37results 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 vehicle operation data collection method and system, computer equipment and medium, and belongs to the field of vehicle data processing. The method comprises the following steps: obtaining vehicle model information and user demand of a vehicle to be collected; determining data analysis service project content options based on a data analysis service project content information table and vehicle model information corresponding information table, and screening data analysis service project content according to the user demand of the vehicle to be collected; generating a natural language text for the data analysis service project content to obtain a vehicle data collection strategy; collecting environmental data and judging based on data collection conditions; when the environmental data falls within the range of the data collection conditions, collecting operation data of the vehicle to be collected according to the data collection project, and obtaining operation data of the vehicle to be collected. The method can dynamically collect data based on the actual operation state, driving environment and user demand of the vehicle, and ensure the effectiveness of operation data collection.

The invention belongs to the technical field of new energy power generation and grid connection, discloses a grid-side converter fault ride-through control structure modeling method, system and equipment and a medium, and aims to solve the problems of difficult modeling, high cost, poor extrapolation and lack of universality caused by dependence on manufacturer internal logic or massive experimental data in the prior art. According to the method, disturbance input and current output data under a limited working condition are acquired, a dimension raising mapping function is constructed to map the input to a high-dimensional feature space, a Koopman operator is solved based on ridge regression to establish a global linear mapping model, active and reactive current reference components are predicted, and the current reference components are optimized. And finally, inputting the current inner loop time domain model to generate a current transient response curve during the fault period. According to the method, high-precision modeling and prediction can be realized only by a small number of samples, and the method has good extrapolation and universality and can be adapted to various fault ride-through strategies.

The invention relates to the technical field of image recognition, and discloses a wind power fan blade defect recognition method and system based on image recognition, and the method comprises the steps: carrying out the blocking cutting, illumination normalization and image enhancement processing of an original image, constructing a defect-free sample and a defect sample, and dividing the samples into a training set and a test set; the method comprises the following steps: taking a GANopen network as a basic framework, fusing the lightweight design of Mamba-YOLO, constructing a joint loss function by adversarial loss, reconstruction loss and coding loss based on a training set, carrying out unsupervised training, optimizing network parameters until convergence, and obtaining a defect identification model; inputting the preprocessed to-be-detected fan blade image into the trained defect recognition model, performing defect recognition and positioning, and outputting the defect type and position; based on an output result of the defect identification model, dynamically adjusting an early warning level and response measures through a self-adaptive early warning mechanism; according to the invention, the efficiency and accuracy of wind power fan blade defect identification are improved.

This application discloses a method for training a recognition model, a recognition method, a computer device, and a computer-readable storage medium. The training method includes the following steps: controlling a single-photon lidar to emit photon pulses towards a target scene and receiving one-dimensional single-photon echoes reflected from the target scene, where the target scene includes at least one object to be recognized; constructing a training sample set based on the one-dimensional single-photon echoes; constructing an initial model and inputting the training sample set into the initial model for iterative training; validating the initial model after each iteration, marking the validated initial model as the recognition model, and outputting it. The application method involves inputting the echo signal to be recognized into the trained recognition model, and obtaining the recognition result based on the processing of the recognition model. The recognition result includes the pose and type of the object in the target scene. Therefore, this application can achieve high-precision recognition of long-distance targets.

A method for evaluating and sampling the value of a dataset is disclosed, which aims to reasonably assess the value of the dataset and guide the sampling of high-value datasets. The method first establishes an evaluation model for assessing the value of individual data points and a function to describe the degree of value redundancy among data points. Then, it constructs a value evaluation model for the dataset by comprehensively considering the value of individual data points and the degree of value redundancy among them. Based on this data set value evaluation model, high-value datasets can be sampled from the data sampling space according to user needs. The main application of this invention is to evaluate the value of datasets and sample high-value datasets. It can guide data sampling in data-driven analysis, modeling, and decision-making tasks, thereby improving the quality of datasets and effectively reducing data acquisition costs while ensuring the effectiveness of the target task.

The invention discloses a method for detecting abnormal electricity consumption of an intelligent electric meter, which relates to the technical field of intelligent meter reading, and comprises the following steps of: 1, data acquisition and preprocessing, 2, data compression and dimension reduction, 3, lightweight data aggregation, 4, transfer learning and fine tuning, 5, self-supervised learning, and 6, model lightweight. According to the method, a lightweight aggregation algorithm is used, that is, data aggregation is carried out through a local weighted average method instead of a complex distributed data aggregation model, so that the calculation complexity is reduced, the aggregation efficiency is improved, and the dependence on hardware resources is reduced; before data aggregation, a data compression technology is used to carry out dimension reduction on power consumption data, so that the data volume is reduced, the aggregation efficiency is improved, and the information loss is reduced; transfer learning and fine tuning are adopted, a BERT pre-training model is utilized to accelerate training of the deep belief network, training data requirements are reduced, and model training efficiency is improved.

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 discloses a kind of automobile body parts resistance spot welding quality evaluation method and system.The method comprises: using the existing equipment of automobile production workshop and process parameter to carry out welding experiment to sheet part same as the material of key position of automobile body, open data record function in the host computer of welding equipment, obtain the data file of different quality types of welding spot;According to the result of spot welding, the data file is classified, and the voltage and current data corresponding to each quality type of welding spot are obtained by analyzing all data files, and the dynamic resistance data is calculated;Lightweight one-dimensional convolutional neural network model is built as spot welding quality evaluation model;The test data is input into the spot welding quality evaluation model after training and optimization, and the corresponding spot welding quality evaluation result is obtained.The method realizes the automatic extraction of the deep characterization features reflecting the spot welding quality result in dynamic resistance data, avoids the complex process of data feature extraction and analysis, and obtains higher detection accuracy.

The present application relates to the technical field of silicone key conductivity test, and discloses a silicone key conductivity reinforcement learning test system and method, wherein the silicone key conductivity reinforcement learning test method comprises the following steps: constructing a silicone key conductivity test environment; establishing a test knowledge base, extracting general feature representation of key conductivity performance through feature mapping and domain adaptation algorithm; constructing a test parameter optimization model, modeling the test process as a Markov decision process, and learning the optimal test strategy based on a reward function; adjusting the test parameters in real time during the test process, performing reliability analysis after obtaining the test results, and feeding back the new test experience to the knowledge base to realize the accumulation and reuse of test knowledge; the present application realizes the self-adaptive optimization of test parameters and the cross-key knowledge transfer by fusing reinforcement learning, transfer learning and meta-learning technology.

The invention provides a standing tree cutting degree parameter automatic inversion method based on a ground-based laser radar and deep learning, and relates to the technical field of forest tree measurement. The method comprises the following steps: acquiring an original three-dimensional laser point cloud based on multi-station scanning, and acquiring a clean trunk point cloud; and generating a high-density diameter-height observation data set through axial skeletonization extraction and slicing processing in combination with circle fitting calculation. A variable parameter cutting degree equation model introducing a relative tree height ratio and a height-diameter ratio factor is constructed, and a residual sum of squares objective function is established; and iteratively solving the optimal shape parameter vector by using a nonlinear least square algorithm, and outputting a model parameter of the target stumpage when a precision evaluation index is met. According to the method, the problem of measurement errors caused by branch and leaf shielding in a complex under-forest environment is effectively solved, a traditional destructive survey mode of cutting down and analyzing trees is replaced, non-destructive, high-precision and automatic inversion of standing tree cutting degree parameters is achieved, and the efficiency and the digital level of forest resource monitoring are improved.

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 invention discloses a traffic signal control method based on machine visual perception and space-time sequence prediction, and relates to the technical field of intelligent traffic signal control. The traffic signal control method based on machine vision perception and time-space sequence prediction comprises the following steps: S1, machine vision perception: acquiring real-time image data of a traffic intersection through image acquisition equipment, preprocessing the image data, and extracting traffic flow key parameters; and S2, space-time sequence prediction: constructing a PCA-LSTM traffic flow prediction model based on principal component analysis PCA and a long short-term memory neural network LSTM, inputting the preprocessed traffic flow historical data, and outputting a short-term traffic flow prediction result. The traffic signal control method based on machine visual perception and time-space sequence prediction is comprehensive in data coverage and low in acquisition cost; and the prediction performance is better: through abnormal data processing and dynamic principal component selection of the PCA-LSTM model, the prediction precision is improved by 2%-5% compared with the traditional LSTM, and the calculation efficiency is improved by 15.01%.

The application relates to a bearing damage positioning method based on Lamb wave dispersion mode matching of acoustic emission signals, wherein different frequency band components of original acoustic emission signals with the same frequency interval are obtained; a time of arrival curve is obtained; a theoretical dispersion curve of Lamb wave propagation in a bearing is solved, then a coordinate system is unified with the signal time of arrival curve; the dispersion curve and the time curve are matched through a Pearson correlation coefficient index; a formula is established according to a speed difference and a time difference, and a bearing damage distance is calculated; the sensor is moved by a certain distance to be measured again, and the damage position is determined through the two results. The method of matching the Lamb wave propagation mode can determine the propagation speed of the acoustic emission signals with different frequencies, the accuracy of the time difference positioning method is greatly improved, the bearing damage position can be directly found through a single sensor, the bearing fault diagnosis can be realized without depending on the bearing rotating speed, the installation difficulty can be effectively reduced, and the detection cost can be reduced.

The invention discloses a data expansion and exoskeleton joint end-to-end torque estimation method based on a diffusion model, and the method comprises the steps: carrying out the normalization processing of time series data from a multi-source sensor, carrying out the fragmentation according to a fixed length, and constructing a training sample with a motion class label; then, training a classifier-free conditional diffusion model by adopting a sample, and simultaneously learning conditional and unconditional denoising mapping relationships in a manner of randomly inactivating category conditions; in the generation stage, based on a classifier-free condition guidance mechanism, multi-modal time series data with specified motion category features are gradually generated from random noise; and finally, fusing the generated synthetic data with real acquired data to train a joint torque end-to-end prediction network, thereby realizing joint torque estimation of input sensor time sequence data. The method improves the prediction precision, generalization ability and stability of the end-to-end torque estimation model in a multi-action and few-sample scene, and has a good engineering application value.

This invention discloses a method, system, storage medium, and vehicle for collecting automotive fault data, relating to the field of automotive fault data collection. The method includes the following steps: providing a data reading interface for the device to be diagnosed to an onboard control device with data transmission capabilities; collecting diagnostic data from the device to be diagnosed corresponding to the diagnostic task through the onboard control device; and storing the diagnostic data as automotive fault data. This invention directly utilizes existing onboard control devices with data transmission capabilities to collect the operating information of the device to be diagnosed; it eliminates the need for external diagnostic instruments as in existing technologies, thereby improving collection efficiency and reducing collection costs. Simultaneously, the automotive fault data collected by this invention is automatically stored, eliminating the need for manual scanning and uploading operations as in existing technologies, thus improving the user experience.

The invention discloses a copper-bismuth composite electrode material with a nanowire array structure, which comprises a foamy copper substrate and a copper-bismuth nanowire array layer deposited with a bismuth component, and the copper-bismuth nanowire array layer deposited with the bismuth component grows on the foamy copper substrate. The invention also discloses a preparation method of the material. The preparation method specifically comprises the following steps: step 1, preparing a sample; step 2, carrying out electroreduction treatment on the sample in an electrolyte to obtain a substrate loaded with a copper nanowire array; and step 3, preparing a bismuth-containing electro-deposition solution at room temperature, placing the copper nanowire array substrate in the step 2 in the bismuth-containing electro-deposition solution, loading a bismuth component on the copper nanowire array substrate in a constant-current electrochemical deposition mode, and after deposition is finished, performing immersion cleaning and drying to obtain the copper-bismuth composite electrode material with the nanowire array structure. According to the copper-bismuth composite electrode material with the nanowire array structure, provided by the invention, carbon dioxide can be reduced into formate with high selectivity, and the stability of an electrode is remarkably enhanced.

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.

The present application relates to the field of industrial detection, and specifically discloses a general defect detection method for industrial manufacturing, comprising the following steps: S1, reference image construction and to-be-detected region calibration; S2, positive and negative sample storage and to-be-detected region extraction; S3, to-be-detected image acquisition; S4, similarity calculation and result output. The present application also discloses a general defect detection system for industrial manufacturing, comprising a reference image calibration module, a sample processing and storage module, an image registration alignment module, a similarity calculation and result output module. In actual use, the present application requires few samples, only 20-30 positive samples and 10-20 negative samples are needed for deployment, greatly reducing the cost of sample collection and labeling and solving the problem of scarce defect samples. In addition, the detection accuracy is high, the core registration algorithm has small error, the similarity calculation model improves the accuracy, and the adaptability is strong. When the product model is updated, the reference image and sample library can be replaced, meeting the flexible production demand.

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.

The invention discloses a self-adaptive layered Kriging model construction method based on a sequential alternate sampling strategy, and belongs to the technical field of multi-fidelity agent modeling. The method comprises the following steps: firstly, generating an initial input sample for each model with different fidelity, calculating the response of the fidelity model, generating a plurality of input samples, establishing an initial Kriging model and an initial layered Kriging model, and establishing a low-fidelity training sample set and a high-fidelity training sample set; and setting an initial iteration frequency, and calculating an initial cost budget. Secondly, judging whether a stopping condition of total cost budget is met or not, if yes, outputting a final layered Kriging model in the last step, and if not, iterating again; according to the method, through a self-adaptive sampling point distribution mechanism, the sample sizes required by the high-fidelity model and the low-fidelity model can be dynamically and intelligently determined, and the modeling efficiency is improved; the method can automatically recognize a function feature region with a significant maximum value and a significant minimum value, and reduces the overall data collection cost to the maximum extent while guaranteeing the final meta-model precision.

The invention relates to a vehicle positioning method and device, a controller, a medium, a program product and a vehicle, the vehicle is equipped with positioning equipment, and the positioning method comprises the following steps: performing feature matching on a target image and a field image acquired by the vehicle in real time; a position of the vehicle is determined based on the coordinates of the positioning device. On the basis, the problem that the vehicle depends on GNSS positioning in a GNSS signal missing scene can be solved, and the blank of vehicle position judgment when no positioning signal exists is filled.

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 present application belongs to the technical field of radar signal processing and target identification, and specifically provides a multi-target array structure inversion method based on two-dimensional power spectrum imaging, to solve the problem that the array structure in the multi-target scattering image is difficult to be stably recovered under the condition of strong aliasing, strong speckle and strong coherent interference background; the present application converts the collected radar scattering data into a power spectrum image, generates a global candidate point set with a level label through log power spectrum construction, sub-aperture decomposition, hierarchical candidate point extraction, consistency voting and density clustering deduplication, and further forms a closed loop through translation and combination prediction and bidirectional matching verification, and the original multi-target array structure is inferred from geometric evidence, which has good robustness, engineering feasibility and identification accuracy; and the present application does not depend on phase compensation, is suitable for observation scenes under multi-target, and meets the application requirements of complex target structure identification, multi-target imaging identification and target classification.

The application provides a simulation scene database construction method and system, the method comprises the following steps: receiving road image data collected by crowdsourcing; identifying the image data by an intelligent camera, and outputting road perception information and traffic flow perception information; constructing OpenDrive roads and Openscenario traffic flows according to the road perception information and the traffic flow perception information; obtaining target motion state description information in the Openscenario traffic flow, classifying and summarizing target motion scenes, and forming a simulation scene database. Through the scheme, the simulation scene database can be quickly constructed, the simulation data acquisition cost is reduced, and the amount of collected data is ensured.

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.