636 results about "Unsupervised learning" patented technology

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.

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.

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.

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 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 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 discloses a nonlinear aerodynamic damping estimation method based on LSTM, and the method comprises the following steps: 1, building a nonlinear state space model of a structure based on structural response, including a state equation, an observation equation and a relation between nonlinear aerodynamic damping and structural vibration amplitude; 2, performing updating and covariance prediction on response data by using unscented Kalman filtering; 3, correcting the Kalman gain in real time by using a long short-term memory network; 4, performing state updating and covariance updating based on the corrected Kalman gain; 5, training the long-short-term memory network through an unsupervised learning mode, optimizing the filtering performance, and defining a mean square error of a posterior observation predicted value and a real observation value as a loss function; and 6, calculating the nonlinear aerodynamic damping according to the estimated nonlinear aerodynamic damping parameters. The invention further discloses a nonlinear aerodynamic damping estimation system based on the LSTM and a storage medium.

The invention discloses an intelligent operation and maintenance alarm generation method based on unsupervised learning, and the method comprises the steps: constructing a dynamic topological graph which represents all service nodes in a system and the mutual relation of the service nodes based on operation and maintenance data, and generating a node state vector which represents the current state of each service node for each service node, the method comprises the following steps: inputting a dynamic topological graph structure and a node state vector into a pre-trained time-space diagram neural network model, calculating an abnormal score of each service node, and when the abnormal score exceeds a dynamically determined alarm threshold value, generating an operation and maintenance alarm, and furthermore, improving the reliability of the operation and maintenance alarm. A root cause node and a fault propagation path are determined based on time priority and anomaly severity, anomaly score distribution change is monitored through KL divergence, and incremental online learning is carried out; according to the method, the limitation problems of high supervision dependence, neglect of space-time topology, fixed threshold value and the like in the prior art are solved, the root cause positioning precision, robustness and generalization capability of alarm are improved, the false alarm rate and the missing report rate are reduced, and the operation and maintenance efficiency and the real-time performance are improved.

The invention provides a fly ash composite material goaf filling body interface quality intelligent evaluation method, and belongs to the technical field of mining engineering and artificial intelligence detection crossing. The method comprises the steps that firstly, filling body interface quality characteristic data are collected and comprise interface sound wave signals, stress strain, coal ash composite material physical parameters and environment working condition data; secondly, constructing a multi-physical field data completion model, performing unsupervised learning on the acquired sound wave, stress, temperature and moisture content data, and generating completion data of global spatial distribution; secondly, constructing a multi-field fusion interface quality index prediction model, and inputting multi-source data into the model to obtain an interface quality index; and finally, combining the quality index to realize interface defect mode classification and grade evaluation, and generating a targeted maintenance strategy. The invention provides an intelligent evaluation method which fuses multi-source data and gives consideration to real-time performance and comprehensiveness, so as to solve the industrial pain points of interface quality evaluation lag, low precision, large destructiveness and the like.

A dynamic adaptive learning method for mineral prediction includes: collecting a dataset including geological data and labels of the geological data; extracting features from the geological data, initializing parameters of a training model and optimizing the parameters to obtain training parameters; performing an associative training on the training model based on the training parameters and the labels in a dynamic adaptive learning framework to obtain a mineral prediction model, algorithms of the associative training including a variational expectation algorithm and a variational maximization algorithm, and the variational expectation algorithm including an unsupervised learning mode, a semi-supervised learning mode, and a fully supervised learning mode; and predicting, by using the mineral prediction model, a mineral to obtain a mineral prediction result. The method can break through limitations of the traditional machine learning technology, offering a more efficient, universal, and stable strategy for geophysical data analysis and mineral resource assessment.

A robotic surgical system network includes a plurality of robotic surgical systems. Each system includes robotic arms, sensors, a surgeon console, and a control system with an integrated AI module. A network interface is associated with robotic surgical system and provides secure data communication. A central or distributed data repository securely stores surgical data aggregated from the robotic surgical systems. The surgical data includes at least one of procedural data, sensor readings, imaging data, AI decision logs, surgical outcomes, or user interaction data. A training module utilizes aggregated surgical data to train or update AI models for the robotic surgical systems using unsupervised learning, transfer learning, or federated learning techniques. A cybersecurity module implements security measures for data transmission and system access, the measures comprising at least one of encryption, multi-factor authentication, or real-time threat detection.

The invention relates to the technical field of intelligent diagnosis, and discloses a micro-grid fault diagnosis method and system based on data driving and unsupervised learning. The method comprises the following steps: collecting current, voltage, temperature and power data of a micro-grid and constructing a time sequence matrix; inputting a time sequence prediction network and a time sequence reconstruction network, and performing parallel processing to obtain a prediction error and a reconstruction error; carrying out weighted fusion on the two errors and constructing a two-dimensional error space to judge normal fluctuation and fault abnormity; and extracting a state variable to generate a dynamic threshold to judge a fault. The false alarm rate and the missing report rate of fault diagnosis are reduced.

The invention relates to the technical field of power utilization monitoring, and discloses a photovoltaic user power utilization abnormity monitoring method and system based on artificial intelligence. The photovoltaic user electricity consumption abnormity monitoring system based on artificial intelligence comprises a data acquisition module which is used for acquiring photovoltaic power generation data, electricity consumption data and environment data of a user; the data preprocessing and feature engineering module is used for cleaning, aligning and normalizing the original data acquired by the data acquisition module and constructing a feature data set for model training and reasoning; and the artificial intelligence analysis engine module comprises an unsupervised learning unit, a supervised learning unit and a deep learning unit. According to the invention, the false alarm rate and the missing report rate can be effectively reduced, the accurate diagnosis of the abnormal type can be realized, and the intelligent and accurate operation and maintenance requirements of power grid enterprises on the power utilization monitoring of photovoltaic users are met.

The invention discloses a retina image unsupervised anomaly detection method for early screening of diabetes mellitus. The method comprises the following steps: carrying out registration and multi-scale attention-guided blood vessel segmentation on a longitudinal time sequence retina image of a patient; extracting a vascular skeleton and constructing a time sequence vascular topological graph, calculating geometric morphology and hemodynamic attributes of each vascular segment, identifying vascular morphology evolution characteristics by comparing topological graphs of adjacent time points, and calculating hemodynamic characteristics such as wall shear stress through simulation; the evolution and hemodynamic characteristics are jointly input into a time sequence encoder for unsupervised learning, and an early diabetes risk score is comprehensively generated by analyzing a reconstruction error, an abnormal score based on density estimation and a time sequence trajectory deviation degree of a potential space; the scheme of the invention does not depend on lesion labels, and can sensitively detect the tiny anomalies at the early stage of pathology from multi-dimensional dynamic changes, thereby providing an objective and quantitative new way for early screening and intervention of diabetes.

The invention provides a video content enhancement method for a low-illumination environment, and the method comprises the steps: achieving the data preprocessing based on an original low-illumination video frame sequence through frame synchronization, color space conversion and local brightness analysis, generating a noise sensitivity thermodynamic diagram through multi-feature unsupervised learning, and constructing a noise perception gating mechanism through the combination of affine transformation. Dynamic modulation of the characteristic channel is realized; in the multi-scale network structure, a channel attention module is used for carrying out layer-by-layer self-adaptive adjustment on a noise sensitive area; a basic illumination image and an edge enhancement image are generated through double-branch decoding, and then weighted fusion is carried out in combination with a noise thermodynamic diagram, so that brightness balance and detail enhancement are realized; a noise smoothing regular term is introduced during end-to-end training, so that the network achieves dynamic balance between an enhancement effect and noise control.

The invention discloses an annual work report generation method, system and device based on a large model, and a medium, belongs to the technical field of natural language processing and artificial intelligence application, and aims to solve the technical problems of low efficiency, unstable quality, poor flexibility and high efficiency in the existing annual work report writing process. According to the technical scheme, the method comprises the steps of obtaining multi-source data related to annual work; preprocessing the multi-source data; a pre-training fine tuning mode is adopted to train and optimize the large model, a large-scale pre-training language model is used as a basis, supervised learning and unsupervised learning modes are combined, and the large model is finely tuned by using related data; inputting the preprocessed data into the trained and optimized large model, and generating a preliminary report according to a structural framework of an annual work report; and receiving user modification opinions through an interactive interface, analyzing the opinions, cooperating with data processing and large model optimization, re-obtaining data or optimized contents, and iteratively outputting a final report.

The invention discloses an image virtual dyeing system based on unsupervised learning and combining a diffusion process and an autoregression model, which comprises a data preprocessing module, a random sequence mask autoregression model, a diffusion loss denoising module, a pathological consistency constraint module and an image generation module, a plurality of pixel areas of a target image are predicted by using a random sequence, pixel position dependency in an image generation process is avoided, capture of a long-range dependency relationship is enhanced, global and local features are effectively captured, local information loss is avoided, global consistency and fidelity of local details are improved, and the image quality is improved. And the quality of the image is gradually optimized through a progressive denoising process in the diffusion loss denoising module, so that the finally generated IHC image is more real, the condition distribution of each image pixel is simulated, a more continuous and smoother IHC dyed image is generated, and the problem of noise and detail loss in the conversion process is avoided.

The invention provides the unsupervised learning-based fault diagnosis method and system for detecting wheel out of round, which belongs to the technical field of machine learning fault diagnosis. Characteristic signals of train wheels will be acquired. The pre-trained detection model is adopted to process the characteristic signals of the train wheels to be detected, so as to obtain the wheel roundness state results. As for the collected characteristic signals, the invention constructs a subway wheel out of round detection method based on unsupervised learning for collected characteristic signals, which are deployed to computer equipment capable of executing computer programs, inputting characteristic signal data collected by a subway wheel out of round monitoring device based on rail wayside response into the computer equipment to obtain the wheel roundness state.

A power transformation equipment temperature anomaly identification and diagnosis method comprises the following steps: collecting state variables, performing cleaning, interpolation complementation and abnormal point elimination on multi-source data through a time synchronization mechanism, and constructing a unified data matrix; extracting statistical features and time sequence dynamic features in the time sequence based on the data matrix, and performing dimensionality reduction on redundant information in combination with a principal component analysis method to form a multi-dimensional fusion feature vector; an unsupervised learning model based on LSTM-AE is constructed, a normal working condition data learning feature reconstruction mode is utilized, and a reconstruction error is taken as a criterion to identify potential temperature anomaly; and calling a preset expert rule base and a knowledge graph, automatically analyzing dominant factors causing anomalies, and identifying typical anomaly types. According to the invention, automatic identification and classification diagnosis of the temperature abnormity of the power transformation equipment under an unsupervised condition are realized, the accuracy and response speed of fault identification are obviously improved, and the intelligence and practicability of equipment operation state monitoring are enhanced.

The invention discloses a transformer insulation state evaluation method based on artificial intelligence multi-parameter fusion perception, and relates to the technical field of electrical equipment detection, and the method comprises the steps: 1, obtaining the multi-modal monitoring data of a transformer, and constructing a dynamic graph structure; wherein the multi-modal monitoring data comprises oil chromatography data, partial discharge data and thermal image data; 2, encoding the dynamic graph structure through an encoder extraction embedding technology, carrying out space-time dimension feature aggregation and evolution modeling based on the encoded dynamic graph structure by utilizing graph convolution and time convolution technologies, inputting preset transformer insulation positive and negative sample pairs into a constructed model, carrying out InfoNCE loss unsupervised learning, and carrying out infoNCE loss unsupervised learning; obtaining a state manifold of a normal transformer, and carrying out multi-modal data space-time alignment and anomaly distinguishing on the state manifold; and step 3, introducing a Bayesian weight after multi-modal data space-time alignment and anomaly distinguishing, and outputting probability distribution of insulation state embedding.

The invention relates to the technical field of industrial automation control, and discloses a desulfurization system pH value intelligent pre-control feedback control method based on big data learning, and the method comprises the following steps: S1, collecting full-chain process data related to pH control; s2, preprocessing the original data and constructing feature vectors; s3, dividing working conditions by adopting unsupervised learning, and independently training an LSTM prediction sub-model for each working condition; s4, solving a comprehensive cost function, and outputting a dynamic optimal pH set value; s5, performing dynamic weight fusion on prediction results of the sub-models to obtain an intelligent feed-forward regulation quantity; s6, fusing the feedforward adjusting quantity and the feedback correcting quantity to form a final control instruction; and S7, periodically retraining all the models to realize self-learning and iterative updating of the system. According to the method, through multi-working-condition model fusion prediction, multi-target online optimization and self-learning iteration, the control precision is improved, the operation cost is reduced, and long-term self-adaption of the control system is realized.

The invention relates to the field of AI visual detection, in particular to an AI visual detection method based on supervised and unsupervised deep learning, which comprises the following steps: establishing a sampling image visual feature by using real-time sampling image data; establishing an AI visual feature analysis model based on supervised and unsupervised deep learning by using the visual features of the sampled image; according to the AI visual feature analysis model, an AI visual detection result is obtained, high-precision and high-robustness visual detection is achieved under a small amount of annotation data through a supervised and unsupervised mixed learning architecture in combination with the discrimination ability of deep learning and the anomaly detection advantage of unsupervised learning, a supervised main model is trained based on the annotation data, and a supervised auxiliary model is trained based on the unsupervised learning. Precise recognition of known defects is ensured, limitation of a traditional supervised model is broken through, generalization ability is improved while high precision is ensured, and the method is suitable for complex and changeable industrial detection scenes.

The invention discloses a motor bearing fault detection system and method based on robust deep learning, and belongs to the technical field of mechanical fault detection and intelligent perception. Feature extraction is carried out on an original vibration signal with a label based on a supervised learning branch network, and the original vibration signal is used as a reference sample; the samples with the same fault category and different fault categories as the reference samples are positive samples and negative samples, and inter-class separation and intra-class aggregation relations in a triple loss optimization embedding feature space are introduced to generate embedding representation; based on an unsupervised learning branch network, encoding the original vibration signal after time domain and frequency domain artificial feature extraction, and introducing triple loss to carry out unsupervised embedding learning to generate high-level feature embedding representation; and the embedded representations output by the two branch networks are fused, dual loss of triple loss and center loss is introduced for training, and a bearing fault detection model after training is completed is used for bearing fault detection.

The invention provides a supervised and unsupervised defect detection method, a model building method and a model building device, and the model building method comprises the steps: building an initial defect detection model which comprises a shared feature extraction module based on a YOLO backbone network, a supervised learning branch based on a YOLO architecture and an unsupervised learning branch based on an anomaly detection architecture; freezing an unsupervised learning branch, and training the shared feature extraction module and a supervised learning branch based on a labeled sample; after training of the shared feature extraction module and the supervised learning branches is completed, the shared feature extraction module and the supervised learning branches are frozen, unsupervised learning branches are unfrozen, and the unsupervised learning branches are trained based on positive samples; unfreezing the supervised learning branch, performing joint training on the shared feature extraction module, the supervised learning branch and the unsupervised learning branch by adopting the labeled sample and the positive sample, and obtaining a defect detection model after the training is completed; according to the method, the defect detection precision can be improved.

The invention discloses an unmanned aerial vehicle spectrum anomaly detection and trajectory generation method based on unsupervised learning, and the method comprises the steps: continuously collecting broadband radio frequency signals through an SDR, and obtaining an IQ data stream; performing short-time Fourier transform to obtain a time-frequency spectrogram; performing robust normalization processing on the time-frequency spectrogram to obtain a standardized spectrogram after slow-varying drift suppression; performing constant false alarm rate detection, and generating a primary abnormal candidate binary mask; identifying spectrogram sub-blocks corresponding to the primary abnormal candidate binary masks from the standardized spectrogram, and inputting the spectrogram sub-blocks into a pre-trained unsupervised deep learning model to obtain an abnormal scoring graph; fusing with the primary abnormal candidate binary mask to generate an abnormal heat map; and clustering the abnormal heat map in a time-frequency dimension, carrying out cross-time window trajectory association and filtering on a cluster, and outputting a time-frequency trajectory of the unmanned aerial vehicle signal. According to the invention, the reliability of anomaly detection is enhanced, the risk of false detection and missing detection is reduced, and the continuity and accuracy of track generation are guaranteed.

A method of training a machine learning model for reconstructing video or audio data based on neuroimaging data of a subject is provided. The method includes: training a neuroimaging data encoder based on neuroimaging data from a neuroimaging training dataset for generating neuroimaging data embeddings; and training a diffusion model based on the neuroimaging data embeddings generated by the neuroimaging data encoder as conditions on the diffusion model. In this regard, the diffusion model is trained to reconstruct video or audio data based on neuroimaging data embeddings of neuroimaging data of a subject obtained in response to a visual or audio stimulus. The neuroimaging data encoder includes a masked autoencoder. The above-mentioned training the neuroimaging data encoder includes training an encoder of the masked autoencoder based on the neuroimaging training dataset using unsupervised learning with masked data modeling for generating the neuroimaging data embeddings. The unsupcrviscd learning with masked data modeling includes generating neuroimaging data embeddings from neuroimaging data from the neuroimaging training dataset, masking a portion of the neuroimaging data embeddings into masked neuroimaging data embeddings and training the masked autoencoder to recover the masked neuroimaging data embeddings. There is also provided a method of using the machine learning model trained for reconstructing video or audio data based on neuroimaging data of a subject.

Computerized methods and systems obtain threat data generated from activity data using unsupervised learning. The activity data is collected from enterprises and describes activities performed on the enterprises. The threat data indicates likelihood that sequences of activities performed on the enterprises are indicative of malicious intent. A supervised ML model that processes sequential data is trained by providing a training set of sequential data to the supervised ML model. The training set includes at least some of the obtained threat data, and data derived from activity data collected from at least some of the enterprises. The trained supervised ML receives new data that describes a sequence of activities performed on an enterprise, and processes the received new data to produce a prediction of whether the sequence of activities performed on the enterprise will lead to a malicious action on the enterprise. In some embodiments, multiple supervised ML models are used.

A system is adapted to automatically identify suspected fraudulent transactions. The system includes a fraud management server configured to perform these operations: receiving unlabeled transactions, each having a number of features, and storing them in a transaction repository; with the features, determining a risk score for each transaction; based on the risk scores, dividing the unlabeled transactions into bins in order of their risk scores; labeling transactions of the first bin legitimate and those of last bin as fraudulent; with the labeled transactions, training a first machine learning model; with the trained first machine learning model, labeling transactions of a second bin and a second-to-last bin as either fraudulent or legitimate; storing the labeled transactions of the first bin, second bin, second-to-last bin, and last-bin in the transaction repository; and with the labeled transactions of the first bin, second bin, second-to-last bin, and last-bin, training a second machine learning model.

The invention discloses a stratigraphic interface unsupervised extraction method, system and device, a medium and a product, and relates to the field of three-dimensional geological modelling, and the method comprises the steps: carrying out the preprocessing of a three-dimensional seismic reflection amplitude image, and determining a preprocessed three-dimensional seismic reflection amplitude image; extracting a feature vector set of the preprocessed three-dimensional seismic reflection amplitude image based on a full convolution U-Net architecture; in an unsupervised learning scene, mapping the multi-dimensional feature vector extracted by each pixel to a plurality of clustering label spaces; based on a self-training strategy, according to the clustering label space, distributing a pseudo target clustering label for each pixel, and determining a target clustering label of each pixel; segmenting a background pixel of the three-dimensional seismic reflection amplitude image and a stratum interface position pixel corresponding to the seismic amplitude extreme value according to the target clustering label, and determining an initial segmentation image; and post-processing the initial segmentation map, and determining a stratum interface position distribution map. The geological boundary identification efficiency is improved.

A system and a method are disclosed for a tool that generates a provider score corresponding to a predicted performance of a provider based on data of claims involving the provider. For a given claim, the tool provides the data as input into a supervised machine learning model and receives as output from the supervised machine learning model a predicted performance of the claim. The tool also inputs the data of the claim into an unsupervised machine learning model that is selected based on a stage of claim processing that the claim belongs to and receives as output from the unsupervised machine learning model an identification of a cluster of candidate claims to which the claim belongs. The tool combines the outputs of the supervised machine learning model and the unsupervised machine learning model to generate the provider score.