114 results about "Statistical learning" patented technology

The invention relates to the technical field of wind power prediction. The invention provides a wind power prediction method and system. The method comprises the following steps: acquiring multi-dimensional meteorological time series data, three-dimensional elevation data and unit operation data of a target wind power plant; constructing a spatial-temporal feature fusion network, extracting time sequence dynamic features, and performing weighted fusion on the spatial correlation features and the time sequence dynamic features to obtain a fusion feature vector; establishing a hybrid prediction model, and taking the fusion feature vector as input to obtain a wind power initial prediction result; introducing a terrain correction factor, constructing a turbulence intensity compensation function, and performing micro-terrain disturbance correction on the wind power initial prediction result; and outputting a final power prediction curve and a confidence interval. The problems that in an existing wind power prediction method, a physical model is insufficient in complex terrain microclimate modeling precision, high in calculation complexity and difficult to meet the real-time requirement, a statistical learning method is limited in high-dimensional nonlinear time sequence feature expression capacity, and prediction errors are remarkably increased under the abnormal working condition are solved.

Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

The invention relates to a positioning method and system based on adaptive multi-sensor fusion, belongs to the technical field of positioning, and aims to solve the problems of low positioning precision and poor stability in a complex environment in the existing positioning technology. According to the method, a VIW system and a GPS are deeply fused, and the advantages of all sensors are effectively integrated. The method comprises the following steps: firstly, evaluating, correcting and screening GPS data through an improved GPS precision factor model and an anomaly detection model based on fuzzy logic and statistical learning; and secondly, acquiring a local pose estimation result by using the VIW system, and realizing time synchronization of the GPS and the VIW system through a time difference compensation strategy. And finally, constructing an optimization objective function containing a VIW system residual term and a GPS residual term, and updating the transformation matrix of the VIW system and the GPS coordinate system. The method is suitable for the positioning requirements of intelligent robots, pilotless automobiles and other equipment in urban canyons, tunnels, indoor and long-distance and high-dynamic scenes.

A system is provided for dynamic scheduling and optimisation of diagnostic tasks in a networked computing environment. The system associates issue tickets with a diagnostic task matrix comprising probable causes, diagnostic tasks, probability values, outcome expectations, and resource parameters. A task scheduling controller generates optimised task sequences based on task success likelihoods, cost, technician availability, and evidentiary sufficiency. As tasks are completed, outcomes are used to update the diagnostic model, enabling automatic self-improvement. A statistical learning model, such as aBayesian or neural network, refines diagnostic probabilities using historical data. Integration with calendaring systems allows real-time rescheduling based on personnel availability. A graphical interface supports live drag-and-drop reconfiguration of task associations, with immediate propagation of updates to task probabilities and cost metrics. The system thereby enhances resolution speed, accuracy, and resource efficiency across evolving operational contexts.

The invention relates to a rock slope support model construction scheme generation method and device, equipment and a medium. According to the method, a three-dimensional geological model fusing geological information and rock mass parameters is constructed, an initial damage field is obtained in combination with micro-seismic monitoring data and statistical learning inversion, and then a constitutive model capable of reflecting the damage and plastic coupling evolution law is established and calibrated; the model is used for dynamically predicting a spatio-temporal evolution path of a potential slip plane in the excavation process, the supporting opportunity and position are accurately judged based on the stress and damage state in the path, a spatio-temporal sequence scheme is generated, and finally optimal supporting parameters are solved through the multi-objective optimization model. And finally, a set of dynamic support construction scheme capable of actively controlling damage development and giving consideration to safety and economical efficiency is integrated and output, technical spanning from passive reinforcement to active intervention and from static design to dynamic optimization is achieved, and the accuracy and reliability of slope support are effectively improved.

The invention belongs to the technical field of information, and discloses an IT performance index early warning method based on time sequence analysis, and the method comprises the steps: S1, inputting historical data; s2, risk detection; and S3, outputting early warning. Through fusion of statistical learning and machine learning technologies, accurate identification and early warning of three scenes of slow degradation of IT performance indexes, mode switching and capacity bottleneck are realized. The method specifically aims to improve the accuracy of performance index trend identification through a variable-point enhanced segmentation regression algorithm, and help an I T manager to more accurately evaluate the influence and trend of performance changes. Designing a cycle-adaptive double-sample hypothesis testing mechanism, and detecting and identifying switching of different load modes, especially significant changes caused by special or emergency events; and constructing a multivariable autoregressive VAR model, establishing a relation model of performance indexes and capacity indexes, evaluating capacity bottleneck conditions, and performing early warning in time.

The invention relates to the technical field of power generation prediction data processing, and discloses a photovoltaic power station power generation prediction method based on multi-model fusion, which combines a photovoltaic physical model, a power generation statistical model and a power generation prediction model, not only considers a physical rule, but also combines statistical learning and machine learning to improve the stability and accuracy of prediction, and improves the prediction efficiency. And predicting the generating capacity of the photovoltaic power station by using environmental meteorological data, electrical parameters of the photovoltaic power station and historical power generation data. Accurate photovoltaic power generation power prediction is beneficial to better arrangement of power generation plans and resource scheduling of the power grid. The prediction precision and stability are improved through multi-model fusion, so that power grid dispatching is more accurate and efficient, and power grid instability factors caused by photovoltaic power generation fluctuation are reduced. Therefore, photovoltaic power generation can be connected to a power grid more stably, the power demand is met, and wide application and development of clean energy are promoted.

The invention discloses a few-sample inductance core surface defect detection method based on model interaction, and belongs to the technical field of machine vision and industrial defect detection. The method comprises the following steps: S1, data acquisition and image preprocessing are carried out, and normal samples, labeled samples and unlabeled samples are constructed; s2, constructing an unsupervised statistical model based on statistical learning; s3, constructing a supervised semantic segmentation model; s4, inputting an inductance core image to be detected into the unsupervised statistical model and the supervised semantic segmentation model at the same time for processing, and generating a segmentation result; s5, detecting result differences are quantified; s6, performing parameter updating on the unsupervised statistical model; s7, generating a pseudo label based on an unsupervised statistical model; s8, carrying out weight updating on the supervised semantic segmentation model; and S9, inputting the processed to-be-detected images into the updated unsupervised statistical model and supervised semantic segmentation model in batches for detection to obtain a detection result, and analyzing and calculating system performance indexes.

The invention provides an old people health management detection method and system, and the method comprises the steps: obtaining behavior task data and physiological data of old people through a scale or a non-invasive device, extracting data features, carrying out the fusion, building a cognitive state evaluation model set through combining an integrated learning model, a deep learning model and a statistical learning model, and carrying out the detection of the health management of old people. Training a cognitive state evaluation model set by adopting a differentiated learning strategy, constructing a dynamic evaluation system based on cognitive competence scores, determining evaluation frequency, determining evaluation items by adopting a correlation priority strategy of data features, establishing a nonlinear mapping rule, setting training reference parameters according to the cognitive competence scores, and evaluating the cognitive competence scores according to the training reference parameters. And making a personalized cognitive training scheme based on the dynamic evaluation system and the individual data characteristics of the elderly. The method is high in individuation degree and simple to operate, has continuous and comprehensive data, and can capture dynamic changes of cognitive competence, know differences of old people in real time and provide a timely and effective feedback mechanism.

The invention discloses an HAZOP analysis method and system based on digital delivery, and relates to the technical field of chemical safety risk analysis, and the method comprises the steps: obtaining to-be-analyzed digital delivery data; carrying out digital delivery model integration and dynamic verification by adopting a topological correlation theory and a rule engine method; performing multi-source process data standardization processing and risk element mapping by adopting a statistical learning and semantic matching method; performing model-driven deviation intelligent generation and causal chain analysis by adopting a topological reasoning and analogue simulation method; carrying out cross-professional collaborative optimization and measure closed-loop tracking by adopting a collaborative filtering and closed-loop control method; and carrying out full-life-cycle data linkage and iterative optimization by adopting a feedback learning and knowledge iteration method to generate a standardized report. According to the method and the system, full-process digitization, intelligentization and full-life-cycle management and control of chemical process risk analysis can be realized, and the precision and the efficiency of HAZOP analysis are improved.

The invention specifically discloses a power battery pack multi-parameter inconsistency detection method based on incremental capacity analysis, and relates to the technical field of battery detection. The method comprises the following steps: S1, carrying out short-time pulse constant-current discharge on a power battery pack parallel module comprising n battery cells, and recording terminal voltage change data of the battery pack parallel module in a test step; s2, calculating the total charge change of the battery pack parallel modules in the test step by using a coulomb counting method; s3, calculating the instantaneous rate of the capacity of the battery pack parallel module along with the change of the terminal voltage in the test step by using an incremental capacity analysis method; s4, extracting a statistical feature vector of the capacity change rate by adopting a statistical analysis method; s5, analyzing the statistical feature vector by adopting a statistical learning method; and S6, performing multi-parameter consistency diagnosis by using a machine learning method. The method solves the problems that an existing lithium battery pack parameter consistency detection method is long in detection time and high in cost.

Embodiments are directed to a computer-based tool that can identify an anomalous state of a component in a real-world environment, even if the component experiences gradual and / or seasonal trends. The tool receives data from sensors monitoring a component. The tool uses a trained machine learning model to calculate a predicted behavior of the monitored component. Actual behavior of the component, captured by current sensor readings, is compared to the predicted behavior of the component, calculated by the machine learning model, to compute a divergence. The computed divergence is used by a statistical learning method to determine if the component in the real-world environment is in an anomalous state.

The invention discloses a hybrid deep learning model-based high-resolution department carbon emission prediction method and system, and the method comprises the steps: decomposing carbon emission data into subsequences which comprise a low-frequency subsequence and a high-frequency subsequence; constructing a low-frequency prediction model and a high-frequency prediction model, and respectively predicting the low-frequency sub-sequence and the high-frequency sub-sequence through the low-frequency prediction model and the high-frequency prediction model to obtain a low-frequency prediction result and a high-frequency prediction result; and fusing the low-frequency prediction result and the high-frequency prediction result to obtain a final carbon emission prediction result. Compared with a previous statistical learning measurement model and an evaluation model based on environmental influence, the method has a more accurate effect.

The invention relates to the technical field of assembly scheduling, in particular to an aircraft assembly scheduling enhanced dual-learning-driven evolutionary optimization method and system, and the method comprises the steps: constructing an initial hierarchical coding strategy and an initial decoding strategy in an evolutionary strategy based on the process sequence constraint and the worker station capacity constraint of target aircraft assembly; optimizing statistical learning performance and evolutionary learning performance of the initial hierarchical coding strategy and the initial decoding strategy through a preset search strategy, a preset multi-operator collaborative feedback search strategy and a preset parameter selection strategy; and generating a new evolutionary strategy according to the optimized hierarchical coding strategy and decoding strategy, and executing the new evolutionary strategy to schedule the target aircraft assembly. According to the method, the complex process constraint of aircraft assembly can be converted into layered coding, the layered coding is decoded by using the queue data structure, and different aircraft assembly scheduling schemes are quickly and adaptively generated under the condition of considering the complex characteristics of assembly process grouping, process serial and parallel process relation coupling, station capacity constraint and the like.

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

The invention discloses a rainy day troposphere refractive index profile inversion method based on a buoy platform, and relates to the technical field of marine atmosphere exploration, and the method comprises the steps: obtaining a priori knowledge base based on sounding data statistical learning, setting a threshold value, and searching and constructing an initial parameter profile in the priori knowledge base; decomposing the population into a plurality of sub-populations, distributing each sub-population to different CPU / GPU nodes, and calculating a target function for each individual; obtaining sea surface temperature and sea surface humidity from the buoy platform as boundary constraint conditions, performing inversion by using a multi-target genetic immune algorithm, and obtaining an inversion result when a target function value is less than or equal to a threshold value; and calculating an atmospheric refractive index profile based on an inversion result. According to the method, attitude shaking of the offshore floating platform is considered and corrected; the accuracy of inversion of the troposphere refractive index profile in rainy days is effectively improved, the offshore troposphere refractive index profile is detected in real time, and the calculation efficiency is effectively improved.

The invention relates to the technical field of biostatistics, and discloses a noise language frequency hearing impairment prediction system based on a GEE model and genetic characteristics, and the system comprises a data collection module which collects follow-up visit data of a worker; the data processing module is used for generating a standardized modeling data set; the feature screening module is used for screening features through a statistical learning method to obtain a key feature subset; the time-varying interaction module is used for constructing a generalized estimation equation model and outputting regression coefficient estimation; the risk prediction module outputs the risk probability and the risk layering result of the individual; the decision support module is used for generating hearing protection suggestions of the individuals; according to the method, the generalized estimation equation model is constructed to process follow-up data of occupational noise exposure workers, so that the accuracy and the stability of language frequency hearing loss risk prediction are improved, a basis is provided for formulating a personalized hearing protection scheme and recommending a proper hearing protection device, and the method is suitable for popularization and application. And the transformation of occupational hearing loss from passive treatment to active prevention is facilitated.

The invention provides a desktop learning concentration degree monitoring method and system, and the method comprises the steps: collecting a real-time face image of a learner and a desktop real-time image, obtaining the position and sight angle of the eyes of the learner in the image based on the real-time face image of the learner, determining the type and position of a desktop object based on the desktop real-time image, and carrying out the monitoring of the desktop learning concentration degree. The method comprises the following steps: calculating the overlap ratio of a sight angle area of a learner on a desktop and desktop article positions, judging articles concerned by the learner, counting the types and time of the articles concerned by the learner, regularly recording face images of the learner and images and data of the articles concerned by the learner, and generating traceable record data; according to the invention, the sight tracking data and the intelligent image object recognition data are fused to obtain the desktop attention area and object information of the sight of the learner, the time when the sight of the learner stays on the desktop and the classification and time data of objects concerned on the desktop can be monitored and recorded, and a signal is generated according to the data to remind the learner to keep concentration.

The invention discloses a user self-filling washing requirement and standard process difference verification method, and particularly relates to the technical field of life, and the method comprises the steps: obtaining a corresponding standard process parameter vector from a clothes feature vector through a standard process retrieval algorithm based on a process knowledge base, constructing a knowledge base secondary retrieval index, and traversing the process knowledge base; and obtaining a candidate set meeting the constraint, screening out a record with the highest score, and obtaining a recommendation process and a security boundary. The method is realized based on a standard parameter vector, a rule engine and a statistical learning method, can be conveniently integrated into an existing internet clothes washing platform or a traditional clothes washing management system, can be flexibly expanded according to standard process libraries of different enterprises, and is easy to integrate and expand.

A base station used in a traffic communication system includes a communicator configured to receive, via wireless communication, information transmitted from a vehicle traveling on a road, and a controller configured to identify, based on the information received by the communicator, a traveling behavior including a travel route of the vehicle on the road. The controller is configured to execute first processing for performing statistical learning or machine learning on a plurality of the traveling behaviors respectively identified for a plurality of the vehicles traveling on the road, and second processing for determining, based on a result of the first processing, whether a traffic obstacle is present on the road and a position of the traffic obstacle on the road.

The present invention relates to a multi-objective optimized 1DCNN-GRU-LSTM neural network tunnel face gas concentration prediction method, which belongs to the field of gas concentration prediction in coal tunnel engineering. The present invention includes the following steps: obtaining a gas concentration data set; filling missing values ​​in the obtained gas concentration data set; performing data set division and normalization processing on the data; slicing the data set according to the selected time window length; introducing a convolutional neural network to process historical gas concentration sequence data with time series, and setting parameters; introducing a gated recurrent unit neural network and a long short-term memory neural network to process historical gas concentration sequence data with time series; and optimizing and solving the model more effectively in a multi-objective manner. The present invention establishes a prediction model for gas concentration sequence data through statistical learning and machine learning methods, so that the prediction accuracy of gas concentration is improved.

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

The invention provides an intelligent detection system and method for hidden cracks and fragments of a silicon wafer, and the system comprises an imaging unit, an image processing unit and a control and communication unit, and is used for obtaining a surface image of the silicon wafer located on a conveying belt; the image processing unit is in communication connection with the imaging unit and is used for receiving and processing the surface image, the image processing unit comprises a processor, and the processor is configured to automatically generate an effective detection area excluding an edge area and an electrode area in the surface image based on pre-stored silicon wafer size information and electrode distribution information; the processor is used for segmenting the image in the effective detection area based on a gray threshold range determined through statistical learning, extracting a suspected defect area, performing feature extraction on the suspected defect area, and performing defect classification and judgment according to preset defect feature parameters; and the control and communication unit is connected with the image processing unit, so that efficient and accurate subfissure and fragment detection is realized on the premise of not changing the structure of a production line.

The application relates to the technical field of hydraulic engineering, and discloses a soil and rock dam dam-break probability prediction method, which comprises the following steps: acquiring basic cause variables and state conversion variables leading to soil and rock dam dam-break, adopting a fixed threshold method to perform discretization processing, and generating discretization standards of the variables; a one-dimensional dam-break flow simulation model is constructed; based on the discretization standards, a data set covering multiple grades of flood scenarios is generated; a three-layer Bayesian network model is constructed; the data set is input into the three-layer Bayesian network model for training; a Bayesian smoothing method is adopted to calculate the conditional probability of each node in the three-layer Bayesian network model, so as to correct model parameters; finally, the trained three-layer Bayesian network model is input into the variable data of a to-be-tested soil and rock dam, so as to realize dam-break probability prediction and risk grade evaluation; through fusion of a physical mechanism and statistical learning, the dam-break prediction probability is quantified and improved, and high-precision dam-break risk prediction is realized.

The invention belongs to the technical field of power system dynamic safety analysis and renewable energy source grid connection, and provides a method for determining the permeation limit of high renewable energy sources under multi-dimensional stability constraint. The method takes physical mechanism depth modeling-multi-dimensional stability coupling theory-intelligent data driven screening as a core, constructs a quantitative correlation model of frequency stability, transient stability and small disturbance stability through rigorous theoretical derivation, proposes a global-region double-layer dynamic inertial constraint mechanism, and combines an AI-driven critical scene screening and clustering method to obtain a critical scene clustering algorithm. And finally determining the safe RE penetration limit. According to the method, the limitation of traditional single-dimension evaluation and static constraint is broken through, all theoretical derivation is based on the dynamic characteristics and the statistical learning principle of the power system, moderate data verification is assisted, and a complete theoretical system and an engineering tool are provided for RE safety integration of the low-inertia power grid.

Embodiments are directed to a computer-based tool that can identify an anomalous state of a component in a real-world environment, even if the component experiences gradual and / or seasonal trends. The tool receives data from sensors monitoring a component. The tool uses a trained machine learning model to calculate a predicted behavior of the monitored component. Actual behavior of the component, captured by current sensor readings, is compared to the predicted behavior of the component, calculated by the machine learning model, to compute a divergence. The computed divergence is used by a statistical learning method to determine if the component in the real-world environment is in an anomalous state.

Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.