12results about How to "Reduce collection costs" patented technology

This invention provides a method for modeling a cylinder motion system based on a physical information neural network, comprising: acquiring time-series data of the cylinder motion system, including time-series control quantities and system state quantities; constructing a physical information neural network model with a dynamic feature input layer and a physical parameter input layer, receiving the time-series control quantities and physical parameter vectors respectively; performing a nonlinear transformation on the physical parameter vectors through a physical parameter encoding network to obtain encoded physical feature vectors; fusing the time-series control quantities and physical feature vectors to form a joint input vector; inputting the joint input vector into the backbone neural network, synchronously predicting the system state quantities through multi-layer nonlinear transformations; constructing a composite loss function including a data loss term and a physical loss term; training the model using time-series data, updating the model parameters by minimizing the composite loss function until the model converges, thereby achieving high-precision modeling using only single motion data.

The application provides a microseismic real-time monitoring method and device based on compressed sensing and 5G node collection, solves the problem that microseismic monitoring data is easy to be submerged in noise interference, and microseismic data collection equipment cannot transmit data in real time, or the transmitted data is uneven and incomplete. It comprises the following steps: collecting data, establishing a geological and geophysical model, simulating fracturing based on the parameters of the well to be fractured to demonstrate the parameters of the microseismic monitoring observation system; establishing an adaptive optimal learning dictionary to form a compressed sensing collection scheme; conducting a due diligence investigation of field obstacles and interference sources to form a compressed sensing active obstacle avoidance scheme; using a 5G node collection system to collect field data; applying compressed sensing data reconstruction technology to reconstruct the collected field data to form three-dimensional microseismic monitoring data; carrying out microseismic data processing and interpretation, and feeding back the microseismic analysis results to the fracturing site to realize real-time monitoring of the fracturing construction process.

The application relates to a power distribution facility anomaly detection method based on deep learning, and relates to the technical field of power distribution facility anomaly detection. The application collects electric characteristic signals of a power distribution facility at a set sampling rate, selects electric characteristic signals of a non-anomaly time period located on a slow spectrum submanifold as training data, minimizes a loss function, uses a full connection network to learn a mapping matrix from a nonlinear feature vector of the electric characteristic signals to a latent variable on a low-dimensional slow spectrum submanifold of the electric characteristic signals and a linear dynamics matrix in a latent space, in application, collects electric characteristic signals, uses the last corresponding latent variable of the electric characteristic signals and a predicted future latent variable obtained by iteration using the linear dynamics matrix, reconstructs a predicted non-anomaly electric characteristic signal through inverse transformation between the latent variable and the electric characteristic signal, and performs anomaly analysis by using the difference between the predicted non-anomaly electric characteristic signal and an actual electric characteristic signal.

The application provides a self-supervised speech denoising method and device, and relates to the field of acoustic signal processing, and comprises the following steps: S1, obtaining a DCUnet network, constructing a speech denoising model by adding a TCM module in the DCUnet network; S2, obtaining a noise speech set, training the speech denoising model through the noise speech set, and obtaining a trained speech denoising model; S3, denoising speech through the trained speech denoising model. The application constructs a speech denoising model based on a deep complex domain DCUnet network, combines a complex domain overall processing strategy, improves the reconstruction quality and denoising performance of the speech signal, dynamically captures the change characteristics of the noise scene through the TCM module in the speech denoising model, enhances the adaptability of the model to dynamic signals in a complex environment, trains the speech denoising model by using an ONT strategy, uses noise speech as training data, does not need clear target speech data, reduces the training data collection cost, and improves the generalization ability of the model.

This invention belongs to the field of intelligent seismic exploration technology, specifically relating to an intelligent reconstruction method for passive source seismic wavefields under sparse active source constraints and non-ideal illumination, to address the problems of false phase axes and coherent noise under non-ideal distribution of underground passive sources. To improve the reconstruction effect of passive source seismic wavefields under non-ideal illumination conditions, this method constructs an improved U-Net network, enabling it to intelligently extract data features and derive attention mechanisms along two independent dimensions—channel and space—within the network. The attention mechanism is then multiplied by the input feature map for adaptive feature refinement, achieving intelligent reconstruction of passive source data. Furthermore, a deep learning network, trained through learning, constrains the passive source seismic data prediction network with sparse active source seismic records, enabling the reconstruction of passive source data with co-located active source lines under sparse active source conditions, thus improving the multi-dimensional deconvolution reconstruction effect and computational stability.

The application discloses a kind of generation methods, devices and equipment of vehicle expression dataset and storage medium, by obtaining the scene parameter library containing multiple vehicle environment parameters and the anonymization standard expression library without identity as basic material, expression data is decomposed into multiple action unit combinations using action unit analysis module, action features and parameter features are extracted and fused, initial expression data is obtained by inputting generative adversarial network generator, and then privacy enhancement and quality check form standardized dataset.The application avoids the privacy disclosure risk collected by real drivers, reduces the collection and labeling cost, by fusing environmental parameters and action unit features, the dataset covers multiple vehicle conditions, improves scene adaptability, and privacy enhancement and quality check guarantee data security and quality, which can be directly used for vehicle expression recognition algorithm training, meet the demand of multi-scene, high-quality, compliance data.The technical scheme of the application can be widely applied to the field of vehicle technology.

This invention discloses a SOTIF data acquisition method and related equipment. In this invention, the cloud has a large data acquisition scope, thereby reducing reliance on closed sites or professional data collection fleets, and lowering the acquisition cost of multi-source heterogeneous data. Furthermore, First Automobile Works filters its collected multi-source heterogeneous data using first Shannon entropy and residual risk values. The filtered multi-source heterogeneous data corresponds to traffic environments with high levels of disorder. Therefore, by calling First Automobile Works to collect and upload multi-source heterogeneous data, a large amount of multi-source heterogeneous data that meets the application conditions of SOTIF can be easily obtained, providing data support for SOTIF. Moreover, the SOTIF data acquisition method is executed on the vehicle side, and by uploading the filtered multi-source heterogeneous data instead of the original full-volume data, the data processing burden on the cloud is significantly reduced, improving the implementation efficiency of SOTIF. This invention has wide applications in the automotive technology field.

This invention provides a method, apparatus, device, and medium for detecting obstructive sleep apnea in children. The method includes: filtering an initial feature set using a recursive feature elimination method under nested cross-validation to obtain a target feature set; acquiring input data from a training sample set based on the names of the target features in the target feature set, and training N target machine learning models using the input data; extracting target feature data from the feature data of the object to be detected based on the names of the target features in the target feature set; inputting the target feature data into the N target machine learning models after training; and determining the detection result corresponding to the object to be detected based on the output results of the N target machine learning models. This invention can improve the sensitivity and specificity of detection, reduce the risk of missed or misdiagnosed cases, and lower data collection costs and clinical implementation barriers.

This invention relates to a drilling-anchored asteroid mineral harvesting and trans-domain transportation system and method, belonging to the field of asteroid mineral harvesting and transportation technology. It addresses the problems of poor terrain adaptability and low reliability in existing asteroid mining devices. The system includes a harvesting device, a mineral collection mechanism, and a mothership probe. The harvesting device comprises a mineral collection mechanism, a drilling-anchoring mechanism, a centrifugal acceleration mechanism, a jet propulsion device, and a mining robot shell. The mineral collection mechanism is connected to the centrifugal acceleration mechanism, which is housed within the mining robot shell. The output end of the centrifugal acceleration mechanism is coordinated with the mineral collection mechanism. The mineral collection mechanism is mounted on the mothership. The jet propulsion device is located on the upper side of the mining robot shell, and the drilling-anchoring mechanism is located on the lower side of the mining robot shell. This invention can adapt to various asteroid terrains and environments, enabling reliable anchoring on the asteroid surface for mineral harvesting and transportation, effectively improving the efficiency of asteroid mineral harvesting and transportation, and offering high reliability.

This invention relates to the interdisciplinary fields of digital healthcare, wireless sensing, signal processing, and artificial intelligence, specifically a controllable data augmentation method for radar respiratory monitoring based on parametric environment modeling. This data augmentation method first acquires a clean radar respiratory signal, constructs four types of models with physical constraints and corresponding parameter spaces, and calculates the ground truth values ​​for each interference. Then, based on the physical mechanism, it synthesizes a noisy signal through convolution / time-varying filtering, phase modulation, and additive superposition operators, forming a quadruple data set containing the clean radar respiratory signal, the noisy signal, the ground truth values ​​of interference, and model parameters. After batch generating the dataset, multi-level physical fidelity verification is performed; if the fidelity is not met, subsequent parameter tuning is conducted: scene fingerprints are extracted from real monitoring data, mapped to obtain parameter adjustment amounts, and the parameter space is updated. This invention solves the problems of missing physical modeling and unreasonable synthesis mechanisms, effectively improving the accuracy of simulation data, model generalization ability, and robustness of respiratory monitoring.

The application provides a robot visual operation control method and system based on eye tracking, and belongs to the field of robot vision and artificial intelligence. The method comprises the following steps: a head-mounted device with eye tracking function is used to synchronously collect data streams when an operator performs an operation task, wherein the collected data streams comprise eye movement data streams and scene visual data streams when the operator performs the operation task; for the preprocessed data streams, a dynamic Bayesian network is used to extract gaze-intention coupling features; the preprocessed data streams and the gaze-intention coupling features are input into a robot operation model with a double-path attention fusion that has been constructed, so as to obtain online attention results, and the robot performs corresponding operations according to the online attention results. The method of the application can quickly focus on key areas in complex scenes such as occlusion and interference, and improves the task success rate.

This invention discloses a self-supervised learning prediction method and system for spatiotemporal meteorological-power data, primarily applied to the field of new energy power generation prediction. The method first acquires historical power, historical meteorological data, and future meteorological forecast data from wind farms, performing spatiotemporal interpolation for completion, 3σ outlier removal, Min-Max normalization, and spatial grid alignment preprocessing. Then, it constructs a meteorological-power pre-training framework based on physical simulation, incorporating time-series frequency alignment prediction. This framework includes a core model pre-trained using virtual wind farm data, a time-series alignment network, an adaptive multi-scale spatiotemporal fusion module, and a dual-branch feature extraction network. Next, a self-supervised calibration gated fusion module dynamically fuses spatiotemporal and temporal features. Finally, labeled data is used to fine-tune the complete model and optimize parameters. The system then outputs ultra-short-term and medium-short-term wind power predictions based on the input data. This invention significantly improves prediction accuracy and generalization ability, providing reliable support for power system dispatching.