7results about How to "Guaranteed learning effect" patented technology

This invention relates to the fields of machine learning and edge computing technology, specifically to a blockchain-based trusted distributed hybrid expert model edge computing system. The system includes a task publisher for publishing learning tasks, which may be training or inference tasks; a blockchain network for receiving learning tasks and evaluating expert weights to obtain sparse activation expert index results; a blockchain network for receiving learning task processing results from the edge layer and achieving consensus; a gating network deployed within the blockchain network; an edge layer for downloading the corresponding expert model from the storage layer based on the sparse activation expert index results to process the learning tasks and uploading the processing results to the blockchain network; and a storage layer for storing all expert networks, each with a CID. This invention significantly improves the defense capability of the distributed MoE network against malicious edge node data tampering attacks, thereby ensuring the learning performance of the MoE system.

This invention relates to a method for simulating and analyzing rainfall-induced landslide geological disaster scenarios, belonging to the field of image synthesis, and more specifically to the field of 3D geographic modeling for computer mapping. The method includes: collecting customized model data of a given mountain at the current moment based on a 3D graphical model of the mountain; and using a landslide intelligent prediction model to intelligently predict whether a landslide geological disaster will occur on each side of the given mountain within the next time interval, starting from the current moment, based on the customized model data. This invention also relates to a rainfall-induced landslide geological disaster scenario simulation and analysis system. Through this invention, the technical problem of difficulty in predicting future landslide data for landslide-prone physical entities such as mountains is addressed by intelligently predicting whether a landslide will occur on each side of the given mountain within a future time interval based on a 3D graphical model of the mountain at the current moment, thereby solving the aforementioned technical problem.

The application relates to a seamless switching and context synchronization system and method in a customer service man-machine cooperation process, and deep learning is provided in the application, and more particularly relates to the field of electric digital data processing. The system comprises: a big data acquisition device for acquiring big data track communication content of each past time segment of a set e-commerce application before a current time; and a data analysis device for intelligently analyzing the number of intelligent robots and the number of artificial customer services required for seamless switching in the current time segment based on the big data track communication content by using an intelligent customer service data analysis model. By the application, in the face of the technical problem that seamless switching in the customer service man-machine cooperation process is difficult to achieve in different time segments, a deep learning model can be used to intelligently analyze the number of various customer services required for seamless switching in each time segment according to various types of basic information acquired by big data, so that the above technical problem is solved.

The present application relates to the technical field of power consumption prediction, and particularly relates to a regional power consumption intelligent prediction method based on multi-source heterogeneous data, which comprises the following steps: determining the power consumption prediction influence tendency for the next preset monitoring period based on the power change characteristic value; when it is determined that the next preset monitoring period is a strong influence tendency for power consumption prediction, determining that the power consumption is corrected by the elastic influence weight coefficient, and determining whether the prediction for the power consumption is qualified based on the energy change characteristic value; when it is determined that the prediction for the power consumption is abnormal, correcting the prediction parameter for the power consumption in the next preset monitoring period based on the regional industry change quantitative value, including identifying the main analysis data set, determining whether to increase the model training set or separate the prediction model based on the period quantitative value of the main analysis data, and performing targeted analysis and prediction on the power consumption according to the specific situation of the power elasticity coefficient of the region to be predicted, thereby improving the prediction efficiency of the power consumption in the region.

The application provides a special management method and system for an education research platform, which comprises the following steps: checking the personnel identity of each logged-in person respectively, issuing corresponding research tasks and research permissions, obtaining the permission exercise data generated when each logged-in person executes the research task, constructing the research archives of each logged-in person in combination with the corresponding research permissions, constructing the research access path of the corresponding logged-in person in the research platform according to the permission exercise data, obtaining the corresponding research results of this time and transmitting them to the corresponding login page for display, analyzing the research reports of all the logged-in persons under different disciplines, and transmitting them to the platform homepage for display. The learning plan for each student in the education research platform is formulated, and the learning effect is analyzed, so that the learning effect of the student in the education research platform is effectively improved.

This disclosure relates to an interactive method, apparatus, medium, and electronic device for learning resources. The method includes: in response to receiving a selection operation for a learning resource, displaying in a sidebar of an interactive interface a first display control corresponding to each learning segment included in the selected learning resource and a second display control corresponding to each learning stage included in the current learning segment, wherein the second display control is located below the first display control corresponding to the current learning segment; in response to receiving a selection operation for the second display control corresponding to a learning stage in the current learning segment, controlling the sidebar to exit display and displaying the resource content corresponding to the selected learning stage in the interactive interface. Through the above technical solution, users can select the desired learning stage after selecting a learning resource, thereby obtaining the resource content corresponding to the selected learning stage, resulting in a good user experience.

This application discloses a training method, usage method, apparatus, device, and medium for an image classification model, belonging to the field of artificial intelligence. The image classification model includes a feature extraction network and a multiple instance learning model. The method includes: acquiring a sample image set, wherein each sample image in the sample image set includes at least two instances; training the feature extraction network using the sample images in the sample image set through self-supervised learning based on contrastive learning, obtaining a trained feature extraction network; and training the multiple instance learning model using the sample images in the sample image set through multiple instance learning based on a mutual attention mechanism, obtaining a trained multiple instance learning model. The above scheme can reduce the computational complexity of the image classification model. The embodiments of this application can be applied to various scenarios such as cloud technology, artificial intelligence, smart transportation, and assisted driving.