Complex event identification method based on ontology model and probability reasoning

Abstract

The invention discloses a complex event identification method based on an ontology model and probability reasoning. The complex event identification method comprises the following steps of (1) modeling a sensor and an event in an intelligent environment by using the ontology model; (2) converting the ontology model into a Markov logic network model by using the semantic attribute of the description logic; (3) segmenting the continuously generated sensor data by using a segmentation method based on a place and a fixed time interval to form an event sequence as the input of a Markov logic network model; (4) carrying out probability reasoning on the Markov logic network model, so that the events occurring in the intelligent environment are identified. According to the complex event recognition method, the advantages of a knowledge driving method and a data driving method are fused, the intelligent environment can be accurately modeled, the time constraint relation between events and the uncertainty of sensing data are effectively processed, and the identification accuracy is improved.

Description

technical field [0001] The invention belongs to the field of event recognition in an intelligent environment in the Internet of Things, and in particular relates to a complex event recognition method based on an ontology model and probabilistic reasoning. Background technique [0002] With the rapid development of Internet of Things technology and wireless sensor network, it becomes possible to monitor the physical environment through low-cost and low-power sensors. By arranging a series of sensors in the physical environment, the state of the physical environment can be understood according to the sensing data. Such an environment is an intelligent environment, and the events in it reflect the state changes of the observed objects. The sensors and micro-processing units in the environment are called sensor. Sensors generate massive amounts of data every moment, reflecting the surrounding environment in an intelligent way. By accurately counting, analyzing, and synthesizin...

AI Technical Summary

Problems solved by technology

The knowledge-driven method has accurate modeling at the semantic level, no overfitting phenomenon, strong scalability, and the model is easy to reuse in another intelligent environment, but it cannot effectively deal with the inherent data uncertainty of the intelligent environment; data-driven The method is based on a large amount of sensor data for model training and parameter learning. Although it can effectively deal with data uncertainty, overfitting may occur and the scalability is poor.

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