4results about How to "Reliable information" patented technology

The application relates to the technical field of vehicle control, in particular to a method and device for simulating vehicle end sensor data based on roadside sensors, wherein the method comprises the following steps: generating roadside three-dimensional scene data of roadside sensors based on roadside sensor data of the roadside sensors; generating vehicle body roadside three-dimensional scene data of the roadside three-dimensional scene data in a vehicle body coordinate system based on the roadside three-dimensional scene data and a vehicle body coordinate system of vehicle end sensors; and rendering the vehicle body roadside three-dimensional scene data to obtain simulation data of the roadside sensors simulating the vehicle end sensors. Thus, the problems in the prior art, such as high difficulty, high cost, complex installation, difficult maintenance and the like of data collection and processing, and the difficulty in meeting the increasing demand for vehicle end sensor data, are solved.

The application provides a travel time prediction method fusing uncertainty modeling, and belongs to the field of intelligent traffic and navigation. The method first acquires static road network features, dynamic traffic features and road section external features, constructs a road network directed graph and defines a route R, finds feature values corresponding to road sections in R, constructs initial node embedding and initial edge embedding, and constructs a prediction model; after preprocessing of initial values, each layer of graph convolution network updates node embedding and edge embedding through modeling of interaction between nodes and edges, and finally obtains iterative node embedding and edge embedding; position encoding is performed on the processed route to construct a matrix PE; route embedding is obtained by adding the final node embedding and PE, multi-head attention is performed on the route embedding, and then average pooling is performed to obtain arrival time representation; uncertainty quantization and correction are performed on the arrival time representation to obtain predicted arrival time and upper and lower bounds. The application quantizes the uncertainty of the time prediction result.

Method of measuring bending of a nuclear reactor fuel channel. According to the method a fiber-optic sensor is equipped with a gravity pendulum that is suspended for angular motion at the lower end of the fibre-optic sensor; a flexible hollow carrier rod with the fibre-optic sensor is passed along the central tube of a fuel assembly and using a photoreceiver and a computer the shift of an interference pattern of a reflected light signal in a gas gap between the upper end face of the gravity pendulum and the lower end face of fibre-optic lines connected to the photoreceiver and fastened to the sensor is registered, said gas gap varying during the passage of the fibre-optic sensor as a result of the angular motion of the gravity pendulum away from the axis of the bowed central tube of the fuel assembly. On the basis of the registered shifts of the interference pattern of the reflected light signal, profilograms of the variations of the gas gap for each fibre-optic line of each fibre-optic sensor are recorded; and on the basis of the obtained gas gap profilograms, the magnitude and direction of bending of the central tube of the fuel assembly from the vertical axis are calculated, according to which the presence and magnitude of bending of the nuclear reactor fuel channel are determined.

The application provides an automatic data governance method and system based on a multi-modal large model, comprising: collecting multi-source heterogeneous industrial data and performing standardization processing to form standardized multivariate time series data; constructing a process knowledge base, performing semantic embedding coding on process knowledge text, and storing; constructing and fine-tuning a KTSF multi-modal large model, fusing process knowledge semantics and multivariate time series data through a cross-modal attention mechanism to generate joint semantic representation; based on the prediction of the KTSF multi-modal large model, outputting the residual error between the actual data, dynamically identifying abnormal data; and performing attribution analysis; based on the attribution result, calling the KTSF multi-modal large model to generate a repair value, and intelligently correcting the abnormal data; designing a quality evaluation and feedback learning module for calculating data quality scores and driving model incremental updating; designing a rule self-learning module for automatically refining governance rules through cluster analysis and updating the knowledge base.