Abstract: A tumor radiotherapy planning design method and apparatus, an electronic device, and a computer storage medium are provided, including: obtaining a current optimization parameter vector set, and calculating a current cost function value; randomly correcting the current optimization parameter vector set to generate alternative optimization parameter vector sets; then performing planning parameter optimization, and calculating corresponding total cost function values; determining current optimal or suboptimal alternative optimization parameter vector sets according to the total cost function values, sampling to update the current optimization parameter vector set and the current cost function value according to the total cost function values of the current alternative optimization parameter vector sets, and then performing an iteration repeatedly until a convergence condition is satisfied; and outputting an optimal optimization parameter vector set after the iteration, determining planning parameters, and calcu

Abstract: The present application relates to a search ranking method, a search ranking apparatus, an electronic device and a storage medium. In an embodiment of the method, acquiring search keywords and determining a plurality of initial search results that match with the keywords; acquiring a plurality of characteristic factors associated with each of the initial search results, and a weight of each of the characteristic factors; performing a fusion calculation on each of the initial search results according to the weights of the plurality of characteristic factors to obtain a comprehensive weight of each initial search result; and ranking the plurality of initial search results according to the comprehensive weights. In embodiments of the present application, comprehensive ranking is performed on initial search results of a plurality of columns, which enables the user to quickly find a desired result, saves the operation time, and improves the searching efficiency.

Abstract: The present application relates to a search ranking method, a search ranking apparatus, an electronic device and a storage medium. In an embodiment of the method, acquiring search keywords and determining a plurality of initial search results that match with the plurality of keywords; extracting text similarity, update time dimension, and objective importance of contacts according to the initial search results; acquiring a weight of the text similarity, a weight of the update time dimension, and a weight of the objective importance of the contacts; performing a fusion calculation on each of the initial search results to obtain a comprehensive weight of each of the initial search results; and ranking the plurality of initial search results according to the comprehensive weights. The method enables the user to quickly find relevant information, simplifies the operation, and improves the searching efficiency.

Abstract: A neural network training method, apparatus, a storage medium, and a computer device are provided. The method includes: obtaining a training sample set, each training sample including a standard label; inputting the each training sample into a neural network model including n attention networks, the n attention networks respectively mapping the each training sample to n subspaces, each of the n subspaces including a query vector sequence, a key vector sequence, and a value vector sequence; calculating a space difference degree between the n subspaces by using the neural network model; calculating an output similarity degree according to an output of the neural network model and the standard label corresponding to the each training sample; and adjusting a model parameter of the neural network model according to the space difference degree and the output similarity degree until a convergence condition is satisfied to obtain a target neural network model.

Abstract: The present application relates to a search ranking method, a search ranking apparatus, an electronic device and a storage medium. In an embodiment of the method, acquiring search keywords and determining a plurality of initial search results that match with the plurality of keywords; extracting text similarity, update time dimension, and objective importance of contacts according to the initial search results; acquiring a weight of the text similarity, a weight of the update time dimension, and a weight of the objective importance of the contacts; performing a fusion calculation on each of the initial search results to obtain a comprehensive weight of each of the initial search results; and ranking the plurality of initial search results according to the comprehensive weights. The method enables the user to quickly find relevant information, simplifies the operation, and improves the searching efficiency.

Abstract: The present application relates to a search ranking method, a search ranking apparatus, an electronic device and a storage medium. In an embodiment of the method, acquiring search keywords and determining a plurality of initial search results that match with the keywords; acquiring a plurality of characteristic factors associated with each of the initial search results, and a weight of each of the characteristic factors; performing a fusion calculation on each of the initial search results according to the weights of the plurality of characteristic factors to obtain a comprehensive weight of each initial search result; and ranking the plurality of initial search results according to the comprehensive weights. In embodiments of the present application, comprehensive ranking is performed on initial search results of a plurality of columns, which enables the user to quickly find a desired result, saves the operation time, and improves the searching efficiency.

Abstract: The present application relates to a multi-dimensional search ranking method and apparatus, an electronic device and a storage medium. In an embodiment of the method, acquiring search keywords and determining a plurality of initial search results that match with the keywords; extracting a text similarity, an update time dimension and a click rate associated with each of the initial search results; acquiring a weight of the text similarity, a weight of the update time dimension and a weight of the click rate, and performing a fusion calculation to obtain a comprehensive weight of each of the initial search results; and ranking the plurality of initial search results according to the comprehensive weights. This method facilitates users in quickly finding relevant information, simplifies the operation, and improves the searching efficiency.