International definition
By optimizing the four basic requirements of building structure, system, service and management and their internal relations, we can provide a building environment with reasonable investment, high efficiency, comfort and convenience.
Domestic standard
The revised national standard "Design Standard for Intelligent Building" (GB/T503 14-2006) defines intelligent building as "building as a platform, with information facilities system, information application system, building equipment management system, public safety system, etc., which integrates structure, system, service, management and their optimized combination to provide people with safety, efficiency and convenience.
The original national standard "Design Standard of Intelligent Building" (GB/T503 14-2000) defined intelligent building as "building as a platform, building automation equipment BA, office automation OA and communication network system CA, integrating structure, system, service and management and their optimized combination to provide people with a safe, efficient, comfortable and convenient building environment".
According to the definition of Shanghai, smart home "is a home information platform which is established by using modern computer, information communication and system integration technology, and will be interconnected with home equipment and systems through home network and managed in a unified way to provide a comfortable, convenient, safe, energy-saving and environmentally friendly home living environment".
Internal relations, optimized design, provide an elegant, comfortable, convenient and highly safe environmental space with reasonable investment and high efficiency. Intelligent building can help building owners, property managers and owners realize that they get the greatest benefits in terms of cost, comfort, business activities and personal safety. ".
market analysis
According to statistics, in 20 12 years, the investment in intelligent buildings accounts for about 5%-8% of the total construction investment, and some of them can reach 10%, mainly including the investment in intelligent systems of residential quarters and public buildings. In 20 12 years, the proportion of intelligent buildings in new buildings is 70% in the United States, 60% in Japan and less than 40% in China. Calculated by 86 billion yuan in 20 12 years, even if there is no growth in the next few years, the market scale in 20 12-2020 will reach 1 trillion.
Project composition
Building intelligent engineering, also known as weak current system engineering, mainly refers to communication automation (CA), building automation (BA), office automation (OA), fire control automation (FA) and security automation (SA), referred to as 5A. The systems included are:
Computer management system engineering, building equipment automatic control system engineering, communication system engineering, security monitoring and anti-theft alarm system engineering, satellite and shared TV system engineering, garage management system engineering, integrated wiring system engineering, computer network system engineering, broadcasting system engineering, conference system engineering, video-on-demand system engineering, intelligent residential property management system engineering, visual conference system engineering, large-screen display system engineering, intelligent lighting and audio control system engineering, fire alarm system engineering, computer room engineering.
computing technology
Intelligent control is based on control theory, computer science, artificial intelligence, operational research and other disciplines, and expands related theories and technologies, among which fuzzy logic, neural network, expert system, genetic algorithm and adaptive control, self-organizing control and self-learning control are widely used.
Expert system
Expert systems use expert knowledge to describe specialized or difficult problems. Expert control composed of expert system, whether expert control system or expert controller, has relatively high engineering cost, and also involves problems such as difficulty in automatic acquisition of knowledge, lack of self-learning ability, narrow knowledge range and so on. Although expert system has been successfully applied to solve complex advanced reasoning, the practical application of expert control is relatively few.
fuzzy logic
Fuzzy logic uses fuzzy language to describe the system, which can not only describe the quantitative model of application system, but also describe the qualitative model of application system. Fuzzy logic can be applied to any complex object control, but it is easier to realize simple application control in practical application. Simple control refers to the control of single-input single-output system (SISO) or multi-input single-output system (MISO), because with the increase of input and output variables, the reasoning of fuzzy logic will become very complicated.
genetic algorithm
As an uncertain quasi-natural stochastic optimization tool, genetic algorithm has the characteristics of parallel calculation and fast search for global optimal solution. It can be combined with other technologies to optimize the parameters, structure or environment of intelligent control.
neural network
Neural network is a method that uses a large number of neurons to adjust and learn according to a certain topological structure. It can express rich characteristics: parallel computing, distributed storage, variable structure, high fault tolerance, nonlinear operation, self-organization, learning or self-learning. These characteristics are the institutional characteristics that people have long pursued and expected. It has unique ability in parameter control, structure or environment control, self-organization and self-learning of intelligent control.
Neural network can be applied to the control of any complex object like fuzzy logic, but it is different from fuzzy logic in multivariable control of single-input multi-output system and multi-input multi-output system. In SIMO systems and MIMO systems represented by fuzzy logic, the functions of fuzzy reasoning, fuzzy solving process and learning control are often realized by neural networks. Fuzzy neural network technology and neurofuzzy logic technology: As the main technologies of intelligent control, fuzzy logic and neural network have been widely used. There are similarities and differences between them. Their similarity is that they can be used as general approximators to solve nonlinear problems and can be applied to controller design. The difference is that fuzzy logic can describe the system with language information, but neural network can't; Applying fuzzy logic to controller design, its parameter definition has clear physical significance, so an effective initial parameter selection method can be proposed. Initial parameters (such as weight, etc. ) can only be selected at random. However, in the learning mode, the parameters of the neural network can be set to meet the behavior needed for control after various trainings. Fuzzy logic and neural network both imitate the operation mechanism of human brain. It can be considered that neural network technology imitates the hardware of human brain and fuzzy logic technology imitates the software of human brain. According to the respective characteristics of fuzzy logic and neural network, the combined technology is fuzzy neural network technology and neurofuzzy logic technology. Fuzzy logic, neural network and its hybrid technology are suitable for the combination or comprehensive cross combination of related technologies and control modes of various learning modes, and it is a main feature of intelligent control technology to form intelligent control systems and intelligent controllers with different styles and functions.
For more information about bid writing and improving the winning rate, click on the bottom customer service for free consultation.