7results about How to "Implement automatic selection" patented technology

The embodiment of the application provides a kind of fog computing system, method, electronic equipment and storage medium, applied to fog computing technical field, server, receives the computing instruction sent by user;First computing request for executing target algorithm analysis task is sent to first fog computing device;First fog computing device, for receiving first computing request;According to the algorithm identification and the computing resource information required for executing target algorithm analysis task, it is judged whether itself can execute target algorithm analysis task, and the second computing request of fog computing task is generated and sent to second fog computing device;Second fog computing device, receives second computing request;According to the algorithm identification required for executing target algorithm analysis task, the algorithm required for executing target algorithm analysis task is obtained from server, and target algorithm analysis task is executed.The application can realize the automatic selection of fog computing device and the automatic deployment of fog computing task, and improve the utilization efficiency of resources in fog computing system.

The invention discloses a lithium battery residual service life prediction method based on an MFO-SVM model, and relates to the technical field of lithium battery residual service life prediction.The method comprises the steps that 1, data collection is conducted, and a lithium battery is subjected to repeated charging and discharging experiments at the room temperature of 24 DEG C; 2, characteristic parameter selection: starting from the monitorable performance parameters, namely indirect performance parameters, extracting multi-aspect external characteristic parameters such as voltage and current from the parameters as indirect health factors for evaluating the performance degradation of the battery; step 3, an MFO-SVR prediction model is constructed; and 4, checking and evaluating the constructed MFO-SVR prediction model. The method has the advantages that the potential relationship between the characteristic parameters capable of representing the lithium ion battery RUL and the lithium ion battery RUL is deeply excavated, and four indirect health factors capable of representing the performance degradation of the battery are relatively comprehensively extracted from a battery discharge characteristic curve and a charging process; and the capacity is used as the output, so that the operability is high.

The application discloses a two-wheeled vehicle fast and slow charging compatible charging method and device, equipment and medium. The battery code information in the battery is read and judged, so that it can be quickly determined whether the battery supports fast charging. Then, when the fast charging is not supported, the peak charging current information of the battery is used to quickly determine the charging current to complete the slow charging. In the case of reading the battery code information, the charging current is quickly determined according to the battery code information, and the charging is started. The embodiment of the application can realize automatic selection of the charging mode, and can improve the charging experience of the user to a certain extent.

This invention relates to the field of liquid crystal display technology, and more particularly to an intelligent driving method and system for liquid crystal displays. The method includes the following steps: recording the current decay waveform of the source driver charging the pixel capacitor during pixel charging; calculating the charging time constant based on the current decay waveform; calculating the current pixel capacitance value using the charging time constant and the transistor on-resistance; converting the current pixel capacitance value into an orientation angle corresponding to the pixel orientation state; comparing the current orientation angle with the target orientation angle corresponding to the pre-acquired target grayscale, and calculating the orientation angle deviation; and using a driving sequence containing multiple voltage pulses based on the orientation angle deviation. This invention achieves closed-loop precise driving based on pixel state feedback by deriving the pixel capacitance from the current decay and converting it into the pixel orientation state, overcoming the problem of long-term open-loop blind driving in liquid crystal displays.

This invention discloses an automated scanning, acquisition, and processing device for in-situ core logging data, relating to the field of oil and gas geological exploration technology. It includes: a sampling and analysis platform; a support device horizontally fixed to the sampling and analysis platform, on which a core is placed; a positioning and analysis device fixed to the sampling and analysis platform, located behind the support device and in contact with the top of the core; a protective cover fixedly connected to the rear wall of the sampling and analysis platform, located above the core, with a through-hole in the middle and multiple white light tube groups on the inner walls of the front and rear sides; and an equidistant sampling device fixed to the top of the protective cover, corresponding to the positioning and analysis device. This invention integrates sampling, scanning, and analysis, achieving efficient acquisition and accurate processing of core logging data, and improving the reliability of geological modeling.

The application discloses a video synthetic aperture radar imaging and tracking integrated method, and mainly solves the problems of low imaging automation degree and large calculation amount in the prior art.The implementation scheme is as follows: reading SAR echo data containing N segments of synthetic apertures and setting an imaging grid; projecting and imaging the first two segments of echo data; preliminarily detecting on the image to obtain a target initial state set; performing inter-frame state transition on the targets in the initial state set to obtain a total target state set; setting an imaging region of interest according to the total target state set; performing value function accumulation and backtracking function updating on the second frame of image; performing target value function accumulation and backtracking function updating on the images of the remaining frame numbers according to the region of interest; and performing target track backtracking.The application reduces the calculation amount of video synthetic aperture radar imaging, improves the overall efficiency of video synthetic aperture radar target detection, and can be used for detecting ground moving targets by various airborne radars.

The invention provides a visual language model zero sample classification method based on Lasso regularization dynamic integration. The visual language model zero sample classification method specifically comprises the following steps: S1, constructing a model pool comprising a plurality of pre-trained visual encoders; step S2, in a training stage, adaptively screening out a model subset which contributes significantly to a current task from the model pool by using the sparse characteristic of Lasso regularization, and learning the weight of the model subset; s3, introducing a dynamic regularization coefficient lambda adjustment mechanism based on verification loss to balance the complexity and generalization ability of the model; and S4, in a reasoning stage, performing weighted fusion on the prediction result of the selected model by using the sparse weight obtained by training to obtain a final classification result. According to the method, the accuracy and robustness of the visual language model in the zero sample classification task can be effectively improved.