19results about How to "Improve training accuracy" patented technology

The application discloses an electromagnetic compatibility fault prediction method based on a knowledge graph, relates to the technical fields of electromagnetic compatibility fault diagnosis and artificial intelligence, and constructs an electromagnetic compatibility fault knowledge graph through multi-source heterogeneous data, realizes the structured fusion of data, provides nodes and relations with physical meanings for a Bayesian network, makes the target Bayesian network have interpretability and be capable of tracing fault sources, adopts ALS-PSO to optimize the target Bayesian network, can effectively improve the training precision of the target Bayesian network, and thus can make the target Bayesian network after training accurately realize the prediction of electromagnetic compatibility faults.

The application relates to a large language model assisted domain self-adaption database query statement generation model training method and device, computer equipment and a storage medium. The method comprises the following steps: constructing a sample prompt word corresponding to a sample query question according to the sample query question and sample database mode information of a sample database corresponding to the sample query question; inputting the sample prompt word into a large language model to obtain a sample initial database query statement corresponding to the sample query question; performing pruning processing on the sample database mode information according to the sample initial database query statement to obtain pruned sample database mode information; and training a database query statement generation model by using the sample query question, sample format specification information corresponding to the sample query question and the pruned sample database mode information to obtain a trained database query statement generation model. By using the method, the training accuracy of the database query statement generation model can be improved.

The application provides a video anomaly detection model training method, a video anomaly detection method and equipment, which can be applied to the technical field of computer vision and machine learning. The training method comprises the following steps: obtaining a video source domain and a video target domain; using normal video slices to train an initial video anomaly detection model to obtain a prediction network; inputting randomly selected samples from a frame-level labeled source domain into an adversarial generation network to output first intermediate domain samples; using the first intermediate domain samples and the frame-level labeled source domain to train the initial video anomaly detection model to obtain a teacher network, and constructing a student network based on the teacher network; inputting second intermediate domain samples corresponding to the video target domain and the video target domain into the student network, inputting the first intermediate domain samples and the frame-level labeled source domain into the teacher network, adjusting network parameters of the student network based on output results of the teacher network and the student network, and obtaining a video anomaly detection model.

The invention relates to a channel foreign matter image generation and channel foreign matter detection method based on a generative adversarial network, and the method comprises the steps: image collection: employing an unmanned plane for inspection, and collecting a channel image; image noise reduction: carrying out noise reduction processing on the acquired image by adopting a median filtering technology; model improvement: based on the GAN model, introducing an SE attention mechanism, and improving a loss function to obtain an improved GAN model; model training: training the improved GAN model through a training data set; image generation: generating a channel foreign matter image by using the trained foreign matter image generation model, and forming an extended sample set; foreign matter recognition: training a target detection network in combination with the original sample set and the extended sample set, and detecting and recognizing the channel foreign matter based on the trained target detection network; and foreign matter positioning: according to an identification result, based on a GIS system, determining a spatial position of a foreign matter. The method is beneficial to generating images similar to real channel foreign matter features, and the channel foreign matter detection precision is improved.

The application discloses an information processing method and device, a storage medium and electronic equipment. The method comprises the following steps: obtaining first interaction information between a target account and a plurality of items, and a knowledge graph corresponding to the plurality of items; based on the first interaction information and the knowledge graph, a target recommendation model is used to obtain a target recommendation result corresponding to the target account; in the model training process, the model loss comprises a first loss value determined based on an item relationship matrix and a user-item interaction matrix, and a second loss value determined based on a plurality of account prediction preference scores and actual preference scores of a plurality of items. The application solves the technical problem that in the related art, when a recommendation model is trained, a high-order neighbor cooperation signal is used to learn rich user embedding and item embedding, which causes excessive smoothing, training performance degradation, difficulty in model convergence, and inaccurate recommendation results.

The embodiment of the invention provides a private domain fine tuning corpus determination method and device. The method comprises the following steps: cutting an original text corpus of an enterprise private domain according to a multi-level directory structure to obtain a plurality of target text fragments used for inputting a large language model; acquiring a plurality of private domain fine-tuning corpora through a large language model according to the target text fragment and in combination with a preset guide prompt word; and sorting the plurality of private domain fine-tuning corpora according to the confusion values corresponding to the plurality of private domain fine-tuning corpora, and determining the sorted plurality of private domain fine-tuning corpora as a target private domain fine-tuning corpora for training a private domain model of the enterprise. According to the embodiment of the invention, the problem that the quality of a private domain fine-tuning corpus based on manual calibration is low and the recovery of the question and answer ability of the private domain model is not facilitated in the related technology is solved, and the effect of improving the training accuracy of the private domain model is achieved.

The embodiment of the application discloses a model training method and related device, and relates to machine learning. The method comprises the following steps: obtaining a training data set with classification labels; obtaining target training data for the i-th round of training by sampling; and processing equipment comprising N image processors. The target training data is divided into N sub-training data, and the N sub-training data and the N image processors correspond to each other. At least two of the N sub-training data have a data quantity difference value, and / or at least two of the N sub-training data have a training data quantity difference value. Then, the N sub-training data are classified and predicted by the N image processors. N gradients corresponding to the N sub-training data are calculated based on the corresponding classification labels. When the image processors complete the gradient calculation, corresponding completion identifiers are generated. When the number of the completion identifiers is N, the N gradients are integrated, the initial classification model is updated for the i-th round of parameters, and a classification model is obtained.

The invention discloses a dynamic modeling analysis system for combustion atmosphere data of a biomass oxygen-enriched combustion boiler, and relates to the technical field of dynamic modeling analysis. The system comprises a working condition self-adaptive feature extraction module, a time sequence maintaining model training module, an online self-adaptive correction module and an incremental model updating module. Through a working condition adaptive feature extraction module, a time sequence maintenance model training module, an online adaptive correction module and an incremental model updating module, adaptive feature vectors are generated through feature extraction, a basic model is obtained through training, and online correction and incremental updating are performed by combining feature vector verification threshold judgment. And finally, iterative upgrading is completed through an incremental learning fine tuning model, the reliability of dynamic modeling analysis of the combustion atmosphere data of the biomass oxygen-enriched combustion boiler is improved, and the problem that in the prior art, the reliability of dynamic modeling analysis of the combustion atmosphere data of the biomass oxygen-enriched combustion boiler is low is solved.

The invention relates to the technical field of vehicle fault diagnosis, and discloses a wheel bolt looseness detection method and device, a vehicle and a medium, and the method comprises the steps: collecting an inertial measurement unit signal, a wheel speed signal and a steering wheel hand torque signal of the vehicle; determining target detection data based on the inertial measurement unit signal, the wheel speed signal and the steering wheel hand torque signal; detecting whether a wheel bolt of the vehicle is loosened by using the target detection data to obtain a detection result; and controlling the vehicle to execute corresponding actions based on the detection result. According to the method, the existing sensor of the vehicle is directly used for collecting the signals, then the signals are fused with the multi-dimensional vehicle running state signals, feature extraction and combination processing are combined to form target detection data, the target detection data are used for wheel bolt looseness detection, full-process automation from signal collection, data processing, state detection to vehicle control action execution is achieved, and the detection efficiency is improved. And the detection accuracy and the driving safety are improved, so that the vehicle fault diagnosis requirements of low cost, high real-time performance and easy deployment are met.

The application discloses a power transformation station defect detection method and device, computer equipment and a storage medium, wherein the method comprises the following steps: constructing a data set according to a device defect image of a power transformation station and a corresponding label; training an improved RT-DETR model by using the data set to obtain a trained improved RT-DETR model; obtaining a device defect image of a target power transformation station; inputting the device defect image into the trained improved RT-DETR model to identify a defect position of the target power transformation station; wherein a main network of the trained improved RT-DETR model is an RMNet network, the RMNet network is trained by using a residual structure in a training stage to improve training precision; and in an inference stage, a structure reparameterization is used to equivalently remove the residual structure to improve inference speed. The application can realize real-time online monitoring of device defects of a power transformation station and guarantee safe, stable and reliable operation of a power system.

The application discloses a federated learning system for Internet of Vehicles and a method for constructing a joint optimization model, and the federated learning system for Internet of Vehicles comprises a vehicle terminal, a roadside unit and a V2X wireless communication link; the V2X wireless communication link is disconnected in the vehicle, the V2X wireless communication link is connected to the roadside unit, and the roadside unit is connected to the vehicle terminal; the method for constructing a joint optimization model comprises the following steps: step S1, constructing an optimization objective function; step S2, establishing a constraint system of the optimization objective function; step S3, constructing a coupling relationship between federated learning and communication according to the objective function and the constraint system; step S4, constructing an optimization problem according to step S3; and step S5, solving the optimization problem by using an adaptive alternating optimization algorithm; and the application solves the problems that the prior art cannot simultaneously consider communication reliability and resource utilization efficiency and lacks a federated learning optimization mechanism capable of performing real-time power regulation and bandwidth allocation according to vehicle dynamic characteristics.

The embodiment of the invention provides a Gaussian optimization method and device under a sparse view angle, equipment and a storage medium, and relates to the technical field of computer vision and graphics. According to the method, rough three-dimensional Gaussian of a target scene is obtained, at least one training iteration is carried out, and parameter training is carried out on Gaussian elements in the rough three-dimensional Gaussian until a target Gaussian model is obtained. In a training iteration process, if an iteration round is an optimization iteration round, a pixel importance value is calculated based on a reconstruction error, a semantic prior error and a geometric prior error, for each view angle, a corresponding fine Gaussian primitive is determined based on the pixel importance value, and the rough three-dimensional Gaussian is updated according to all the fine Gaussian primitives. For each visual angle, a corresponding fine Gaussian primitive is determined according to a pixel importance value, a scene key area is focused, insufficient key information modeling caused by undifferentiated distribution of the Gaussian primitive is avoided under a sparse data condition, and the training accuracy of three-dimensional Gaussian sputtering in a sparse scene is improved.

This application relates to the field of railway facility monitoring technology, and in particular to a method for spatiotemporal data fusion analysis of railway facilities based on BeiDou positioning. The method includes determining a data extraction strategy based on key distribution density and key temporal deviations, performing data screening and compensation by combining interval recurrence stability coefficients or interference correlation indices, and achieving long-term scheduling optimization and short-term real-time prediction through target prediction models and reinforcement learning algorithms. This application can solve the problems of heterogeneous multi-source monitoring data, time asynchrony, and lack of unified spatiotemporal correlation analysis, improving the accuracy and intelligence level of railway facility operation status monitoring, and providing support for the safety, reliability, and intelligent decision-making of railway transportation.

The application discloses a kind of oil and gas exploration road image segmentation model training method, device, equipment and medium, the method comprises: oil and gas exploration road sample image is input to the convolution operation of image segmentation model being constructed in advance to oil and gas exploration road sample image, and convolution road feature map is obtained;Convolution road feature map is input to the feature coding of convolution road feature map in coding module in model, and coding road feature map is obtained;Coding road feature map is input to the feature fusion of coding road feature map in residual attention space pyramid module of model, and fusion road feature map is obtained;Fusion road feature map is input to the feature decoding of fusion road feature map in decoding module of model, and prediction road segmentation region image is obtained;According to actual road segmentation region image and prediction road segmentation region image, model training is carried out to image segmentation model, and oil and gas exploration road image segmentation model is obtained.

The present application relates to a kind of methods for predicting porous medium structure using machine learning, comprising: obtaining the image of porous medium prepared in different working conditions and corresponding parameter information;According to phase state characteristics, image is processed, and image dataset is obtained;From image dataset, optionally two images of different working conditions are selected, and with corresponding parameter information, a training sample is formed;Using several training samples, generative adversarial network model is trained, which uses unsupervised image-to-image conversion algorithm for training, for each input training sample, the reconstruction image of original image and predicted image are output, the model is verified by the output predicted image, and the prediction model is obtained after training;The image of porous medium prepared in known working condition and corresponding parameter information are input into prediction model together with the preset parameter information of target object, and the predicted image of target object is obtained, so that the structure prediction of target object can be efficiently and accurately obtained by the present application.

The application discloses an age prediction model training method and device, equipment and a storage medium. The method comprises the following steps: constructing a sample training set with age category labels; inputting the sample training set into a pre-constructed neural network model for training to obtain age prediction probabilities corresponding to each training sample in the sample training set; determining age prediction values according to the age prediction probabilities and tolerance error values corresponding to each age category; training the neural network model according to the age prediction values, and obtaining an age prediction model according to the trained neural network model. The embodiment of the application improves the prediction accuracy of the age prediction network model.

The embodiment of the invention provides a point cloud data processing method and device, a vehicle and a storage medium. The method comprises the following steps: extracting at least one single-frame point cloud containing a target object from an original point cloud sequence based on four-dimensional label information of the target object; and on the basis of the corresponding relationship between the vehicle pose information and the single-frame point cloud, performing registration operation on the at least one frame of single-frame point cloud to obtain the reconstructed point cloud data of the target object, thereby improving the integrity of the point cloud data, and improving the training precision of the autonomous vehicle perception model.

The invention discloses a distributed learning resource optimization system and method based on an open wireless access network, and relates to the technical field of distributed learning, the system comprises a plurality of access units, a concentration unit and a control unit, the access units are in one-to-one correspondence with user groups, the control unit solves a distributed learning resource optimization model, and the concentration unit is in one-to-one correspondence with the user groups. The access unit issues the maximum number of retransmission times, wireless resource blocks, power and computing resources to target users in the user group for the target users to carry out local training and local gradient uploading, and the access unit carries out local aggregation on the local gradients of all the target users in the user group to obtain a local aggregation gradient of the user group; and the concentration unit globally aggregates the local aggregation gradients of all the user groups to obtain a global aggregation gradient. According to the invention, the training time delay can be reduced, the model convergence performance is improved, and the reliability and training precision of distributed learning are fully improved.

This application discloses a method and apparatus for use in a node during model training in wireless communication. The node first receives at least a first dataset and a second dataset; then receives or sends a first signaling; and sends first reporting information; the first dataset is used for training a first model, and the second dataset is used for training a second model; the first reporting information depends on the second model; the first signaling is used to indicate whether the second model depends on the first model. This application improves the reliability of model training and reduces the time consumed by model training by optimizing the training method of AI / ML models, thereby improving transmission performance and spectral efficiency.