Abstract: A method for matching a vehicle and a charging pile includes a) when a user of the vehicle inserts a charging connector of the charging pile into the vehicle, the vehicle sends, to a charging control platform of a cloud, a vehicle location and time stamp information corresponding to a point-in-time when the charging connector is inserted into the vehicle, and b) the charging control platform, after receiving the vehicle location and the time stamp information, scans a status of all charging piles whose distance to the vehicle location is less than a predetermined threshold, and when it is found through the scan that there was one and only charging pile whose working status is changed from an available status to an occupied but not charging status at the point-in-time, then the vehicle is successfully matched with the charging pile and the charging pile is controlled to start vehicle charging.

Abstract: Methods and systems are provided for determining advertising reach based on machine learning. In particular, a reach calculator is provided to determine reach for advertisement campaigns in real time through the use of machine learning. The reach calculator increases the speed at which reach calculations can be done by using a trained machine learning model and a set of aggregated features as opposed to using a direct calculation approach that directly analyzes a massive amount of user data.

Abstract: Systems and methods for a framework for generating predictive models for media planning. In some aspects, control circuitry receives, from a database, reference data associated with a program. The control circuitry receives a future date for insertion of an advertisement during transmission of the program. The control circuitry determines a prediction period between a current date and the future date. The control circuitry determines whether the prediction period exceeds a threshold period. If the prediction period does not exceed the threshold period, the control circuitry selects a first type for a predictive model. If the prediction period exceeds the threshold period, the control circuitry selects a second type for the predictive model. The control circuitry trains the predictive model according to the selected type and based on the reference data. The control circuitry predicts, based on the predictive model, an average audience for insertion of the advertisement.

Abstract: Systems and methods for a framework for generating predictive models for media planning. In some aspects, control circuitry receives, from a database, reference data associated with a program. The control circuitry receives a future date for insertion of an advertisement during transmission of the program. The control circuitry determines a prediction period between a current date and the future date. The control circuitry determines whether the prediction period exceeds a threshold period. If the prediction period does not exceed the threshold period, the control circuitry selects a first type for a predictive model. If the prediction period exceeds the threshold period, the control circuitry selects a second type for the predictive model. The control circuitry trains the predictive model according to the selected type and based on the reference data. The control circuitry predicts, based on the predictive model, an average audience for insertion of the advertisement.

Abstract: Methods and systems are provided for determining advertising reach based on machine learning. In particular, a reach calculator is provided to determine reach for advertisement campaigns in real time through the use of machine learning. The reach calculator increases the speed at which reach calculations can be done by using a trained machine learning model and a set of aggregated features as opposed to using a direct calculation approach that directly analyzes a massive amount of user data.

Abstract: Methods and systems have been provided for predicting viewership information for advertising events (e.g., during display of a media asset or within a media guidance application). Performance of an artificial neural network (ANN) based approach and a support vector machine (SVM) based approach have been described, along with a hybrid approach that combines information from the ANN and SVM approaches. These techniques described with the ANN and SVM are equally applicable to other machine learning techniques.

Abstract: Methods and systems have been provided for matching advertising requests (e.g., for advertisements in an advertising campaign) to advertising events (e.g., during display of a media asset or within a media guidance application) based on predicted viewership information determined using machine learning techniques. Performance of an artificial neural network (ANN) based approach and a support vector machine (SVM) based approach have been described, along with a hybrid approach that combines information from the ANN and SVM approaches. These techniques described with the ANN and SVM are equally applicable to other machine learning techniques.