Simulation data generation method for simulating real physical environment

Abstract

The invention provides a simulation data generation method for simulating a real physical environment. The simulation data generation method includes the steps: 1) receiving a series of real environment data; 2) dividing an acquisition time slot of the real environment data into a plurality of time intervals; 3) fitting space distribution of physical environment parameters based on a Gaussian process model according to data values of a plurality of different positions at the same time for each time interval to obtain the value of the physical environment parameter at an optional designated position at the time corresponding to the time interval; 4) modeling time trend according to the obtained data values at each position at different time to obtain a time trend function at the position; 5) generating simulation data of the physical environment parameters according to the designated time and position. Data points at different positions at each time interval are regarded as data points acquired at the same time. Massive simulation environment data can be generated at small overhead, and the simulation data generation method is high in configurability and universality.

Description

technical field [0001] The invention relates to the technical field of physical environment simulation, in particular to a method for generating simulation data for simulating a real physical environment. Background technique [0002] The Internet of Things application system is an important means for people to perceive and control the physical world. Most IoT applications use the real physical environment as input, and the real-time state of the physical environment will directly affect the execution results of the IoT application. Therefore, accurate and batch generation of IoT simulation data is of great significance to the development, debugging and testing of IoT applications . In the development stage of these systems, whether the impact of changes in physical world data on the execution status of the IoT system can be estimated will directly determine the reliability of the system. [0003] There have been attempts to model the physical environment in the traditiona...

AI Technical Summary

Problems solved by technology

The main problem of this type of method is that it has high computational overhead, which is unacceptable for the massive environmental data required for IoT experiments

[0005] To sum up, the schemes of simulating real physical environment changes in the prior art are either too expensive or based on industry knowledge, making it difficult to form a general method that can be used in the field of Internet of Things

This makes it difficult for existing technologies to use low computing overhead to evaluate the impact of changes in data characteristics in the environment on the Internet of Things system, resulting in the simulation effect of the Internet of Things often not reflecting the numerically related processing flow.

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