7results about How to "Optimizing Model Parameters" patented technology

This application provides a digital key positioning method, device, vehicle, and storage medium. The method includes: acquiring multi-anchor point ranging values ​​corresponding to the digital key; wherein the multi-anchor point ranging values ​​include ranging values ​​from the digital key to different ranging modules on the vehicle; calling a first model to identify the multi-anchor point ranging values ​​and determining a first identification result; if the first identification result indicates that the digital key is located inside the vehicle, calling a second model and a third model to identify the multi-anchor point ranging values; wherein the second model is trained based on a first training set, and the third model is trained based on a second training set, the second training set including training data that resulted in identification errors during the training of the second model; and determining the position of the digital key based on the identification results output by the second model and the third model. This method can more accurately determine the position of the digital key in complex environments, effectively improving positioning accuracy and robustness.

This invention provides a non-contact, highly anti-interference, and highly accurate wind turbine blade monitoring system and method based on lidar and multi-dimensional data fusion. The method includes the following steps: S1, data acquisition; S2, data preprocessing; S3, feature extraction; S4, data fusion; S5, equilibrium state diagnosis; and S6, dual verification. The system includes a lidar acquisition module (1), a data synchronization module (2), a signal processing module (3), a data fusion module (4), an intelligent diagnosis module (5), and a dual verification module (6). This invention is applied in the field of wind power equipment condition monitoring technology.

This invention discloses a pre-trained online Gaussian process device and method for predicting hydrogen fuel cell voltage, applicable to marine propulsion systems. Addressing the problems of traditional models relying on real-ship data, weak generalization ability, and inability to adapt to battery aging, the device includes a sensor module, a memory, and a processor. The sensor module collects pressure, current, temperature, and voltage data; the memory stores the model and program; and the processor performs data processing and prediction calculations. The method pre-trains a Gaussian process regression model using experimental data, updates it online according to preset rules using real-time ship data, and outputs the prediction results after data preprocessing, model construction, updating, and prediction. This invention achieves high-precision prediction in zero-sample / few-sample stages, dynamically adapts to complex operating conditions and battery aging, operates efficiently without redundancy, and ensures reliable operation of marine hydrogen fuel cell propulsion systems.

The invention discloses a meter box topology identification method based on a high-speed broadband power line carrier technology. According to the method, a master station issues a ranging task to a concentrator, a carrier communication link is established with a plurality of terminal nodes through the concentrator, ranging data of the terminal nodes are collected, and signal strength data and signal-to-noise ratio data are received. According to background noise, normal noise, periodic impulse noise and sudden impulse noise existing in the low-voltage power line, noise analysis, frequency domain processing and filtering suppression are carried out on collected signals, so that effective data capable of reflecting communication link characteristics can be obtained. And after abnormal value detection and standardization processing are carried out on multiple rounds of measurement results, weights of different characteristics are determined based on correlation between communication characteristics and an actual topological relation, and a weighted linear regression model is constructed and used for predicting meter box affiliation of a terminal node or a relative position of the terminal node in a topology. And judging a topological structure of the terminal node according to a model output result, and verifying an identification effect through a model evaluation index.

The invention provides a lightweight multi-scale detection method and system for unmanned aerial vehicle small target aerial photography, and the method comprises the steps: obtaining visible light or infrared image data of unmanned aerial vehicle aerial photography, carrying out the preprocessing of an image, and obtaining a preprocessed image; constructing a detection model based on a YOLOv11 model, extracting multi-scale features, and performing weighted fusion processing on the multi-scale features to obtain a cross-layer information aggregation feature map; constructing an unmanned aerial vehicle aerial photography small target data set, obtaining a training set sample, inputting a detection model, optimizing model parameters, and obtaining a lightweight multi-scale detection model; performing parallel detection on the cross-layer information aggregation feature map based on a lightweight multi-scale detection model, and outputting a detection result; the detection result is transmitted to the terminal in real time according to a set mode; the feature extraction capability is enhanced by constructing a detection model, effective complementation of shallow spatial information and deep semantic information is realized, a multi-scale feature map with rich semantic information is generated, and the small target detection precision is improved.

The application provides a training method, device and storage medium of a collaborative retrieval enhancement generation system. The method is applied to the field of artificial intelligence technology. The method comprises the following steps: performing a retrieval operation by a retriever based on input query content, and outputting a candidate document set associated with the query content; wherein the candidate document set comprises multiple candidate documents; performing sorting processing on the multiple candidate documents in the candidate document set by a rearranger to obtain a target document subset; generating an actual response result for the retrieval operation based on the query content and the target document subset by a generator; calculating a reward value corresponding to the actual response result, and optimizing the rearranger and the generator based on the reward value to obtain a trained collaborative retrieval enhancement generation system. The method can effectively solve the problems of asymmetric dependence, excessively high sorting sensitivity and limited generalization ability of the traditional RAG system.

This invention relates to the field of photoelectric detection and fault diagnosis technology, specifically disclosing an intelligent fault diagnosis system for a photoelectric pod image acquisition device. The system includes a data acquisition and preprocessing module, a cross-domain feature alignment and knowledge distillation module, a few-sample meta-learning diagnosis module, and a diagnosis decision and visualization module. By adapting general fault knowledge to the target domain through cross-domain knowledge transfer and combining it with a few-sample meta-learning mechanism, the system can achieve high-precision diagnosis with very few fault samples and possesses the adaptive capability to quickly adapt to new fault modes.