4results about How to "Improve push accuracy" patented technology

This invention discloses an adaptive intelligent jacking device for steel-concrete composite beam bridges, relating to the field of bridge construction equipment technology. It includes a bridge beam and a foundation support assembly. The foundation support assembly is equipped with a multi-stage jacking mechanism, a foundation anchoring mechanism, and an intelligent control system. The foundation support assembly includes an upper mounting plate and a lower mounting plate, with a horizontal adjustment mechanism connecting the support column to the upper mounting plate. The multi-stage jacking mechanism includes a primary jacking mechanism, a secondary jacking mechanism, and a tertiary fine-tuning mechanism. This invention provides stable support for the entire device through the foundation support assembly. Combined with the horizontal adjustment mechanism, the horizontal state of the upper mounting plate can be adaptively adjusted to adapt to foundations with varying flatness, avoiding uneven stress on the device and reducing construction safety hazards. The multi-stage jacking mechanism, employing a combination of primary jacking, secondary jacking, and tertiary fine-tuning, significantly improves jacking accuracy, ensuring the bridge beam is accurately pushed to the design position, and has good engineering application value.

This application discloses a data processing method, apparatus, computer-readable storage medium, and computer device, relating to the field of Internet technology. It involves acquiring user behavior data and obtaining item latent semantic vectors based on the user behavior data; performing clustering processing on the item latent semantic vectors to obtain a first clustering result; determining the cluster identifier to which each item latent semantic vector belongs based on the first clustering result; and constructing a user latent semantic profile based on the cluster identifier. In this way, by clustering item latent semantic vectors, determining the cluster identifier to which each item latent semantic vector belongs based on the first clustering result, and then using the cluster identifier as a representation dimension of items to construct a user latent semantic profile, the efficiency of data processing is improved, the accuracy of user profiles is increased, and thus the accuracy of recommendation system pushes is enhanced.

Embodiments of the present application provide a data processing method and device, computer equipment and a readable storage medium. The method comprises: obtaining sample spliced media features and N-dimensional labeled interaction labels of a sample object for sample multimedia data; calling a shared network in an initial recognition model to extract shared media features associated with N media recognition tasks from the sample spliced media features; calling a dedicated network i in the initial recognition model to extract dedicated media features i from the sample spliced media features; calling a recognition network i in the initial recognition model to identify an i-dimensional predicted interaction label according to the shared media features and the dedicated media features i; and training the initial recognition model according to the N-dimensional labeled interaction labels and the N-dimensional predicted interaction labels to obtain a multi-task recognition model. The media recognition accuracy of the trained initial recognition model can be improved, and the push accuracy of the multimedia data can be improved.

The application discloses an insurance product information pushing method and device, a storage medium and a terminal, relates to the technical field of data processing, and aims at solving the technical problem of how to improve user determination effectiveness and thus improve the pushing accuracy of insurance product information. Mainly includes: obtaining user transaction history data, user basic attribute data and context basic features of an insurance product; determining information cross features based on the user transaction history data, the user basic attribute data and the context basic features, and performing classification processing on the information cross features and user features of a user to be screened through a user classification model that has completed model training to determine target users after classification, wherein the user classification model is obtained by training based on an interaction time training sample set; and pushing product information of the insurance product to the target users.