883results about How to "Reduce computing pressure" patented technology

The invention discloses an Internet of things networking system and method based on edge computing. The method comprises the following steps that: terminal equipment transmits scene data to intelligent gateway equipment, wherein the scene data include numerical data collected by a sensor as well as streaming media data collected by a pickup and the camera; the intelligent gateway equipment performs service processing on the received scene data, and is responsible for uplink and downlink data transmission; and a data aggregation platform receives periodically reported data from the intelligentgateway equipment and stores the received data in a database for an intelligent algorithm platform to train and optimize an algorithm file, wherein the algorithm file is used for updating an intelligent decision module of the intelligent gateway equipment to optimize service scenes. The scheme realizes the edge computing of the service scenes through the intelligent gateway equipment, thereby effectively improving the processing efficiency and real-time performance. In conjunction with the updating of a service algorithm module of an intelligent gateway, flexible adaptation to the service demands and environmental changes of different scenes can be realized, and the flexibility of the intelligent gateway is improved. When a center or network fails, normal operation of terminal scene services can be ensured, thereby achieving relatively high practical and popularization value.

The invention relates to a Gaussian distribution resampling Rao-Blackwellized particle filter based mobile robot simultaneous localization and mapping method. The method comprises the following steps: S1, robot initial pose is estimated according to robot pose and mileometer control information; S2, a scan matching method is executed according to a map; S3, particle sampling is carried out in proposal distribution of trajectory; S4, weight of each particle is calculated and weight of each particle is updated; S5, particle resampling is carried out on the basis of Gaussian distribution: specifically, by sorting particle weight, high-weight particles are dispersed to obtain resampled new particles; and S6, the map is calculated according to robot pose and observation information, and map revision is carried out. By the method, reliable grid map precision can be obtained.

The invention discloses a video recommendation method and device, a computer device and a storage medium, and belongs to the field of machine learning. The method comprises the steps of inputting a video into a first feature extraction network, performing feature extraction on at least one continuous video frame in the video, and outputting video features of the video; inputting the user data of the user into a second feature extraction network, performing feature extraction on the discrete user data, and outputting the user features of the user; performing feature fusion based on the video feature and the user feature to obtain a recommendation probability of recommending the video to the user; and determining whether to recommend the video to the user according to the recommendation probability. According to the method, for the user characteristics and the video characteristics, different networks are adopted for characteristic extraction, information in the user characteristics andthe video characteristics is prevented from being lost, and the video recommendation accuracy is improved.

The invention provides a method for protecting account security based on an asynchronous dynamic password technology. The method is characterized by including the steps that a user and a service provider agree on the encryption mode of challenge codes and the calculation formula of dynamic codes; after a log-in request of the user is verified successfully, an authentication server generates the challenge code A, the challenge code B and the dynamic code C, the challenge code A is encrypted to obtain the challenge code B, and the challenge code A is calculated through the calculation formula to obtain the dynamic code C; the authentication server sends a feedback message containing the challenge code B to a mobile phone of the user; the user decrypts the challenge code B according to the encryption mode and then substitutes the decrypted challenge code B into the calculation formula to obtain the dynamic code D; the user inputs the dynamic code D and an account name in the log-in interface on the client side to log in. A system is high in reliability, the secrecy degree for transmitting the challenge codes and other information is high, leakage of code information of the user can be effectively avoided, and moreover user operation is safe and reliable.