Scientific and Technological Innovation Thesis and Intelligent Computing Thesis: Exploration on Innovation Mode by Computational Intelligence Principle Abstract: The cultivation of scientific and technological innovation ability is an important aspect of undergraduate education, and it is of great significance to strengthen college students' scientific and technological innovation under the background of the country's vigorous promotion of scientific and technological innovation. Cultivating innovative talents is the center of university construction. Based on the principles and methods of computational intelligence, combined with the practice of national college students' innovation project, this paper makes a preliminary exploration on the method of constructing an efficient innovation team. Keywords: computational intelligence; Scientific research and training; At present, we should improve our independent innovation ability and build an innovative country. Higher education bears the important responsibility of cultivating innovative talents. Students' scientific and technological activities are of great significance for improving students' scientific and technological innovation ability and cultivating top-notch innovative talents. It is particularly important to build a group of innovative teams of college students who are determined to forge ahead and innovate boldly to improve students' innovative ability and teamwork ability. At present, the research of team theory needs to be deepened. It is a new attempt to use the basic theoretical principles and methods of computational intelligence to guide the construction of innovative project teams for college students. 1 Introduction to the basic theories and methods of computational intelligence was first defined by American scholar James C.Bezedek 1992. Broadly speaking, it is a calculation method based on bionics and some mechanisms of biological systems, such as biological evolution, cellular immunity, neural network, etc., which is abstractly described by mathematical language. It is intelligence based on numerical calculation and structural evolution, and it is an advanced stage of the development of intelligence theory. The main methods of computational intelligence are: artificial neural network, fuzzy system, evolutionary computation, etc. 1. 1 fuzzy computing fuzzy system is based on fuzzy set theory and fuzzy logic reasoning, trying to simulate the ability of human brain to express and solve imprecise knowledge at a higher level. In fuzzy systems, knowledge is stored in the form of rules. It uses a set of fuzzy IF—THEN rules to describe the characteristics of objects, and solves the uncertainty problem through fuzzy logic reasoning. Fuzzy system is good at describing and using the knowledge in the subject field, and has strong reasoning ability. 1.2 artificial neural network artificial neural network system is a complex network system formed by a large number of simple processing units, that is, neurons. In artificial neural network, the calculation is completed by the data flow in the network. In the process of data flow, each neuron receives the input data stream from its connected neuron, processes it, and then transmits the result to other neurons connected to it in the form of output data stream. The topological structure of the network and the connection weight (Wi) between neurons are determined by the corresponding learning algorithm. The algorithm constantly adjusts the structure of the network and the connection weight between neurons until the neural network produces the required output. Through this learning process, the artificial neural network can continuously and automatically acquire knowledge from the environment, and store this knowledge in the network in the form of network structure and connection weights. Artificial neural network has good self-learning, self-adaptation and self-organization, as well as the characteristics of large-scale and distributed information storage and processing, which is very suitable for dealing with incomplete and inaccurate information processing problems that need to consider multiple factors at the same time. 1.3 Evolutionary Computation During the evolution of nature for billions of years, organisms have formed an internal mechanism to optimize their own structure, and they can constantly learn from the environment to adapt to the ever-changing environment. For most living things, this process is accomplished through natural selection and sexual reproduction. Natural selection determines which individuals in the population can survive and reproduce: sexual reproduction ensures the mixing and recombination of genes in offspring. Inspired by this evolutionary process in nature, evolutionary computing starts with simulating the biological evolutionary process in nature, and explores the development and evolution law of some human intelligent behaviors from the gene level, thus solving the problem of how intelligent systems learn from the environment. 2 Enlightenment of the principle of computational intelligence in the practice of innovation team From the perspective of system theory, the construction of innovation team is a complex system optimization and control problem. Complex systems are: 1) adaptive/self-organizing. 2) Uncertainty. 3) Emergence characteristics. 4) finality. 5) evolution. 6) openness. These computational intelligence methods have the characteristics of self-learning, self-organization and self-adaptation, and have certain research value for the construction of innovative teams. 2. 1 The intelligent characteristics of self-learning, self-organization and adaptive calculation under the guidance of experts mention that fuzzy systems are good at describing and using empirical knowledge; Neural network is good at learning directly from data, and it is more advantageous to combine artificial neural network with expert system to build a hybrid system than to work alone. Under the guidance of relevant experts, the innovation team highlights the characteristics of students' free formation, independent management and self-service. On the premise of clarifying the team tasks, we should make some principled provisions on the number of team members, membership conditions, internal control system, etc., and give the team leader full power, such as determining the composition of team members, controlling internal funds, and dividing and assessing team members, so as to ensure their direct and effective management of team work. 2.2 The characteristics of cooperative and competitive consciousness computing intelligence mention that evolutionary computing is good at solving complex global optimization problems, with strong robustness and global optimization. The evolution of population is a natural selection process of survival of the fittest. The cornerstone of team building is the theory of cooperative competition. Deutsch has long pointed out that if people are in a decentralized and unrelated independent competitive relationship and think that there is no relationship between goals, then, with limited resources, people will be more selfish and their interests will conflict, which will lead to internal friction and interpersonal tension, and ultimately lead to low productivity and creativity. Dcutsch believes that people should have the same goal in the organization and cooperate with each other under the same goal. People with cooperative relationships will respect each other and enjoy information and resources. They will regard the progress of others as their own promotion, exchange views with each other and learn from each other's strengths. The progress of modern science shows that almost every scientific and technological achievement today is the result of multidisciplinary cooperation. The construction of scientific research team in colleges and universities is to implement this concept well and build this concept between moderate competition and cooperation. 2.3 Collaborative learning team integrated with the idea of computational intelligence In the study of human intelligent behavior, it is found that most human activities involve social groups composed of many people, and the solution of large-scale complex problems requires the cooperation of many people or organizations, and the relationship between teachers and students also emphasizes cooperation and common development. With the development of computer network, computer communication and concurrent programming, distributed artificial intelligence has gradually become a new research hotspot in the field of artificial intelligence. Although each agent works autonomously, many agents work cooperatively in the same environment, and the means of cooperation is mutual communication. The combination of computational intelligence and distributed artificial intelligence is to study how logically or physically dispersed intelligent actions coordinate their knowledge, skills and planning, and solve single-objective or multi-objective problems, so it also provides an effective way for designing and establishing large-scale complex intelligent systems or computer-supported collaborative learning. 2.4 Choose a team leader with strong comprehensive ability. It is mentioned that the control of complex system should use intelligent methods to deal with all kinds of uncertainties, which requires team leaders to have comprehensive ability to deal with complex problems. The scientific and technological innovation team should be a group composed of different types of people to achieve specific goals. To inspire and gather everyone's strength and be responsible for internal planning, organization, command, coordination and control, there must be a core figure, that is, academic leaders. Excellent academic leaders are essential elements of scientific and technological innovation teams in colleges and universities. Team leader is the center of communication and coordination inside and outside the team, the main source for other team members to obtain information such as work direction, specific tasks and work objectives, and the central link and link for the team to maintain morale, vitality and cohesion, which largely determines the academic level, scientific research style and cultural atmosphere of the whole team. At the same time, strengthen the overall coordination and organization of the team and improve the internal cohesion of the team. 2.5 Strengthen communication and share resources. The intelligent characteristics of computing refer to the adaptability and evolutionary mechanism based on information transmission. Then, the team members formed a partnership of close cooperation and sharing resources. Through close cooperation with each other, team members are no longer an independent individual, but a collective that takes responsibility and actively faces challenges. In this group, the resultant force of team members is far greater than the sum of each member's abilities. Therefore, in the construction of scientific research team, good communication channels can promote unity and cooperation among members, so that every member of the organization can do everything for the development of the organization. Then, the team members formed a partnership of close cooperation and sharing resources. Through close cooperation with each other, team members are no longer an independent individual, but a collective that takes responsibility and actively faces challenges. In this group, the resultant force of team members is far greater than the sum of each member's abilities. Therefore, in the construction of scientific research team, good communication channels can promote unity and cooperation among members, so that every member of the organization can do everything for the development of the organization. 2.6 Under the adjustment of computational intelligence mechanism, equipped with members with complementary advantages, the nonlinear complex system has emergent properties characteristics. Emergence means that components of different roles interact according to local or global behavior rules through various interaction methods. The type and state of components, the interaction between components and the behavior of the system are constantly changing with time. After a certain period of time, the local interaction between subsystems or basic units in the system has evolved into some unique and new properties as a whole, forming some patterns, which are embodied in emergent properties. The interaction between subsystems will lead to the macroscopic overall characteristics which are significantly different from those of a single subsystem. Emergence is also reflected in qualitative change. After the interaction between subjects begins, the system can self-organize, self-coordinate and self-strengthen, then expand and develop, and finally undergo qualitative change, that is, emerge. Conclusion The theory of computational intelligence is effective in dealing with the optimization and control problems of complex systems, and the inspiration of the principle of computational intelligence in the practice of innovative teams is various. At present, the research of team theory needs to be deepened. It is a new idea to use the principles and methods of computational intelligence to guide the construction of college students' innovative project teams. Reference: [1] Wang Haiying. Construction of academic team based on multi-agent co-evolution mechanism. After-school education in China. 25438+00, 7.[2] Wang Haiying. Evolution Mechanism of Enterprise Innovation Management Team Based on Cooperative Agent [C20 10 IEEE International Conference on Advanced Management Science (IEEE ICAMS 2010) .2010-07. [3] Li Huibo. Team spirit. Xinhua Publishing House. 2004.