Abstract: The present disclosure discloses a split mobile phone radiator. The split mobile phone radiator includes a protective cover. An accommodating groove is formed in the top of the inner wall of the protective cover. A refrigeration module is separably arranged on the top surface, far away from the accommodating groove, of the protective cover. The refrigeration module includes a semiconductor refrigeration mechanism, a radiating mechanism used for cooling the semiconductor refrigeration mechanism, and a rear cover wrapping around the semiconductor refrigeration mechanism and the radiating mechanism. A heat conducting silica gel sheet is arranged on one side, close to the protective cover, of the rear cover. According to the present disclosure, the whole refrigeration module fits the top of the protective cover, so that the mobile phone radiator is directly attached to the back side of the mobile phone, and the mobile phone radiator has a split function.

Abstract: The present invention discloses a method and an apparatus for measuring leaf nitrogen content (LNC), and belongs to the spectral analysis and artificial intelligence (AI) field. The method includes the following steps: (1) obtaining a single-band image of a target leaf illuminated by a light source in a single feature band; (2) repeating step (1), to collect image information in four feature bands; (3) combining the collected images in the four feature bands into a four-channel spectral image; (4) training a deep learning model by using the spectral image and a corresponding nitrogen content label, to obtain a nitrogen content prediction model; (5) transplanting the trained nitrogen content prediction model into an AI control system; (6) collecting information about a to-be-predicted leaf sample, predicting nitrogen content by using an AI sensor equipped with the AI control system, and outputting the predicted nitrogen content.

Abstract: The present disclosure discloses a split mobile phone radiator. The split mobile phone radiator includes a protective cover. An accommodating groove is formed in the top of the inner wall of the protective cover. A refrigeration module is separably arranged on the top surface, far away from the accommodating groove, of the protective cover. The refrigeration module includes a semiconductor refrigeration mechanism, a radiating mechanism used for cooling the semiconductor refrigeration mechanism, and a rear cover wrapping around the semiconductor refrigeration mechanism and the radiating mechanism. A heat conducting silica gel sheet is arranged on one side, close to the protective cover, of the rear cover. According to the present disclosure, the whole refrigeration module fits the top of the protective cover, so that the mobile phone radiator is directly attached to the back side of the mobile phone, and the mobile phone radiator has a split function.

Abstract: The present disclosure provides a wireless charger with a cooling function, including a front shell and a radiating rear shell. A radiating module and a wireless charging coil are mounted in a mounting space enclosed by the front shell and the radiating rear shell. The wireless charging coil and/or the front shell is provided with radiating holes communicated with the mounting space. The wireless charging coil is arranged in the mounting space, and is located at the lower end of the front shell. The radiating module includes: a radiating mechanism and a semiconductor refrigeration sheet. The radiating mechanism is arranged below the wireless charging coil. The semiconductor refrigeration sheet is located between the wireless charging coil and the radiating mechanism. A refrigerating surface of the semiconductor refrigeration sheet faces the wireless charging coil, and a heating surface of the semiconductor refrigeration sheet faces the radiating mechanism.

Abstract: The present invention discloses a method and an apparatus for measuring leaf nitrogen content (LNC), and belongs to the spectral analysis and artificial intelligence (AI) field. The method includes the following steps: (1) obtaining a single-band image of a target leaf illuminated by a light source in a single feature band; (2) repeating step (1), to collect image information in four feature bands; (3) combining the collected images in the four feature bands into a four-channel spectral image; (4) training a deep learning model by using the spectral image and a corresponding nitrogen content label, to obtain a nitrogen content prediction model; (5) transplanting the trained nitrogen content prediction model into an AI control system; (6) collecting information about a to-be-predicted leaf sample, predicting nitrogen content by using an AI sensor equipped with the AI control system, and outputting the predicted nitrogen content.