5 results about "Resource quality" patented technology

The invention discloses a browser optimization method and system based on dynamic resource loading and intelligent caching, belongs to the technical field of browser optimization, and aims to solve the technical problems of how to improve browser resource loading efficiency, reduce bandwidth consumption and improve user experience. Comprising the steps of predicting a next page possibly accessed by a user or required resources through a lightweight learning model based on a historical browsing path of the user, and obtaining the resources required by the user; carrying out on-demand dynamic loading on resources required by the user; the loaded resources are cached through a multi-level caching strategy; comparing the resources of the local cache of the browser with the latest version of the server side based on the resource fingerprints, and if the resource versions are inconsistent, downloading the resources of the latest version from the server and updating the local cache of the browser; a priority scheduling and parallel loading strategy is adopted for resources of various levels; and dynamically adjusting a resource loading strategy and resource quality according to the current network state.

The embodiment of the invention discloses a resource early warning method and device based on a computing node cluster and electronic equipment. A specific embodiment of the method comprises the following steps: determining whether to call a computing node cluster for resource processing; for each interaction object, determining a corresponding model calculation amount in an information prediction process; selecting a first computing node supporting calling of each interaction object from a computing node cluster; for each interaction object, generating first historical resource overdue summary information and first future resource overdue summary information; generating second historical resource overdue summary information and second future resource overdue summary information; generating target resource quality information; and sending the corresponding encrypted information to a target monitoring terminal so as to carry out target alarm when a resource quality alarm condition is satisfied. According to the embodiment, through reasonable allocation of hardware resources, the target resource quality information is accurately and efficiently generated on the basis of reducing waste of computing resources.

This invention relates to the field of BIM resource library technology and discloses a method for the construction and management of an enterprise-level BIM resource library driven by user data surveys. The method includes acquiring existing BIM resource files and user survey data from the target enterprise; performing demand analysis on the user survey data to generate a demand list and resource quality indicator thresholds; determining the resource organization structure based on the demand list; writing resource metadata according to preset naming and information filling rules when resources are added to the library; configuring role permissions and audit strategies; generating audit logs; accepting resource additions; and performing resource retrieval, online preview, download, upload, and version updates via the web-based management portal and modeling software client plugins, recording behavior logs during the call process, and performing statistical analysis on the behavior logs and audit logs to form a closed-loop optimization. This invention analyzes user survey data and generates a BIM resource library based on the analysis results, solving the problem that BIM resource libraries are complex and unsuitable for specific companies.

The present disclosure provides a method and device for determining recommended resources, and a storage medium, relates to the technical field of data processing, in particular to the technical field of big data, information flow, intelligent recommendation, and can be used in the information flow recommendation scene. The specific implementation scheme is as follows: obtaining a candidate resource to be recommended; determining a first resource gear where the candidate resource is located; comparing the resource quality of the candidate resource with the quality requirement corresponding to the second resource gear to obtain a comparison result, wherein the quality requirement corresponding to the second resource gear is higher than the quality requirement of the first resource gear; adjusting the recommendation weight of the candidate resource according to the comparison result to obtain a target recommendation weight; and determining a target resource to be recommended according to the target recommendation weight. Thus, by comparing with the quality requirements of different gears, the recommended resources are inclined to high-quality resources, and the resource recommendation quality is improved.

The present application relates to the technical field of education resource pushing, in particular to a cross-platform education resource intelligent aggregation and accurate pushing method and system, which is applied to an education resource service platform, and through collecting historical learning behavior records of learners on the platform and historical evaluation information of various resource providers before the learners request resources, a multi-dimensional data base is constructed; based on the above data, learning preferences and resource quality are analyzed, and multiple candidate resource aggregation schemes meeting the adaptation conditions are generated; then, the applicability and accuracy of each candidate scheme are evaluated, the knowledge relevance, learning matching degree and resource credibility are comprehensively considered, and the optimal recommended resource scheme is selected; finally, the scheme is accurately pushed to the learners, and personalized resource acquisition is realized. The method effectively breaks the cross-platform data island, improves the intelligent level of resource aggregation, enhances the accuracy and practicality of recommendation, and optimizes the learning experience.