56results about How to "Eliminate noise interference" patented technology

The invention relates to the technical field of battery health state analysis, and discloses a battery health state comprehensive evaluation method, system and device and a storage medium, and the method comprises the steps: carrying out the preprocessing of data collected by a battery management system, and obtaining a standardized data sequence; calculating a first battery characteristic parameter according to the standardized data sequence; performing covariance matrix adaptive Kalman filtering processing on the first battery characteristic parameter to obtain a second battery characteristic parameter; constructing a capacity-internal resistance change rate index and a temperature-efficiency correlation index based on the second battery characteristic parameters to obtain a dual-threshold evaluation matrix; inputting the dual-threshold evaluation matrix into a deep reinforcement learning model, and performing forward propagation calculation and iterative training through an Actor network and a Critic network to obtain a feature fusion weight coefficient; and carrying out weighted calculation on the dual-threshold evaluation matrix to generate a battery health state value and a residual life prediction value, and realizing multi-dimensional evaluation of the battery health state.

The invention relates to the technical field of data processing, and discloses a BIM-assisted steel structure construction safety management early warning method, device and equipment. The method comprises the following steps: carrying out multi-source heterogeneous data acquisition and space-time alignment processing on a steel structure construction site to obtain an engineering data stream; carrying out dynamic noise reduction and feature extraction on the engineering data flow to obtain a standardized multi-modal feature vector, and carrying out time sequence correlation analysis and mode mining to obtain a potential risk index set; performing multi-level threshold calculation and context sensitivity evaluation on the potential risk index set to obtain an adaptive early warning trigger matrix; performing multi-dimensional cross validation and causal chain analysis on the self-adaptive early warning trigger matrix to obtain a graded and classified risk assessment report; and carrying out scene analysis and response scheme generation on the graded and classified risk assessment report to obtain an executable multi-step emergency plan. According to the invention, the efficiency and accuracy of BIM-assisted steel structure construction safety management early warning are improved.

The invention relates to the technical field of image processing, and discloses an industrial article appearance detection method and system based on machine vision. The method comprises the steps that a workpiece image is collected, and a standardized image is obtained through Gaussian filtering, histogram equalization and Otsu algorithm preprocessing; feature vectors are extracted through edge detection, Hough transform and a gray-level co-occurrence matrix; training a defect identification model by adopting a CNN of an attention mechanism; inputting an image for real-time detection; fusing the detection data and the environmental parameters by using a D-S theory; and establishing a database based on a distributed architecture to generate a quality report. According to the method, the detection accuracy is improved through multi-source information fusion, and efficient management and analysis of mass detection data are realized by adopting a distributed architecture.

The invention discloses an electrical automatic control method and system. The method comprises the following steps: acquiring temperature data and load current data in an electrical cabinet; denoising the temperature data by adopting a wavelet transform algorithm; filtering the load current data by adopting a Kalman filtering algorithm; establishing a temperature rise compensation model based on a long short-term memory (LSTM) neural network, wherein the output of the temperature rise compensation model is a temperature rise predicted value in the electrical cabinet; setting a multi-objective function of temperature rise control in the electrical cabinet; solving by adopting a multi-target ant colony algorithm to obtain an optimal control strategy of the cooling fan; and starting, stopping and speed adjustment of the cooling fan are controlled according to the optimal control strategy of the cooling fan. Aiming at frequent starting and stopping of a cooling fan of an electrical cabinet in the prior art, a temperature rise compensation model is established based on a long short-term memory (LSTM) neural network, a multi-objective optimization function including temperature rise deviation and starting and stopping times of the cooling fan is set, an improved multi-objective ant colony algorithm is adopted for solving, and starting and stopping of the cooling fan are reduced.

The invention relates to the technical field of infrared image recognition, and discloses a hot target extraction method and system based on infrared image recognition, and the system comprises a multi-sensor collection module, a data dimension reduction module, a data verification module, a segmentation optimization module, and a dynamic feedback module. Multi-modal data dimension reduction processing is achieved by collecting infrared images, visible light images and environment dynamic parameters and combining Laplacian feature mapping, and a target area is extracted through geometric verification and topological analysis. An energy optimization model and a distribution alignment method are adopted to complete accurate segmentation of a target area, and meanwhile, segmentation model parameters are adjusted in real time based on environment dynamic parameters, so that the system stability is ensured. According to the method, high-precision and robust extraction of the thermal target is realized in a complex dynamic environment, the problems of fuzzy target boundary, noise interference and insufficient dynamic environment adaptability in the prior art are effectively solved, and the method is suitable for the fields of military reconnaissance, fireproof monitoring, electric power inspection and the like.

The invention provides a multi-data fusion fire-fighting early warning method and platform, and relates to the technical field of fire fighting, and the method comprises the steps: collecting a multi-dimensional environment data set of a target region; performing noise filtering on the multi-dimensional environment data set to obtain a multi-dimensional environment de-noised data set; comprehensively analyzing the plurality of data sources based on the multi-dimensional environment de-noised data set to generate fire-fighting comprehensive data, and fusing the multi-dimensional environment de-noised data set to obtain a multi-dimensional environment fused data set; building a fire risk model according to the fire-fighting comprehensive data, synchronizing the multi-dimensional environment fusion data set to the fire risk model for evaluation according to a plurality of data sources, and obtaining a fire risk level of the target area; and generating a fire-fighting early warning instruction based on the fire risk level, and sending the instruction to the remote control terminal. According to the method, the technical problem that a traditional fire early warning method is single in monitoring means and is easily interfered by environmental factors to cause false alarm or missing alarm is solved, and the accuracy and response speed of fire early warning are remarkably improved.

The invention relates to a double-end traveling wave distance measurement method and device suitable for a power distribution network and a storage medium, and the method comprises the steps: firstly, carrying out the preprocessing of a fault phase current traveling wave through the normalization and rough characteristic frequency band zero-phase band-pass filtering of a double-end current traveling wave; using spline wavelet transform to calibrate a proper modulus maximum point as initial estimation of a wave head starting point; further estimating a wave head on the double-end time domain original traveling wave waveform by using a threshold method, and obtaining an accurate wave head starting point through double-end amplitude interpolation correction; and finally, calculating the position of a fault point according to a double-end traveling wave distance measurement principle. According to the method, a fault phase current waveform is preprocessed into a current traveling wave in a rough characteristic frequency band through a zero-phase filtering means, interference outside the characteristic frequency band is eliminated, a wave head break point serves as a wave head starting point, the break point is estimated step by step through a wavelet modulus maximum value and a signal amplitude threshold value method, and the fault phase current waveform is obtained. And more accurate approximation of the break point is obtained through double-end amplitude interpolation, so that the range finding precision exceeding the sampling rate limit is achieved.

The invention relates to the technical field of medical information extraction, in particular to an image feature selection method for a schizophrenia medical record Spark model. The method comprises the following steps: acquiring brain magnetic resonance image data and resting state functional magnetic resonance imaging data of a plurality of subjects; performing multi-scale layered segmentation on the brain image of each subject by using a preset layered brain region template for the brain magnetic resonance image data to obtain brain region positioning data including a local level, a regional level and a global level; and performing adaptive hierarchical noise reduction processing on the resting state functional magnetic resonance imaging data in a Spark distributed environment to obtain brain function time sequence data after noise reduction. According to the method, the brain magnetic resonance image data is accurately divided into a local level, a regional level and a global level by using the preset layered brain region template, so that local structure details and global network characteristics of the brain region are considered, and effective integration of multi-scale information is realized.

The invention discloses an urban emergency refuge congestion risk assessment method and system based on image enhancement. The method comprises image acquisition, image enhancement and risk assessment. The invention belongs to the field of image processing, and particularly relates to an urban emergency refuge congestion risk assessment method and system based on image enhancement. According to the scheme, global-local dynamic weights are introduced, and texture information of a dense area and the overall site layout are considered; the fusion of real-time and historical data features is optimized through feature consistency constraint; constructing loss based on the dynamic region weight, and optimizing the urban emergency refuge image; a dynamic area sensing weight is introduced, a crowd dense distribution center is focused, and invalid features of an open area are inhibited; the density perception weight is fused with the multi-scale expansion convolution features, and fine dense region boundary segmentation is achieved; carrying out risk assessment on the real-time urban emergency refuge image based on the dynamic region weight and the historical correlation; and the congestion risk assessment effect is improved.

The invention relates to a network abnormal behavior detection method based on dynamic data balance and generation, and the method comprises the steps: obtaining original network flow data, inputting the original network flow data into a dynamic abnormal data balance model, and obtaining an enhanced data set; the method comprises the steps of marking original network flow data based on an abnormal data balance model, performing selective undersampling on marked data, adjusting a ratio of normal data to abnormal data, obtaining a balance data set, generating new abnormal data according to the balance data set, and merging the new abnormal data with the balance data set to obtain an enhanced data set; and inputting the enhanced data set into a TimeDiT noise reduction model for denoising to obtain a target data set after noise reduction, inputting the target data set into an LSTM-Transform weighted training model for network abnormal behavior detection, and weighting abnormal data in the target data set in combination with a weighted loss function to obtain a detection result. According to the invention, the network traffic abnormal behavior detection efficiency and accuracy can be improved.

The invention relates to a monitoring ball signal extension system and method based on a multi-mode networking module, and belongs to the technical field of power transmission and transformation construction safety monitoring. The system comprises a signal detection module, a networking mode selection module, a signal processing module, a signal transmission module, a monitoring system integration module and an external signal enhancement device. The signal detection module monitors signal intensity and interference conditions; the networking mode selection module automatically selects a networking mode in sequence; the signal processing module optimizes the video signal; the transmission module transmits signals; the monitoring system integration module interacts with the monitoring platform; the external signal enhancement device comprises an antenna gainer and a signal repeater. The system can realize automatic networking, adapts to a complex environment, ensures stable transmission of signals, improves imaging quality, enhances monitoring flexibility, optimizes data processing, is suitable for safety monitoring of a power transmission and transformation construction site, and ensures smooth engineering.

The invention belongs to the technical field of hydroelectric generating set vibration prediction, and particularly relates to a hydroelectric generating set mechanical vibration trend prediction method and device, equipment and a medium, and the method comprises the steps: processing a vibration signal of a hydroelectric generating set through employing a variational mode decomposition algorithm; training the deep belief network by using the training set to obtain a trained deep belief network model; using a sparrow search algorithm to optimize parameters of the deep belief network model to obtain a vibration trend prediction model; and collecting a vibration signal of the hydroelectric generating set, processing the vibration signal by using a variational mode decomposition algorithm, inputting the processed vibration signal into the vibration trend prediction model to obtain a vibration prediction value, and taking the obtained vibration prediction value as an input of next prediction to realize multi-step prediction of the vibration trend of the hydroelectric generating set. By means of multi-step prediction, operation and maintenance personnel can know the vibration trend of the hydroelectric generating set in a period of time in the future in advance, potential fault hidden dangers can be found in time, and the operation safety and stability of the hydroelectric generating set are improved.

The application discloses a comprehensive linkage alarm system for fire hazards and structure instability of timber buildings, and relates to the technical field of timber safety monitoring, which comprises the following modules: a multi-dimensional heterogeneous signal acquisition module for collecting data and constructing real-time state monitoring data sets; a structure spectrum benchmark modeling module for constructing a thermal-rigidity attenuation theoretical model; a thermal-frequency correlation analysis module for generating a thermal-frequency deviation degree index; a structure state determination module for identifying timber pyrolysis states and structure instability risks; a cascade response module for executing corresponding fixed-point spraying control, evacuation path planning and alarm output; and a self-adaptive feedback correction module for warehousing data and correcting thermal aging parameters. The application establishes a correlation model of thermal and rigidity by fusing thermal imaging and vibration data, effectively eliminates environmental wind load noise, realizes accurate identification of internal hazards of timber buildings, and improves the accuracy of building monitoring and the efficiency of emergency response.

The invention discloses a remote sensing image step-by-step detection network based on Transform region suggestion. The network comprises a first input layer, a label embedding layer, a feature fusion layer, a Transform encoder, a Transform decoder and a full connection layer, a second input layer, a variational auto-encoder, an embedded layer and a single attention layer; the first input layer is connected with the label embedding layer, the label embedding layer is connected with the feature fusion layer, and the feature fusion layer is connected with the Transform encoder. According to an existing remote sensing image target detection technology, a large-size image is processed by reducing an input remote sensing image, or the remote sensing image is cut into small image slices, and target detection in the remote sensing image is carried out by setting a re-collection rate; therefore, the technical problems of long detection time, more resource utilization, small target information loss and high omission ratio are solved.

The application belongs to the technical field of power systems and their automation, and specifically discloses a method for stabilizing electromagnetic oscillation of a photovoltaic grid-connected system considering dynamic characteristics of a converter. According to the application, a target current absorbed by a target inverter from a DC side in the photovoltaic grid-connected system is calculated based on a target signal processing device; a target DC bus voltage value is calculated based on a DC bus voltage equation; a difference between a DC bus voltage reference value and the target DC bus voltage value is calculated, and the action of the target converter is controlled according to the voltage difference. In the above manner, the target current is calculated by taking the filtered DC bus voltage measurement value as the divisor and the power as the dividend, so that the target current no longer sharply increases with the drop of the actual voltage under high-frequency dynamics, and the oscillation between the circuit and the constant power load is suppressed by introducing positive damping in the control loop, thereby significantly improving the stability of the photovoltaic grid-connected system and reducing the cost of stabilizing electromagnetic oscillation.

The invention relates to the technical field of acoustic emission signal processing, in particular to an effective signal rapid extraction method in pipeline circumferential weld circumferential crack acoustic emission. The method comprises the following steps: step 1, collecting a circumferential crack acoustic emission signal of the circumferential weld of the pipeline, setting a threshold value, and extracting a peak-to-peak signal; 2, processing the signal, and based on VMD, obtaining a stationary eigenmode function component containing a potential acoustic emission multi-mode component after optimizing the number of decomposition layers through center frequency selection; 3, initially determining a frequency value of a potential multi-mode component through spectral analysis, and taking the frequency value as a driving force parameter set; and 4, sending the signal to the improved Duffing oscillator system after parameter setting, solving an equation by using a four-order Runge-Kutta method, drawing a phase plane trajectory diagram, and determining whether the signal contains the above-mentioned power parameter set frequency according to whether the phase diagram transitions from critical chaos to a large-scale periodic state so as to eliminate false weak signals and extract effective signals.

The present invention discloses a method and system for setting thresholds under different working conditions based on the fusion of MVMD noise reduction, KNN classification and adaptive threshold. Obtain n groups of vibration signal data of the equipment at different times; perform MVMD decomposition to obtain multiple basic modal components IMF; calculate the correlation coefficient between each IMF and the original signal; add the basic modal components IMF whose correlation coefficient is greater than the correlation coefficient threshold to obtain a reconstructed signal; calculate the effective value and kurtosis of the velocity of each group of reconstructed signals; set the KNN classification parameters, select the parameter k with the highest accuracy; calculate the thresholds under different working conditions; determine the final threshold according to the standard value. The KNN classification method is used to separate the characteristic values ​​of different operating conditions of the equipment, and then the adaptive threshold method is used to calculate the thresholds of different working conditions. Different from the defect of the traditional method that cannot set the threshold according to the working condition and cause a pre-alarm.

The invention relates to the field of intelligent power grids and intelligent fusion terminals, in particular to a transformer health monitoring system based on an intelligent fusion terminal TTU, and the system comprises a waveform data collection module which is used for collecting the waveform data of a transformer; the environment data acquisition module is used for acquiring operation environment data of the transformer; the denoising and reconstruction module is used for carrying out denoising elimination and waveform reconstruction on the waveform data of the transformer; the feature extraction module is used for performing feature extraction, enhancement and fusion on the preprocessed waveform data and the operation environment data of the transformer to obtain a fusion feature vector; the state analysis module is used for inputting the fusion feature vector into a mixed regression model for health state prediction to obtain a transformer health index prediction value; and the TTU decision module is used for matching the control decision scheme and controlling the working state of the transformer. According to the invention, the accuracy and reliability of transformer health prediction and the reliability of overall operation of a power grid can be improved.

The present invention discloses a fiber optic gyroscope fault diagnosis method based on MiniRocket network model, comprising the following steps: calibrating multiple fiber optic gyroscopes of the same model using a standard angular velocity source, obtaining the output signal of each fiber optic gyroscope and the corresponding temperature stress signal; marking the output signal of each fiber optic gyroscope, and performing noise reduction processing through the EEMD algorithm to construct a data set; training the MiniRocket network model, and tuning the model hyperparameters in combination with the sparrow optimization algorithm; and performing fiber optic gyroscope fault diagnosis of the same model based on the optimized MiniRocket network model. The method can effectively improve the accuracy and reliability of fiber optic gyroscope fault diagnosis through precise data acquisition, effective preprocessing means, efficient feature extraction and model optimization, and real-time fault diagnosis, especially being able to accurately distinguish intermittent faults from permanent faults, thereby achieving better maintenance of the inertial navigation system.

The invention discloses a slope-near foundation bearing capacity evaluation method and device and electronic equipment, and relates to the field of civil engineering. The method comprises the following steps: acquiring images of a near-slope foundation at a plurality of angles; according to the picture of the adjacent slope foundation, a slope surface model corresponding to the adjacent slope foundation is constructed, and the slope surface model is composed of a plurality of soil strip units; determining the width, the height and the slope inclination angle of each soil strip unit; according to the width, the height and the slope inclination angle, the vertical normal stress of each soil strip unit on a vertical interface and the top surface normal stress of each soil strip unit on the top surface are determined, the top surface is the upper top surface of the soil strip unit, and the vertical interface is a surface perpendicular to the lower top surface; and according to the vertical normal stress and the top surface normal stress, the bearing capacity of the near-slope foundation is obtained. According to the technical scheme, the slope model is constructed, the gradient characteristics of the adjacent slope foundation are considered, and the bearing capacity of the adjacent slope foundation can be evaluated more accurately.

This invention discloses an LMD processing device, method, equipment, and storage medium for power transient signal analysis, comprising: a signal acquisition module, a preprocessing module, an LMD core processing module, a time-frequency analysis module, and a result output and storage module. The preprocessing module is connected to the signal acquisition module, the LMD core processing module is connected to the preprocessing module, the time-frequency analysis module is connected to the LMD core processing module, and the result output and storage module is connected to the time-frequency analysis module. The signal acquisition module captures digital signals of power grid transient signals converted from line signals. The preprocessing module preprocesses the analog signals to obtain a preprocessed signal. The LMD core processing module decomposes the local mean of the preprocessed signal into a PF component set. The time-frequency analysis module performs time-frequency analysis on the PF components of the PF component set to obtain a time-frequency feature matrix. The result output and storage module stores and displays the time-frequency feature matrix. This invention can reduce the impact of device output boundary distortion caused by endpoint effects and improve positioning accuracy.

The application discloses a microseismic event positioning method based on a microseismic signal, and is characterized by comprising the following steps: S1, collecting the microseismic signal by using a microseismic collection device; the microseismic collection device comprises a plurality of three-component microseismic detectors which are arranged at equal intervals in a long drill hole and are used for collecting the microseismic signal generated by a microseismic event in a deep coal seam; the P wave and the S wave of the microseismic signal are collected simultaneously by using the microseismic collection device, and the time difference between the P wave and the S wave is used to directly provide the absolute distance from each receiver to the seismic source, so that the positioning process is converted into a spherical surface intersection problem with each receiver as a spherical center and with a calculated distance as a radius. The method not only eliminates systematic errors caused by the uncertainty of the time of earthquake occurrence, but also provides a large amount of redundant data for cross verification, so that the accuracy and reliability of the positioning result are greatly improved. The accuracy and comprehensiveness of signal collection are improved.

This invention discloses a method and apparatus for accurate bridge damage identification and location based on multi-source data fusion. The method includes: performing spatiotemporal registration and data fusion on the time-series data of the bridge's global planar deformation obtained by ground-based synthetic aperture radar and the time-series data of the bridge's point-like three-dimensional deformation obtained by the BeiDou satellite navigation system to obtain a fused deformation field of the entire bridge area; calculating multiple damage characteristic indicators reflecting different deformation physical mechanisms of the bridge based on the fused deformation field, and determining the corresponding preliminary damage level based on the damage judgment threshold of each damage characteristic indicator; using the preliminary damage levels of each damage characteristic indicator as independent evidence sources, performing evidence fusion to resolve indicator conflicts, and determining the bridge damage location and damage level based on the fusion result. This method can effectively improve the accuracy and reliability of bridge damage identification and location, providing a scientific basis for bridge operation and maintenance decisions.

The invention discloses an energy storage energy management method, device and system and a medium, the energy storage energy management system comprises a local energy management system, a local energy management system and a local energy system, the local energy management system obtains operation data of each device in the local energy system, the model prediction precision of a locally deployed energy prediction model is improved; based on the data type of the operation fusion data, performing data fusion processing on all the operation data to obtain the operation fusion data; calling an energy prediction model to perform energy prediction on the operation fusion data to obtain a strategy prediction result; when the model prediction precision reaches the target precision, obtaining a corresponding local pre-stored strategy according to the operation fusion data, and adjusting the local pre-stored strategy according to a strategy prediction result to obtain a target control strategy; according to the invention, the accuracy of energy management and control can be improved, so that the reliability of an energy storage energy management system is improved.

The application relates to the technical field of artificial intelligence, and discloses a speech synthesis method and device, computer equipment and a storage medium, which comprise the following steps: obtaining reference speech data and speech synthesis text; performing emotion extraction on the reference speech data to obtain reference emotion embedding; fusing the reference speech data, the speech synthesis text and preset noise features to obtain original fusion features; determining a dynamic emotion injection coefficient based on a preset denoising time step parameter; performing denoising processing on the original fusion features based on the dynamic emotion injection coefficient and the reference emotion embedding to obtain target synthesis spectral features; and performing audio conversion based on the target synthesis spectral features to obtain target speech data. The application can be applied to the speech synthesis scene of financial technology and medical health, and improves the emotion expression accuracy of speech synthesis.

This disclosure provides an image detection method, apparatus, electronic device, and storage medium. The image detection method includes: acquiring a multiplex immunofluorescence image to be tested, wherein the multiplex immunofluorescence image is imaging data after multiplex immunofluorescence staining of a tissue sample to be tested; performing tissue segmentation based on the multiplex immunofluorescence image to obtain a tissue region image; and performing image sharpness detection on the tissue region image to obtain a detection result. The image detection method provided by this application can effectively eliminate background noise interference in multiplex immunofluorescence images caused by staining at different locations in the image, greatly reducing the proportion of background in the input image for subsequent image detection, and improving the accuracy and reliability of the image detection results.

The invention discloses a data processing method for an AR intelligent camera device, and the method specifically comprises the steps: obtaining original image data shot by the AR intelligent camera device in real time, and carrying out the processing of the original image data through employing a self-adaptive illumination compensation algorithm, and obtaining first image data; performing denoising processing on the first image data to obtain second image data; whether any athlete in the second image data is shielded or not is judged, if yes, complementation is carried out according to the historical track of the athlete, and third image data is formed; marking illegal behaviors in the third image data to form fourth image data; and putting the fourth image data into an AR information display template for fusion processing to generate AR display image data. According to the invention, the ornamental value and interactivity of sports event live broadcast can be effectively improved, high-quality and personalized AR enhanced experience is provided for audiences, and the satisfaction degree and participation degree of users are greatly improved.

This invention discloses an algorithm for removing high-power noise interference based on vortex waves, belonging to the field of signal processing technology. In this invention, the transmitted signal is a vortex wave signal. At the receiving end, an antenna array (number of array elements N≥2) receives the signal containing high-power noise interference. The received mixed signal is then subjected to vortex wave-based noise reduction processing. The vortex wave noise reduction principle is as follows: The average amplitude or the average of the maximum and minimum amplitude values ​​and the average phase of the electric field of N echo signals are taken. Then, the average amplitude and phase values ​​are combined to form a noise reduction electric field. Alternatively, one echo signal from the N echo signals can be arbitrarily selected, and the noise reduction electric field is subtracted from that echo signal. Compared with existing plane wave-based signal processing algorithms, this invention can extract vortex wave signals under low signal-to-noise ratio conditions, which is beneficial for improving the anti-interference capability in the field of signal processing and has significant practical application value.

The invention discloses a vehicle mobile phone volume change track detection method, device and equipment and a storage medium. According to the method, a Python automation script is used for controlling terminal equipment to execute a sound detection process, a target sound source is automatically triggered, a volume gear is adjusted, a sound scene is switched, and an operation environment is provided for sound data collection; according to an operation environment, a browser sound collection interface is synchronously called to capture physical parameters of loudspeaker sound of terminal equipment in real time, original collection data are obtained, the original collection data are calibrated and filtered, standardized sound data are generated, and the standardized sound data are displayed in real time through a visual interface. And after the detection is completed, the full-amount detection data is automatically packaged and stored according to the preset format, so that the full-automatic operation of the volume change track detection of the vehicle-mounted terminal and the mobile phone can be realized, the detection efficiency is remarkably improved, the subjective error of manual operation is eliminated, the environmental noise interference is effectively overcome, and the objectivity and accuracy of the detection data are ensured.

The application relates to an industrial defect image scoring method and system based on DINOv3 and label reconstruction, which comprises the following steps: S1, data acquisition and label reconstruction: acquiring an industrial defect image and one or more original scoring labels corresponding to the industrial defect image; the original scoring label has a preset continuous scoring interval, and a preset qualified or defective demarcation quantile point exists in the interval; all original scoring labels representing qualification and having a scoring value lower than the demarcation quantile point are uniformly truncated to a preset truncation target value. By adopting the technical scheme, the original scoring labels having a scoring value lower than the demarcation quantile point are uniformly truncated to the preset truncation target value by setting the preset demarcation quantile point, the truncated average of the multi-scoring samples is taken as a supervision label value, the labeling noise of the low-scoring samples is effectively reduced, the multi-person labeling discrepancy is alleviated, and the stability of the supervision signal is improved.