention discloses a probabilistic anomaly early warning method for structural monitoring data fusion based on streaming over-thresholding and evidence-based reasoning. The method includes: establishing historical datasets for each sensor as sample data; separating the sample data by temperature effects and extracting the response components after removing the temperature effects as the initial dataset for anomaly detection; determining the initial threshold; constructing an over-threshold dataset; fitting the over-threshold dataset and obtaining the posterior probability distributions of shape and scale parameters through Bayesian inference; calculating the probability distribution of early warning trigger values; calculating the anomaly certainty index; dynamically updating the probability distribution of early warning trigger values ​​using the streaming over-thresholding method; and synthesizing the results using the evidence fusion method to obtain the fused anomaly certainty index. This invention can form a fused anomaly certainty index, thereby better handling conflicts between indicators, avoiding false alarms caused by single sensor failures, and providing data support for structural maintenance.