2756 results about "Data point" patented technology

A system and method are provided for examining data from a source. The method is executed by a device having a processor and includes receiving a set of historical data and a set of current data to be examined, from the source. The method also includes generating multiple statistical models based on the historical data and a forecast for each model. The method also includes selecting one of the multiple statistical models based on at least one criterion, and generating a new forecast using the selected model. The method also includes comparing the set of current data against the new forecast to identify any data points in the set of current data with unexpected values. The method also includes outputting a result of the comparison, the result comprising any data points with unexpected values.

The invention provides a valve actuator dispatching control method and system based on AI intelligence, and relates to the technical field of industrial automation control, and the method comprises the steps: collecting the running state parameters of a steam turbine in real time, determining the relation between the steam inlet amount and the valve opening degree under the current working condition to obtain a preliminary valve opening degree estimated value, and carrying out dynamic response compensation; the method comprises the following steps: carrying out comprehensive coupling analysis on a theoretical valve opening value and mechanical parameters such as response time, inertial parameters and an opening limit threshold value of a valve actuator, calculating a dynamic adjustment weight and carrying out boundary constraint processing to obtain a processed parameter, and calling a preset valve opening-silicon controlled trigger angle nonlinear mapping model in a single chip microcomputer based on the parameter; and calculating a final silicon controlled trigger angle by inquiring adjacent data points and a linear interpolation method. By fusing intelligent prediction, multi-dimensional adjustment and a closed-loop feedback mechanism, the valve control precision and response speed are improved, and efficient and intelligent operation of industrial automatic control is achieved.

The invention discloses an intelligent definition method and system for industrial edge data acquisition, a medium and equipment. The method comprises the following steps: acquiring operation data of field equipment in real time; performing protocol analysis and cleaning on the data to generate standardized data points, dynamically detecting the communication state and adaptively adjusting the acquisition frequency; classifying and aggregating the standardized data points into a running state feature set and calculating feature indexes; edge side anomaly detection is carried out based on the feature set, and a protocol container library is synchronously called to match a device communication protocol to generate matching information; an acquisition strategy optimization instruction is generated in combination with the abnormal result and the matching information, and transmission characteristic indexes and instructions are packaged; and structuring storage feature indexes according to equipment types and time dimensions to form a historical operation database, and managing a storage period by adopting a sliding window mechanism. According to the invention, protocol adaptive analysis, dynamic acquisition adjustment and edge intelligent analysis are realized through software definition, the hardware dependence and field debugging risk are reduced, and the data acquisition efficiency and the intelligent level are improved.

The invention relates to the technical field of data analysis, in particular to an intelligent hydrological data analysis method and system, and the method comprises the following steps: collecting water level, flow velocity and precipitation data, calculating the change rate of adjacent moments, marking hydrological parameter abrupt change points, carrying out the statistics of time intervals, predicting the duration of extreme events, and obtaining the distribution characteristics of the hydrological abrupt change points. According to the method, by analyzing the time interval and the duration time of the data points, the distribution characteristics of hydrological abrupt change can be mastered in the early stage, response can be made in advance, the propagation sequence and the diffusion rate of the abrupt change points in different river reaches can be analyzed, and accurate division of the influence range and the risk level of hydrological events is enhanced; effective management of hydrological event space risks is achieved, understanding of hydrological key influence factors is deepened by screening key variables and evaluating the evolution relation of the variables along with time, accuracy of short-term and long-term hydrological risk prediction is enhanced, and a scientific basis is provided for dynamic early warning of hydrological abnormal events.

The invention discloses a multi-modal fused intelligent question answering and knowledge retrieval method and system, and the method comprises the steps: building a multi-modal data index model oriented to a heterogeneous knowledge source, carrying out the feature mapping of text, image, table, chart, audio and video contents through a unified semantic embedding space, and generating a cross-modal index set; after a query request is received, performing semantic matching and structure matching on the cross-modal index set by using a multi-channel retriever to obtain candidate evidence fragments; and based on an evidence granularity decomposition strategy, performing minimum evidence unit division on text statements, table units, chart data points and multimedia frame contents in the candidate evidence fragments, and establishing a semantic consistency graph among the units. According to the method, high-credibility traceable generation of question and answer results is realized through multi-modal fusion and space-time consistency constraint, and the retrieval precision and interpretation transparency in a complex knowledge scene are remarkably improved.

The invention provides a mangrove forest restoration area carbon sink quantity data monitoring system, and relates to the technical field of ecological carbon sink metering, and the system comprises an acquisition module which is used for collecting a carbon exchange original data set of an atmosphere-vegetation-soil interface based on a multi-type sensor network arranged in an intertidal wetland of a mangrove forest restoration area; the data generation module is used for carrying out space-time coordinate matching based on the original data set and generating a multi-dimensional carbon flux fusion data set of geographic position and time joint indexing; and the identification module is used for calculating a local space-time dispersion and a global correlation threshold value based on the fused data set, and identifying and marking abnormal data points. According to the invention, real-time and multi-dimensional monitoring of the carbon sink amount of the mangrove forest restoration area is realized, the monitoring efficiency and the data reliability are improved, and support is provided for assessment of restoration and management strategies.

The invention discloses a welding seam track extraction method and system based on RANSAC parameter fitting, and the method comprises the steps: S1, obtaining three-dimensional point cloud data, and obtaining the normal vector and local curvature of each point cloud data point; selecting an initial seed point from the three-dimensional point cloud data according to the local curvature of each point cloud data point; s2, establishing a feature similarity evaluation system, and adding the initial seed points and the corresponding similar adjacent points into the same plane; according to different planes where the point cloud data points are located, plane area coarse segmentation is carried out; and S3, a plurality of geometric fitting models are constructed for the welding track based on the RANSAC algorithm, the geometric fitting model with the highest matching degree is selected from the geometric fitting models, and the welding track in the welding area is extracted. According to the technical scheme, the welding seam track can be rapidly and effectively extracted, the universality of welding seam track recognition is improved, and the extraction error of the welding seam track is remarkably reduced.

The present disclosure relates to a method and apparatus for detecting anomalous shaking of a vehicle, a device, a vehicle, a medium, and a program product, the method comprising: acquiring a target time sequence corresponding to an original time sequence signal, the original time sequence signal being a time sequence signal corresponding to a vehicle status signal, and the target time sequence being a stable time sequence obtained from preprocessing the original time sequence signal; randomly selecting a plurality of sample points from the target time sequence, constructing a plurality of isolation trees, selecting from the target time sequence a data point to be detected, traversing isolation trees among the plurality of isolation trees according to said data point, and, on the basis of the depths of said data point in the isolation trees, determining anomaly scores of said data point; on the basis of the anomaly scores of said point, determining an anomalous point list composed of anomalous points that have been detected; and, according to a time interval between the anomalous points in the anomalous point list, identifying an anomalous shaking interval. According to the present method, the problem of missed detection during anomaly detection on vehicle status information can be reduced.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention relates to the field of foundation detection and multi-source big data intelligent analysis, and discloses a multi-source big data intelligent analysis fusion processing method based on foundation detection equipment, and the method comprises the following steps: carrying out the standardization processing, sampling frequency unification, missing value filling, and completion time and space alignment of multi-source data collected by the foundation detection equipment; based on the preprocessed multi-source data, constructing a similarity weight matrix between the data points for measuring the correlation between the data points; a manifold learning algorithm is adopted to embed multi-source data into a low-dimensional manifold space, and low-dimensional embedding representation used for describing a data local geometric structure is constructed; the method is based on a high-order Gaussian process model. By introducing the high-order Gaussian process method of multi-modal interaction modeling, the internal correlation of the modals and the complex interaction relationship between the modals can be comprehensively described. The problem that the nonlinear relation of the multi-modal data cannot be captured is solved, and more accurate feature extraction and fusion of the data are realized.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine heterogeneous data visualization processing method combined with artificial intelligence, and the method comprises the steps: collecting multi-source heterogeneous data above and under a coal mine to construct a real-time heterogeneous data matrix; feature categories are divided based on data dynamic relevance, and stable data areas are screened through a dynamic analysis window to generate an optimization matrix; performing pattern recognition on the unstable region to extract an abnormal data cluster, and calculating an abnormal feature factor of a data point; analyzing the feature distribution difference to generate a stable feature index, and calculating an abnormal risk value in combination with the distance, the abnormal feature factor and the stable feature index; and mapping the center coordinate of the stable region to a three-dimensional space of the coal mine, and positioning a key information region in combination with the abnormal risk value. An apparatus includes a memory, a processor, and a computer program. The coal mine heterogeneous data processing efficiency and the safety monitoring accuracy are improved, intelligent positioning of the key information area is achieved, and powerful support is provided for coal mine safety production.

The invention relates to the technical field of industrial internet, and discloses a multi-dimensional data integration and dynamic risk assessment method for enterprise purchase anomaly detection, which comprises the following steps: establishing a reliability verification matrix of multi-dimensional purchase data, and extracting abnormal data points; identifying a cross correlation mode of the abnormal data points, and constructing a purchase behavior interaction graph of the target enterprise; analyzing a risk evolution path of the purchase risk factor, and calculating a risk conduction entropy value; and according to the risk conduction entropy value, generating a risk grading label of the purchase risk factor so as to output a purchase anomaly detection report of the target enterprise. According to the invention, the accuracy of enterprise purchase anomaly detection can be improved.

The invention discloses a hydraulic engineering supervision and analysis system and method based on digital twinning, and belongs to the technical field of hydraulic engineering operation monitoring and management, and the method comprises the steps: recognizing data points with periodic fluctuation characteristics and sparse spatial distribution, extracting a weak disturbance data subset, and building a response evolution curve; identifying a potential abnormal region through neighborhood statistics and clustering; for the abnormal region, inverting possibly existing micro-damage types and distribution directions based on historical time sequences and environmental boundary conditions to obtain an inversion defect feature set, and inputting the inversion defect feature set into a twinborn model to execute local heat-seepage-force coupling calculation to obtain a stress response evolution curve in a future preset period; calculating a risk score value according to the growth rate, step characteristics and frequency abrupt change of the curve, and outputting risk level and evolution trend information when the score exceeds a set threshold value; according to the invention, early recognition and dynamic evolution analysis of tiny hidden dangers can be realized, and the intelligent and refined level of hydraulic engineering safety supervision is improved.

The invention relates to the technical field of health early warning, in particular to a human health early warning method and system based on physical examination data analysis, which comprises the following steps: collecting physical examination data of a human body, analyzing index changes of blood pressure, blood sugar, cholesterol and body mass index of the human body, sorting the physical examination data of the human body through time sequence sorting, and generating a time sequence sorting result. According to the method, time sequence sorting and spatial dimension evaluation are performed on the physical examination data, so that the health risk can be accurately identified and the early warning level can be dynamically evaluated, the data analysis precision is improved, the risk prediction dimension is increased, early warning is more timely and accurate, and the accuracy of early warning is improved by performing layout evaluation on the physical examination data points in a finite dimension space. Potential health abnormalities can be identified on multiple levels, and multi-dimensional analysis provides a new way for identifying complex health problems, so that health management is more active and predictable, health monitoring and risk management are optimized, and the health condition of a patient is improved.

The invention discloses a machine learning method and system based on privacy protection enhancement, and relates to the technical field of data processing. The method comprises the steps of monitoring real-time data streams of a plurality of data sources based on real-time input data, analyzing relationships among various data, drawing a data association network diagram, evaluating information leakage risks and privacy sensitivity of a plurality of data points by analyzing interconnection density and path distribution of nodes in the network diagram, and generating sensitivity evaluation indexes. According to the method, the security and privacy protection level in the data processing process are improved by identifying the risk points in the data in real time and specifically adjusting the noise level of the data points, and the encryption processing efficiency and security are improved by comprehensively considering the data processing efficiency, security level requirements and computing resource consumption; the stability of encrypted data is tested by simulating network attack conditions, the capability of coping with network attacks is enhanced, input data is enabled to support various data analysis tasks, and the privacy and security of the data are ensured.

The invention discloses an anomaly detection method based on global reachability density and an adaptive threshold mechanism, which comprises the following steps: collecting single-index historical water quality monitoring data of a target monitoring station, extracting a subsequence with a fixed length through a sliding window mode, constructing a neighbor index structure and extracting neighborhood information, acquiring a k neighbor set of each data point; respectively calculating the local accessibility density of each data point in the local neighborhood of the data point; respectively calculating the global reachability density of each data point; performing statistical summary on GRD values of all data points in the sample set, calculating a mean value and a standard deviation of the GRD values, and defining a dynamically adjusted distance threshold value based on a variable coefficient; and an abnormal score function fusing local and global density features is further constructed, and abnormal point determination is realized according to a distance threshold. The method is suitable for complex time sequence water quality monitoring data, can effectively improve the recognition accuracy of weak anomalies, local drifts and extreme abrupt changes, and has high universality and robustness.

The invention provides an electric energy metering system operation state comprehensive evaluation method and system, and the method comprises the steps: receiving a real-time energy consumption data stream, carrying out the processing through an adaptive threshold anomaly detection algorithm and a Bayesian inference method, and generating a real error range estimation corresponding to each data point; generating a target prediction result by using the time sequence prediction model in combination with historical same-period data, seasonal factors and a reinforcement learning mechanism; comparing the target prediction result with the real-time energy consumption data flow to obtain a comparison result, and generating a comprehensive performance fluctuation evaluation result based on the comparison result and the multi-dimensional state space model; constructing a comprehensive evaluation index system by using an integrated learning algorithm and a fuzzy logic algorithm, and obtaining a comprehensive score corresponding to each ammeter by using the comprehensive evaluation index system; according to the method, the prediction accuracy of the electric energy metering system, the comprehensiveness of health condition evaluation and the scientificity of maintenance decision are improved, and support is provided for efficient management and optimal scheduling of an intelligent power grid.

Systems, methods, and devices that relate to assessing uncertainty associated with entities are disclosed. In one example aspect, the method receives artifacts relating to an entity and categories for assessing uncertainty. For each category, a generative model retrieves and standardizes data points from the artifacts. A rule-based model inputs the standardized data points to output a rating. The generative model then generates an assessment of the rating and data points according to a predefined structure. The method outputs a summary, rating, and standardized data points for each category. These outputs can be used by other systems for assessing the uncertainty of the entity and taking action based on the assessment.

The invention discloses a multi-stage equipment energy efficiency optimization control system and method for an efficient machine room, and relates to the technical field of equipment optimizing.The method comprises the steps that environment parameter data and design parameter data of a refrigerating machine room are obtained, an environment correction factor is analyzed, and the cold load rate is dynamically corrected based on the environment correction factor; on the basis of preset delay time, phase-space reconstruction is carried out on the historical corrected cold load rate sequence, the final embedded dimension is analyzed, and a hybrid prediction model is constructed on the basis of the final embedded dimension; matching optimal operation parameters based on physical resonance characteristics of equipment in combination with comprehensive energy efficiency indexes of the machine room; and performing data structure compression on the optimal operation parameters and the comprehensive energy efficiency indexes of the corresponding machine room, performing screening analysis on data points, and updating a parameter library. And invalid data interference is reduced, so that the system can realize self-adaptive optimization in long-term operation, and the data storage and calculation efficiency is improved.

The invention relates to the technical field of power grid state monitoring, in particular to an intelligent monitoring and early warning method and system for a high-voltage power grid, and the method comprises the steps: collecting the multi-dimensional parameter data of a power grid node, and obtaining the multi-dimensional parameter data of the power grid node based on the relative deviation of the data of each dimension in a local window and the mean value of the data of each dimension in combination with the correlation coefficient of the data of each dimension; calculating parameter fluctuation attention at a target moment so as to correct parameter data of each dimension; processing the data points through a clustering algorithm to obtain a plurality of clusters, and selecting the cluster center of the cluster with the most data points as a power stability index; and calculating the relative deviation between the data point and the index, and generating a state early warning coefficient so as to estimate and evaluate the abnormality of the power grid node region and generate an early warning signal. According to the method, parameter fluctuation is accurately quantified by fusing the deviation degree and correlation of the multi-dimensional data of the local window, and a foundation is built for monitoring and early warning.

A system and method for dynamic model training of a predictive machine learning model accesses data points of a training dataset including a plurality of model covariates. The predictive machine learning model is configured to generate an output including a risk rank representative of a mortality risk. The method selects one of the covariates and generates a historical data distribution for the selected covariate by applying the model to the training dataset including a plurality of historical application records. The method determines a current data distribution for the selected covariate. When comparison of the current data distribution with the historical data distribution indicates a data distribution shift exceeding a predetermined threshold, the method automatically updates parameters of the predictive machine learning model and retrains the predictive machine learning model using the updated parameters. Comparison of the current data distribution with the historical data distribution may employ covariate shift adaptation.

The invention discloses a power supply equipment fault prediction method and device based on deep learning, and relates to the technical field of power system equipment fault prediction and deep learning application. The method comprises the following steps: acquiring a power grid topological structure, an equipment operation state, a historical fault record, a real-time equipment load and environmental condition data; forming a space-time correlation basic diagram according to the power grid topology and the equipment operation state, and calculating the correlation strength by using a diagram neural network; calculating fault time delay and determining a transmission path set by using a long short-term memory network in combination with association strength and historical fault records; fusing multiple data to calculate a cross-regional fault propagation probability, and generating a predicted fault path list; and the fault prediction output of the long-short-term memory network input is updated, and the real-time operation data verification optimization of the power grid is combined, so that accurate cross-regional cascade fault prediction is realized, and safe and stable operation of the power grid is ensured.

The invention relates to the technical field of frequency converter anomaly detection, in particular to a hydraulic engineering frequency converter state detection method and system. The invention aims to improve the accuracy of abnormal data point detection in the frequency converter performance parameter time sequence data, and particularly focuses on the influence of environmental factors on performance parameters. Performance parameter time sequence data and environment parameter time sequence data are synchronously obtained, discrete wavelet transform is used for decomposing performance parameters in a multi-layer mode, and approximation coefficients are mainly analyzed to insight the operation state of the frequency converter. And after the suspected abnormal data segments are preliminarily screened, environment abnormal indexes are calculated in combination with environment parameters, and fluctuation degree values of the suspected abnormal data segments are analyzed. And based on the environmental anomaly index and the fluctuation degree value, adaptively adjusting the initial threshold value of each approximation coefficient to obtain an adaptive threshold value, and finally detecting abnormal data based on the adaptive threshold value. According to the method, abnormal data points caused by environmental factors can be effectively eliminated, and abnormal data points caused by abnormity of the frequency converter can be accurately identified.

The invention relates to the technical field of real-time data analysis, in particular to a real-time data analysis system based on big data, which comprises an event capture module, a trend identification module, a priority processing module and a data isolation module. According to the method, the data stream is monitored in real time, the timestamps, the change amplitude and the frequency of the data points are extracted, the timeliness and the accuracy of data processing are enhanced, real-time change analysis is carried out on the data, instant understanding of the data stream is enhanced through key change marks, the response speed of market changes is increased, and the market competitiveness is improved. By analyzing the detailed background and development trend of key events, the prediction accuracy and the operation foresight are improved, resources are dynamically reconfigured according to the priority of the events, the key events are ensured to be processed preferentially, the resource utilization efficiency and the operation response speed are greatly improved, data isolation guarantees the concentration and safety of high-priority event processing, and the method is suitable for popularization and application. And the data processing efficiency is obviously improved.

The invention relates to the technical field of mechanical automation control, in particular to a bearing pressing machine control method and system. The method comprises the following steps: acquiring multi-dimensional monitoring data in a press fitting process; based on the difference between the monitoring data at the current moment and the data at each moment in the preset window, constructing a deviation degree sequence of each dimension at the current moment; adjusting the length of a preset window based on the correlation of the deviation degree sequence to obtain a target window at the current moment; determining a truncation distance of clustering analysis according to the data point distribution sparseness of the monitoring data in the target window; taking the truncation distance as a parameter, and performing clustering analysis on the data points in the target window through a density clustering algorithm to identify abnormal data points; and when the monitoring data at the current moment is abnormal, the bearing press-fitting machine is controlled to execute a preset response action. According to the method, the analysis window and the clustering truncation distance are adaptively adjusted, so that the accuracy of abnormal state recognition under the multi-stage working condition is improved.

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

The invention relates to the technical field of electric digital data processing, in particular to a data processing method for quality monitoring of a power distribution equipment production line, which comprises the following steps of: calculating a time decay dynamic fluctuation index based on acquired continuous power distribution equipment sensor data flow, and recording data points and timestamps of which variable quantities exceed a threshold value; and obtaining a time decay sampling point sequence. According to the dynamic sensing and time decay modeling mechanism of the continuous power distribution equipment sensor data flow, the time decay dynamic fluctuation index is constructed to capture numerical value abrupt change in a short period, key change points where quality abnormity possibly exists can be positioned in time, timestamps can be recorded, and the response sensitivity to data behaviors before and after event triggering is improved. Dense original data segments are actively intercepted after anomaly recognition, multi-stage discrete wavelet decomposition of time domain signals is carried out, smooth trend and multi-scale mutation features are extracted in a refined mode, and the depicting capacity of key process detail changes of the circuit breaker is enhanced.

Embodiments of the present application relate to the field of computer vision. Disclosed are a target six-degree-of-freedom pose estimation method and apparatus based on binocular vision and particle swarm optimization. The method comprises: obtaining a binocular image of a target, wherein the binocular image at least comprises a rotation matrix, a displacement vector and geometric features of the target; decomposing the rotation matrix into a rotation vector and a rotation angle by means of a rotation matrix decomposition method, and performing six-degree-of-freedom uniform sampling on the rotation vector and the rotation angle to obtain a data point set; establishing a mathematical relationship between the rotation matrix and the displacement vector on the basis of the geometric property of the geometric features and the data point set; and performing target pose estimation on the binocular image of the target by designing an objective function and an application optimization algorithm to obtain a pose estimation result. The present application solves the problems in the related art of low accuracy, poor universality and poor robustness.

The invention discloses a submarine topography super-resolution reconstruction method based on window displacement multi-source fusion, and aims to solve the problem of precision attenuation caused by absence of multi-beam truth value evaluation and resolution improvement of an existing neural network submarine topography reconstruction method. The method comprises the following steps of: obtaining gravity anomaly, gravity vertical gradient, vertical line deviation component, submarine topography background and ship survey water depth multi-source data of a target sea area; constructing a residual U-Net attention neural network model, and performing training by taking multi-source data extracted by a 11 * 11 window and position codes as input; moving the trained model sampling window in a staggered manner according to a target resolution step length; extracting data points in the window during movement and injecting position codes; and outputting a high-resolution water depth predicted value based on the position code and the window data. The reconstruction precision is improved by fusing physical quantities, the multi-resolution robustness is guaranteed by a window displacement mechanism, and the medium-frequency feature reconstruction capability is enhanced by a residual U-Net attention model.