32 results about "Morphological filter" patented technology

The invention discloses an ultra-high voltage transmission line fault analysis system based on multi-dimensional data, and belongs to the technical field of big data analysis. The method is used for solving the technical problem that the robustness of a system is poor when an existing single-algorithm technical scheme is implemented. Through multi-source data synchronous acquisition and improved EEMD denoising, noise interference of an extra-high voltage line in a complex electromagnetic environment is suppressed; signal characteristics are dynamically tracked through adaptive Kalman filtering, space-time alignment and standardization processing are carried out, dimensional difference and time delay are eliminated, improved wavelet packet transformation, a deep belief network and adaptive morphological filtering are carried out in parallel, multi-dimensional characteristics are dynamically weighted and fused through an attention mechanism, a fault characteristic vector is constructed, and fault diagnosis is carried out. According to the method, efficient extraction and optimization of fault features are achieved, accurate reasoning of fault types and positioning can be achieved through the improved fuzzy Bayesian network, the particle swarm optimization algorithm adaptively updates parameters based on real-time errors, and the adaptive robustness of a complex power grid environment can be effectively improved.

The present application belongs to the technical field of image recognition, and particularly relates to a liquid crystal screen defect detection method based on image analysis. The method comprises: acquiring a liquid crystal screen surface image frequency domain signal and wavelet packet decomposition, and extracting a low frequency and medium-high frequency coefficient matrix; performing convolution on the low frequency coefficient matrix with an adaptive morphological filter kernel, fitting to generate a uniform illumination reference matrix; after weighted correction and denoising of the medium-high frequency coefficient matrix, inverse wavelet packet transformation is performed on the medium-high frequency coefficient matrix and the uniform illumination reference matrix to generate a reconstructed difference image; the local pixel gradient direction vector and the gray level co-occurrence matrix contrast feature of the reconstructed difference image are input into an isolation forest classifier to output a defect position and category. The present scheme shifts illumination separation to the frequency domain, avoids damage to defect pixels by spatial domain filtering, eliminates non-uniform illumination interference, retains the edge gradient and texture structure of small defects, and solves the problem of missing detection of small dark spots under a gradual illumination background.

The invention provides an additive manufacturing powder laying defect recognition early warning method and system, and belongs to the technical field of selective laser melting. According to a proposed adaptive threshold segmentation algorithm fusing adjacent pixel distribution, a segmentation threshold boundary is dynamically adjusted according to statistical distribution characteristics of local pixels of an image, so that accurate positioning and effective extraction of defect features are realized, and the capture and recognition capability of weak defects is improved; specific structural element operation is executed through a morphological filtering technology to optimize the binarized image, so that isolated noise points are effectively eliminated, broken defect areas are connected and enhanced, the completeness and continuity of defect features are improved, and the reliability of subsequent quantitative analysis is improved; a multi-dimensional quantitative index system based on the area, the equivalent diameter and the like of the defect boundary is constructed, and comprehensive evaluation and judgment are carried out in combination with a process capability index and a continuous triggering mechanism, so that dynamic monitoring and timely early warning of overall and local defect states of the workpiece are realized.

The invention relates to a method for measuring the reactivity of pozzolanic materials and correlating this reactivity with strength development in composite cement. The method comprises dissolving pozzolanic materials in an alkaline solution and preparing a 1.0–5.0 M Sodium Hydroxide (NaOH) solution. This solution, containing the dissolved materials, is placed on a flow cell, configured to accommodate particles up to 100 µm. The flow cell is heated between 20 °C and 70 °C, and optical images of the particles are collected using an optical microscope. Particle count and area are measured by image processing with spatial and morphological filters to identify particles in the solution over time. The change in particle count or area is computed, and the normalized count represents dissolution plotted over time, describing the dissolution rate. This particle dissolution behaviour is correlated with compressive strength data obtained from samples tested within 1–28 days.

The invention relates to a variable-speed bearing fault feature extraction method and device, relates to the technical field of bearing fault diagnosis, and aims to solve the problem that the variable-speed bearing fault feature extraction precision is not high. The method comprises the steps that a particle swarm optimization algorithm is introduced into a multi-scale adaptive morphological filtering operator, key parameters of the adaptive morphological filtering operator are obtained, filtering signals of all scales are obtained through calculation according to the multi-scale adaptive morphological filtering operator and the key parameters, and the filtering signals of all the scales are added to obtain a primary filtering signal; introducing a particle swarm optimization algorithm into an adaptive stochastic resonance algorithm to obtain a system cooperative adjustment parameter of the adaptive stochastic resonance algorithm when the signal-to-noise ratio is maximum, and inputting the primary filtering signal into the adaptive stochastic resonance algorithm to obtain a secondary filtering signal; and calculating the secondary filtering signal by using an angular domain resampling algorithm to obtain an order spectrogram containing bearing fault features. The method has the effect of improving the fault feature extraction precision of the variable-speed bearing.

The application discloses a kind of wind generator bearing fault diagnosis method and system, it is related to wind power generation equipment fault diagnosis technical field.The method includes with specific sensor acquisition bearing vibration data and pre-processing;It is denoised by enhanced combination differential morphological filter ECGMF;Intrinsic time scale decomposition ITD algorithm is used to decompose signal after denoising, extract component instantaneous amplitude and spectrum analysis obtains fault characteristic;Deep support vector machine DSVM model is constructed in combination with support vector machine SVM and neural network;Characteristic vector is input into deep support vector machine DSVM model training and judging fault output result.The system contains vibration data acquisition, enhanced combination differential morphological filtering, intrinsic time scale decomposition, deep support vector machine and diagnostic result output module, corresponding to realize the function of each step above, through each link cooperation, effectively extract fault characteristic and accurately diagnose, can promote the accuracy and efficiency of wind generator bearing fault diagnosis.

The invention relates to the technical field of image processing and computer vision, and discloses a cross-country road image segmentation and target integrated intelligent detection method comprising the following steps: S1, image input and gray level conversion; s2, carrying out Vibe background modeling and foreground extraction; s3, morphological filtering and denoising are carried out; s4, connected domain marking is carried out through an improved one-time scanning method; s5, optimizing a bounding box; s6, outputting a result; according to the method, the Vibe algorithm and morphological filtering are combined, dynamic noise such as vegetation shaking and light and shadow changes is effectively restrained, the accuracy and the anti-jamming capability of foreground segmentation are remarkably improved, the improved one-time scanning method is adopted for connected domain detection, the problem of target breakage is solved while the real-time processing efficiency is guaranteed, and the method is suitable for large-scale popularization and application. Through a bounding box optimization strategy, broken targets can be combined to ensure integrity, positions can be accurately restored, redundant boxes can be removed, and the method effectively aims at the complex environment of cross-country roads.

The present invention provides a voice recognition method, which includes the steps of: receiving a plurality of voice signals; performing a time-domain to frequency-domain conversion operation on the plurality of voice signals to generate a plurality of frequency-domain signals; using a morphological filter to perform filtering operations on the plurality of frequency-domain signals to generate a plurality of filtered backgrounds; generating a plurality of initial voice fingerprints according to the plurality of filtered backgrounds, wherein each of the plurality of initial voice fingerprints comprises times and frequency points corresponding to a plurality of peaks in the corresponding filtered background; generating at least one voice fingerprint according to the plurality of initial voice fingerprints; and storing the at least one voice fingerprint in a memory for subsequent voice recognition operation.

The application provides a ground point bottom model establishment method based on a point cloud minimum elevation clustering algorithm, which is divided into the following steps: open pit point cloud data collection, open pit point cloud data preprocessing, open pit point cloud data gridding, open pit point cloud bottom model establishment based on a DBSCAN density clustering algorithm, missing point cloud interpolation, and open pit final ground point bottom model point cloud data establishment. Specifically, first, open pit full point cloud data is collected, and the point cloud data is subjected to thinning processing; second, the point cloud data is gridded, all grid point cloud data is traversed, and the DBSCAN density clustering number of the lowest point in the grid is calculated in the order of elevation from low to high, and when the clustering number reaches N, the point is considered to be the minimum ground point; finally, missing point cloud is filled in through a nearest neighbor interpolation method, and then the final open pit ground bottom model point cloud data is obtained. The method aims to calculate the DBSCAN density clustering number of the lowest point in the grid, so that the open pit ground bottom model point cloud data can be quickly and accurately collected, which provides important guidance value for the establishment of an open pit DEM model by using an auxiliary progressive morphological filter and the intelligent acceptance of open pit blasting.

The application discloses a kind of based on morphological extraction point cloud local surface feature method, steps are as follows: S1 acquires point cloud data and carries out data pretreatment to point cloud data;S2 searches the neighborhood point of each point of point cloud in three-dimensional space, calculates normal information, then based on normal information, the curvature of each point is calculated, and curvature value is obtained;Then refer to the curvature value range corresponding to local surface feature and filter out target surface;S3 uses progressive morphological filter to adjust filter parameter constantly, according to the curvature change of selected local area, the size of filter window is adjusted accordingly, non-target point in point cloud data after curvature screening is filtered out, and the point of target local area is extracted;S4 uses dilatation, corrosion three-dimensional morphological operator and dilatation corrosion combination open operation and close operation to optimize target local area, and extract the local surface feature area of complete smooth point cloud data.The method is extracted to local area with obvious curvature change, high accuracy, and wide adaptability.

The invention belongs to the technical field of space flight and aviation remote sensing geological exploration, and particularly relates to a uranium ore altered mineral automatic extraction method for spaceborne hyperspectral data, which comprises the following steps of: 1, preprocessing the spaceborne hyperspectral data and optimizing wavebands; 2, constructing a multi-dimensional spectrum geometric feature set; 3, constructing a'net mineralization 'coupling model; step 4, carrying out adaptive threshold segmentation based on histogram curvature extreme values; and 5, morphological filtering post-processing is carried out, and a uranium ore altered mineral distribution diagram is output. According to the method, full-automatic and high-precision extraction of alteration information is achieved by constructing a positive and negative feature coupling model and combining histogram curvature extreme value analysis, and the problems that in existing hyperspectral mineral extraction, manual repeated threshold adjustment is seriously relied on, multi-index combined logic is rigid, and complex background interference is difficult to adapt are effectively solved.

The invention discloses a speech recognition system, which comprises a processing circuit and a memory. The processing circuit is configured to: receive a plurality of sound signals, respectively; respectively performing a time domain-to-frequency domain conversion operation on the plurality of sound signals to generate a plurality of frequency domain signals; using a morphological filter to filter the plurality of frequency domain signals, respectively, to generate a plurality of filtered background images; respectively generating a plurality of initial voice fingerprints according to the plurality of filtered background images, wherein each initial voice fingerprint in the plurality of initial voice fingerprints comprises time and frequency points corresponding to a plurality of peak values in the corresponding filtered background image; generating at least one voice fingerprint according to the plurality of initial voice fingerprints; and storing the at least one voice fingerprint in the memory for voice recognition.

The present application relates to a kind of frequency hopping modulation mode identification method, comprising the following steps: step one, receive FH signal, and FH signal is carried out data preprocessing, utilize SPWVD time-frequency conversion algorithm to extract FH signal time-frequency energy graph;Adopt adaptive wiener filtering and morphological filtering algorithm to remove time-frequency energy graph noise;Then the frequency energy graph is converted into time-frequency energy gray scale graph;Step two, feature extraction, extract the geometric invariant moment of time-frequency energy gray scale graph, pseudo Zernike moment and the modulation identification characteristic parameter of three types of characteristics of rui li entropy Feature set is used as FH modulation identification feature set;Step three, feature selection identification, realize the selection of optimal feature set and the identification of multiple types of FH modulation mode by reinforcement learning feature selection algorithm and support vector machine classifier.The present application realizes the identification of multiple types of FH modulation mode, under the condition of low signal-to-noise ratio, the recognition accuracy is high, robustness is strong, and the algorithm calculation complexity is reduced.

The invention provides a voltage sag detection method and device based on a Teager energy operator and morphological filtering, and relates to the technical field of power system sag protection and electric energy quality monitoring. The method comprises the following steps: firstly, converting a three-phase voltage signal into a voltage component under a two-phase static coordinate system through Clark conversion; secondly, a virtual instantaneous voltage power modulus containing a fundamental wave offset mechanism is constructed, and sag characteristic components can be effectively extracted; then, a morphological filtering combination filter is used for conducting denoising processing on the differential power signals, high-frequency impulse noise interference is eliminated, and pure characteristic signals are obtained; and finally, calculating an energy spectrum of the pure characteristic signal by using a Teager energy operator, and carrying out voltage sag detection in combination with a self-adaptive dynamic threshold, so as to judge the starting and ending moments of the voltage sag. According to the invention, the voltage sag can be accurately detected within 1ms, and the anti-noise performance is excellent.

The invention provides a copper foil defect mode analysis method and device, and the method comprises the steps: obtaining and preprocessing copper foil edge image data, extracting defect region features, and generating a feature vector set for describing defect form differences; adjusting structural element parameters of a preset morphological filter according to the defect morphological evolution degree, and generating a filter configuration scheme group matched with the defect region features; performing multi-scale morphological processing on the defect areas through a filter configuration scheme group, and generating sawtooth characteristic intensity and contour evolution complexity of each defect area; generating a defect classification result by combining a preset copper foil product quality grading standard according to the sawtooth characteristic strength and the contour evolution complexity of each defect area; and generating optimized detection parameter configuration according to the defect classification result.

The application discloses a photoacoustic microscopic image correction and restoration method and device based on random subsampling and morphological filtering, and the method comprises the following steps. O Performing distortion estimation, estimating the maximum amplitude by solving the Euclidean distance of the blood vessel center line and edge with morphological characteristics, and obtaining a morphological filtering parameter d; collecting K relatively stable photoacoustic microscopic signals, and performing equal-interval sampling respectively to serve as a positive sample set I D ; fitting the image information of the positive sample I D with a multi-Gaussian distribution, simulating jitter, and serving as a negative sample set I d ; constructing a PADC network model based on random subsampling and morphological filtering, wherein the PADC network model comprises a PADC encoder structure, a PADC decoder structure and a skip layer; training the constructed PADC network model to obtain a trained PADC network model; and correcting and restoring the photoacoustic microscopic image to be processed by using the trained PADC network model. The application can effectively correct and restore the system motion artifacts caused by NURD in the maximum value image.

The application provides a morphological filtering image loss optimization method and device of an optoelectronic tracking system, and the method comprises the following steps: acquiring a basic morphological operation sequence formed by a morphological filtering algorithm adopted by the optoelectronic tracking system; determining a first pixel number required to be added and a second pixel number required to be deleted corresponding to each basic morphological operation based on the number of structure elements corresponding to each basic morphological operation; and performing optimization processing on each basic morphological operation, so that the optimized basic morphological operation performs the following operations: adding a corresponding first pixel number of pixels before and after a to-be-processed image sequence to obtain a reconstructed image sequence; performing the basic morphological operation on the reconstructed image sequence to obtain an operation result sequence; and deleting a corresponding second pixel number of pixels before and after the operation result sequence to obtain an output image sequence. According to the scheme, pixel loss can be effectively avoided, and the performance of the optoelectronic tracking system is improved.

The application provides a residual magnetism calculation verification method based on an arc starting point algorithm, comprising the following steps: collecting analog quantities of current and voltage and performing digital-analog conversion; when the collected data has an abnormal value, performing filtering processing on the sampling data by using a morphological filtering algorithm; detecting a closing period and judging whether a closing point exists, if not, returning to step S1, and if yes, executing step S4; extracting excitation inrush current waveform data and judging whether N continuous data are less than a current threshold value and the N+1th data is greater than the current threshold value, if not, returning to step S1, and if yes, outputting a mutation point and calculating a corresponding phase angle of the mutation point; and comprehensively calculating residual magnetism in a transformer core at the last time of switching off by derivation at the arc starting point. The method is simple and effective, is not affected by inaccurate magnetic flux estimation before and after magnetic saturation, requires only closing data, and can more accurately give a residual magnetism value.

The invention provides a morphological method and device for removing noise of a ship and a medium, and belongs to the technical field of seismic exploration. The method comprises the following steps: step 100, inputting seismic shot gather data; step 200, determining a parallel minimum granularity unit by adopting an SPARK parallel framework, and forming a target set of morphological filtering calculation; step 300, constructing structural elements of morphological filtering, and then predicting noise; step 400, performing boundary noise process treatment; and step 500, separating interference noise of the large ship to obtain de-noised seismic shot gather data. According to the method, two-dimensional mathematical morphology filtering and boundary processing of bidirectional structure elements are combined for denoising calculation, interference noise can be accurately predicted, denoising is accurate, parallel calculation is adopted, the calculation amount can be greatly reduced, the calculation efficiency is greatly improved, and the calculation cost is saved.

The invention provides a processing method of radix paeoniae rubra decoction pieces, and relates to the technical field of traditional Chinese medicine processing. The processing method of the radix paeoniae rubra decoction pieces comprises the following steps: S1, based on a machine vision sorting technology, carrying out impurity identification on a radix paeoniae rubra raw material by adopting a YOLOv5 target detection algorithm, removing immature rhizomes with the diameter of less than 1cm through a morphological filtering algorithm, reserving qualified medicinal materials with the skin shrinkage degree of 0.2-0.4 mm, and generating a primary screening data set of the radix paeoniae rubra raw material; a machine vision sorting technology is combined with a morphological filtering algorithm to realize accurate identification and elimination of raw material impurities, the raw material screening precision is improved to a submillimeter level, component fluctuation caused by mixing of immature rhizomes is effectively avoided, a near infrared spectrum rapid detection technology is fused with a partial least squares regression algorithm to construct a component prediction model, and the accuracy of component prediction is improved. The method realizes lossless rapid screening of paeoniflorin content, improves screening efficiency by more than 20 times compared with a traditional chemical analysis method, and avoids environmental pollution caused by solvent consumption.

This invention relates to the field of signal source monitoring, specifically to a train operation status monitoring method based on DAS spatiotemporal sequence imaging. The invention employs a time-domain frame-segmented wavelet packet feature extraction method to preprocess the train vibration signal, removing trailing vibration signal interference. Then, based on image binarization and morphological filtering methods in image processing algorithms, the train trajectory is feature-enhanced. Image segmentation is used to detect local straight lines, k-means clustering is used to classify the corresponding straight lines of the train, and polynomial fitting is used to extract the train trajectory. Finally, based on the obtained train trajectory, the wavelet packet energy feature value matrix is ​​truncated, and an adaptive threshold method is used to obtain the train's forward and backward trajectories, thereby achieving accurate monitoring of train length and speed, effectively solving the accuracy problem of existing DAS systems in monitoring train operation status.

The application provides an encoder code disc baseline detection system, comprising: a high-speed image sensor module for acquiring an original image of an encoder code disc baseline; a morphological filtering module for preprocessing the original image; a baseline segmentation module for screening baseline features of the preprocessed image, a baseline feature extraction module for extracting baseline features of the image; and a cache module for realizing caching of a to-be-processed image in an image processing operation process. Through the high-speed image sensor module, the various image processing modules and the cache module inside the system, the collection, processing and transmission operations of the encoder code disc baseline image are completed, the baseline position acquisition delay of the entire encoder code disc installation system is reduced, the precision of the encoder code disc installation system is improved, and the application can be widely applied to the field of industrial manufacturing intelligentization.

The invention provides a power grid fault rapid diagnosis system fused with an AI algorithm, and relates to the technical field of power grid fault diagnosis. Three-phase voltage and current data of a power distribution network and distributed photovoltaic real-time output data are collected, and data noise is suppressed by using an adaptive morphological filter; extracting fault feature data under photovoltaic disturbance; further constructing a fault feature encoder through a comparative learning algorithm, and extracting fault feature representation which is not influenced by photovoltaic fluctuation; and a fault propagation relationship among nodes in the topological structure of the power distribution network is analyzed by combining a causal reasoning enhanced graph neural network, and a fault tracing path is generated. Thus, the accuracy of fault feature extraction in the photovoltaic disturbance environment can be effectively improved, the accuracy and efficiency of fault tracing and positioning are improved, rapid and accurate power grid fault diagnosis can be achieved, and therefore the safety and reliability of power distribution network operation are guaranteed.

The invention provides a signal de-noising processing method, device and equipment and a storage medium, and the method comprises the steps: carrying out the wavelet de-noising of an electric signal based on a wavelet transform filter, and obtaining an electric signal after the wavelet de-noising; and weakening a pseudo-Gibbs effect in the electric signal after wavelet denoising based on a morphological filter to obtain a target electric signal without myoelectricity noise. According to the method and the device, effective processing and purification of the electric signals are realized through measures such as wavelet denoising based on wavelet transformation and pseudo-Gibbs effect weakening by the form filter, particularly, myoelectricity noise possibly existing in the electric signals is further removed through the form filter, the purity and the quality of the target electric signals are improved, and the method and the device are suitable for popularization and application. And powerful support is provided for subsequent analysis and application of the target electric signal.

The invention discloses a morphological filter-based fan blade acoustic emission crack detection and evaluation method and system. The method comprises the following steps: acquiring a blade acoustic emission signal through an acoustic emission sensor, and carrying out low-pass filtering and direct current offset removal pretreatment; dynamically determining the length of morphological structure elements according to the signal variance, and adaptively selecting corrosion or expansion operation to enhance crack characteristics; extracting multi-dimensional crack characteristics including peak values, variances, duration, spectral characteristics and the like; and calculating a crack severity index based on weighted fusion, and evaluating the severity and development situation of the crack in combination with a time sequence change trend. The system comprises a signal acquisition module, an adaptive filtering module, a feature extraction module and a comprehensive evaluation module. According to the invention, high-sensitivity and strong-interference-resistance crack detection and health state monitoring can be realized, and the method is suitable for early damage diagnosis and intelligent operation and maintenance of the fan blade.

This invention belongs to the technical field of wear condition identification for hot forging press components, specifically relating to a feature signal enhancement method for wear condition identification of hot forging press components, including: S1, enhancing the feature signal x of inferior vibration signal x poor The repair signal x is obtained through processing. re S2. Construct a closed-open self-complementary top-hat morphological filter FCO-NSTH to enhance the characteristic frequency amplitude of the vibration signal to be enhanced; S3. Adaptively select the optimal structural element parameters of the closed-open self-complementary top-hat morphological filter using a particle swarm optimization algorithm; S4. Use the closed-open self-complementary top-hat morphological filter FCO-NSTH with adaptively selected optimal structural element parameters to enhance the repair signal x. re The characteristic frequency amplitude is enhanced to obtain an enhanced vibration signal; S5. The enhanced vibration signal obtained in S4 is input into the intelligent model to identify and predict the wear state of hot forging press components. This method can accurately identify the wear state of hot forging press components by combining with the intelligent model.

The application provides a method and related equipment for enhancing a while-drilling seismic signal. The method comprises: acquiring a while-drilling seismic signal; calculating a time-varying structure element according to a local variance of each trace of the while-drilling seismic signal; performing morphological dilation and corrosion operations at different scales for each time point and spatial trace of each while-drilling seismic signal to calculate a multi-scale morphological gradient value; constructing a weight matrix according to the multi-scale morphological gradient value; and performing weighted morphological filter iteration reconstruction on the while-drilling seismic signal based on the weight matrix to obtain an enhanced while-drilling seismic signal. The embodiment of the application realizes real-time enhancement of the while-drilling seismic signal by dynamic structure element generation and multi-scale morphological gradient weighting, and combines weighted morphological filter iteration reconstruction, thereby significantly improving the signal-to-noise ratio, retaining lithology interface reflection characteristics, suppressing noise and interference, and providing reliable technical support for underground coal mine seismic monitoring.

The application discloses a kind of fusion multi-scale morphological and adaptive energy threshold electrocardiosignal processing method and system, comprising: obtaining electrocardiosignal, utilize multi-scale morphological filter, to electrocardiosignal Parallel signal path calculation and baseline path calculation, the morphological reconstruction signal of weighted subtraction calculation result is obtained;By non-linear energy operator to morphological reconstruction signal is amplified, and energy sequence is obtained;Energy value in energy sequence is detected gradually, if the energy value is greater than main threshold, then directly confirm as R wave, and update main threshold;If the energy value is less than main threshold, judge distance last R wave time whether greater than 1.5 times average RR interval, if not greater than, then read next energy value;Otherwise, by backtracking search to obtain backtracking maximum value, judge backtracking maximum value whether greater than auxiliary threshold, if greater than, then mark as supplementary R wave;Otherwise, read next energy value;Based on confirmed R wave, output denoised electrocardiosignal waveform and heart rate parameter.

The invention relates to the technical field of electrical variable measurement, and discloses a charging pile output current characteristic on-line extraction method comprising the following steps: synchronously obtaining a current sampling sequence and a control duty ratio sequence; determining a current rising slope and a current falling slope of the energy storage inductor according to the duty ratio and the power conversion parameter; determining a left side horizontal span and a right side horizontal span of a filtering operator according to a ratio of the rising slope to the falling slope, and constructing an asymmetric morphological filtering operator; the asymmetric morphological filtering operator is used for processing a current sampling sequence, extracting a non-periodic transient characteristic edge and packaging, power control parameters are used for reconstructing the geometrical morphology of the filtering operator, physical matching of the measurement operator and sawtooth ripple topology is achieved, waveform inflection point distortion caused by a symmetric window is eliminated, and the measurement accuracy is improved. The millisecond transient characteristic envelope integrity is maintained while the switch ripple is suppressed, and high-fidelity electric heating boundary data is provided for loop health assessment.