Panda population prediction method based on multi-environmental-membrane system

Abstract

The invention discloses a panda population prediction method based on a multi-environmental membrane system. The panda population prediction method comprises a processor, wherein the output end of theprocessor is electrically connected with a configuration file; the output end of the processor is electrically connected with a script file; the output end of the script file is electrically connected with a memory; and the output end of the memory is electrically connected with a robustness testing device and a sensitivity testing device. The panda population prediction method can perform symbolization on the evolutionary behavior of a concept model by inputting original pedigree data of pandas of two bases of GPBB and CCRCGP, designing the concept model of a multi-environment membrane system, and designing a calculation model of the multi-environment membrane system, selects a MeCoSim software platform to carry out panda population prediction simulation on the multi-environment membranesystem of the calculation model, takes core parameters such as original panda data and the statistical birth rate as initial data, selects a parameter uncertainty probability distribution method to predict the combined action of probability parameter values of different panda individual age groups, and is convenient for completing work of predicting the population number according to species evolution behaviors.

Description

technical field [0001] The invention relates to the technical field of ecological species modeling methods, in particular to a giant panda population prediction method based on a multi-environment membrane system. Background technique [0002] The study of ecosystem population diversity is conducive to better grasp the law of population development. The most commonly used methods in ecosystem data modeling are mathematical analysis methods such as ordinary differential equations and partial differential equations. However, ordinary differential equations cannot well capture the spatial dynamics and random effects of ecosystems. The above shortcomings are solved, but the modeling process is too complicated, and any minor changes need to rebuild the model. [0003] In the process of the existing giant panda population prediction method, it is difficult to predict the population number according to the evolutionary behavior of the species. Therefore, we propose a giant panda p...

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Problems solved by technology

The most commonly used methods in ecosystem data modeling are mathematical analysis methods such as ordinary differential equations and partial differential equations. However, ordinary differential equations cannot capture the spatial dynamics and random effects of ecosystems well. Although partial differential equations can solve overcome the above deficiencies, but the modeling process is too complicated, and any small changes require re-establishment of the model

[0003] In the process of the existing giant panda population prediction method, it is difficult to predict the population number based on the species evolution behavior. Therefore, we propose a giant panda population prediction method based on the multi-environmental membrane system

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