Study On The Evolution Mechanism Of Knowledge Network Of R&D Projects

The R&D project is a knowledge-intensive project with a long-term development process,a large number of participants,and a wide range of knowledge areas.With the realization of the project goal,the amount of knowledge in the development process shows a geometric growth,the knowledge relationship between the personnel and the social relationship are complex and intertwined,and the knowledge network is born in the interaction behavior.How to standardize and guide the development of knowledge networks and improve innovation performance has become an urgent problem to be solved.This paper considers the internal knowledge network of enterprise R&D project team as the research object,analyzes the motivation and environmental factors of knowledge network evolution from the perspective of project management and knowledge management,and conducts simulation analysis of network evolution based on complex network theory,in order to build a more reasonable Providing a good foundation for knowledge network structure,improving knowledge network level,and efficiently innovating knowledge.On the basis of combing the literature,this paper proposes the motivation of knowledge network evolution from the three dimensions of demand,behavior and individual,namely task demand,knowledge activity and node attribute.Considering the particularity of the knowledge network in the context of the project,this paper proposes 10 environmental factors from the three aspects of cultural atmosphere,knowledge management system and organizational foundation,and qualitatively analyzes the impact of these factors on member behavior and network evolution.The Netlogo software was used to carry out simulation experiments based on the NW small world model,and the results were discussed and analyzed,and management suggestions were given.Firstly,from the three perspectives of task type,task pressure and task complexity,the role of task requirements in network evolution is analyzed,which is mainly reflected in the knowledge learning of members and the frequency of knowledge flow.Secondly,from the source of knowledge growth,the knowledge flow and knowledge learning within the team are used to promote the evolution of the network.On the one hand,in order to meet the task requirements,the project members establish connections with each other,the network density increases,the average path length decreases,and certain In the phenomenon of aggregation,the nodes exchange knowledge under the influence of absorption and feedback,and the knowledge stock of both parties grows in the process of knowledge flow.On the other hand,through the continuous completion of tasks,members gain empirical knowledge from them,and members with active learning will not only improve the knowledge stock but also increase the heterogeneity of network knowledge through external private contacts.Thirdly,it analyzes the influence of attributes on their own knowledge and behavior from the perspective of the ability and will of the node.It believes that environmental factors indirectly affect the scale and structure of the network through the influence of knowledge flow willingness and knowledge learning willingness.Finally,Netlogo software is used to simulate the NW small world model to simulate the knowledge learning and knowledge flow behavior of nodes.The average shortest path length,clustering coefficient and degree distribution reflect the time series variation of the network structure.The average knowledge stock and standard deviation are used to reflect the knowledge distribution and knowledge level changes,and the simulation results are discussed and analyzed.The main conclusions are as follows:(1)With the increase of the probability of reconnection,the average knowledge stocks tend to rise first and then fall,and the variance of knowledge stocks grows more and more slowly.(2)Task requirements have a positive impact on knowledge flow andknowledge learning within a certain range.(3)The stronger the absorption capacity of the node,the larger the overall knowledge stock,but the influence of the node feedback ability on the knowledge stock is not significant.(4)The nodes with high initial knowledge level mainly rely on knowledge learning to realize knowledge accumulation.The nodes with low knowledge level mainly rely on internal knowledge flow to obtain the knowledge stock,and the nodes with medium knowledge level perform similarly in the two knowledge behaviors.(5)Environmental factors affect the knowledge learning effect by influencing the willingness of knowledge to change the relationship of knowledge,and indirectly affect the evolution of network size and structure.