Application loading method based on e-commerce cloud computing and artificial intelligence computing center

Abstract

The invention relates to the technical field of e-commerce cloud computing, in particular to an application loading method based on e-commerce cloud computing and an artificial intelligence computingcenter. According to the method, application loading data is divided into a series of relational network circle data in advance, and then demand trajectory field analysis is carried out on each pieceof relational network circle data through a target artificial intelligence calculation model to identify a demand trajectory field type to which each piece of relational network circle data belongs; and thus, the big data acquisition strategy updating accuracy is improved. After that, a demand trajectory field analysis result is further obtained, so that when it is detected that the relational network circle data meeting the preset service demand condition exists in the relational network circle data, it is rapidly determined that the target application loading object may be a big data collection service interaction field carrying key application loading interaction, and after the targeted strategy is updated, the subsequent low-confidence big data acquisition data volume is reduced, and the big data acquisition pertinence is improved.

Description

technical field [0001] The invention relates to the technical field of e-commerce cloud computing, in particular to an application loading method and an artificial intelligence computing center based on e-commerce cloud computing. Background technique [0002] With the rapid development of mobile Internet and e-commerce, e-commerce services usually provide users with a series of service extension components that can be customized, such as service extension components for e-commerce live broadcasting, service extension components for e-commerce information sharing, etc. With the maturity of cloud computing technology, users' e-commerce service terminals are interconnected with high-performance computing resources in the cloud, and service extension components can be better applied to e-commerce service terminals. [0003] In related technologies, in the process of mining big data for e-commerce users, it is usually based on each user's personalized intention and preference to...

AI Technical Summary

Problems solved by technology

[0004] In order to at least overcome the above-mentioned deficiencies in the prior art, the object of the present invention is to provide an application loading method based on e-commerce cloud computing and an artificial intelligence computing center, which divides the application loading data into a A series of relationship network circle data, and then analyze the demand track field of each relationship network circle data through the target artificial intelligence calculation model to identify the demand track field type to which each relationship network circle data belongs, thereby improving the accuracy of big data collection strategy update accuracy

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