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Edge computing is a technology that moves data processing and analysis functions from the traditional cloud computing center to the edge devices near the data source, and advances computing power and storage resources from the cloud to the edge of the network. its goal is to reduce the delay of data transmission and network congestion, improve the efficiency and real-time performance of data processing, and improve the performance and reliability of the overall system. Edge computing technology can make the data processing and analysis process as close to the data source as possible by running applications and algorithms on physical devices, sensors, smartphones and other terminal devices. in order to achieve faster response, lower latency and more efficient and real-time data processing. For example, in the field of self-driving, edge computing can make the vehicle analyze and respond to the changes of the surrounding environment more quickly and improve the safety and efficiency of driving. In the field of smart home, faster response and better user experience can be achieved by putting tasks such as speech recognition and image processing on edge devices.

Real-time and data privacy protection are important advantages of edge computing technology. Edge computing deploys computing resources and intelligent algorithms on edge devices closer to the data source, so that data processing and analysis can be carried out locally without the need to transfer to the cloud for processing. This local processing method greatly reduces the delay of data transmission and makes real-time data processing possible. In addition, edge computing pushes data processing and analysis to the edge of the network, so that the data does not have to be transmitted to the cloud for processing, thus reducing the risk of data being stolen or tampered with in the process of transmission. Edge computing can process and analyze data locally, reduce the possibility of data exposure in the external environment, and improve the degree of data privacy protection.

It is reported that Weimei holography (NASDAQ:WIMI) applies artificial intelligence technology to edge computing and actively explores the technological innovation and application of artificial intelligence edge computing. Artificial intelligence plays a vital role in edge computing, which can further improve the efficiency and flexibility of edge computing, and help to achieve intelligent, efficient and personalized services. By deploying artificial intelligence algorithms on edge devices, we can respond to user requests more quickly, reduce network latency, and make intelligent decisions and controls according to real-time data and user needs.

Artificial intelligence can carry out real-time data analysis and processing on edge devices. Edge devices usually have limited computing and storage resources, while artificial intelligence technology can make complex data analysis and processing on edge devices by means of model compression and optimization algorithms. For example, in an autopilot system, people can recognize faces or vehicles in real time through artificial intelligence algorithms, and conduct real-time behavior analysis, without the need to transmit a large amount of data to the cloud for processing. In addition, artificial intelligence can also improve the intelligent level of edge devices. By deploying deep learning models and machine learning algorithms on edge devices, edge devices can have stronger intelligent capabilities and can independently analyze and make decisions without relying on cloud instructions and feedback. For example, smart home systems can automatically learn users' habits and preferences through artificial intelligence algorithms, and actively adjust the working status of devices according to the needs of users.

At the same time, artificial intelligence can improve the security and privacy of edge computing. Data in edge computing is usually sensitive, while artificial intelligence can provide a more secure and reliable edge computing environment by means of data, privacy protection and anomaly detection. For example, in intelligent systems, artificial intelligence can protect patient data and privacy, and ensure the security and privacy of patient data. Thus it can be seen that artificial intelligence plays an important role in edge computing, which can not only improve the data processing ability and intelligent level of edge devices, but also improve the security and privacy of edge computing.

Cloud edge collaboration is an important development direction of artificial intelligence edge computing technology in the future. With the increasing intelligence of edge devices, edge computing will be able to undertake more computing tasks and data processing work. Cloud-edge collaboration technology closely collaborates between cloud and edge devices and intelligently distributes computing tasks between cloud and edge devices to achieve more efficient computing and data processing. Through cloud-edge collaboration, we can reduce the computing pressure on the cloud, improve the real-time and response speed of edge computing, and better protect the privacy and security of user data. Adaptive learning is another important development direction of edge computing technology of artificial intelligence in the future. Edge devices usually have limited computing and storage resources and are unable to train and learn complex models. Adaptive learning technology can automatically adjust the complexity and size of the model according to the computing power and storage capacity of edge devices, and realize real-time learning and reasoning on edge devices. Through adaptive learning, edge devices can flexibly optimize and update the model according to different application scenarios and requirements, so as to improve the effect and accuracy of learning.

With the increasing intelligence of edge devices, edge devices in the future will have stronger computing and perception capabilities, and will be able to achieve more complex artificial intelligence tasks. Intelligent edge devices will be able to process data and make decisions independently, reduce dependence on the cloud, and improve real-time and response speed. Edge computing ecosystem is another important development direction of artificial intelligence edge computing technology in the future. The edge computing ecosystem will include edge devices, cloud computing resources, network communications and application services. The future edge computing ecosystem needs to establish perfect technical standards and specifications to realize the interoperability and data sharing between devices. The edge computing ecosystem also needs to establish an open cooperation mechanism to allow more developers to participate in it and jointly promote the development of artificial intelligence edge computing technology.

At present, the artificial intelligence edge computing technology of WIMI micro-beauty holography is developing rapidly, and it has brought many advantages in real-time, cost-effective and so on. With the continuous progress of technology and the expansion of application scenarios, the development prospect of artificial intelligence edge computing technology will be broader.

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