6results about How to "Quick recall" patented technology

The invention discloses an activity recall matching method, system and device in a game scene and a medium, and the method specifically comprises the steps: setting activity configuration data according to business demand data of game activities, and storing the activity configuration data in a MySQL database, the activity configuration data comprising basic configuration data and advanced configuration data; constructing a Redis cache cluster, monitoring the MySQL database through a Canal component, and synchronizing the activity configuration data to the Redis cache cluster in real time; and acquiring user portrait attribute data from a preset game user database in real time, and determining an activity recall result of each user by matching the user portrait attribute data with the activity configuration data based on a preset activity rule matching engine. Accurate matching and personalized pushing of the activities are achieved, the activity conversion rate and the user participation degree are remarkably improved, and powerful support is provided for continuous optimization of game operation strategies.

The invention discloses an intelligent library literature quick retrieval method based on deep learning, and particularly relates to the technical field of natural language processing, and the method comprises the steps: converting text data in a data set into vectors in a high-dimensional space through a pre-trained Transform model, and converting the text data into dense vectors for feature extraction; a SimCSE model is adopted to generate positive sample pairs for codes of the same text content in different Dropout environments, unsupervised training is carried out on the model by utilizing the positive sample pairs, so that deep semantic representation of the text is learned, and finally, a model fusion strategy is carried out on the two models; then, an In-batch Networks method is used, other samples in the same batch are used as negative samples in the model training process, a dynamic learning rate adjustment mechanism based on performance feedback is adopted, the learning rate is automatically adjusted according to the expression of the model on a verification set, and the training process is optimized, so that the model is accelerated.

The invention discloses a file editing method and device and electronic equipment, and belongs to the technical field of electronics. The method comprises the steps that under the condition that a first editing operation is executed on a first file, an editing thought text is generated, and the editing thought text is used for describing the first editing operation; and associating the editing thought text with the first file to obtain the edited first file.

This invention discloses an intelligent product configuration method and system based on multi-level semantic matching, comprising: acquiring a demand dataset, extracting feature information from demand items and concatenating them to form demand text; using a text embedding model for vectorized encoding, performing an approximate nearest neighbor search in a product vector index library to obtain a first candidate product set; pairing the demand text with the feature text of the first candidate products, inputting it into a semantic re-ranking model to complete relevance modeling and outputting a matching score, and re-ranking and filtering based on the score to obtain a second candidate product set; inputting the demand text and the product information of the second candidate product set into a large language model, which performs multi-candidate joint evaluation and outputs a structured evaluation result; after parsing and verifying the structured evaluation result, generating and outputting a configuration result containing the selected product identifier. This invention achieves multi-level semantic matching and intelligent product configuration, improving matching accuracy and decision reliability.

The application discloses a kind of based on five-layer dynamic structure's employment supply and demand accurate matching device, and the present application relates to human resource service technical field.It includes data acquisition module, operation matching module, result output module, operation matching module covers semantic enhancement layer, knowledge partition layer, label matching layer, dynamic tuning layer, feedback closed loop layer;The information feature of the present application is regularized after multi-stage semantic enhancement, relies on three-level theme tree and index routing to improve search efficiency;Through physical environment and psychological characteristics double-path label matching, all-around evaluation adaptation degree, still can dynamically adjust search operator weight and post recall quantity, optimization matching order.Combined with user job-seeking to the whole cycle behavior data completes stability test, drives model iteration, with parameter rollback and demand drift detection capability, can adapt to market labor demand change, matching result dimension is comprehensive, precision is high, effectively improves employment matching success rate and employment stability.

A multi-period map correlation system based on graph optimization comprises a single SLAM mapping module, a data integration module, a global search module, a point cloud fine matching module and a global graph optimization module. The application focuses on the changing mine environment, constructs a global map for the data collected in different periods, and completes the information correlation between the global maps, provides a basis for long-period map updating, thereby converts the pose estimation problem into a joint optimization problem, realizes accurate correlation of multi-pass mapping, and significantly improves the positioning accuracy and environmental adaptability of the SLAM technology in the mine scene.