724 results about "Statistical model" 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, which relates to the technical field of payment security, discloses a payment-scene-oriented interaction intention identification and error correction system comprising an input analysis module, an intention simulation module, a dynamic decision module, a biological verification module, an audit evidence storage module, and a cross-scene knowledge migration module. According to the method, multi-modal data such as voice, texts, images and touch tracks are integrated, structured feature vectors are generated through a cross-modal attention network, the problem of incomplete single-modal coverage is solved, cross-modal data consistency verification is achieved based on a unified semantic tag system, and the reliability of input sources is graded by combining equipment fingerprints and geographic positions, so that the reliability of the input sources is improved. A high-risk transaction protection capability is enhanced, a generative adversarial network is utilized to construct a virtual attack sample library, attacks such as tampering with characters similar in shape and AI faking voiceprints are simulated, unknown threats are actively defended through cosine similarity matching, a user historical behavior statistical model is integrated, and known risks such as high-frequency small-amount transfer are passively intercepted. And a closed-loop incremental learning continuous optimization model is supported.

The invention relates to the field of energy consumption analysis, and discloses an AI-based energy consumption data analysis and prediction system, which comprises the steps of collecting environmental parameters and running states of equipment, dynamically identifying the current system working condition by using a working condition identification algorithm combining incremental clustering and historical mode matching, and predicting the energy consumption data. Collected data is divided according to time, space and working condition dimensions, multi-scale features are extracted, normalization parameters can be dynamically adjusted along with changes of working conditions, a drift index is calculated through comparison of a drift threshold value and historical distribution, an optimal normalization updating strategy is selected according to the drift index, the normalization parameters are dynamically updated, and energy consumption trend prediction is conducted through a statistical model. A prediction result is combined with a working condition label to carry out weighted correction, error analysis and deviation detection are carried out in combination with a drift index, working condition prediction and historical error data, and an analysis result is fed back to a working condition sensing module, a feature adaptive module and a normalization control module. The method has the advantage of improving the stability and reliability in a dynamic environment.

The invention discloses a multi-target water supply scheduling method and system based on big data driving, and the method comprises the steps: collecting the multi-source data of a plurality of water supply targets, and enabling the scheme to obtain more reliable data support in the scheduling planning of the water supply targets; based on multi-source data serving as input features, the scheme combines the multi-source data with a graph space-time prediction model, a season statistical model and a gradient lifting regression model to obtain water consumption predicted by the three models respectively, prediction results of the three models are integrated through error adaptive weighting, more reliable predicted water consumption requirements are obtained, and the prediction efficiency is improved. And after the linear distribution model of the water source and the water supply targets is solved, initial water supply distribution schemes in one-to-one correspondence with the multiple water supply targets are obtained, the water supply accuracy of the water supply targets can be improved, multiple costs are used as targets to be minimized, and after multi-constraint combined solving, the water supply accuracy of the water supply targets can be improved. Therefore, the scheduling cost optimization is considered under the condition that the multi-target water supply scheduling meets flexibility and reliability.

The invention relates to the technical field of warehouse management, in particular to an unmanned warehouse management method and system with a real-time inventory function, and the method comprises the steps: building a signal strength-probability statistical model through return signal data generated in a historical inventory process, and carrying out the stability verification of each RFID tag return signal in a current inventory period, and under the condition that the stability is confirmed, carrying out preliminary positioning based on the time difference of arrival and determining the probability of the storage unit. And finally, the preliminary positioning is verified in combination with the probability of the storage unit and the checking information of the last checking period, so that the situation that whether the position of the RFID tag is changed really cannot be confirmed due to the fact that verification cannot be carried out when the positioning jumps is avoided. According to the invention, low-cost and high-reliability material positioning can be realized by using an existing RFID system, so that real-time and accurate checking is realized, and an information basis is provided for management of unmanned warehouses.

The invention provides a multichannel transcranial direct current stimulation cognitive function assessment method and system, and belongs to the field of neuroscience and brain cognitive function assessment. The method comprises the steps that electroencephalogram signal data of a subject under multi-channel transcranial direct current stimulation are collected and preprocessed; respectively performing micro-state analysis, brain function connectivity analysis and oscillation power analysis on the preprocessed data; constructing a linear mixed effect model, and inputting the results of the micro-state analysis, the brain function connectivity analysis and the oscillation power analysis into the model for quantitative evaluation of the cognitive function state; and a time sequence database is constructed, and the time sequence database is used for storing cross-time-point analysis results and analyzing the long-term change trend of the cognitive function based on a statistical model. Dynamic, accurate and high-temporal-spatial-resolution evaluation of the cognitive state of the subject is achieved, the long-term change trend of the cognitive state is dynamically monitored, and technical support is provided for early warning and rehabilitation effect tracking of neurodegenerative diseases.

The invention discloses a surface topography modeling method and particularly provides a roughness calculation formula based on Fourier series and polynomial combination, Fourier series can effectively describe periodic roughness features of a surface, a polynomial function can describe an overall deformation trend of the surface, the advantages of the Fourier series and the polynomial function are combined, and the surface topography modeling method has the advantages that the surface topography modeling efficiency is improved. The method can accurately simulate the random roughness and overall deformation characteristics of material corrosion, terrain, sea wave plane and other phenomena, and avoids the problem that the traditional surface topography modeling method is difficult to accurately describe the randomness and nonlinear characteristics of a complex surface based on simple statistical model or experimental data fitting. According to the method, the morphological characteristics of phenomena such as material corrosion, topography and sea wave planes can be simulated more accurately, and technical support is provided for analysis and prediction in related fields.

The invention discloses a slope deformation trend prediction method based on three-dimensional point cloud and deep learning, and relates to the technical field of geological disasters, and the method comprises the following steps: S1, obtaining multi-time sequence three-dimensional point cloud data of a target slope, S2, carrying out the preprocessing, obtaining a standardized time sequence point cloud data set, and carrying out the prediction of the deformation trend of the target slope. S3, extracting slope deformation characteristic parameters from the standardized time sequence point cloud data set, S4, constructing a prediction model, S5, integrating the data into a model training sample, and training and optimizing the deep learning prediction model, and S6, inputting the data into the trained deep learning prediction model, and outputting a deformation trend prediction result of a target slope. And S7, carrying out reliability evaluation on the deformation trend prediction result, and generating a final prediction report. According to the method, through the deep learning model fusing the CNN and the attention mechanism LSTM, the spatial relevance and the time dynamics of slope deformation can be mined at the same time, compared with a traditional statistical model, the prediction precision is improved, and the method is especially suitable for long-term deformation trend prediction.

The invention discloses an integrated wind power prediction method and system based on a multi-source data set. The method comprises the following steps: acquiring historical meteorological factors and fan operation data; preprocessing the historical meteorological factors and the fan operation data to obtain a data set; based on the data set, key features are obtained through a Boruta algorithm, the key features are processed through a sliding window mechanism and a VMD algorithm, and an enhanced feature matrix is obtained; inputting the enhanced feature matrix into a deep learning model for prediction, and obtaining a preliminary prediction value; and carrying out residual error correction and fusion on the preliminary prediction value to obtain a final wind power prediction result. The method effectively improves the capability of processing wind energy intermittency, volatility and randomness, avoids the defects that a physical model is complex in calculation and a statistical model is difficult to process nonlinear and non-stationary features, reduces the over-fitting risk of a single deep learning model, can improve the prediction accuracy and stability, and improves the prediction efficiency. And the method has better generalization ability in practical application.

The invention relates to the technical field of artificial intelligence and data analysis, in particular to an unsupervised anomaly detection method and system based on comparative potential fusion. The method aims at solving the problems that in the prior art, an unsupervised anomaly detection method is limited in feature expression ability, sensitive in noise, insufficient in potential feature discrimination and lack of statistical interpretability in detection results. According to the method, the global potential features generated by comparison learning and the self-encoder reconstruction residual error are fused, the statistical model is combined for self-adaptive threshold judgment, the problems of insufficient feature expression and high noise sensitivity in multi-source heterogeneous time series data anomaly detection are effectively solved, and the method has the advantages that the detection precision and robustness are improved, and the dependence on labeled data is reduced.

The invention relates to the technical field of power system scheduling, and discloses an intra-day look-ahead scheduling rapid solving method considering large-scale new energy cluster power generation volatility. Comprising the following steps of S1, new energy cluster space-time fluctuation scene generation based on a neuron cellular automaton, S2, power grid dynamic security domain definition and simplification based on a physical information neural network, S3, scheduling rapid optimization solution based on model prediction path integration, and S4, scheduling scheme dynamic elasticity and stability evaluation based on a Kupman operator theory. The new energy cluster space-time fluctuation scene generation method based on the neuron cell automaton can effectively generate a space-time scene reflecting large-scale new energy cluster power generation volatility, supports uncertainty analysis, has the advantages of being high in calculation efficiency and scene authenticity, and is suitable for large-scale new energy cluster power generation. The problem that scene generation is inaccurate due to the fact that a traditional statistical model ignores space-time coupling is solved.

An adaptive random access system and method with learned query optimization for compacted data files that enhances random access performance through machine learning and pattern recognition. The system incorporates a query pattern learning module that analyzes historical access patterns and user behavior to build statistical models of data usage. An adaptive estimator module improves location estimation accuracy by incorporating learned patterns rather than relying solely on mathematical calculations. A predictive boundary detector uses learned codeword patterns to more accurately identify boundaries in compacted data, reducing misalignment errors. An intelligent search engine coordinates optimization strategies including context-aware search string parsing and encoding strategy selection based on learned performance data. A dynamic codebook optimizer reorganizes sourceblock layout based on access frequencies and co-occurrence patterns to improve retrieval speed. An enhanced search cache implements predictive caching algorithms that anticipate user queries and proactively load relevant data.

The invention discloses an analysis and prediction system based on disease data, which belongs to the technical field of disease data analysis and comprises a cross-modal distillation data fusion module, a time domain causal chain analysis module, a multi-level disease risk prediction module, a disease critical state real-time monitoring module, a self-adaptive intervention strategy generation module and a clinical collaboration and feedback optimization module. A time decay factor and a smoothing coefficient are introduced through the time domain causal chain analysis module, the causal strength between variables is updated in real time, nonlinear changes of disease progression are adapted, and individual risk scores are generated through the multi-level disease risk prediction module in combination with the dynamic weight and the time decay factor of the causal atlas. A spatial smoothing function is introduced to suppress the influence of an abnormal value, group risks are quantified through a modal mean value and a standard deviation, individual weights are dynamically adjusted through a feedback mechanism, the long-term stability of prediction is improved, causal strength and feature importance are used for joint modeling, and the problem that a traditional statistical model ignores a potential causal path is avoided.

The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power generation prediction method of a multi-algorithm hybrid model, which comprises the following steps: S1, historical data preprocessing: carrying out meteorological condition clustering on historical photovoltaic data by adopting a BIRCH algorithm, dividing the historical photovoltaic data into a sunny day data set, a cloudy data set and a cloudy and rainy data set, and screening key influence factors through a Pearson's correlation coefficient; s2, EEMD (ensemble empirical mode decomposition) and reconstruction: performing ensemble empirical mode decomposition on the preprocessed photovoltaic data, and reconstructing the data according to the energy distribution and contribution rate of an IMF component; and S3, constructing a hybrid prediction model: processing a non-stationary IMF component by using a BiLSTM neural network, processing a stationary IMF component by using a Holt-Winters model, carrying out weighted fusion on a prediction result through back propagation, and outputting a final photovoltaic power prediction value. According to the method, the advantages of deep learning and a statistical model are combined, refined preprocessing and decomposition reconstruction are carried out on data, and the precision and stability of photovoltaic power prediction are effectively improved.

The invention discloses a construction method of a soil microorganism nitrogen metabolism function prediction model, and relates to the technical field of environmental microbiology and agricultural information, and the method comprises the following steps: S1, constructing a spatial-temporal heterogeneity representation engine, and dynamically decoupling a multi-level gradient of a soil environment through an adaptive spatial-temporal mesh generation algorithm; according to the construction method of the soil microorganism nitrogen metabolism function prediction model, the adaptive capacity of the soil microorganism nitrogen metabolism function prediction model in a complex environment is improved, and multi-level environment gradient dynamic analysis from a centimeter-level rhizosphere microdomain to a kilometer-level landscape scale is realized; the problem of cross-scale feature fusion caused by temporal-spatial resolution mismatch of a traditional model is solved, the simplified hypothesis of a traditional statistical model on the synergistic-antagonistic relationship of microbial functional genes is broken through, a dynamic coupling mechanism of a nitrogen metabolism path under environmental disturbance is accurately quantified, and the prediction error is reduced compared with the prior art.

The invention discloses a multi-disaster chain type marine disaster risk assessment method based on regional disaster-bearing body distribution, relates to the technical field of marine disaster prevention and control, and aims to clarify the range of a research region, extract all disaster-bearing bodies in the research region, analyze the interaction relationship of the disaster-bearing bodies and establish an association probability matrix. By integrating a disaster chain theory, a probability statistical model and GIS spatial analysis, systematic identification and quantification of multi-disaster chain type marine disaster risks are realized, a logical relationship among a native event, a secondary event and a derivative event is defined, a disaster chain type structure chart is constructed, the total probability of a disaster-bearing body is calculated through a probability iterative algorithm, and the risk of the multi-disaster chain type marine disaster risk is identified and quantified. According to the method, direct and indirect conduction risks are comprehensively reflected, individual risk values are calculated in combination with disaster-bearing body vulnerability, and disaster chain overall risk values are generated through superposition, so that the limitation of isolated analysis of a single disaster in a traditional method is avoided, and complex interaction and cumulative effects of a disaster chain can be more comprehensively captured.

The invention provides a remote sensing image target statistical method and system fusing a large language model and visual prompt driving. The method comprises the following steps: acquiring a remote sensing instance segmentation image to be processed and a visual prompt thereof; inputting a to-be-processed remote sensing instance segmentation image and a visual prompt thereof into the trained remote sensing image target statistical model, and outputting a remote sensing image target statistical result; the training comprises the following steps: introducing a large language model and visual cue into an encoder architecture of a GrondingDINO model to obtain a remote sensing image target statistical model; inputting a remote sensing instance segmented image and the visual cue thereof into an encoder, and outputting an image feature, a visual cue feature and a text feature; the feature intensifier carries out fusion processing on the output of the encoder; a language-guided query selection module calculates cross-modal query according to the fusion processing result, and a cross-modal decoder obtains a target statistical result of the image based on the fusion processing result and the cross-modal query; and training by using the training data and outputting the trained model.

The invention discloses a crested ibis habitat ecological quality trend analysis method and system, and relates to ecological quality monitoring. The method comprises the following steps: S1, constructing an intelligent sensing network, synchronously obtaining multi-source data of a habitat, identifying activity events of crested ibis, and generating a multi-dimensional habitat parameter table; s2, collecting environmental samples, and generating a microbial functional gene abundance matrix through metagenome sequencing and bioinformatics analysis; s3, taking the activity events of the crested ibis as behavior tags, and generating habitat function health indexes by coupling the parameter table and the matrix training machine learning prediction model; s4, performing spatial interpolation and trend analysis based on the habitat function health index to generate an ecological quality space-time evolution graph; and S5, based on the ecological quality space-time evolution graph, performing quantitative analysis by using a spatial differentiation statistical model, and generating a trend analysis report. By fusing multi-source data, real-time dynamic evaluation of habitat ecological quality and quantitative analysis of driving factors are realized, and a direct decision basis is provided for accurate protection.

The invention relates to the technical field of risk assessment optimization, and discloses a risk assessment method based on statistical model optimization. The method comprises the following steps: acquiring a running state data set of a target object containing multi-dimensional monitoring index time sequence data; and inputting the operation state data set into a pre-trained reference risk assessment model, and generating an initial risk score and risk distribution characteristics. And iteratively adjusting parameters of the reference risk assessment model through a dynamic correction algorithm according to the generated risk distribution characteristics, and generating an optimized risk assessment model. And adopting the optimized risk assessment model to re-assess the same operation state data set, and outputting a corrected risk score and a key risk area identifier. According to the method, the risk distribution characteristics of the specific data of the target object under the reference model are analyzed, and the model parameters are dynamically adjusted, so that the risk assessment standard better fits the actual risk mode, the accuracy and pertinence of risk identification are improved, and a more reliable basis is provided for a risk management and control decision.

The invention discloses a risk data processing method and system based on a multi-rule engine, belongs to the technical field of big data processing, and aims to solve the technical problems of field limitation, rule stiffness, processing capability bottleneck, insufficient intelligent degree and limited expansibility faced by data processing. Comprising the following steps: constructing a multi-rule engine comprising a business rule module, a statistical model module and a real-time analysis module; carrying out standardization processing on the collected risk data; monitoring the standardized data through a message queue, calling a multi-rule engine, and performing rule parallel evaluation on the labeled data based on a specific field rule, a machine learning model and a streaming computing method; arbitration is carried out based on a dynamic weight distribution algorithm of scene features; and executing a corresponding risk handling operation based on the handling strategy.

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 relates to the technical field of image analysis, in particular to a metal composite material surface defect intelligent detection method based on machine vision, which comprises the following steps: acquiring an original image and converting the original image into an observation matrix; decomposing the matrix into a low-rank matrix and a sparse matrix through a robust principal component analysis algorithm; utilizing a nuclear norm constraint low-rank matrix to establish a background statistical model, and utilizing a norm constraint sparse matrix; positioning a defect area in the sparse matrix and extracting a gray level co-occurrence matrix feature vector; performing connected domain analysis on the sparse matrix, calculating geometrical morphology parameters and evaluating a stress concentration coefficient; and establishing a self-adaptive decision model to carry out fusion judgment so as to judge the physical damage. According to the method, through low-rank sparse matrix decoupling and multi-dimensional statistical geometric feature fusion analysis, accurate stripping of weak defects and quantitative evaluation of physical damage attributes under a complex background are realized.

A facility for estimating a value relating to a occurrence is described. The facility receives a first occurrence that specifies a first value for each of a plurality of independent variables that include a distinguished independent variable designated to be monotonically linked to a dependent variable. The facility subjects the first independent variable values specified by the received occurrence to a statistical model to obtain a first value of the dependent variable. The facility receives a second occurrence that specifies a second value for each of the plurality of independent variables, the second value of the distinguished independent variable varying from the first value of the distinguished independent variable in a first direction. The facility subjects the second independent variable values specified by the received occurrence to the statistical model to obtain a second value of the dependent variable, the second value of the dependent variable being guaranteed not to vary from the first value of the dependent variable in a second direction that is opposite the first direction.

The invention discloses a data management method based on a real-time data warehouse, and the method comprises the steps: obtaining column type data blocks through vectorization processing according to collected data, dividing data types according to the access frequency of the column type data blocks, and correspondingly setting storage positions; in response to the data request, determining a data access mode according to the cache hit rate of the data request; and according to the real-time monitoring index, a load trend is predicted through a time sequence prediction statistical model, and vectorized nodes and cache capacity are adjusted. Through vectorization processing, cache hit rate analysis and dynamic resource adjustment driven by load prediction, the core problems of low storage efficiency, poor resource utilization rate, high response delay and the like of a traditional data warehouse architecture are solved, and multi-dimensional breakthrough in performance, cost and stability is achieved.

The invention relates to the technical field of user interface development and evaluation, and provides a method for judging web interface interaction complexity, and the method comprises the steps: S1, dividing a web interface into a plurality of function modules; s2, counting the number of UI elements in each function module through an element number statistical model; s3, analyzing a data dependence or transmission relationship among the functional modules in the web page through a data flow hierarchy complexity model, and determining a transmission link depth of data among the modules; s4, counting the single-page data volume of the web interface through a single-page data volume statistical model; and S5, calculating the web interface interaction complexity based on the number of UI elements in each function module, the transmission link depth and the single-page data volume. Compared with a traditional function counting or code quantity statistical method, the hidden complexity such as bidirectional data flow and condition triggering refreshing in the module dependency chain can be accurately recognized, and a scientific basis is provided for development resource allocation.

The invention relates to the technical field of vehicle damage detection and identification based on computer vision, and discloses a vehicle damage detection and identification method based on computer vision. The method comprises the steps of image acquisition, polarization image acquisition, Stokes parameter calculation, polarization feature extraction, anomaly evaluation, connected region analysis and the like. A polarization statistical model is established by setting an image coordinate system, collecting a multi-angle polarization diagram and calculating the polarization degree and the polarization angle of each pixel and taking an artificially selected nondestructive area as a reference, so that anomaly measurement and adaptive threshold segmentation are performed on the pixels, and a structured vehicle damage detection result is output in combination with corrosion, expansion and connected domain analysis. Physical interpretability and detection precision are enhanced by using polarization characteristics, recesses or scratches can be accurately identified even in a vehicle body area with a smooth surface or complex reflection, a standardized report containing position information and abnormality is provided, and automation and accuracy of vehicle damage assessment are effectively improved.

A bot detection service associated with an overlay network operates to score traffic as a probability of being a bot, as opposed to returning a binary classification (i.e., bot or human). According to the approach herein, scoring is determined through probability estimates, wherein a score (the probability) is based on considering a set of detections concurrently. In one embodiment, all (or substantially all) triggered (current) threat detections contribute to the score. The preferred approach penalizes requests that fail all (or substantially all) combinations of detection algorithms. According to a further feature, an automated tuning (autotuning) is also applied, e.g., using real-time empirical statistical models, to adapt the measurement of false positive probability for one or more threat detection algorithms to suit customer traffic trends. The approach herein is also extensible to include any number of future threat detection algorithms.

The invention relates to a runway visual range prediction method based on multi-modal fusion, and the method comprises the following steps: S1, constructing a time-space matched data set which comprises Himawari-9 satellite thermal infrared channel brightness temperature image data, airport site meteorological element data and airport observation RVR / MOR data, carrying out the cleaning work of the data, and dividing the data into a training set, a verification set and a test set; s2, constructing an RVR / MOR forecasting model based on a Cross ViViT model, training model parameters by using a training set and a verification set, adjusting and optimizing the parameters, and performing precision evaluation on the model by using a test set; s3, mapping the high-dimensional semantic representation presented by the constructed RVR / MOR prediction model into a specific RVR prediction value for utilization; the problems that at present, only a traditional statistical model or a depth model based on a single mode is relied on, a complex RVR generation mechanism is often difficult to describe accurately, and particularly, response lag or prediction distortion phenomena easily occur in sudden weather events, so that the prediction effect is affected are solved.

The invention discloses a distribution transformer on-line health diagnosis system and method. The distribution transformer on-line health diagnosis system comprises a low-voltage transformer area, a cloud platform and a big data application layer. The on-line health diagnosis of the distribution transformer comprises the steps of data acquisition, analysis, storage, statistical model establishment for prediction and artificial intelligence deep learning for prediction. Various data in the operation process of the transformer are collected in real time through the terminal data collector, meteorological indexes such as the temperature and the humidity of the operation environment of the transformer are combined, processing and analysis are carried out through combination of statistical model analysis and an artificial intelligence algorithm, transformer fault diagnosis is carried out, and the operation condition of the transformer is monitored. And once it is found that the operation state of the transformer has a deterioration trend, early warning is given out in advance, and early warning of the sub-health state of the transformer is achieved.

The invention provides a laser stripe center extraction method based on gray coefficient binarization, and aims to solve the problems of non-uniform brightness distribution of laser stripes, difficulty in extraction of weak stripes, breakage of center lines and the like. The method comprises the following steps: enhancing an image; realizing self-adaptive threshold segmentation through a local gray scale statistical model and a peak variable coefficient by using a gray scale coefficient binarization method; extracting a sub-pixel-level center point by combining a secondary positioning method of skeleton constraint; and carrying out fitting smoothing by adopting bicubic interpolation and a smooth spline method. According to the method, the problem of extraction of strong and weak stripes under complex working conditions is effectively solved, and the precision, connectivity and robustness of stripe center positioning are remarkably improved while background noise is suppressed.