205 results about "Automatic learning" patented technology

An object of a system according to an embodiment is to automatically learn a behavior pattern of a target person, detect an abnormality, and issue an alert.SOLUTION: A system according to an embodiment includes a GPS device, a AI learning unit, an abnormality detection unit, and an alert generation unit. The GPS device keeps track of the current location of the subject. The AI learning unit automatically learns a daily behavior pattern based on the position information of the target person acquired by the GPS device. The abnormality detection unit detects an abnormality by comparing the behavior pattern learned by the AI learning unit with the current position information. The alert issuing unit issues an alert based on the abnormality detected by the abnormality detection unit.SELECTED DRAWING: Figure 1

The invention discloses an unmarked steel rail surface defect screening method based on self-supervised learning, and relates to the technical field of steel rail maintenance. Comprising the following steps: S100, acquiring steel rail surface image data and carrying out data preprocessing to generate an enhanced image pair; s200, constructing a defect screening basic feature encoder through a multi-scale visual pre-training model, and generating a final multi-scale fusion feature vector based on the enhanced image pair; and S300, constructing a dynamic pseudo tag generation unit, and calculating the cosine similarity between the final multi-scale fusion feature vector and the nearest neighbor normal sample feature vector. According to the method, a multi-scale visual pre-training framework is constructed, deep visual features representing the normal state and the abnormal state of the surface of the steel rail are automatically learned from massive original steel rail images on the premise that manual labeling is not needed, and a dynamic pseudo-label generation mechanism and a cross-scene migration adaptation unit are combined, so that the real-time performance of the system is improved. High-precision automatic screening of steel rail surface defects is achieved, and the generalization ability of the model in a complex environment is improved.

The invention discloses a deep coal bed gas seam network density prediction method, and relates to the technical field of reservoir development. The method comprises the following steps: extracting an average seam net distance from microseismic event point data as a seam net density representation label; establishing a multi-source data fusion framework taking a fracturing section as a sample unit, and splicing geological and engineering parameters into a feature vector; a machine learning algorithm is adopted to construct an intelligent prediction model, and a complex nonlinear mapping relation between multi-source features and labels is automatically learned. According to the method, intelligent prediction of the fracture network density is realized, and reliable support can be provided for fracturing effect evaluation and construction parameter optimization.

The invention relates to the technical field of new energy power prediction, in particular to an extreme weather offshore wind power generation situation prediction method based on artificial intelligence, and the method comprises the steps: obtaining data under historical extreme weather, dividing the data into a training set and an optimization set, removing noise, extracting environment data, and carrying out the feature data fusion. The method comprises the following steps: determining a freezing proportion according to an extreme weather disaster grade, freezing partial layer parameters of a pre-trained conventional power generation prediction model, training an unfrozen layer, constructing an extreme weather power generation prediction model, periodically obtaining data in an optimization set through constructing a simulation time axis, carrying out automatic learning, and finally obtaining environmental data in real time for prediction. And judging the prediction accuracy according to the similarity between the prediction result and the optimization set data, and if the prediction accuracy is not accurate, analyzing an abnormal reason and correcting related parameters. The method provided by the invention effectively overcomes the difficulty of inaccurate offshore wind power generation prediction in extreme weather, and significantly improves the accuracy and reliability of wind power generation prediction under extreme weather conditions.

The invention discloses a target detection-based molten pool working condition monitoring method in a laser directional energy deposition process and application, which uses a deep learning-based target detection network to identify the molten pool working condition, and comprises the following steps of: 1, constructing an image data set of target detection, 2, constructing a target detection model, and 3, identifying the molten pool working condition in the target detection model. Comprising a multi-level feature extraction module, a feature fusion module and an output module, 3, training of a target detection model, and 4, obtaining a gray level image detection result of laser directional energy deposition to be monitored through the target detection model. According to the method, molten pool position information can be automatically detected from the gray scale molten pool image in the laser directional energy deposition process, working condition types can be recognized, intelligent recognition and classification of the working conditions of the molten pool are achieved, the proposed target detection model can directly and automatically learn deep features of the molten pool from the original gray scale image, complex pretreatment steps are avoided, and the working efficiency is improved. The adaptive capacity of the model is remarkably improved through deep learning, and reliable technical support is provided for intelligent monitoring of the laser directional energy deposition process.

The invention provides a spatial intelligent visual physical process inference method based on an implicit physical large model, and belongs to the field of spatial intelligent and artificial intelligence modeling calculation. The problems of low prediction precision and lack of physical consistency for complex physical scenes in the prior art are solved. The method comprises the following specific steps: acquiring multi-modal data of an environment, and representing the multi-modal data in a unified coordinate system; preprocessing the multi-modal data, designing a geometric coding model, a dynamic prediction model and an energy conservation constraint, introducing a space-time attention mechanism, predicting the motion state of an object according to the multi-modal data, and obtaining prediction data of the motion state of the object; acquiring real observation data of object motion, comparing the difference between the prediction data and the real observation data, and performing correction and weight updating on the prediction data of the model through a self-adaptive residual term; according to the method, visual information and implicit physical law modeling are fused, and the self-supervised physical consistency constraint is utilized, so that automatic learning and prediction of a potential physical process are realized.

The invention discloses a semantic-driven intelligent query method, system and device based on a security event library and a medium, and the method comprises the steps: improving the query accuracy through deep semantic understanding and cross-modal correlation analysis; potential threat association difficult to recognize can be found through multi-modal semantic association and threat graph construction; the query habit and professional domain knowledge of the user can be automatically learned in combination with a query optimization mechanism driven by reinforcement learning, personalized customization of a query strategy is realized, the manual adjustment frequency is reduced, and the man-machine cooperation efficiency is improved; on the whole, based on a dynamic optimization mechanism of a plug-in architecture and a large vertical model, the system can quickly adapt to changes of a network security environment of a power system, the response speed to novel threats is increased, and the adaptability of the system is enhanced.

The invention provides a satellite-borne single-photon laser radar point cloud self-supervised filtering method and system, and relates to the technical field of laser radar data processing, and the filtering method comprises the steps: carrying out the point cloud self-supervised filtering through a core hypothesis that a source point cloud and a target point cloud which are randomly split from a same coarse filtering point cloud slice should have similar distribution; a completely self-supervised deep learning filtering framework is constructed, and efficient denoising of the satellite-borne single photon point cloud can be realized without manual annotation or external prior knowledge. The statistical consistency of the point cloud is used as a supervision signal, and the limitation that structural noise with a specific mode cannot be effectively filtered out through a traditional threshold value method is overcome. Distribution characteristics of the point cloud are automatically learned through a deep learning model, coordinate correction is carried out, full-automatic processing is achieved, and the processing efficiency of mass satellite-borne point cloud data is improved; according to the method, the fine filtering point cloud after coordinate correction is directly output, and terrain details can be better kept.

The invention belongs to the technical field of wireless communication safety monitoring and intelligent supervision, and particularly relates to a distributed low-cost hotel view-stealing and photo-stealing intelligent supervision system which comprises a wireless probe and a sky-wing cloud wireless analysis management and control platform. The wireless probe is connected to a hotel room router, collects space electromagnetic signals and terminal and AP wireless data, and pre-processes and reports the space electromagnetic signals and the terminal and AP wireless data; the cloud platform is constructed based on OpenStack, network mapping is updated through an ANO adversarial online automatic learning algorithm, wired / wireless attributes and event characteristics are subjected to correlation analysis by means of a VAE-CWGAN model, risks are identified, and an alarm is issued. The system realizes lightweight deployment, low-cost operation and maintenance, real-time intelligent supervision and room-level accurate positioning, can detect hundreds of wireless security incidents and dozens of cameras, is suitable for the fields of secrecy, national security and public security, and assists in security guarantee of smart civilized cities.

The invention discloses an AI large model fused API asset intelligent management method and system, and relates to the technical field of API data security management, and the API asset intelligent management mainly comprises the following steps: (1) carrying out multi-source API data collection and preprocessing; (2) on the basis of the preprocessed API data, extracting API features by fusing natural language processing and flow feature analysis, and generating an API comprehensive feature vector fusing API document features and flow features; and (3) identifying API assets based on the API comprehensive feature vector, firstly identifying normal API assets to form an enterprise API asset list, and then identifying shadow APIs and zombie APIs based on the enterprise API asset list. According to the scheme, the accuracy of API asset identification can be improved; meanwhile, a deep learning model and algorithm are adopted, features can be automatically learned and extracted, manual intervention is reduced, and efficiency is improved; moreover, according to the scheme of the invention, intelligent identification and anomaly detection of API assets can be realized, shadow APIs and zombie APIs can be found in time, and the security and stability of the system are guaranteed.

The invention discloses a virtual simulation data intelligent generation and enhancement method based on deep learning. The method comprises the following steps: step 1, collecting and converging multi-source heterogeneous simulation data; 2, simulation data preprocessing and feature extraction; step 3, constructing and configuring a depth generation model; step 4, iterative training and optimization of the depth generation model; step 5, intelligently generating and synthesizing new simulation data; step 6, generating data quality verification and enhancement feedback; step 7, carrying out controllability adjustment and directional enhancement on the generated data; and 8, model online learning and data generation closed-loop optimization are carried out. The method can automatically learn the internal law and distribution of the simulation data, efficiently generate highly-vivid and diversified new simulation data meeting specific requirements, and deeply enhance the existing limited data, thereby significantly reducing the simulation data acquisition cost, improving the data quality and richness, and improving the simulation data acquisition efficiency. And powerful data support is provided for development and test of a virtual simulation system and simulation-based AI model training.

The invention belongs to the technical field of hydraulic engineering safety management, particularly relates to an intelligent monitoring system for hydraulic engineering seepage, and aims to solve the problems that an existing intelligent monitoring system is difficult to adapt to time-varying effects such as material aging and fracture development in an engineering operation period in a use process, so that a simulation result gradually deviates from an actual state, the precision is reduced and the working efficiency is low. In order to solve the problem of poor prediction extrapolation in the face of extreme working conditions, the invention provides the following scheme that the method comprises a sensing array module, and the sensing array module is connected with a data acquisition module. The problem that a simulation result of a traditional fixed parameter model is gradually distorted is fundamentally solved, automatic learning can be carried out along with engineering operation, and a real physical state is continuously approached, so that high precision of seepage trend prediction and scientificity of early warning are still kept under time-varying conditions such as material aging and extreme working conditions.

The invention provides an equipment identification method and system based on packet traffic semantic feature enhancement, and electronic equipment, and the method comprises the steps: converting the original Internet of Things traffic into a general packet-level traffic semantic feature which can be understood by a large language model, and then carrying out the fine adjustment of the large language model through the packet-level traffic semantic feature, and the large language model can automatically learn potential Internet of Things equipment traffic characteristics and execute equipment classification identification decisions. According to the technical scheme, accurate identification of the Internet of Things equipment is realized, a network administrator can grasp the type and state information of the access equipment in real time, access or abnormal behaviors of unauthorized equipment are effectively identified, safety protection measures are taken in time, and the overall safety of an intelligent environment system is guaranteed.

The invention provides a PCBA manufacturing process optimization method and device, equipment and a storage medium, and the method comprises the steps: obtaining a mounting point data set of a to-be-assembled PCB, and dividing the mounting point data set into a plurality of mounting period subsets according to the number of mounting heads of a chip mounter and the type distribution of components; inputting the mounting period subset into a deep learning model, performing feature coding on the mounting point data through an encoder to obtain a feature matrix, and sequentially determining a matching relationship between each mounting point and a mounting head through a decoder according to the feature matrix to obtain a mounting point distribution scheme; and sorting the mounting points in each mounting period subset according to coordinate information and mounting angle information of each mounting point in the mounting point distribution scheme to obtain an execution sequence of the mounting head. According to the method, the global feature relationship and constraint conditions between the mounting points are automatically learned through the deep learning model, the optimization strategy is automatically adjusted according to the characteristics of different PCBs, and the mounting path length is shortened.

The invention provides a multi-modal fusion evaluation method and system for green tea grading. The method is applied to the technical field of artificial intelligence deep learning, and comprises the steps that to-be-detected hyperspectral data of to-be-detected green tea are collected through a constructed collection system, and the to-be-detected hyperspectral data comprise to-be-detected spatial data and to-be-detected spectral data; performing feature extraction on the to-be-detected spatial data by adopting a residual network model to obtain an image feature vector; inputting the spectral data to be detected into the Transform model to obtain a spectral feature vector; performing interaction association on the image feature vector and the corresponding spectral feature vector by adopting a cross attention mechanism to generate a fusion feature vector; and inputting the fusion feature vector into a trained grading model to obtain a grading result, and training the grading model by taking the green tea grade as a label. The feature representation is automatically learned from the original image of the green tea to classify the grade or evaluate the quality.

The application provides an aircraft engine defect identification system based on borehole exploration image real-time analysis. It is characterized by including: a borehole video acquisition module that acquires video images of the internal structure of aircraft engine equipment components and transmits them to an image processing engine in real time; the image processing engine automatically identifies cracks, pits, burns, notches, deformations, corrosion, material loss and other defects in the aircraft engine equipment components in each frame of video image through a neural network image recognition analysis algorithm, and transmits the defect identification analysis results to a data background; the data background is used to match the defect identification results found by the image processing engine with the records in the aircraft engine defect index database, confirm and record the defects and trends; the system realizes real-time tracking and monitoring of the defect state of aircraft components, automatically learns and accumulates a defect feature database, accurately identifies and quickly verifies defects, and promotes the application and development of intelligent maintenance and inspection technology in the field of aircraft operation and maintenance.

This invention provides a plasma spectral identification method, apparatus, electronic device, and storage medium, comprising: inputting the plasma spectral information to be identified into a trained LGBM spectral identification model to obtain target classification spectral parameters corresponding to the plasma spectral information to be identified, and performing spectral wavelength feature analysis on the plasma spectral information to be identified based on the target classification spectral parameters; wherein the trained LGBM spectral identification model is obtained by training on plasma spectral information samples carrying real spectral parameter labels. The method of this invention automatically learns the implicit physical information between plasma spectral wavelength features by employing a machine learning LGBM algorithm, achieving accurate identification and classification of plasma spectra. It is easy to operate and can greatly reduce the technical cost of plasma spectral diagnostics.

This invention relates to the field of atmospheric particulate matter analysis and discloses a deep learning-based method for calibrating organic aerosol concentrations. This invention addresses the technical problems in existing organic aerosol concentration calibration techniques, such as insufficient utilization of multi-source information, inadequate use of temporal evolution information, and fixed feature fusion methods lacking dynamic weight adjustment. By leveraging multi-source observation vectors, it fully utilizes observation information from different physical properties to establish a stable nonlinear mapping relationship under complex atmospheric conditions and mixed aerosol backgrounds. Through multi-source observation features and historical state features, the model can characterize the dynamic evolution of organic aerosol concentrations. Employing a bidirectional long short-term memory network and SE attention mechanism to process multi-source observation features and historical state features, it can automatically learn and dynamically adjust the importance weights of feature channels, effectively suppressing noise and redundant feature interference. Furthermore, it utilizes forward and backward time-series modeling to fully capture the contextual relationships between consecutive time points.

The invention relates to a manufacturing method (100) for an electronic trajectory control device for a vehicle. The manufacturing method (100) comprises synchronizing measurements of the rotational speeds of each of the vehicle's wheels at initial times. The manufacturing method (100) comprises determining a first set of trajectory parameters at these initial times. The manufacturing method (100) comprises developing (101) candidate programs for the electronic trajectory control device by machine learning from the first set of parameters and the synchronized measurements of the rotational speeds of each of the wheels. (Fig. 1)

The application discloses a three-layer artificial intelligence detection system, which comprises at least one sensor, a gateway and a service platform. The sensor is used for sensing a device and obtaining sensing signal data of the device. The sensor comprises a first artificial intelligence module and a first communication module. The gateway comprises a second artificial intelligence module and a second communication module. The service platform comprises a third artificial intelligence module, a data storage management module and a third communication module. The sensor, the gateway and the service platform communicate with each other through the first, second and third communication modules. The received detection data and related programs are processed, managed and executed in layers through the multi-layer artificial intelligence technology, so that the following benefits can be achieved: distributed layer processing, high efficiency, high speed, low cost, easy development, sustainable automatic learning and updating of artificial intelligence models, and instant synchronous processing of a large amount of data.

The invention belongs to the technical field of electroencephalogram signal analysis, and particularly relates to an electroencephalogram signal-based fatigue state detection system, which comprises a signal acquisition module, a signal preprocessing module, a feature extraction module and a fatigue state judgment model, the signal acquisition module is used for establishing stable low-impedance connection between each electrode and scalp by wearing an electrode cap and using conductive paste, and recording an original EEG signal; the signal preprocessing module is used for carrying out band-pass filtering and self-adaptive wave trapping on an original EEG signal and then removing physiological artifacts through self-adaptive filtering and independent component analysis; the feature extraction module is used for extracting artificially designed frequency domain, time domain and nonlinear features from the preprocessed signals, automatically learning deep features through 1D-CNN and an attention mechanism, and splicing the two types of features to form a fusion feature vector; the fatigue state judgment model calculates a fatigue score based on the fusion feature vector, and obtains a comprehensive score through time smoothing and trend analysis.

The invention discloses a successive grounding fault line detection method for a small current grounding system, and relates to the technical field of power distribution network fault detection, and the method comprises the steps: employing CEEMDAN mode decomposition to carry out the decomposition of the zero-sequence current of all lines except a bus, and obtaining the intrinsic mode function of each line; performing frequency domain sorting and splicing on the intrinsic mode functions of the lines to obtain a time frequency matrix; mapping the time-frequency matrix into a zero-sequence current image through a piecewise linear difference method; and taking the zero-sequence current image as input, and obtaining a predicted fault line based on a pre-trained successive grounding fault line detection model. According to the method, CEEMDAN mode decomposition is adopted to carry out time-frequency double-domain signal processing, an intelligent fault diagnosis system is constructed in combination with an automatic feature extraction function of an OfficientNet-B0 deep learning model, cascading fault features of the small-current grounding system can be automatically learned, and the recognition accuracy and the anti-interference performance are remarkably improved.

The invention relates to the technical field of information communication particularly suitable for specific application fields, and discloses a machine learning-based ionic liquid screening method for catalyzing CO2 cycloaddition reaction. The method comprises the following steps: constructing an ionic liquid three-dimensional structure database; calculating and generating a three-dimensional electrostatic potential and electron density data field through a density functional theory; voxelizing the data field into a standardized multi-channel three-dimensional tensor; utilizing a three-dimensional convolutional neural network regression model to automatically learn space electron characteristics and predict catalytic activity; and performing high-throughput virtual screening on the candidate library. Through end-to-end learning of the mapping relation between the three-dimensional electronic structure and the catalytic activity of the ionic liquid, the prediction accuracy and the screening efficiency are improved.

This invention discloses an intelligent traffic prediction and scheduling method based on user behavior data, comprising: acquiring the user's actual driving trajectory and matching it to a digital road network; extracting expert trajectory data containing state-action sequences and contextual features; constructing a generative adversarial learning model and using the expert trajectory data for adversarial training, wherein the generator learns to imitate the user's driving behavior, and the discriminator learns to distinguish between the real user trajectory and the trajectory simulated by the generator; using the output of the discriminator after training convergence, assigning a personalized cost value of the user's subjective preference to each state-action pair in the digital road network; based on the constructed personalized cost network, using the A* path search algorithm to plan an optimal personalized driving route for the user; this invention, through inverse reinforcement learning, can automatically learn the user's inherent preferences from the user's behavior, and the planned route is more in line with the user's true intention, improving user satisfaction and system adoption rate.

This invention relates to the field of intelligent reliability engineering technology for electromechanical products, specifically an adaptive failure prediction method for motors based on digital twin-based intelligent optimization. Through physically-driven intelligent modeling, a physical information neural network is used to automatically learn time-varying coupling coefficients, achieving an organic fusion of data-driven approaches and physical constraints. Failure paths are dynamically reconstructed by using a particle swarm optimization algorithm to identify dominant failure modes in real time and dynamically adjust the weights of the failure propagation chain. A prediction-verification-correction closed loop is implemented, leveraging digital twins and Bayesian inference to achieve continuous self-evolution of the model and constantly improve prediction accuracy. Intelligent design of test schemes is employed, using a multi-objective genetic algorithm to optimize and accelerate test conditions, improving test efficiency and failure mode coverage. Probabilistic lifetime prediction outputs a remaining lifetime distribution with confidence intervals, quantifying prediction uncertainty.

This invention relates to a method for autonomously controlling a device. The device typically moves in the real world and is not subject to physical design constraints. The advantage of this method is that the device can perform an autonomous learning process and continuously improve the learned knowledge or behavior. It overcomes the disadvantage of prior art methods that require the generation of training data first, which is often the case with traditional artificial intelligence methods. In general, this method is widely applicable, and the device can automatically learn and continuously revise its own knowledge base in the process. Furthermore, the invention also relates to an apparatus designed to implement the method, a system component comprising a plurality of the proposed apparatus, a computer program product, and a computer-readable storage medium that executes the method or causes a computer to execute the method.

The invention discloses a wind power data anomaly detection and fault location method and related equipment, and the method comprises the steps: obtaining the operation data of a wind power station, cleaning the operation data, and obtaining the cleaned operation data; performing multi-stage abnormal data screening on the cleaned operation data to obtain target abnormal data; monitoring the cleaned operation data in real time by adopting a local abnormal factor algorithm, and triggering a fault diagnosis process when an abnormality is detected; and when the fault diagnosis process is triggered, extracting a feature vector of the target abnormal data, inputting the feature vector into a pre-trained deep learning model for fault diagnosis and positioning, and outputting a fault type and a fault position. The objective of the invention is to improve the accuracy of wind power data anomaly detection, effectively distinguish real faults from temporary fluctuations, and reduce missing report or false report. Abnormal data are further optimized and screened, the problem that fault features are difficult to extract is solved, and the accuracy of fault diagnosis is improved; complex fault modes can be automatically learned from mass data, accurate and rapid fault positioning is achieved, and diagnosis delay is avoided.

A system and method for learning a predictive function that can automatically learn different operating modes for a multi-modal system and predict the number of operating states for a multi-modal system and additionally the detailed structure for each state. Once learned, the predictive function (model) can be used to determine a mode of a new sample (an asset). Based on the determined components that maximize a log likelihood function, a mode of the new sample is detected into the model via dependency graphs. One aspect includes enforcing a lower bound for the number of sample points to form an operational mode for an asset. While a mode relates to sample points which maximizes like log-likelihood, an ability is provided to remove artifact modes due to noisy data by considering a sufficient sample data condition and maximizing log-likelihood. Domain knowledge can be incorporated into the model via dependency graphs.