1150 results about "Unsupervised learning" patented technology

The invention provides an intelligent equipment fault diagnosis and reasoning method and system based on unsupervised learning. Belongs to the technical field of industrial equipment fault diagnosis and prediction. 2) unsupervised abnormal feature extraction and detection; (3) dynamic causal reasoning based on unsupervised learning; (4) a dynamic self-optimization inference engine; (5) intelligent fault reasoning and exploration; (6) enhancing the dynamic knowledge graph; and (7) fault prediction and decision support. Aiming at the problems that a large amount of annotated data is needed, causal analysis is lacked, and multi-source data fusion is difficult, non-annotated efficient detection and classification, fault mode diagnosis, prediction and decision support of equipment faults are realized through feature extraction of an unsupervised depth model, construction of a dynamic causal relationship, and fusion of a real-time self-optimization inference engine and a dynamic knowledge graph; therefore, the downtime and the maintenance cost are greatly reduced.

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

A computer-implemented method of predicting quality of a food product sample after a mixing process, based on properties of the food product, comprises: building a hybrid model by: training an autoencoder in an unsupervised learning step using historical process data of food product samples; training a supervised model in a supervised learning step using the output of the autoencoder; and predicting the quality of the food product by inputting process data of current samples into the hybrid model and classifying the samples.

Embodiments of the disclosure relate to unsupervised learning with synthetic data and attention masks. According to example embodiments of the disclosure, a plurality of sample images are generated by providing a plurality of text prompts into a trained generative model, respectively. For a sample image of the plurality of sample images, at least one attention map is obtained from a generative model, the at least one attention map being determined by the generative model for generating the sample image, an attention map indicating visual elements of an object within the sample image. Training of a target model is performed according to unsupervised learning at least based on the plurality of sample images and attention maps for the plurality of sample images, the target model being configured to perform an image processing task.

Various aspects of the present disclosure relate to a device that receives a request by a location server to query for positioning measurements which are labelled or unlabelled, which is dependent on whether a supervised or unsupervised learning model is used. The requested positioning measurements, each associated with a label, are provided to a training system that trains one or more artificial intelligence (AI) inference models. The one or more AI inference models are deployed to a location management function (LMF), which makes predictions based on subsequent positioning measurements received from the device.

The invention discloses an intelligent early warning diagnosis method based on an intelligent power plant, and the method comprises the steps: detecting an abnormality through employing an unsupervised learning method, and correcting a detection result in combination with a heuristic rule; adjusting the length of a dynamic time window according to the running state of the equipment, ensuring the requirement of data trend analysis, carrying out smoothing processing on trend data captured in the adjusted time window, and extracting fault trend characteristics; and performing abnormal distribution analysis on the fault trend characteristics, performing layered early warning on the faults according to abnormal levels, constructing a step-by-step troubleshooting model based on a Bayesian network, and analyzing fault causes step by step. According to the intelligent early warning diagnosis method, the influence of equipment faults on a power plant system can be effectively reduced, and the overall safety and stability of a power plant are improved.

The invention belongs to the technical field of artificial intelligence, and discloses a computer network fault detection system based on an artificial intelligence technology, which comprises a data acquisition module, a feature extraction and preprocessing module and a fault positioning and repairing module. According to the invention, the detection link is accurate and comprehensive, the data acquisition module performs multi-source fusion to obtain rich materials, the feature extraction and preprocessing module generates multi-dimensional vectors to capture complex features, and multi-level fault identification performs comprehensive troubleshooting and reduces misjudgment; the diagnosis process is intelligent and efficient, the hybrid fault diagnosis model fuses supervised and unsupervised learning, processes known faults and detects unknown anomalies, and supervised learning branches are optimized to improve performance; the repair work is timely and reliable, the automatic repair scheme generation method has strategy library matching, dynamic adjustment and rollback mechanisms, quick response and flexible repair can be achieved, and the subsequent diagnosis accuracy is improved through repair verification multi-dimensional evaluation and closed-loop feedback.

The invention discloses an industrial machine vision recognition and analysis system, relates to the technical field of machine vision recognition, and is used to solve the problem of poor industrial machine vision recognition. The invention combines total variation denoising, contrast-limited adaptive histogram equalization, random geometric transformation and other technologies to enhance image diversity, improve the system's generalization ability for images in different environments, extract image texture and edge features, provide high-quality data for subsequent analysis, use unsupervised learning to extract image potential features and patterns, and locate abnormal areas through template matching, so as to adaptively discover defects in the production process, dynamically update the model based on a small amount of labeled data, optimize feature representation in real time, and adapt to environmental changes. Finally, semi-supervised learning and pseudo-label generation technology are used to expand the training set, improve the generalization ability of the model, maintain efficient learning when labeled data is limited, reduce manual labeling costs, and improve the accuracy of automated quality inspection.

Methods, systems, and computer storage media for providing a data analysis pipeline using a data analysis pipeline engine in a data intelligence system are described. A data analysis pipeline refers to a structured sequence of data processing steps that support transforming raw data into meaningful insights or actionable outcomes. The data analysis pipeline engine is an unsupervised learning pipeline based on clustering, topic modeling, and Large Language Models (LLMs). For example, the data analysis pipeline can use advanced machine learning techniques to automatically categorize emails into semantically similar clusters, enabling the data intelligence system to quickly identify and prioritize potentially high-risk emails for further investigation. The data analysis pipeline employs AI agents for context-aware graph induction relevance assessment. The AI agents employ induction and deduction loops to build and refine a data feature hypergraph (e.g., vulnerability hypergraph) that encompasses identified relevant data providing a holistic view of a contextual landscape.

An AI-based method for visual inspection of parts manufactured on a shop floor includes acquiring a set of real images of nominal parts manufactured on the shop floor to create training datasets. A self-supervised pre-trainer module is used to pre-train a loss computation neural network in a self-supervised learning process using a first dataset on pretexts defined by real-world conditions pertaining to the shop floor. The first dataset is labeled by automatically extracting pretext-related information from image metadata. A main anomaly trainer module is used to train a main anomaly detection neural network to reconstruct a nominal part image from an input manufactured part image in an unsupervised learning process using a second dataset. The main anomaly training measures a perceptual loss between an input image and a reconstructed image by measuring a difference between feature representations thereof at one or more layers of the pre-trained loss computation neural network.

The invention discloses a cross-modal remote sensing target detection method and system based on spatial consistency constraint and deep feature alignment, belongs to the field of computer vision and remote sensing science and technology and the technical field of machine learning and deep learning, and solves the problem that a conventional cross-modal method does not fully consider feature hierarchy difference. According to the invention, an improved teacher-student network model is constructed and comprises a student branch network, a teacher branch network and an optimization module for performing pseudo-label optimization, non-monitoring learning and supervised learning on the student branch network and the teacher branch network; training the improved teacher-student network model by adopting the target domain data set and the source domain data set to obtain a trained improved teacher-student network model; and carrying out cross-modal remote sensing target detection on a to-be-detected target domain image by adopting the trained improved teacher-student network model. The method is used for cross-modal remote sensing target detection.

The invention belongs to the technical field of power system security, discloses a load frequency control system attack detection method based on reinforcement learning, and aims to improve the recognition and defense capability of a power system on complex network attacks and overcome the defects of a traditional detection method in the aspects of attack sample generation, unknown attack recognition and system adaptability. According to the method, an attack agent based on a Markov decision process is constructed, and an improved reinforcement learning algorithm is adopted to generate a high-concealment confrontation sample; designing a bimodal detection architecture fusing LSTM supervised learning and auto-encoder unsupervised learning, and introducing an adaptive weight fusion mechanism to realize attack type identification and anomaly detection; and incremental learning and a parameter dynamic adjustment mechanism are combined, so that the detection model has continuous learning and evolution capabilities. The method can be applied to a power grid dispatching center or an intelligent micro-grid, real-time monitoring and attack defense of a load frequency control system are achieved, and the operation safety and robustness of a power system are remarkably improved.

The invention discloses a vibration signal anomaly detection method based on unsupervised learning, which relates to the technical field of vibration anomaly detection, and comprises the following steps: collecting vibration signals of electromechanical equipment during normal operation and synchronously recording working condition information; the collected vibration signals are preprocessed; setting a plurality of window segmentation lengths, and enabling each segment of vibration signal to generate a multi-stage sub-sequence; extracting time-frequency domain features of the vibration signals in the subsequences; the time-frequency domain features form feature vectors in a feature matrix splicing mode, feature standardization processing is carried out on the feature vectors, the feature vectors are fused with real-time working condition feature vectors obtained through working condition information, and a fused feature matrix is generated; and constructing an OCSVM model, and carrying out vibration anomaly detection on the electromechanical equipment by utilizing fusion feature matrix training. The method has the advantages that robustness and abnormal interpretability of single-class data are enhanced, the misjudgment rate is reduced through the dynamic confidence interval algorithm and probability distribution modeling, and the defect of insufficient model generalization is overcome through cross-modal feature fusion and working condition correlation modeling.

The invention provides a drilling risk determination method and device. Before specific implementation, a graph auto-encoder is introduced and used, and a preset detection model which is based on physical constraints and has a good application effect is obtained through unsupervised learning and training. In specific implementation, current target logging data of a target well and a logging data set of a current time period can be firstly obtained; determining a current working condition according to the target logging data; according to the current working condition, the logging data set of the current time period and a preset geological-engineering pre-drilling evaluation profile, a target dynamic threshold value which aims at a current target well and is based on working condition constraints and considers the change condition of data processed in the current time period before graph self-coding is determined; processing the target logging data by using a preset detection model to obtain a corresponding target reconstruction error; and detecting whether a drilling risk exists according to the target reconstruction error and the target dynamic threshold. Therefore, the drilling risk can be accurately detected and identified, and the false alarm rate is reduced.

The invention discloses a multi-source federal cross-domain and source-domain enhanced millimeter wave action recognition method and system, and the method comprises the steps: generating a micro-Doppler spectrogram through a millimeter wave radar, and extracting the motion features of human body actions through a signal processing module; in the federal multi-source domain adaptation module, dynamically evaluating and fusing knowledge of a plurality of source domains by adopting a voting-based pseudo-tag method and a weighted knowledge aggregation mechanism, and optimizing the generalization ability of a target model; and through a generalization gap optimization method, the performance of the source domain model is improved, and the robustness of the system in different environments is ensured. Through combination of a federated learning framework and a multi-source domain adaptation technology, unsupervised learning under the condition that a target domain has no annotated data is realized, only a single set of millimeter wave equipment is needed, a millimeter wave communication protocol is compatible, and the method has the characteristics of privacy protection, unsupervised learning, multi-source knowledge fusion and strong generalization ability. The method is suitable for application scenes of smart home, health monitoring, man-machine interaction and the like, and has wide practical application value and research prospect.

A battery life early prediction method based on pre-classification and LSTM-CNN comprises the following steps: extracting features by using capacity difference data in a discharge process, determining a classification interval of a sample through an unsupervised learning clustering method, dividing the sample into a long life group and a short life group according to the actual life distribution condition of the battery, according to the method, a brand-new superposition model of LSTM and CNN networks suitable for battery sample classification and life prediction is constructed and trained, new samples are classified through an LSTM-CNN classification model, different life prediction models are matched, and then the life of the battery is predicted through the LSTM-CNN prediction model, so that early prediction of the life of the battery by using a small amount of data is realized, and the prediction efficiency of the life of the battery is improved. The model structure is simple, and the prediction accuracy and efficiency are remarkably improved.

The invention discloses a DC power supply health state prediction model optimization method based on an extreme learning machine, and relates to the technical field of power supply intelligent management. According to the method, an extreme learning machine basic model is improved, multi-layer ELM-AE is adopted to carry out greedy unsupervised learning on input data layer by layer, advanced features of an original data set are extracted, a mixed kernel function is adopted as a regression layer of a supervised learning stage, the complex nonlinear problem is solved, and the method has the advantages of being high in robustness and high in robustness. The adaptability of the prediction model to diversified characteristics under different battery working conditions is improved, the generalization ability and prediction precision of the model are improved, and the convergence speed and prediction precision of the model are remarkably improved by optimizing the model parameters through the improved black-wing calculation algorithm.

The invention provides a power station photovoltaic module defect detection method and device based on unsupervised learning. The method comprises the steps that firstly, a thermal imaging image to be detected and a visible light image in a target area provided with a photovoltaic module are collected; cutting a thermal imaging photovoltaic module image from the thermal imaging image, and cutting a visible light photovoltaic module image from the visible light image; afterwards, a teacher network model and a visible light photovoltaic student network model in the photovoltaic defect detection model are used for processing the visible light photovoltaic module image, and position information of the visible light abnormal photovoltaic module is determined and returned; and the teacher network model and the thermal imaging photovoltaic student network model in the photovoltaic defect detection model are used to process the thermal imaging photovoltaic module image, and the position information of the thermal imaging abnormal photovoltaic module is determined and returned. Wherein the photovoltaic defect detection model is obtained by training in a label-free supervision mode.

The invention discloses a medical image noise reduction and reconstruction method based on unsupervised learning, relates to the technical field of medical image processing, and solves the technical problem that an existing diffusion model is not ideal in detail recovery and noise reconstruction effect on a medical image. The method comprises the following steps: constructing at least two different first frequency domain subsets and second frequency domain subsets according to a medical image; the first frequency domain subset and the second frequency domain subset are input into a trained generation parallel network, a preliminary reconstruction image is generated, and the generation parallel network comprises at least a first generation network and a second generation network which are mutually independent; mapping the features of the preliminarily reconstructed image to a potential space, performing diffusion reconstruction in a denoising process, and encoding to obtain diffusion reconstruction features; and decoding the diffusion reconstruction features through a decoder to obtain a final reconstruction image of the medical image. According to the invention, high-frequency detail recovery of the medical image can be realized, and the high-quality medical image is reconstructed and generated.

The invention discloses a network security threat monitoring method and system based on artificial intelligence, and relates to the technical field of digital information transmission, and the method comprises the steps: obtaining data of a plurality of data sources in a network environment, carrying out the multi-level data fusion of the data through an artificial intelligence model, and forming unified data representation; the artificial intelligence model comprises at least one selective fusion layer, and features are extracted from different data sources and are jointly expressed; based on a data fusion result, performing anomaly detection by adopting an unsupervised learning method; and modeling the network flow data into a graph structure according to the detected abnormal behavior, and analyzing the graph structure by adopting a graph neural network GNN. According to the method, the technical problems of difficulty in multi-dimensional data fusion, delay in threat detection, insufficient response capability and the like in the existing network security protection technology are solved.

The invention discloses an AI-based network security risk identification and management and control system, which is characterized by comprising network security equipment and a server, the network security equipment is used for monitoring flow data flowing into / out of the server, and the AI-based network security risk identification and management and control system also comprises an interface conversion module which is used for connecting different network security equipment, original operation logs of different network security devices are collected; data collection: collecting multi-source data from multiple channels of network traffic, server logs, application program logs and user behavior records; preprocessing the data; preprocessing the data, removing noise data, filling missing values, and extracting useful features from the original data; simulation training: training the model to identify normal and abnormal behaviors through machine learning by utilizing supervised learning, unsupervised learning or semi-supervised learning methods; the deep neural network is used for modeling a complex data mode, and the detection precision is improved.

The present invention discloses a radar automatic recognition method and device for low-altitude small targets. The method first performs radar data preprocessing; then uses a deep neural network to extract the features of the processed data, and then respectively uses supervised learning and unsupervised learning to perform binary classification and anomaly detection; finally, determines whether it is a drone, a bird or other low-altitude floating objects according to the results of binary classification and anomaly detection. This method solves the problem of identifying low-altitude floating objects, has a very wide range of applications, strong generalization ability, less computation, lower cost, and higher recognition accuracy.

The invention discloses a box-type transformer substation monitoring method based on unsupervised learning, and the method comprises the following steps: S1, collecting multi-source sensing data in the operation process of a box-type transformer, and generating a standardized input data set; s2, selecting a data sample in a normal operation state, constructing an unsupervised learning model, and obtaining a feature representation set; s3, inputting data, extracting current operation state characteristics, and recognizing a voiceprint abnormal state by combining outlier detection; s4, performing window sliding and statistical analysis on the time sequence data, and outputting a trend abnormal interval and an abnormal index type; s5, constructing a variable association graph structure, and identifying potential abnormal variables and propagation paths; s6, integrating the various types of abnormal information and the feature representation set, and constructing a voiceprint map library; and S7, integrating and processing the abnormal result and the voiceprint map database, and executing visual display and intelligent early warning. According to the invention, box transformer substation abnormity monitoring and early warning based on unsupervised learning are realized, and the fault identification accuracy and response efficiency are improved.

The invention relates to a power system network security threat monitoring and early warning method and device, power equipment, a computer readable storage medium and a program product, and belongs to the field of power system network security. The method comprises the following steps: acquiring historical data and real-time data; obtaining first network feature data based on historical data, performing unsupervised learning training on the initial detection model, and taking abnormal data as second network feature data; marking the second network feature data as third network feature data; marking the first network feature data to obtain fourth network feature data to perform self-supervised learning training on the model, and obtaining first network attack features to construct a network attack feature library; performing semi-supervised learning training on the model based on the first network feature data and the third network feature data to obtain a detection model, and updating a network attack feature library; and detecting real-time data based on the detection model and the network attack feature library. The method can improve the accuracy of power system network security threat monitoring and early warning.

The invention provides a network abnormal behavior feature detection method and device, and a computer readable storage medium. The network abnormal behavior feature detection method comprises the following steps: constructing an attention-based trajectory group anomaly detection model, constructing an abnormal behavior detection model, and constructing a normal and anomaly modeling model based on unsupervised learning; inputting the network behavior data of the networking device into the trajectory group anomaly detection model to obtain group trajectory preliminary anomaly detection data; inputting the group trajectory preliminary abnormal detection data into an abnormal behavior detection model to obtain non-abnormal behavior data in a data transmission process between the networked devices; and inputting the non-abnormal behavior data into the normal and abnormal modeling models to obtain network abnormal behavior feature data. According to the invention, the accuracy of network abnormal behavior detection between networked devices can be improved.

The invention relates to the technical field of transformer detection, in particular to a millimeter wave radar-based transformer vibration anomaly detection method, which comprises the following steps of: acquiring an original reflection signal on the surface of a transformer, performing time domain and frequency domain analysis on the original reflection signal, and constructing a vibration feature vector; based on the constructed vibration feature vector, unsupervised learning is carried out on the vibration mode of the normal transformer through a deep self-encoding network, and potential features of the normal vibration mode are automatically learned; converting the new reflected signal into a new vibration feature vector, reconstructing the new vibration feature vector by using a deep self-encoding network, calculating a reconstruction error, and if the reconstruction error exceeds a predetermined threshold, judging that the vibration mode is an abnormal vibration mode; a self-adaptive threshold strategy is adopted, and a preset threshold is automatically adjusted so as to adapt to the change of the operation states of different transformer devices along with time. According to the invention, a self-adaptive threshold strategy is introduced, so that running characteristics of different transformer devices and fluctuation of an external environment can be adapted in real time.

The invention is applied to the field of mechanical fault diagnosis signal processing, and particularly provides a pulse neural network mechanical fault diagnosis method based on STFT dimension transformation, and the method comprises the steps: collecting a one-dimensional mechanical vibration signal, carrying out the wavelet decomposition, carrying out the wavelet reconstruction of a low-frequency component and a denoised high-frequency component, and carrying out the wavelet reconstruction of the low-frequency component and the denoised high-frequency component; obtaining a denoised one-dimensional vibration signal; performing short-time Fourier transform, and converting the time-frequency two-dimensional matrix into a time-frequency two-dimensional matrix; inputting the time-frequency two-dimensional matrix into an improved HH threshold neuron model, carrying out Poisson sparse coding on the time-frequency two-dimensional matrix, and only carrying out pulse response on signal significant features; constructing a suprathreshold coding convolutional network with residual connection, inputting a sparse coding matrix, training by adopting an unsupervised learning rule based on STDP, and adaptively adjusting a network synaptic weight; and inputting to a trained above-threshold coding convolutional network, and obtaining pulse emission activity of neurons of an output layer through network forward propagation to determine a fault diagnosis result.

The invention relates to the technical field of big data and data security, in particular to a cloud-edge collaboration and AI fusion data classification and grading method and system and a storage medium, cloud computing and edge computing are combined, the AI technology is used for data classification and grading, and rapid processing and efficient analysis of data are achieved; for structured data and unstructured data, different recognition and processing methods are adopted, including regular expression, keyword matching, dictionary matching, neural network, natural language processing and other algorithm models; particularly, a neural network and a natural language processing technology are combined with an unsupervised learning method and a semi-supervised learning method to realize an automatic classification and grading method of unstructured data in stages, rapid data processing and response can be carried out on an edge layer through a cloud edge collaboration mechanism, and the efficiency is improved. And deep analysis and model training are carried out by using the powerful computing power of the cloud layer.

The invention relates to the technical field of battery health state management, and discloses a lithium battery internal state estimation method based on a physical information neural network, and the method comprises the steps: obtaining a loss function for carrying out the optimization training of the internal parameters of a neural network model through an e-SPM model, enabling the internal parameters of the neural network model to be subjected to the optimization training through the loss function, and achieving the estimation of the internal state of a lithium battery. And internal state estimation of the lithium battery is realized. According to the invention, the current, voltage and temperature of the lithium battery are directly mapped to the internal electrochemical state of the lithium battery, so that the output result of the physical neural network is more explanatory and is easy to understand and analyze, and the output state quantity is substituted into the simplified physical model of the voltage by adopting an unsupervised learning mode. In the training process, a large amount of labeled data is not needed, and the experimental data acquisition difficulty is reduced.

The invention discloses a software operation anomaly detection method and system, a medium, a product and equipment. The method comprises the steps that multi-modal data generated by software to be detected in the operation period is acquired; performing feature extraction on data of each mode in the multi-mode data to obtain feature information corresponding to each mode; fusing the feature information corresponding to each mode through a self-attention mechanism to obtain a fused feature vector; using a supervised learning module and an unsupervised learning module contained in an anomaly detection model to perform anomaly detection based on the fused feature vector to obtain an anomaly detection result, thereby comprehensively considering the performance of the software in different modes during operation, comprehensively, quickly and accurately identifying the fault existing during the operation of the software, and improving the fault detection accuracy. And the accuracy and reliability of software operation anomaly detection are improved.