Quantitative analysis method of ThingML (Modeling Language) model under uncertain environment

Abstract

The invention discloses a quantitative analysis method of a ThingML (Modeling Language) model under uncertain environment. The quantitative analysis method comprises the following steps: 1) expanding the language and the semantics of the ThingML, causing the ThingML to model uncertainty in the environment, and providing required quality-of-service requirements for a user; 2) utilizing Java to analyze to obtain the meta-model of the ThingML, and obtaining each required field of the ThingML; 3) converting the meta-model of the ThingML into a NPTA (Networks Priced Timed Automata) model, which includes foreground and back-end configuration; and 4) converting the quality-of-service property of the user into a query statement of UPPALL-SMC (Statistical Model Checking), and carrying out quantitative analysis on the system. The quantitative analysis method can evaluate the quality of service of the application of the Internet of Things so as to revise a corresponding design level to guarantee the quality of service.

Description

technical field [0001] The invention belongs to the field of computers, and in particular relates to a quantitative analysis method of a ThingML (object modeling language-a modeling language for Internet of Things application) model in an uncertain environment. Background technique [0002] Under the existing ThingML-based development method, there are the following deficiencies: [0003] 1. Quantitative analysis cannot be performed, that is, the performance indicators of the code generated by ThingML cannot be evaluated before the code is deployed. Although correctness can be guaranteed, the quality of service of IoT applications cannot be guaranteed. [0004] 2. There is no uncertainty description semantics, because the entities in the Internet of Things are in the real environment, and the real environment is uncertain, that is, it will show some changes. Uncertainty analysis cannot be performed due to the lack of semantics for describing environmental uncertainties. ...

AI Technical Summary

Problems solved by technology

[0003] 1. Quantitative analysis cannot be performed, that is, the performance indicators of the code generated by ThingML cannot be evaluated before code deployment

Although correctness can be guaranteed, the quality of service of IoT applications cannot be guaranteed

[0004] 2. There is no uncertainty description semantics, because the entities in the Internet of Things are in the real environment, and the real environment is uncertain, that is, there will be some changes

Uncertainty analysis cannot be performed due to the lack of semantics for describing environmental uncertainties

[0005] This inadequacy leads to a problem: it is difficult to evaluate the quality of the ThingML model itself, so the reliability of the software cannot be guaranteed

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