110 results about "Local outlier factor" patented technology

A fault detection method for refrigeration units based on an improved deep learning model is provided, including the following steps: S1: obtaining operating parameters of a refrigeration unit in a normal operating state and in states with different fault types as data sets; S2: detecting local outliers in the data set by using a local outlier factor algorithm and removing the local outliers, and then expanding the data set by using adaptive synthetic sampling; S3: normalizing the data set; S4: constructing a fault detection model; and S5: inputting the parameters of the tested refrigeration unit into the fault detection model, and judging whether the tested refrigeration unit has a fault and the type of the fault.

The invention provides an industrial anomaly detection and root localization method and system based on data driving, and the method comprises the steps: carrying out the cleaning, feature extraction and normalization processing of original data collected in an industrial production process, and constructing a feature space; based on a local anomaly factor LOF and a mahalanobis distance MD method, jointly detecting local anomaly and global anomaly, and identifying an abnormal working condition; extracting space and time correlation characteristics of the abnormal variables through Pearson correlation weighting and Granger causal test to form a space-time correlation matrix; constructing an abnormal causal network based on the matrix, and tracing an abnormal root and a propagation path through depth-first search and abnormal propagation intensity evaluation; and finally, dynamic optimization of the anomaly detection and diagnosis method is realized based on parameter self-adaption and model incremental learning. According to the method, the anomaly detection accuracy and the anomaly traceability interpretation capability can be effectively improved, and the intelligent level and the self-adaptive capability of data processing are enhanced.

The invention provides an electronic current transformer operation data calculation system, and relates to the technical field of data processing, and the system comprises a data processing module which is used for carrying out the sliding time window scanning of an equipment dependence adjacency matrix, calculating the local reachable density under a dynamic neighbor topological distance through a local outlier factor algorithm, if it is detected that the harmonic total distortion rate gradient enters a statistical hypothesis test rejection domain or the winding temperature rise rate exceeds a material thermal aging critical threshold, extracting a multi-dimensional abnormal feature vector set; the risk quantitative index generation module is used for generating a risk quantitative index group from the multi-dimensional abnormal feature vector set through a pre-trained fault propagation probability model; and the processing instruction set generation module is used for establishing a multi-objective optimization function based on the risk quantification index group, performing non-dominated sorting evolution on a processing instruction chromosome by adopting an NSGA-II genetic algorithm, screening a dynamic optimization instruction sequence matched with a real-time load fluctuation coefficient through a Pareto frontier solution set, and generating a structured processing instruction set. The operation reliability of the power system is improved.

The invention discloses a power distribution switch terminal communication state analysis method and system, and belongs to the technical field of power distribution network communication. The method comprises the following steps: acquiring dual-source parameters of a terminal and a communication link, and preprocessing to form a time sequence data sequence; an improved local outlier factor algorithm is combined with spatio-temporal clustering to identify abnormal communication events; extracting an analysis sequence containing event context based on the dynamic window; respectively performing communication state evaluation on a terminal side and a link side through a multi-task deep learning model, and outputting state classification and severity score; and when the evaluation results on the two sides conflict, intelligent decision making is performed by adopting a multi-level arbitration mechanism based on an evidence theory and integrating factors such as confidence difference, a historical trend and topological association, so that accurate responsibility determination of a fault source is realized. The method solves the problems that the traditional threshold alarm is high in false alarm rate and the fault source cannot be positioned, and remarkably improves the accuracy and operation and maintenance efficiency of communication state diagnosis.

The invention discloses a water quality monitoring intelligent early warning method and system based on multi-dimensional data analysis, and belongs to the technical field of water quality monitoring. The method comprises the following steps: processing missing values and noise through a dynamic weighted data filling algorithm to obtain complete water quality data; carrying out multi-parameter collaborative dimensionality reduction by adopting an improved t-SNE algorithm, and mapping high-dimensional water quality parameters to a three-dimensional feature space; constructing a dynamic watershed partition model based on a Delaunay triangulation network and DEM data; calculating a spatial anomaly score through a weighted local outlier factor; performing time anomaly detection in combination with STL (Standard Template Library) decomposition and a Transform model; multi-scale fluctuation detection is realized by using wavelet packet decomposition; fusing space, time and parameter dimensions to construct a three-dimensional judgment matrix, and generating a comprehensive anomaly score; and triggering a multi-level early warning mechanism according to a scoring result. The system can monitor the water quality change in real time, and gives out early warning in time when abnormity occurs, so that the water quality safety is guaranteed.

The invention relates to the technical field of monitoring and alarming, in particular to an online monitoring method and system for a coking coal detection chamber, and the method comprises the steps: collecting parameter data of a current time node in the coking coal detection chamber and parameter data in a plurality of historical set time windows; calculating an abnormal score of the parameter data of the current time node under any historical set time window by using an improved local outlier factor algorithm to obtain abnormal scores of the parameter data of the current time node under all historical set time windows, and when the mean value of the abnormal scores in all the historical set time windows is greater than a set abnormal threshold value, determining that the current time node is abnormal, and triggering an alarm device to perform early warning. The problem that an existing algorithm is not high in detection accuracy is solved.

The invention discloses a project cost prediction method fusing MDS-LOF and GBRT. The method comprises the steps of S1, selection and primary processing of project cost data; s2, performing project feature analysis and reserving core data; s3, performing dimension reduction processing by using a multi-dimensional scaling analysis (MDS) method; s4, using a local outlier factor (LOF) algorithm to identify abnormal values and removing the abnormal values; s5, training is carried out by using the GBRT prediction model and the processed data, and a trained model is obtained; and S6, outputting and verifying the trained prediction model. According to the invention, by constructing the adaptive prediction model, the influence caused by sudden policy regulation and control is weakened, so that the project cost prediction result is more in line with the market law of the building construction cost, and scientific basis and reference value are provided for project early-stage decision making and cost prediction of investment subjects such as enterprises and governments in a complex policy environment period.

An infrared-spectroscopy-based method for monitoring temperature-induced deformation of a component of an intelligent moxibustion robot. The method comprises: collecting an infrared spectrum and a frequency spectrum of each detection position on a target component of a moxibustion robot; on the basis of frequency differences between peaks and troughs in the frequency spectrum, constructing a motion influence confidence factor of each detection position; using a box-counting method to acquire a scale relationship graph of the infrared spectrum of each detection position; on the basis of scale relationship graphs of all the detection positions, determining an overall absorbance difference index of the target component; on the basis of the overall absorbance difference index and the motion influence confidence factor, using a local outlier factor (LOF) outlier detection algorithm to calculate an LOF of each detection position in a thermal data sequence; and on the basis of a thermal alarm threshold value, determining a temperature-induced deformation risk of the target component. The method can eliminate spectral difference variations caused by shadows and reflectance properties, thereby improving the precision of determining high-temperature deformation of components.

The invention provides a personnel trajectory studying and judging method and system based on a local outlier factor detection algorithm, and the method comprises the steps: collecting the trajectory data of an operator, carrying out the data preprocessing of the collected trajectory data, and obtaining a standardized trajectory data sequence; based on the standardized trajectory data sequence, extracting spatial features, time features and context features of the trajectory points; calculating a local outlier factor value of each track point by adopting a local outlier factor algorithm based on the extracted features, and generating an outlier sequence; a sliding window technology is adopted to analyze the outlier sequence, when all local outlier factor values in a window are larger than 1, an abnormal track is judged, and corresponding alarm information is generated for track points judged to be abnormal and pushed in real time; and receiving the alarm information and pushing the alarm information to a manager. According to the invention, through multi-feature fusion and real-time monitoring, accurate identification of abnormal tracks is realized, and the operation safety management level is effectively improved.

The invention relates to the technical field of concrete structure monitoring, in particular to a concrete structure service entity durability key index monitoring and analyzing method. Comprising the following steps: performing multi-source data acquisition on a concrete structure through a sensor network, including temperature, humidity, resistivity, steel bar electrochemical parameters, cracks, porosity and the like; acquired data is subjected to synchronous preprocessing and multivariable empirical mode decomposition, and key evolution factors such as temperature and humidity collaboration and electrochemical activity are extracted. Based on the factors, a space-time graph neural network model is constructed, and multi-dimensional features and evolution laws among nodes of the structure are deeply analyzed. And then, a local outlier factor algorithm is adopted to detect structural abnormal nodes and distribution thereof, and finally, historical and environmental data are combined to perform adaptive classification and classification abnormity, and a comprehensive monitoring analysis report is output. According to the invention, the intelligent, precise and dynamic level of concrete structure key index monitoring is comprehensively improved, and the safety guarantee and operation and maintenance efficiency of the concrete structure are improved.

The invention discloses a power consumer group intelligent identification method, system and device based on a PSO-KMeans algorithm, and a medium, and belongs to the technical field of power big data analysis, and the method comprises the steps: collecting original load data of power consumers, carrying out the preprocessing of the original load data, obtaining standardized load data, extracting multi-dimensional features, and constructing a weighted feature matrix based on an entropy weight method; performing coarse clustering by utilizing spectral clustering to generate an initial clustering center set, taking the initial clustering center set as an initial particle position of an improved adaptive inertia weight PSO algorithm, optimizing a K-Means clustering center, and outputting a global optimal clustering center; and performing clustering by taking the result as a K-Means initial center to obtain a final user group, and identifying abnormal power utilization suspected users in combination with a contour coefficient and a local outlier factor. According to the method, high-quality and high-stability user grouping and accurate anomaly detection are realized, and efficient technical support is provided for power user management and demand side response.

The invention relates to the technical field of electric digital data processing, in particular to a real-time monitoring method and system for sludge solidification stirring equipment, and the method comprises the steps: obtaining multi-dimensional parameter data of the sludge solidification stirring equipment at a plurality of historical time nodes; constructing the parameter data of the same dimension into a parameter sequence in a set time window; acquiring the local fluctuation degree of the parameter data of any time node in the parameter sequence to which the parameter data belong, and the linear correlation degree between the parameter data and other parameter sequences; determining the parameter data greater than a linear correlation degree threshold as strong cooperation parameter data; and respectively calculating first abnormal scores of the parameter data and the strong cooperation parameter data of any time node by using an improved local outlier factor algorithm, averaging the first abnormal scores to obtain a second abnormal score, and responding to the parameter data of which the second abnormal score is greater than a set threshold value as abnormal parameter data. The problem that an existing algorithm is not high in detection precision is solved.

The invention discloses a road slope deformation early warning and monitoring system for geological data analysis. The system comprises a parameter acquisition module, a parameter association mapping module, a feature extraction enhancement module, a model training reasoning module, an early warning threshold judgment module and an early warning response module. The parameter acquisition module acquires data such as slope displacement and the like and transmits the data to the parameter association mapping module, the parameter association mapping module analyzes a parameter coupling relationship and then transmits a feature tensor to the feature extraction enhancement module, and the module disassembles and fuses features and transmits the features to the model training reasoning module. The model training reasoning module utilizes an optimized multi-head attention mechanism and a road slope local outlier factor model to calculate an anomaly degree, the anomaly degree is transmitted to the early warning threshold value judgment module to be matched with an early warning level, and then the early warning response module triggers a corresponding response. And the feature extraction enhancement module and the model training reasoning module comprise a plurality of units which play roles respectively. The system comprehensively captures the deformation characteristics of the slope, and improves the early warning accuracy and reliability.

The invention discloses an information data processing system based on brain wave signals, and particularly relates to the field of medical signal processing and brain science application, and the system comprises a data importing and filtering module which supports the input of an original electroencephalogram file and carries out band-pass filtering on the original electroencephalogram file; the bad track detection module is used for automatically detecting and marking bad tracks based on a local outlier factor algorithm, and comparing local density differences between each channel and surrounding channels; the artifact suppression module is used for correcting non-engraving plate transient artifacts in the electroencephalogram; the data restoration and interpolation module is used for carrying out interpolation on the detected bad track and restoring the overall channel layout; the frequency band and ratio characteristic module is used for calculating energy, relative power and ratio of delta, theta, alpha, SMR, beta and betaH frequency bands; the spectrum-space-time characterization module is used for constructing the features into two-dimensional tensors; and the judgment module is used for outputting the mental state category and the attention score. The mental state can be identified based on the juvenile electroencephalogram.

The invention provides an energy storage equipment detection method and device, and electronic equipment, and relates to the technical field of power system energy storage. The method comprises the following steps: acquiring charging and discharging data of each single battery in the energy storage equipment; constructing a multi-dimensional feature vector based on the charging and discharging data of each single battery; processing the multi-dimensional feature vector to obtain a local outlier factor value of each single battery; and determining whether each single battery is abnormal or not based on the local outlier factor value of each single battery. The method solves the technical problems that an existing energy storage equipment abnormal behavior detection method is insufficient in mass data processing capacity and limited in detection algorithm efficiency.

The invention discloses a power grid operation abnormal area identification method and system, and the method comprises the steps: calculating new energy output data based on meteorological data, constructing a unit combination model through combining a generator set parameter and a power grid structure parameter, carrying out the whole-year time sequence production simulation considering a power flow network frame, and generating power grid operation time sequence data; performing feature extraction on the power grid operation time sequence data by using a convolutional neural network to obtain a deep feature vector, extracting a time-space correlation feature in the deep feature vector, outputting an abnormal probability, and screening an area of which the abnormal probability exceeds a preset threshold value as a candidate abnormal area; for candidate abnormal regions, constructing an electrical association neighborhood for each candidate region, calculating local reachable density and a local outlier factor value based on a deep feature vector, and performing abnormal degree quantitative sorting; and performing electrical logic verification, equipment state association verification and time trend verification on the sorted abnormal regions, and performing risk grade division based on a local outlier factor value and an influence range.

The present application relates to the technical field of mine machinery control, and particularly relates to a mine hoist intelligent control method and system. The method comprises: obtaining vibration acceleration and depth and speed data of a hoisting container; constructing a speed adaptive penalty factor negatively correlated with real-time speed, and performing variational modal decomposition on the vibration signal; constructing a dynamic reference potential energy positively correlated with real-time speed square and depth logarithm; combining envelope amplitude relative over-scale and inverse speed weighted difference mean to calculate an impact risk index; and performing fault determination through local outlier factor analysis. The present application realizes relative quantity monitoring of the whole stroke by introducing kinetic energy theorem and cantilever beam stiffness characteristics, avoids false alarms in deep wells, and significantly improves fault sensitivity of low-speed inspection by using inverse speed weighting.

The application discloses a battery pack abnormal monomer detection method and system based on a local outlier factor algorithm. The method comprises the following steps: sampling and collecting voltage data and current data of each battery monomer in a battery pack by using a sliding window method to obtain a monomer voltage matrix; first voltage features F1 and second voltage features F2 are obtained according to the monomer voltage matrix U; a voltage change consistency feature F3 of each monomer in the sliding window is calculated according to the monomer voltage matrix U and the current data of the battery monomer; the first voltage features F1, the second voltage features F2 and the voltage change consistency feature F3 are integrated into feature points, and the feature points are integrated and mapped into data points; a local outlier factor LOF algorithm is used to perform abnormal detection on the data point set to obtain abnormal data points, and the abnormal data points are abnormal battery monomers in the battery pack. The application can improve the accuracy of abnormal monomers in the battery pack and can judge the type of abnormal conditions.

The present application relates to the technical field of electric energy metering, and particularly relates to an insulation performance evaluation system and method for insulation voltage transformers, comprising an insulation monitoring and sensing optimization module, a central storage unit, a data processing and abnormality analysis module, and a device performance evaluation module; the insulation monitoring and sensing optimization module builds an intelligent sensor network and a data acquisition and processing terminal, and realizes real-time monitoring of the insulation voltage transformer; the data processing and abnormality analysis module extracts key features, and uses a local outlier factor algorithm to perform real-time analysis on the key features, and can timely discover abnormal changes in insulation performance; the device performance evaluation module calculates a comprehensive gain value index to perform detailed performance evaluation on the device with problems, which is helpful for operation and maintenance personnel to understand the device state and formulate reasonable maintenance, replacement or optimization strategies. The present application is used to solve the technical problems of incomplete insulation performance monitoring and insufficient evaluation for insulation voltage transformers.

The present invention provides a machine-learning method for detecting data change points in a dynamic social network. The method comprises: capturing a sequence of graph snapshots of a graph representing the dynamic social network; extracting data features from each graph snapshot; applying a sliding-window statistical analysis on the extracted data features of the sequence of graph snapshots to detect a first set of change points; applying a local outlier factor algorithm on the extracted data features of the sequence of graph snapshots to detect a second set of change points; combining the first and second sets of change points to form an output set of change points for the sequence of graph snapshots. The provided method can effectively handle the high-dimensional complex network data, particularly in dynamic environments, to provide more accurate and comprehensive information.

The invention discloses a bridge stay cable monitoring method and system, and the method comprises the steps: determining the size of a sliding window according to the sampling frequency and sampling time of stay cable state data, and determining a target data flow according to the sliding window; performing abnormal data point detection on the target data stream based on a local outlier factor algorithm and a preset weight factor; and preferentially transmitting the abnormal data points to the data center, adjusting the size of the sliding window according to the number of the abnormal data points, re-determining the target data stream according to the adjusted sliding window, and continuing to detect the abnormal data points. Before the stay cable state data is transmitted to the data center, the abnormal data point is preferentially sent to the data center in time, the data transmission efficiency can be effectively improved, and the response delay of the data center is reduced. According to the number of the abnormal data points and the size of the sliding window, the problem of density imbalance of the abnormal data points in the sliding window is avoided to a certain extent, and the accuracy of abnormal data point detection can be improved.

The invention discloses an energy storage battery inconsistency screening method based on an anomaly detection model, and the method comprises the steps: collecting the SOC time series data of a plurality of battery clusters, constructing the multi-dimensional feature data of the SOC between the battery clusters, quantifying the SOC coordination relation between the plurality of battery clusters from different angles, and more comprehensively reflecting the real state of the consistency between the clusters. The anomaly detection model fusing the isolated forest algorithm and the local anomaly factor algorithm is constructed, and the anomaly detection model is trained and tested by using the standardized multi-dimensional feature data, so that the accuracy of a model output result is improved. And inputting standardized multi-dimensional feature data obtained by processing a to-be-detected battery cluster into the anomaly detection model to obtain an anomaly score of the to-be-detected battery cluster, performing contrast screening on the anomaly score of the to-be-detected battery cluster according to a set SOC consistency state grade between the battery clusters, and generating a visual report for a screening result. Therefore, related personnel can visually know the detection condition of the to-be-detected battery cluster.

The application provides a power system secondary circuit fault diagnosis method and system, and belongs to the technical field of power equipment state monitoring and intelligent diagnosis. The method comprises the following steps: acquiring first characteristic data of a target monitoring point in a secondary circuit of a power distribution device and storing historical characteristic data; performing outlier analysis based on the first characteristic data of the target monitoring point; comparing the first characteristic data of the target monitoring point with a reference set, first performing global anomaly detection by using a robust Z-score method, and then performing local anomaly detection on the first monitoring point by using a local outlier factor algorithm; and based on the historical characteristic data of a second monitoring point, performing trend analysis by using a state space model method to identify an abnormal change trend, so as to realize fault diagnosis of the secondary circuit. The application can quickly identify significant global individual anomalies, effectively detect early weak anomalies, can identify a slow development of a persistent degradation trend, and improves the accuracy and reliability of the secondary circuit fault diagnosis.

A kind of observation data anomaly identification method based on SIP-LOF, comprising the following steps: first, the observation data to be detected is extracted by PLR-SIP method All data points, then two data points of starting and ending are used as segmentation point, the point of maximum y direction distance between two segmentation points is calculated, then the point of maximum y direction distance between adjacent two segmentation points is calculated in turn, obtain multiple segmentation points, and sub-sequence is formed between adjacent two segmentation points;Extract multiple characteristic values of sub-sequence, construct multidimensional space according to all characteristic values, then all characteristic values are mapped in multidimensional space, and the distance between each characteristic value is calculated;After arranging distance value according to size, the k distance of each sample and the first k neighbors are found, then the reachable distance of each sample to k neighbors, local reachable density are calculated, then local outlier factor LOF is calculated, and LOF value is obtained.The present application can quickly and automatically locate abnormal data in mass data.

The application relates to an input data quality evaluation method for photolithography process window analysis, and the method comprises the following steps: S1, obtaining standardized data; S2, obtaining a robust regression residual analysis confidence degree by using a RANSAC regression algorithm; S3, obtaining an isolation tree confidence degree based on an isolation tree anomaly score; S4, obtaining a local outlier factor confidence degree by using a local outlier factor method; S5, comprehensively obtaining an unsupervised confidence degree based on the isolation tree confidence degree and the local outlier factor confidence degree; S6, obtaining a Gaussian confidence degree; S7, comprehensively obtaining a comprehensive confidence degree based on the robust regression residual analysis confidence degree, the unsupervised confidence degree and the Gaussian confidence degree, and obtaining an input data quality evaluation result for photolithography process window analysis. Compared with the prior art, the application has the advantages of improving the input data quality of photolithography process window analysis.

The application discloses a code pre-configuration method fusing calculation power constraint and outlier analysis, comprising the following steps: performing calculation power signature marking on a function function set according to a hardware fingerprint at a management end to obtain a function set recording maximum instantaneous calculation power occupation; performing combination explosion pruning under a target scene calculation power threshold to generate a candidate sequence; inputting the candidate sequence into a space-time joint distribution model based on historical normal output to obtain a predicted output; calculating a local outlier factor of the predicted output and an expected output to form an outlier factor set; obtaining a reordered sequence according to a weighted ranking of the outlier factor and calculation power occupation; and finally selecting a sequence with the minimum outlier factor and lower calculation power occupation than a threshold to realize code pre-configuration. The application can automatically select a function function number sequence most suitable for a target scene before deployment, reduces deployment risks, and improves the predictability and reliability of the whole system.

The invention relates to the technical field of mining machinery control, in particular to an intelligent control method and system for a mine hoist. The method comprises the following steps: acquiring vibration acceleration, depth and speed data of a hoisting container; constructing a speed adaptive penalty factor in negative correlation with the real-time speed, and performing variational mode decomposition on the vibration signal; constructing dynamic reference potential energy in positive correlation with the real-time speed square and the depth logarithm; calculating an impact risk index in combination with a difference mean value of the envelope amplitude relative standard exceeding degree and inverse speed weighting; and carrying out fault judgment through local outlier factor analysis. By introducing the kinetic energy theorem and the rigidity characteristic of the cantilever beam, relative quantity monitoring of the whole stroke is achieved, false alarm of the deep well is avoided, and the fault sensitivity of low-speed inspection is remarkably improved through inverse speed weighting.

The invention discloses an LOF-based machine tool machining abnormality diagnosis method, system and equipment and a medium, and relates to the technical field of machine tool machining monitoring. The method comprises the steps that position information, program instruction information and vibration signals of all shafts of a machine tool are collected; constructing and forming a mapping unit sequence; constructing a local outlier factor algorithm to construct a density distribution model; calculating a local outlier factor of the to-be-measured data point; and selecting a to-be-tested data point based on the local outlier factor, and obtaining corresponding position information and program instruction information. The method does not need to manually set a fixed threshold value, does not need to depend on a fault sample, achieves the time-space fusion of multi-source data and the precise recognition of the machining abnormality, can precisely position the machining position and program instruction corresponding to the abnormality, solves the problems that an existing diagnosis method is poor in adaptability, is difficult to land, and is difficult to trace the abnormality, guarantees the precision machining quality, and improves the precision machining efficiency. And the machining requirements of the high-end manufacturing field are met.

The invention discloses an evolutionary multitask-based sonar data noise robust anomaly detection method, which comprises the following steps of: 1, preprocessing acquired sonar data, and constructing a noise-containing data set in a label overturning manner; 2, screening out noise-free samples by using a local outlier factor algorithm; 3, the number of iterations is set, and populations are initialized on the noise-containing data and the noise-free data respectively; 4, taking a true positive rate TPR and a false positive rate FPR as objective functions; 5, performing iterative optimization on the two populations by adopting an evolutionary multi-task algorithm; and 6, taking an optimal noise individual in the noise sonar population as a sonar noise robust classification model to realize classification of target sonar data. According to the method, the abnormal sample can be identified in a noise scene, and a reliable classification result is obtained, so that the robustness of sonar data anomaly detection can be improved.

This invention discloses a non-contact laser-ultrasound method and system for detecting internal defects in high-entropy alloys. The method first pre-treats the surface of the high-entropy alloy sample, then adaptively adjusts the laser pulse parameters according to the thermal conductivity characteristics of the high-entropy alloy to excite internal ultrasonic waves via thermoelastic effects. After non-contact signal reception using a laser interferometer, signal pre-processing is performed using a wavelet packet transform and principal component analysis fusion algorithm. Defect features are then extracted using a local outlier factor and support vector machine fusion algorithm to distinguish between defects and lattice distortion interference signals. Defect parameters are analyzed using an improved ultrasonic propagation model, combining mechanical parameters and lattice distortion coefficients. Finally, cross-validation and error correction ensure detection accuracy. The system comprises seven modules, including sample pre-processing, laser excitation, and ultrasonic reception, with a main control module coordinating the collaborative work of each module. This invention achieves non-contact detection, improving detection adaptability and accuracy, and enabling automated batch online detection.