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Our country’s equipment manufacturing industry ranks among the best in all developing countries, but compared with developed countries, there is still a long way to go. It is not only the backwardness of various technologies, but also the interference of other countries. Although our country's equipment manufacturing industry is not as advanced as the advanced technology of developed countries, we still have to stick to our original aspirations, do not underestimate ourselves, and be good at absorbing and learning from the strengths of others to make up for our own weaknesses. While not working behind closed doors and while absorbing technology from other countries, we can make use of our strengths to make up for our weaknesses and develop our own industrial technology. This paper studies the evolution trend of innovation network structure and at the same time studies the evolution mechanism of advanced equipment manufacturing innovation network structure from the perspective of complex systems. The explained variable in this article is green total factor productivity. The variable adopts the Malmquist–Luenberger global super-efficiency index model. There are two main explanatory variables. One is the heterogeneity that affects the efficiency of industrial evolution, including factor heterogeneity, structural heterogeneity, and environmental heterogeneity, and the other is the interaction term of equipment manufacturing specialization agglomeration degree dummy variable multiplied by factor heterogeneity. The regional economic development level is added to the model as a control variable. In the selection of measurement indicators, the per capita GDP is used as the control variable. The experimental results show that each sample is tested in pairs, and the standard error level of the mean is 0.018, which is less than 0.05, indicating that the efficiency of the equipment manufacturing industry’s economic correlation spatial network has a significant impact on the overall economic development level of the industry. The reduction in spur helps to increase economic output.
Our country’s equipment manufacturing industry ranks among the best in all developing countries, but compared with developed countries, there is still a long way to go. It is not only the backwardness of various technologies, but also the interference of other countries. Although our country's equipment manufacturing industry is not as advanced as the advanced technology of developed countries, we still have to stick to our original aspirations, do not underestimate ourselves, and be good at absorbing and learning from the strengths of others to make up for our own weaknesses. While not working behind closed doors and while absorbing technology from other countries, we can make use of our strengths to make up for our weaknesses and develop our own industrial technology. This paper studies the evolution trend of innovation network structure and at the same time studies the evolution mechanism of advanced equipment manufacturing innovation network structure from the perspective of complex systems. The explained variable in this article is green total factor productivity. The variable adopts the Malmquist–Luenberger global super-efficiency index model. There are two main explanatory variables. One is the heterogeneity that affects the efficiency of industrial evolution, including factor heterogeneity, structural heterogeneity, and environmental heterogeneity, and the other is the interaction term of equipment manufacturing specialization agglomeration degree dummy variable multiplied by factor heterogeneity. The regional economic development level is added to the model as a control variable. In the selection of measurement indicators, the per capita GDP is used as the control variable. The experimental results show that each sample is tested in pairs, and the standard error level of the mean is 0.018, which is less than 0.05, indicating that the efficiency of the equipment manufacturing industry’s economic correlation spatial network has a significant impact on the overall economic development level of the industry. The reduction in spur helps to increase economic output.
As the “chaotic” edge of innovation system has strong innovation potential and is easy to form and develop the emerging technology innovation network, the formation process of emerging technologies and their relationship with the development trajectory of original technologies are analyzed, and the evolution mechanism of innovation network of advanced manufacturing industry is deeply studied combined with life cycle theory. Firstly, empirical analysis is carried out by collecting patents data in the industrial robotics field. Then, the IPC co-occurrence network and patentee citation network are plotted by combining patents citation analysis with social network analysis. Next, the technical characteristics and knowledge flow characteristics of an advanced manufacturing innovation network are verified by calculating various indicators of the network. Finally, the empirical results show that the technology structure in the field of industrial robotics has high heterogeneity, wide integration among technical fields, and knowledge flow network has a small-world effect, characterized by easy flow, wide flow direction, high efficiency, fuzzy network boundary, and numerous and diversified core the key players in innovation.
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