4results about How to "Close to actual needs" patented technology

The invention discloses a multi-task intelligent coordination execution method and system based on a pension accompanying robot, and belongs to the technical field of multi-task intelligent coordination execution, and the method comprises the steps: S1, carrying out the initialization of the system according to a current environment and preset parameters; s2, collecting information of the user and the surrounding environment thereof through an integrated sensor; s3, performing fusion analysis processing according to the collected information, predicting the behavior trend of the user, identifying the current demand of the user according to a data analysis result and a prediction result, and automatically generating a task; by integrating advanced data fusion, behavior pattern recognition and prediction, demand recognition and task evaluation, the intelligent level and service quality of the system are remarkably improved, and more accurate behavior pattern recognition and trend prediction are realized through membership function and sliding window prediction; and in combination with the behavior pattern and the trend prediction result, tasks meeting user requirements are generated, and priorities are set through a multi-dimensional evaluation model, so that the efficiency and rationality of task execution are ensured.

This invention provides a controllable image description method, system, device, and medium based on multimodal fusion. The method includes the following steps: based on the original image features X... ν * and object regression box features X ν b The images are fused to generate sample image features X. ν Extract each word from the labeled language text, and label and positionally encode the extracted words to generate sample text description features X. w Generate a trajectory regression box based on the mouse trajectory segment, and generate sample trajectory features X based on the trajectory regression box. r Using a pre-built TO-Transformer feature fusion model, the sample image features X are processed. ν The sample text description feature X w and the sample trajectory feature X r Feature fusion is performed to generate sample fusion features; based on the sample fusion features, a controllable image description model is generated using a pre-built output model, thereby effectively improving the controllability and efficiency of image description.

The application discloses a kind of knowledge graph construction methods and devices for ship power system design task.The method includes the following steps: mode layer design is carried out, and the concept of ship power system and the relationship between concepts are defined;Unstructured data is preprocessed;The entity and the relationship between entities are marked;The entity data set and the entity relationship data set marked are trained to obtain the corresponding model;Triplet extraction is carried out based on the obtained model, imported into the graph database, and the concept knowledge graph of ship power system is formed;Modeling is carried out, and the knowledge graph of ship power system design task is formed;Atlas fusion is carried out, and the final knowledge graph is formed.The application can realize the construction of the knowledge graph of ship power system design task, make the knowledge graph construction in the field more convenient, efficient and accurate, provide knowledge base for subsequent use of atlas to build query platform, and improve the retrieval efficiency of ship designers.

The present invention provides a secure data sharing calculation method and system based on a semi-honest MASCOT protocol, the method comprising: when a new participant joins the request in the preprocessing stage, the predetermined participant extracts the obtained shards and the corresponding key values; for each shard under each extracted key value, the predetermined participant randomly selects a value in a finite field to generate a shard under the corresponding key value of the new participant, and sends the generated shards and the corresponding key value to the new participant; based on the sent shards, the predetermined participant updates the value of the shard corresponding to itself under each key value; the predetermined participant extracts the obtained multi-group shards and the corresponding key values; for each multi-group shard under each extracted key value, the predetermined participant randomly selects multiple values ​​in a finite field to generate a multi-group shard of the new participant, and sends the generated multi-group shards to the new participant; the predetermined participant updates the multi-group shard corresponding to itself under each key value based on the sent multi-group shards.