Abstract: A method includes a device in an area 1 that requests a ultra-wideband (UWB) base station in the area 1 to measure an initial position, and starts inertial measurement unit (IMU) measurement. The device moves to an area 2, and the device requests to measure distances between each UWB base station in the area 2 and at least three locations. The device returns to the area 1, and requests the UWB base station in the area 1 to measure an end point position. The device corrects coordinates of the device in the area 2 calculated using data obtained through the IMU measurement based on the measured end point position. Calculate the coordinates of each UWB base station in the area 2 based on the corrected coordinates in the area 2 and the measured distances between each UWB base station and the device in the area 2.

Abstract: This application relates to a UWB base station anomaly detection method. The method includes: obtaining a first distance that is between each of the N UWB base stations and the UWB tag and that is obtained through measurement; obtaining an estimated value of spatial coordinates of the UWB tag in the first coordinate system based on the first distance and spatial coordinates of each UWB base station in the first coordinate system; obtaining a second distance between each UWB base station and the UWB tag based on the estimated value and the spatial coordinates of each UWB base station in the first coordinate system; and determining, based on the first distance and the second distance that correspond to each UWB base station, whether a UWB base station in the N UWB base stations is abnormal.

Abstract: Embodiments of the present disclosure provide a data processing method and an electronic device, which relate to the computer field. The method includes: acquiring a time-series dataset, the time-series dataset comprising multiple time-series data items each comprising a time and multiple attribute parameters corresponding thereto; acquiring a target attribute parameter being at least one of the multiple attribute parameters; determining, based on a time-series model, at least one influence factor of the target attribute parameter, the at least one influence factor indicating at least one attribute parameter influencing the target attribute parameter and at least one time corresponding to the at least one attribute parameter; and outputting the at least one influence factor. As such, according to the embodiments of the present disclosure, for a target attribute parameter, at least one influence factor can be determined based on a time-series dataset and a time-series model.

Abstract: Embodiments of the present disclosure relate to a data processing method and an electronic device, and relate to a field of computers. The method comprises: acquiring data to be processed, the data to be processed indicating at least one of: first state information, a first action, and second state information after executing the first action when the first state information is satisfied; determining result data based on the data to be processed using a trained data generation model, the result data indicating third state information after executing a second action when the first state information is satisfied, and the data generation model being obtained based on a training set and a causal model corresponding to at least one data item in the training set; and outputting the result data.

Abstract: Embodiments of the present disclosure relate to an information processing method and an electronic device and relate to a computer field. The method comprises: obtaining input information from a user, the input information at least indicating at least one of: attribute information of at least one target object, or information of a current perception category of the at least one target object; determining a target decision for the at least one target object based on the input information using a trained decision model; and outputting the target decision. In this way, the embodiments of the present disclosure can output a target decision corresponding to the input information of the user based on the trained decision model, so as to provide a reference to the user for decision making and facilitate the user to maintain the perception category of the target object.

Abstract: A gesture recognition method and a related apparatus are provided, to obtain a first point cloud data set by filtering an original point cloud data set collected by a radar apparatus. The first point cloud data set includes a plurality of frames of first point cloud subsets, the first point cloud subset includes a first cluster center, the first cluster center is a cluster center of a plurality of pieces of point cloud data in the first point cloud subset, a maximum horizontal distance between any two first cluster centers meets a first preset condition, and duration of the first point cloud data set meets the first preset condition. Point cloud data whose motion track does not match gesture motion can be effectively filtered out. Gesture recognition is performed by using the first point cloud data set obtained by filtering.

Abstract: Embodiments of the present disclosure relate to method, device and computer-readable storage medium for data processing. A method for data processing comprises: obtaining, based on similarities between characteristics of a first target dataset and characteristics of a predetermined dataset, a respective first performance of a plurality of candidate causal model configurations corresponding to characteristics of the predetermined dataset; selecting, based on the respective first performance, a target causal model configuration from the plurality of candidate causal model configurations; and processing the first target dataset using a causal model which is built based on the target causal model configuration. Embodiments of the present disclosure also provide device and computer-readable storage medium capable of implementing the above method. Besides, the embodiments of the present disclosure can adaptively build a good casual model.