1129 results about "Supervised training" patented technology

The invention relates to the technical field of equipment fault diagnosis, and discloses an equipment fault diagnosis and prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-modal data in real time through a plurality of sensors installed on equipment; s2, preprocessing the collected data; s3, constructing a hybrid deep learning model; s4, dynamic weighted fusion is performed on the features of different modal data by using an attention mechanism, and comprehensive feature representation is generated; s5, using the marked fault data and normal data to supervise and train the model; s6, inputting equipment operation data acquired in real time into the trained model, and judging the state of the equipment; and S7, generating a potential fault early warning signal based on a prediction result of the model. A piezoelectric vibration sensor and a thermal infrared imager are arranged on a motor bearing through vibration, temperature and sound sensors, vibration waveforms, thermal imaging slices and time-frequency diagrams are synchronously captured, and composite state characteristics such as mechanical wear and temperature anomaly of equipment are comprehensively reflected.

The invention relates to the field of fault early warning, in particular to a wind power fault dynamic early warning method and system based on multi-source heterogeneous data fusion. According to the method, multi-source data such as SCADA operation data, CMS vibration monitoring data and meteorological environment data of a wind turbine generator are collected in real time, standardization processing is carried out, and a multi-dimensional feature vector is constructed. And generating a fusion data set by using an adaptive weighted fusion algorithm, constructing a fault prediction model based on a deep convolutional neural network, and outputting a health state assessment value and a fault risk level in real time after historical fault sample supervised training. And when the risk level exceeds a threshold value, generating an early warning signal containing a fault type and a positioning and repairing suggestion, dynamically adjusting a monitoring parameter weight, iteratively updating a model, and realizing adaptive optimization of an early warning strategy. The problem that an existing method depends on single data source and multi-source data fusion is solved, and accurate dynamic early warning is achieved.

The invention discloses a large-scene three-dimensional reconstruction method based on three-dimensional Gaussian sputtering, and relates to computer graphics. The method comprises the following steps: collecting a multi-view image set of a large scene; obtaining a scene sparse point cloud according to the multi-view image set; performing monocular depth estimation on the multi-view image by using a pre-trained depth prediction network to obtain monocular depth estimation priori; the method comprises the following steps of: performing global training on a scene by utilizing scene sparse point cloud and monocular depth estimation prior to obtain an initial three-dimensional Gaussian model, and performing space grid division on the initial three-dimensional Gaussian model to obtain a plurality of scene blocks with axis alignment bounding boxes; setting image view angle data of each scene block; performing deep supervised training on the Gaussian ellipsoids in the plurality of scene blocks by using a parallel GPU (Graphics Processing Unit); combining the trained scene blocks to obtain a final three-dimensional Gaussian model; in view of low geometric structure reconstruction precision caused by only depending on color information of a multi-view image in large-scene three-dimensional rendering, the method improves the reconstruction precision of large-scene rendering.

The invention discloses a hydropower station equipment fault analysis method based on state data mining, and relates to the technical field of hydropower station equipment intelligent fault diagnosis, and the method comprises the steps: collecting key operation parameters through deploying multiple types of sensors, and constructing a unified state time series data set; carrying out supervised training by adopting an LSTM network, extracting a dynamic feature vector, and constructing an AI state analysis model; introducing a micro-fluctuation abnormal coefficient WBYX, and evaluating the operation stability of the equipment; a coupling disturbance collaboration coefficient OHRD is calculated, and a fault conduction relation among multiple devices is identified; and calculating a trend evolution coefficient QSYH based on the state vector included angle offset, and analyzing whether the equipment operation trend is abnormal or not. By setting a multi-level threshold value, generation of a hierarchical early warning mechanism and a response strategy is realized, and the operation safety and the fault prediction capability of hydropower station equipment are effectively improved. The method is suitable for hydropower station key equipment state monitoring and intelligent operation and maintenance management in a complex environment.

The invention discloses a deep learning-combined power transformer partial discharge signal extraction and diagnosis method. The method comprises the following steps of S1, setting a multi-channel synchronous acquisition system in a power transformer body area to acquire a multi-dimensional original partial discharge data set; s2, preprocessing the acquired multi-dimensional original partial discharge data set; s3, performing time alignment and amplitude matching on the processed signal, and dividing the processed signal into a sliding time window to construct a standard input tensor; s4, constructing an attention enhancement model fused by the convolutional neural network and the bidirectional gating circulation unit; s5, performing supervised training on the attention enhancement model by using the labeled sample; s6, inputting the real-time signal into the training model, and outputting a discharge type label; s7, risk grade evaluation is carried out in combination with statistical characteristics; and S8, generating a structured diagnosis report and uploading the structured diagnosis report to a monitoring platform. According to the invention, multi-source signals and a depth model are fused, and intelligent diagnosis and risk assessment of transformer partial discharge are realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology and the like, and discloses a voice generation method which comprises the following steps: constructing a multi-language voice synthesis model, obtaining plain text data and paired voice text data, and constructing an expansion vocabulary; updating a language perception embedding layer and model parameters, and converting an input text into a mark sequence; and the encoder extracts context semantic features, extracts pronunciation rule features, and the decoder fuses the features to generate an acoustic feature sequence, and converts the acoustic feature sequence into target voice data. According to the invention, the multi-language speech synthesis model is combined with the language perception embedding layer, so that the speech generation capability of a low-resource language is improved; the text conversion accuracy is improved by expanding the vocabulary, the target language learning ability is enhanced by unsupervised training, the low data environment adaptability is optimized by supervised training, and the speech naturalness and fluency are improved by feature fusion.

The invention discloses a semi-supervised multi-temporal satellite image time-varying information extraction method, and belongs to the technical field of remote sensing image processing. The semantic change detection performance of the model and the detection precision of complex shape change ground objects are improved. The method comprises the following steps: constructing a semantic change detection model, carrying out full-supervised training on the semantic change detection model by using a binary change detection supervised loss function and a semantic segmentation supervised loss function by using a small amount of labeled dual-temporal remote sensing images to obtain an initial model, and obtaining a semantic change detection prediction result of each pair of images; using a pseudo label optimization strategy to optimize the semantic change detection prediction result of each pair of images; combining the obtained pseudo label data with high confidence and a small amount of labeled dual-temporal remote sensing images into a new training set, using the new training set to perform semi-supervised training on the initial model in a semantic change detection model, and using a consistency regularization combination loss function to perform supervised training to obtain a new model; and until a preset number of iterations is reached.

An online system may provide an instruction prompt to a machine-learned language model. The instruction prompt may include an instruction to generate an evaluation label of a training sample of a classification model and a textual format related to how data is arranged. The evaluation label may be used in a supervised training of the classification model. The online system may provide a batch of evaluation request prompts to the machine-learned language model. Each evaluation request prompt includes data that is at least partially arranged in the textual format described in the instruction prompt. The online system may receive a plurality of responses from the machine-learned language model. Each response includes the evaluation label corresponding to each evaluation request prompt. The online system may store at least evaluation labels and the data in the evaluation request prompts as training samples for the supervised training of the classification model.

The invention discloses a method for improving the intelligent classification precision of distillate oil, and the method comprises the steps: employing a method based on a threshold value during spectrum cleaning, and adding S-G to smooth and sharpen spectral features during data preprocessing; during model supervised training, the high-dimensional feature center of distillate oil types is increased, and the identification capability of the model on inter-class differences is enhanced. The method can effectively improve the fraction oil spectrum classification precision, improve the model generalization ability, and provide a more accurate and efficient intelligent classification scheme for refinery enterprises.

The invention provides a training method of a document image tampering detection model and a tampering detection method and device.The training method of the document image tampering detection model comprises the steps that multi-scale visual domain features are extracted from a sample document image, and multi-scale frequency domain compressed sensing features are extracted from frequency domain information; acquiring tampered area edge mask data from the document image; fusing the multi-scale visual domain features and the multi-scale frequency domain compressed sensing features to obtain multi-modal fusion features; performing semi-supervised training on the multi-scale sensing network by taking the multi-scale visual domain feature as a sample feature of a first prediction head, taking the multi-modal fusion feature as a sample feature of a second prediction head, taking a real label or a pseudo label as a sample label and taking joint loss as a loss function to obtain a document image tampering detection model; according to the method provided by the invention, document image tampering pixel-level detection under low labeling cost is realized, and the detection precision of a document image tampering detection model is improved.

Implementations relate to providing a user feedback mechanism that enables a user to provide feedback towards one or more specific portions of a response. The response can be generated based on processing of a user query using a generative model such as a large language model (LLM). The one or more specific portions can be a textual portion that includes textual content, and / or a media content portion that include media content such as one or more images, one or more videos, one or more audio pieces, etc. The feedback towards one or more of the specific portions of the response can be utilized in training or fine-tuning the generative model (or an additional generative model) via approaches such as supervised training or reinforced learning.

The invention relates to the technical field of RV speed reducers, in particular to an intelligent monitoring and fault diagnosis integrated RV speed reducer control system and method. Collecting multi-mode operation data of the RV speed reducer, wherein the multi-mode operation data comprises vibration, temperature, sound and current data; feature extraction is carried out on the multi-modal operation data, an adaptive feature fusion method based on an attention mechanism is designed, weights of different features are dynamically adjusted, and comprehensive and intelligent state monitoring is achieved; constructing a fault diagnosis model based on a multi-branch convolutional neural network, and performing supervised training on the model by using historical operation data and fault labels to realize high-precision fault classification; a knowledge distillation technology is adopted to compress the trained model, the model is deployed into the RV reducer, and online real-time diagnosis is achieved; according to the method, the value of multi-source heterogeneous data is fully mined, and the intelligent level of RV reducer fault diagnosis is improved.

The invention provides an industrial product defect automatic classification method and system, and the method comprises the steps: collecting original industrial product defect image data, and constructing a labeled image sample set and an unlabeled image sample set; constructing a training image sample set based on the labeled image sample set and the unlabeled image sample set in combination with a plurality of image synthesis strategies; based on the training image sample set, introducing a transfer learning strategy and fusing an attention mechanism, and constructing and optimizing an industrial product defect classification model; performing semi-supervised joint training and online learning based on the training image sample set and the real-time small-batch image sample set; and constructing an industrial product defect identification log based on the real-time image flow sample set, the classification model parameters and the corresponding classification prediction function. On the basis of multi-strategy image enhancement and semi-supervised training, transfer learning and a channel attention mechanism are fused, expansion of industrial product defect image samples and fine defect identification are achieved, and the method is suitable for an intelligent defect detection system in various industrial manufacturing fields.

The invention discloses a high-resolution hyperspectral image reconstruction method based on wavelet domain feature fusion and contrast learning, and belongs to the technical field of image fusion and super-resolution reconstruction. The method comprises the following steps: constructing a fusion network model comprising a wavelet transformation module, a cross-modal feature fusion module, a high-frequency contrast learning module and an image reconstruction module; performing end-to-end supervised training by using a training data set containing the low-resolution hyperspectral image and the high-resolution multispectral image; and after training is completed, inputting a test image pair to realize image reconstruction. According to the method, the detail retention capability is improved by combining wavelet decomposition and a directional fusion mechanism, the cross-modal high-frequency feature alignment capability is enhanced through comparative learning, a fusion image with high spatial resolution and high spectral consistency is finally generated, and the method is suitable for multi-modal image reconstruction tasks such as remote sensing, medical and natural images.

The invention provides a distributed optical fiber temperature sensing logging data blind denoising method and system based on physical self-supervised learning. The method comprises the following steps: acquiring original noisy distributed temperature sensing (DTS) logging data, and generating a self-supervised training sample through a space-time alternating downsampling strategy by using the space-time coherence of the original noisy distributed temperature sensing (DTS) logging data; constructing a physical self-supervised blind denoising network model by using a simplified structure, and inputting a self-supervised training sample for training; in the training process, a multi-physical constraint loss function optimization model is introduced until a loss function converges, and a denoising model is obtained; and inputting to-be-processed complete DTS logging data into the denoising model to obtain denoised DTS logging data. According to the method, manual labeling and large-scale labeling of the data set are not needed, the inherent physical characteristics and structural information of the DTS data can be fully utilized, efficient and accurate blind denoising of the DTS logging data is achieved, and the problem that an existing deep learning denoising method needs to depend on a large amount of labeled data is solved.

The invention belongs to the technical field of computer artificial intelligence, and relates to a citrus intelligent planting management-oriented large model field quantification and adaptive model deployment method, which comprises the following steps of: firstly, constructing a large model calibration data set and carrying out preprocessing, and inputting a Transform model to execute forward reasoning; then carrying out left multiplication rotation on a weight matrix of a to-be-quantized layer of the model and right multiplication rotation on an activation matrix, executing GPTQ quantization, calculating a dynamic adaptive smoothing factor of each channel of the activation matrix, executing normalization and symmetric quantization according to hidden dimension grouping, and generating a quantized Transform model; then obtaining citrus industry text data to construct a fine tuning instruction set, and performing supervised training on the quantitative model to generate a full-quantitative and full-precision model; and finally, by training a task complexity classifier, selecting a full-quantization or full-precision model as a target deployment model according to a task complexity level, so that efficient quantization and intelligent task adaptation of the model are realized.

The invention relates to the field of audit data anomaly detection, and provides a tracking audit anomaly detection method and system based on multi-modal deep learning, and the method comprises the steps: carrying out the preprocessing of historical audit data, mapping the historical audit data to a unified feature space, and obtaining unified feature representation data; performing block processing on the unified feature representation data, and performing operation mode feature extraction and integration to obtain audit behavior feature processing data; extracting short-term local dependency features and long-term global dependency features, and performing weighted fusion to obtain audit comprehensive feature data; performing self-supervised training on the auditing comprehensive feature data, generating a pseudo mark for unlabeled data, and training together with an abnormal sample to obtain an abnormal detection model; and identifying the real-time audit data based on the anomaly detection model to obtain an anomaly audit score, and adopting a corresponding anomaly disposal scheme. According to the invention, the accuracy and real-time performance of auditing anomaly detection are improved, and risk early warning and handling response of auditing data are realized.

The invention provides a camouflage object semantic segmentation method, device and equipment based on a self-supervised dual construction model, and a storage medium. Relates to the technical field of computer vision. The method comprises the following steps of: an image reconstruction stage: randomly masking pixels in an input image, extracting the masked image through a backbone network to obtain semantic features, and aggregating the semantic features by using a boundary self-adaptive feeling module to reconstruct the masked pixels; in the model refining stage, the weight of a boundary self-adaptive feeling module is randomly initialized, meanwhile, semantic features extracted by a backbone network are reserved, and full-supervised training is carried out to obtain a preliminary segmentation result; and a label reconstruction stage: using a backbone network to extract semantic features from the noise label, the partial label and the complete label so as to carry out feature extraction on the preliminary segmentation result, and then using a distance adaptive asymmetric module to carry out reconstruction so as to obtain a final segmentation result. According to the invention, the camouflage object can be accurately segmented.

The invention discloses an intelligent data anomaly detection method and system based on an Internet of Things integrated management and control technology, and the method comprises the steps: S1, collecting and preprocessing intelligent data, and constructing a standardized data set; s2, performing preliminary anomaly detection on the standardized data set by adopting an isolated forest algorithm, and screening out potential abnormal data points; s3, constructing a self-supervised training sample, performing enhancement detection on the abnormal data point set, and generating a correction factor; s4, recalculating an abnormal score for each abnormal data point based on the correction factor; s5, abnormal data points output through re-correction are classified and stored, and log information is generated; and S6, dynamically optimizing the model parameters according to a detection result fed back in real time. According to the method, an efficient and scientific optimization scheme can be provided in Internet of Things intelligent data anomaly detection, and remarkable technical values and economic benefits are brought to practical application.

The invention discloses a user security feature recognition method based on behavior pattern analysis, and aims to solve the problems of inaccurate recognition of power utilization security features of power consumers and insufficient robustness in the prior art. The method comprises the following steps: preprocessing and segmenting original power consumption time sequence data; then, a self-supervised learning model based on an expert hybrid architecture is constructed, the architecture integrates five neural networks to construct an expert model, expert weights are dynamically distributed through a gating network, and a power utilization mode deep embedding vector is output through self-supervised training; clustering the embedded vectors by using a clustering algorithm, determining an optimal clustering number in combination with an elbow method and a contour coefficient method, generating a user portrait, and performing visualization and feature analysis; and finally, according to the user portrait data, carrying out transaction behavior pattern recognition on the input to-be-recognized user data, and outputting a security feature recognition result. The method can comprehensively and accurately identify the power utilization safety characteristics of the user, and is suitable for scenes such as intelligent power grid safety monitoring.

The invention discloses a winter wheat LAI and SPAD estimation method based on a lightweight semi-supervised model, and relates to the technical field of agricultural remote sensing monitoring, and the method comprises the steps: obtaining multispectral image data of a winter wheat key growth period, and carrying out the preprocessing of the multispectral image data to generate a standardized multichannel vegetation index image; the method comprises the following steps: constructing a lightweight semi-supervised model MCVI-SANet, and carrying out self-supervised training on the MCVI-SANet by adopting a semi-supervised training strategy driven by VICReg; and inputting the multi-channel vegetation index image into the trained MCVI-SANet, and outputting quantitative estimation results of the LAI and SPAD of the winter wheat. Through combination of a saturation perception mechanism and semi-supervised learning, estimation deviation caused by dense canopy vegetation index saturation and data noise is effectively eliminated, and the estimation precision and generalization ability in a complex agricultural scene are improved while the lightweight deployment characteristic of the model is ensured.

The invention discloses a weak supervision image semantic segmentation system and method based on an attention mechanism, and the method comprises the following steps: obtaining to-be-segmented image data and an image-level label, and extracting a multi-level semantic feature map; fusing the multi-level feature map with the space and the channel dimension to generate an attention feature map; combining the attention feature map with an image-level label to obtain a target area positioning map; inputting the attention feature map and the target area positioning map into a double-edge reconstruction network to generate a high-quality pseudo-label map; carrying out progressive decoding structure convolution on the pseudo label graph and the multi-level semantic feature graph to generate a segmentation prediction graph; constructing a supervision signal based on the segmentation prediction map and the pseudo-label map to obtain a supervision training result; dynamically updating the supervised training result to generate an updated pseudo-label graph; and continuously iterating based on the updated pseudo-label graph until the loss function is converged, and outputting an image semantic segmentation result. According to the invention, weak supervision image semantic segmentation based on the attention mechanism is realized.

The invention provides a self-supervised end-to-end vision reconstruction method and system, and relates to the technical field of computer vision processing. The method comprises the following steps: firstly, acquiring multi-camera parameters and different-view-angle image data, including focal length, lens distortion and main coordinate point data of each camera, and pixel point coordinates of a current frame of a first camera and a reference frame of a second camera, then constructing an end-to-end training model, and calculating a re-projection error of pixel points of the current frame and the reference frame to obtain a multi-view-angle image; and solving the parameter update quantity by using a Gaussian Newton iteration method, iteratively optimizing the camera pose, the pixel corresponding relation and the depth data, and reconstructing a three-dimensional coordinate by combining the obtained data set after the re-projection error is converged, and converting and splicing to realize three-dimensional scene reconstruction. By implementing the scheme, end-to-end self-supervised training can be realized under the condition of not depending on manual annotation, so that three-dimensional visual reproduction is realized.

The invention relates to a scientific research path generation method and system of a big language model based on knowledge graph enhancement, and belongs to the field of artificial intelligence. The method comprises the steps that a literature data set is analyzed based on a large language model, and a heterogeneous knowledge graph fusing knowledge triples and evidence metadata is constructed; performing self-supervised training on the atlas through a heterogeneous graph neural network to generate a knowledge embedding matrix; designing a semantic aligner to embed the map and align the map with the semantic space of the large language model; searching seed nodes according to user query and extracting context sub-graphs; converting the sub-graph into a graph lexical element sequence; and constructing a mixed prompt input large language model in combination with a natural language instruction, and generating a structured scientific research path. According to the method and the system, the quality and the credibility of a scientific research path can be accurately found, a literature reading sequence and an experiment reproduction sequence are clarified, and a more efficient technical engine is provided for knowledge discovery.

A method for generating a segmentation mask of at least one image comprising generating a plurality of superpixels for the at least one image, automatically generating labels for the generated plurality of the superpixels, wherein the automatically generating labels comprises generating the labels for training a semantic segmentation model based on a plurality of segmentation masks by generating a label for each superpixel by identifying the most similar reference superpixel from a reference data set of reference superpixels, wherein each reference superpixel is associated with a class for supervised training of the semantic segmentation model, wherein the labeled superpixels form the segmentation mask of the at least one image, and computer-aided checking of the generated segmentation mask for correctness, wherein the checking for correctness comprises labeling unlabeled superpixels and correcting labels of incorrectly labeled superpixels by assigning the incorrectly labeled superpixels to the correct class.

The invention discloses an optical cable micro-disturbance identification method based on physical simulation and self-supervised time sequence decoupling, and relates to the technical field of optical cable identification, and the method comprises the steps: constructing a physical digital twin simulator, and generating a high-fidelity training set; constructing a deep learning model, wherein the deep learning model adopts a lightweight time sequence decoupling network; training the model by adopting a staged training strategy, and sequentially carrying out self-supervised noise distribution pre-training, simulation supervised training and spectral domain physical consistency fine tuning operation; inputting DAS time sequence data collected in real time into the trained model, and outputting the data as an optical cable identity ID and a physical position; the lightweight time sequence decoupling network comprises a physical guide preprocessing module, a lightweight U-Net separation module, a sparse gating module and an intelligent parallel decoding module. Through the technical means of simulation-driven data generation, staged training strategies and the like, the defects of the prior art in the aspects of reducing the data cost, improving the detection capability in a low SNR environment, realizing multi-source blind source separation and the like are overcome.

The invention relates to the technical field of artificial intelligence, and discloses an emotion recognition method and device based on a voice large model, equipment and a medium, which are applied to a patient emotion recognition scene in the medical field, and the method comprises the steps: obtaining voice segment information and text description information corresponding to the voice segment information; performing feature coding and feature alignment on the voice fragment information and the text description information to generate joint features, and performing self-supervised training on the pre-trained voice large model through the joint features to generate an initial voice large model; collecting a voice instruction pair, and performing model fine tuning on the initial voice large model to obtain a fine-tuned voice large model; obtaining voice information with diversified emotion tags, optimizing the target function of the fine-tuned voice large model, and generating a target emotion recognition model; and obtaining to-be-recognized voice segment information, and performing emotion recognition based on the target emotion recognition model to obtain an emotion recognition result. The accuracy of emotion recognition is improved.

Disclosed herein are systems and methods for training an AI language model for assessing and improving happiness and wellbeing of a human user, the method comprising receiving a pre-trained AI language model; receiving a first training data set comprising non-user-specific training data comprising brain imaging data; applying one or more supervised training protocols based on the first training data set to modify the pre-trained AI language model to generate a non-user-specific language model configured for happiness and wellbeing support of human users; receiving a second training data set comprising user-specific training data comprising brain-imaging data for a specific user; and applying one or more supervised training protocols based on the second training data set to modify the non-user-specific language model to generate a user-specific language model configured for happiness and wellbeing support of the specific user.

The invention relates to the technical field of fault prediction and computer big data processing, in particular to a generalized zero sample composite fault diagnosis method, device and system based on anti-fact reasoning. According to the generalized zero sample composite fault diagnosis method based on the anti-fact reasoning, a two-stage generalized zero sample composite fault diagnosis model based on the anti-fact reasoning is constructed. According to the model, internal causal components of fault data are pointed out from the angle of causal theory, and then a structural causal model is constructed to describe decoupling and generation of fault features under the guidance of anti-factual reasoning. On the basis, a generative model is improved through a reinforced discriminator in the first stage so as to realize binary classification of a single fault and a composite fault. In the second stage, a single fault category is predicted through supervised training of a classifier, and meanwhile, a traditional zero sample learning method is designed to classify composite faults. According to the method, the diagnosis precision of the model is greatly improved, and the problem of deviation of model diagnosis on visible classes and invisible classes is solved.

The invention provides a robot action generation method and device, and the method comprises the steps: responding to a target task received by a robot, and obtaining image data and text description information associated with the target task; on the basis of the image data and the text description information, potential action information and fused visual representation information are determined by utilizing a large language model obtained by supervised training provided based on a potential action model; compressing and splicing the image data, the potential action information and the fused visual representation information to obtain control sequence information; and de-noising and splicing the state information and the control sequence information corresponding to the robot to generate action sequence information, so that the robot executes a corresponding action based on the action sequence information. By means of the method, the smoothness and coherence of actions generated by the robot are improved.