15 results about "Resource quality" patented technology

The invention discloses a higher vocational college quality evaluation system construction method based on production and teaching integration, and particularly relates to the technical field of production and teaching integration teaching assistance. Based on enterprise support resources and student actual receiving information, preset and actual knowledge network topological graphs are constructed respectively, and standardized knowledge points are extracted through natural language processing; generating a migration difficulty weight in combination with expert scores and industry standards, and forming a directed weighted graph; a support resource quality coefficient is generated by comparing a topological structure, semantic content and course stage integrating degree of the topological graph, calculating a skill knowledge matching degree, an aging synchronization rate and an operation integrating degree and performing conjoint analysis, and if the coefficient is lower than a threshold value, early warning is triggered to prompt optimization of resource supply; and by taking maximization of a quality coefficient as a target, redividing a course stage boundary based on a dynamic programming algorithm, correcting an operation integrating degree formula in combination with a migration path efficiency coefficient, preferentially adjusting a high migration difficulty stage, and realizing real-time alignment of a teaching rhythm and an industrial demand.

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 invention discloses a computing power network resource quality evaluation method and device, and is used for solving the problem of poor evaluation accuracy of a related computing power network resource quality evaluation technology. The method comprises the following steps: acquiring sampling data and an index comment set of a computing power index; the computing power index is multi-dimensional; through a pre-trained index proportion distribution model, based on the sampling data and the index comment set, obtaining a weight corresponding to the computing power index of each dimension and a first membership degree result of the computing power index to the comments of multiple levels; determining an adjustment factor corresponding to the calculation power index of each dimension based on the sampling data; wherein the adjustment factor comprises one or more of a time factor, a performance factor and a stability factor; and determining a second membership result of the computing power network for the multiple grades of comments based on the adjustment factor, the weight and the first membership result corresponding to the computing power index of each dimension, and determining a target comment in the multiple grades of comments based on the second membership result.

The application relates to the technical field of machine learning, in particular to a resource classification model training method and a resource recommendation method and device, which can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and auxiliary driving; the method comprises the following steps: acquiring preset multimedia resource screening conditions; the preset multimedia resource screening conditions comprise a resource interaction condition and a resource quality grade condition; seed multimedia resources are screened out from an original multimedia resource pool based on the resource interaction condition and the resource quality grade condition; preset multimedia resources are extracted from the original multimedia resource pool; positive samples are generated based on the seed multimedia resources, and negative samples are generated based on the preset multimedia resources; a preset classification model is trained based on the positive samples and the negative samples, so that a resource classification model is obtained. The application can improve the quality of multimedia resources to be recommended and improve the retention rate of a viewing object on a multimedia resource platform.

The application relates to the technical field of network resource evaluation, and discloses a network resource quality evaluation method based on multi-modal data. The method acquires multi-modal network data, collects key performance indicators and user behavior data of network resources, associates data points, calculates quality evaluation values, generates a resource quality evaluation set, and then sorts adjacent resource pairs according to network topology distance based on the quality evaluation values and resource identifiers in the set to obtain a quality consistency partition annotation set. Then, resource points in the quality consistent section are extracted, performance indicator time series and user behavior sequences are acquired, change rates are compared in combination with resource load data, influence degrees under network condition fluctuations are evaluated, and influence degree analysis results are generated. Abnormal points with influence values greater than average influence benchmark values and located in quality conflict sections are identified to form a resource abnormal point set. Detailed information of the abnormal points is acquired, abnormal points with potential diffusion risks are marked, and a network resource quality evaluation report is generated.

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.

The present invention discloses a method for allocating resources between 5G network slices based on scalable adjustment, which solves the problems of complex resource allocation, low security and low management efficiency of existing slices. The method includes determining the slice's high-quality and non-high-quality channel services based on the signal-to-noise ratio; calculating the signal-to-noise ratio coefficient for the high-quality channel service based on the signal-to-noise ratio, and calculating the synthesis coefficient for the non-high-quality channel service based on the throughput and delay, and calculating the slice service performance coefficient; determining the slice's physical resource occupancy rate, and allocating the corresponding resource occupancy coefficient according to the size of the physical resource occupancy rate, which is the slice resource coefficient; calculating the slice service quality coefficient according to the congestion rate; determining the slice allocation priority according to the slice service performance coefficient, resource coefficient, and service quality coefficient, and allocating unallocated resources. The present invention evaluates the allocation priority from multiple aspects of service performance, resources, and quality, ensures that the service is in a slice environment with a good environment, and provides real-time protection for improving customer perception.

The present invention provides a game resource loading decision-making method and device under comprehensive performance, which relates to the technical field of dynamic game resource management, and includes: predicting the movement route of a game character to obtain multiple predicted movement routes, multiple route prediction probabilities, and multiple predicted movement speeds; retrieving the resource quality level, and combining multiple predicted movement speeds, aiming at comprehensively optimizing the loading quality and performance of the map, optimizing the loading quality of the map resources of multiple routes to obtain multiple optimal loading quality levels; preloading the map resources of multiple routes according to multiple optimal loading quality levels. Through the present invention, the technical problem that the existing method lacks dynamic adaptability to real-time game scenarios and player behaviors, resulting in insufficient timeliness and accuracy of game resource allocation, can be solved. By configuring resources according to predicted resource requirements, the scientificity, timeliness, and accuracy of resource allocation can be improved, the overall resource utilization rate can be optimized, and the game experience can be significantly enhanced.

The invention relates to the technical field of network resource evaluation, and discloses a network resource quality evaluation method based on multi-modal data. The method comprises the following steps: acquiring multi-modal network data, collecting key performance indexes and user behavior data of network resources, associating data points, calculating quality evaluation values, and generating a resource quality evaluation set; sorting adjacent resource pairs according to a network topology distance based on the quality evaluation value and the resource identifier in the set to obtain a quality consistency partition labeling set; then extracting resource points of a section with consistent quality, obtaining a performance index time sequence and a user behavior sequence, comparing a change rate in combination with resource load data, evaluating an influence degree under network condition fluctuation, and generating an influence degree analysis result; identifying abnormal points of which the influence values are greater than the average influence reference value and which are in the quality conflict section, and forming a resource abnormal point set; and obtaining detailed information of abnormal points, marking the abnormal points with potential diffusion risks, and generating a network resource quality evaluation report.

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 invention relates to a multimedia resource generation model training method and a multimedia resource generation method, and the training method comprises the steps: inputting each piece of resource generation description information and preset empty description information into a preset generation model, and guiding the preset generation model to generate positive sample multimedia resources based on preset guiding parameters; inputting each piece of resource generation description information and preset null description information into a preset generation model, and guiding the preset generation model to generate at least one negative sample multimedia resource and at least one resource quality defect corresponding to any negative sample multimedia resource based on the scaled guide parameters; constructing positive and negative sample resource pairs; and based on the positive and negative sample resource pairs, performing iterative training on the to-be-trained multimedia resource generation model to obtain the multimedia resource generation model. According to the embodiment of the invention, the generation effect of the multimedia resource generation model can be improved.

The present disclosure relates to a training method for a multimedia resource generation model and a multimedia resource generation method. The training method includes inputting each resource generation description information and preset empty description information into a preset generation model, and guiding the preset generation model to generate a positive sample multimedia resource based on preset guidance parameters; inputting each resource generation description information and preset empty description information into the preset generation model, and guiding the preset generation model to generate at least one negative sample multimedia resource based on scaled guidance parameters, with at least one resource quality defect corresponding to any negative sample multimedia resource; constructing positive and negative sample resource pairs; and iteratively training the multimedia resource generation model to be trained based on the positive and negative sample resource pairs to obtain a multimedia resource generation model. The disclosed embodiments can improve the generation effect of the multimedia resource generation model.

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 discloses a training method for a content resource detection model, a content resource detection method, a system, an apparatus, a computer device and a storage medium, and belongs to the field of artificial intelligence technology. This application is based on the data of the target field, trains a basic model that supports multiple CV tasks in the target field, and then uses the data of the target business to train the basic model for pertinence in the business scenario, thereby obtaining a first detection model. While ensuring the performance of the model, the generalization of the first detection model is effectively improved, thereby using a small amount of labeled data corresponding to the target resource quality detection task to train the first detection model, and the content resource detection model corresponding to the target resource quality detection task can be quickly obtained. From a large range of fields to business scenarios and then to downstream specific businesses, the training method of the model is refined layer by layer, and the different samples corresponding to each level are fully utilized during the model training process, which greatly improves the accuracy of content resource detection.

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.