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Application of industrial big data in the automotive industry

application of industrial big data in the automotive industry

02:25 source://

original title: application of industrial big data in the automotive industry

automotive enterprises are basically controlled by the group, which is relatively lack of big data support. Now, if we want to really do a deep and thorough management and control of the group, we must be based on big data, and take into account the differences between the business objectives and the existing reality and theory of various businesses Only after the business objectives, management objectives and product objectives are specifically quantified can we control them according to this objective, such as Leeb hardness tester, Rockwell hardness tester, Brinell hardness tester, shore hardness tester, shore hardness tester, Babbitt hardness tester, microhardness tester, Mohs hardness tester, Vickers hardness tester and Wechsler hardness tester. Therefore, we need to fully integrate big data technology into all business processes

first, we need to accurately identify user needs with big data. What exactly are user needs? We actually need to use data to support. The data source is not comprehensive, and the analysis of data is not accurate, so we need to accurately identify user needs with big data. The second is the accurate analysis of competitive products. Every enterprise studies the cars of competitors. We have established a competitive product analysis library, which requires the processing technology of big data to dig out the most valuable ones. If dozens of models are collected early, it is difficult to judge by looking for them manually. The third is unified modeling and simulation optimization based on big data. This model can be reused, which greatly improves the efficiency of our research and development. Fourth, collaborative development of knowledge sharing based on big data uses electronic tensile machine in the process of mixture development. Fifth, virtual test and calibration based on big data. Analyzing the extreme high and low temperatures in winter and summer, a new product of a car can be complete after a year. If there is big data for simulation, the original historical data can be used to accelerate, which can improve the success rate of our product research and development, improve the level of research and development, and shorten the research and development cycle

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