State threat assessment method based on fuzzy dynamic Bayesian network

Abstract

The invention discloses a state threat assessment method based on a fuzzy dynamic Bayesian network. The method includes extracting data to be processed from acquired data; processing the acquired data in a jamming proofing manner, and processing the data processed in the jamming proofing manner in a fuzzy manner; performing knowledge representation on the process of battlefield state threat assessment, determining concept and expression of events and attributes, and establishing a dynamic Bayesian network model about battlefield threat assessment; inputting fuzzy variables into the network model to obtain the fuzzy dynamic Bayesian network, performing inference on the fuzzy dynamic Bayesian network to acquire a state forecasting result, and acquiring the threat assessment grade based on the state forecasting result. According to the method, the dynamic Bayesian network is established, knowledge representation is performed on data changing with the time, timeliness and effectiveness of the data are enhanced, the data processed in a fuzzy manner are inputted as inputting evidences, accuracy and efficiency of inference are improved, and a semantic model of the state threat assessment can be expressed better.

Description

technical field [0001] The invention relates to the field of computer software engineering, in particular to a situational threat assessment method based on a fuzzy dynamic Bayesian network. Background technique [0002] The modern battlefield is complex and ever-changing, and battlefield information is highly uncertain and incomplete. Combat commanders need to consider many complex factors and make timely and accurate decisions. Therefore, it is particularly important to estimate and analyze the threat situation on the battlefield. At the same time, war is an open system that can interact with the external environment. The information collected may contain some inaccurate data, and changes in subtle factors in the external environment will have a huge impact on the outcome of the battlefield. Therefore, when evaluating the threat of the battlefield situation, it is necessary to collect a large amount of information for processing, consider many complex factors, and conduct...

AI Technical Summary

Problems solved by technology

[0003] There are still some deficiencies in some current methods of inferring battlefield situation threats, such as the use of expert systems for reasoning, which is relatively slow and highly dependent on expert knowledge; template matching methods do not take into account real-time changes in the battlefield; fuzzy The reasoning method has a stronger ability to express numerical values, but it has no self-learning function

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