Abstract: An integrated inflator/deflator air pump, a mounting structure and an inflatable product. The air pump includes a pump body. Function buttons are pressed to drive a linkage plate to move, such that a first microswitch in the pump body is triggered so as to start a blower assembly. The function buttons are pressed to drive a reversing block to move up and down, such that a blower air inlet or a blower air outlet of the blower assembly is in communication with a first air port of the pump body. The mounting structure includes a mounting seat, and the pump body is embedded in the mounting seat. By cooperation between a positioning groove and a positioning block, when the pump body is mounted in the mounting seat, the first air port of the pump body can directly correspond to a third air port of the mounting seat.

Abstract: A method of dispatching power, an electronic device, and a storage medium, which relate to a field of artificial intelligence technology, and in particular to technical fields such as natural language processing and knowledge graph. The method includes: acquiring a power dispatching question information; determining a question semantic information corresponding to the power dispatching question information, wherein the question semantic information includes an identification of a target substation, a question intention, and a target parameter; and determining an answer information according to the question semantic information and a preset rule base, and outputting the answer information, wherein the answer information is used for a power dispatching of the target substation.

Abstract: Provided are image signal processing driving method, control module, processing module and medium, being applied to an image signal processing module including a plurality of ISP cores or an ISP core supporting time division multiplexing. The image signal processing module is equipped with driver architecture.

Abstract: Provided are image signal processing driving method, control module, processing module and medium, being applied to an image signal processing module including a plurality of ISP cores or an ISP core supporting time division multiplexing. The image signal processing module is equipped with driver architecture.

Abstract: A method of dispatching power, an electronic device, and a storage medium, which relate to a field of artificial intelligence technology, and in particular to technical fields such as natural language processing and knowledge graph. The method includes: acquiring a power dispatching question information; determining a question semantic information corresponding to the power dispatching question information, wherein the question semantic information includes an identification of a target substation, a question intention, and a target parameter; and determining an answer information according to the question semantic information and a preset rule base, and outputting the answer information, wherein the answer information is used for a power dispatching of the target substation.

Abstract: A method is for calculating a personalized patient model including an external surface model of a patient and an organ model of the patient. In an embodiment, the method includes acquiring metadata of the patient, the metadata being assigned to at least one metadata category; ascertaining, using the patient metadata acquired, the external surface model of the patient and an internal anatomical model of the patient, the internal anatomical model including a body cavity of the patient; ascertaining the organ model of the patient using the patient metadata acquired and using the internal anatomical model of the patient ascertained; and calculating the personalized patient model, the external surface model of the patient ascertained and the organ model of the patient ascertained, being combined.

Abstract: A method is for calculating a personalized patient model including an external surface model of a patient and an organ model of the patient. In an embodiment, the method includes acquiring metadata of the patient, the metadata being assigned to at least one metadata category; ascertaining, using the patient metadata acquired, the external surface model of the patient and an internal anatomical model of the patient, the internal anatomical model including a body cavity of the patient; ascertaining the organ model of the patient using the patient metadata acquired and using the internal anatomical model of the patient ascertained; and calculating the personalized patient model, the external surface model of the patient ascertained and the organ model of the patient ascertained, being combined.