农业和水产养殖系统统一建模和仿真的可编程过程结构基础

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2024-03-01 DOI:10.1016/j.inpa.2022.10.001
Monika Varga, Bela Csukas
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引用次数: 0

摘要

本研究论文定义了一种非常规建模和模拟方法的理论基础和计算实施,其灵感来自于解决生物、农业、水产养殖和环境系统问题的需要。具有挑战性的实际问题是开发一个框架,用于自动生成因果关系正确、基于平衡的统一模型,该框架还可用于有效耦合各种模型(复杂的特定领域模型、基于传感器数据处理的模型、上层优化驱动的模型等)。这项创新所要解决的科学问题是,结合系统理论的功能基础、网状理论的结构方法和基于代理建模的计算原理,开发可编程过程结构(PPS)。PPS 为底层复杂系统自动生成易于扩展和连接的统一模型提供了一个新颖的框架。PPS 模型可以从一个状态和一个过渡元原型以及面向过渡的过程结构描述中生成。模型由统一的状态和过渡元素组成。包含原型元素的本地程序也来自元原型,负责具体情况的计算。PPS 结构的完整性和一致性以元原型为基础,用于区分基于保护法的措施和信号。模拟的基础是状态和转换元素之间的数据流,以及这些元素与其计算原型之间基于统一的数据传输。这种架构及其基于人工智能语言(Prolog)的实现方式,可以方便地整合各种领域和任务的特定模型。一个简单的例子有助于更好地理解。在一些初步应用的基础上,讨论了最近整合的通用方法的功能,重点是最近研究的农业和水产养殖案例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Foundations of Programmable Process Structures for the unified modeling and simulation of agricultural and aquacultural systems

This research paper defines the theoretical foundations and computational implementation of a non-conventional modeling and simulation methodology, inspired by the needs of problem solving for biological, agricultural, aquacultural and environmental systems. The challenging practical problem is to develop a framework for automatic generation of causally right and balance-based, unified models that can also be applied for the effective coupling amongst the various (sophisticated field-specific, sensor data processing-based, upper level optimization-driven, etc.) models. The scientific problem addressed in this innovation is to develop Programmable Process Structures (PPS) by combining functional basis of systems theory, structural approach of net theory and computational principles of agent based modeling. PPS offers a novel framework for the automatic generation of easily extensible and connectible, unified models for the underlying complex systems. PPS models can be generated from one state and one transition meta-prototypes and from the transition oriented description of process structure. The models consist of unified state and transition elements. The local program containing prototype elements, derived also from the meta-prototypes, are responsible for the case-specific calculations. The integrity and consistency of PPS architecture are based on the meta-prototypes, prepared to distinguish between the conservation-laws-based measures and the signals. The simulation is based on data flows amongst the state and transition elements, as well as on the unification based data transfer between these elements and their calculating prototypes. This architecture and its AI language-based (Prolog) implementation support the integration of various field- and task-specific models, conveniently. The better understanding is helped by a simple example. The capabilities of the recently consolidated general methodology are discussed on the basis of some preliminary applications, focusing on the recently studied agricultural and aquacultural cases.

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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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