65 results about "Wavelet filtering" patented technology

The invention relates to the technical field of information processing, in particular to a mine microseism positioning control system and method.The mine microseism positioning control method comprises the steps that microseism signals are synchronously collected through a geophone, an accelerometer, an acoustic emission sensor and an optical fiber sensor, and data timestamps are aligned based on a GPS / Beidou clock; blind source separation is carried out on the signals after wavelet filtering, independent seismic source components are extracted, multi-sensor features are fused through a graph neural network, and a space-time joint feature matrix is constructed; classifying micro-seismic event types by using a ResNet-LSTM hybrid model, generating a seismic source coordinate initial solution based on deep reinforcement learning, and iteratively converging to an optimal solution through a particle swarm optimization algorithm; and mapping a positioning result to a three-dimensional geological grid model, simulating an energy diffusion path, triggering a three-level alarm according to the event energy density, and automatically pushing an emergency instruction to a mine safety management system. Therefore, the problem of low precision of the existing mine microseism positioning control system is solved.

The invention relates to the technical field of relay protection loop detection, and discloses a relay protection loop fault detection method and system, comprising data acquisition, data processing, feature extraction, fault diagnosis and early warning and alarming. Multi-dimensional state data of current, voltage, temperature and vibration are collected in real time, wavelet filtering, mean or median filtering and normalization preprocessing are performed, comprehensive characteristic parameters are extracted in combination with time domain and frequency domain analysis and wavelet packet decomposition, a fault is accurately diagnosed and positioned through a fault diagnosis model, early warning and alarming are achieved based on graded thresholds, and the fault diagnosis accuracy is improved. The fault key information is synchronously transmitted to the operation and maintenance terminal, database storage and display terminal display are matched, the problems of poor real-time performance, one-sided feature extraction, no graded early warning and the like of traditional detection are effectively solved, the fault detection efficiency, the diagnosis accuracy and the operation and maintenance response speed are greatly improved, the operation risk of a power system is remarkably reduced, and the safety and stability of the power system are guaranteed.

The invention relates to the field of data processing, in particular to a composite insulator vulcanization uniformity detection method and device. The method comprises the following steps: acquiring an NMR spectrogram of a vulcanized insulator; performing first-layer decomposition processing on the NMR spectrogram by using a wavelet filtering algorithm to obtain a detail coefficient; a noise detail coefficient and a signal detail coefficient in the approximation coefficient are distinguished; performing second-layer decomposition processing on the approximation coefficient to obtain a detail coefficient; calculating the signal purity; in response to the fact that the signal purity is smaller than a preset purity threshold value, noise detail coefficients in the detail coefficients obtained through second-layer decomposition processing are screened out; continuing decomposition and calculating the signal purity, and stopping decomposition when the signal purity is not less than a preset purity threshold value; after the noise detail coefficient is corrected and adjusted, a denoised NMR spectrogram is reconstructed; therefore, vulcanization uniformity detection is realized. Therefore, the accuracy of vulcanization uniformity detection is improved by improving the accuracy of denoising.

The invention discloses a Mini LED lamp panel needle mark defect detection method and system, and the method comprises the following steps: S1, carrying out the preprocessing of a Mini LED lamp panel surface image, suppressing high-frequency noise, and enhancing the defect features; s2, extracting defect features by adopting a self-adaptive convolution adjacency analysis method: S21, dynamically adjusting the shape and size of a convolution kernel through a deformable self-adaptive convolution kernel, and matching the local form of the needle mark defect; and S22, carrying out correlation analysis on the pixel points and neighborhoods thereof, and eliminating isolated noise interference. According to the method, defect forms are dynamically matched through the self-adaptive convolution kernel, isolated noise is eliminated through adjacency analysis, defects of different sizes are covered through multi-scale fusion, wavelet filtering and a parallel computing architecture are combined, the detection precision and the anti-jamming capability are remarkably improved, stable recognition of the tiny needle marks in the complex environment is achieved, meanwhile, high-speed real-time processing is guaranteed, and the detection accuracy and the anti-jamming capability are improved. The method is suitable for diversified industrial scenes and material requirements, reduces the false detection and omission ratio, and promotes the intelligent upgrading of precision manufacturing quality inspection.

The invention relates to the technical field of power distribution network fault detection, and provides a neutral point small current grounding power distribution network single-phase grounding fault section positioning method based on a space-time diagram wavelet neural network. The method comprises the following steps: constructing a training data set covering multiple types of single-phase earth fault scenes, and carrying out time sequence electrical quantity sampling; data preprocessing is completed through operations such as data cleaning and missing completion; constructing a spatial-temporal feature extraction module fusing a graph wavelet neural network and a gating circulation unit network, and carrying out spatial-temporal joint modeling on node voltage and current time sequence information; and outputting a node-level fault positioning probability through a full connection layer and a Softmax classifier. According to the method, the response capability of the model to topological disturbance is enhanced through frequency domain wavelet filtering, the information transmission efficiency is improved in combination with a residual mechanism, the fault section identification precision under the conditions of sparse observation data and weak electrical disturbance characteristics is effectively improved, and the method is suitable for a small current grounding fault positioning task in a complex operation environment of a power distribution network.

The invention relates to the field of data processing, in particular to a data processing method and system of a thulium laser treatment device. The method comprises the following steps: acquiring a laser data time sequence and a biological reflection data time sequence; dividing the laser data time sequence and the biological reflection data time sequence into a plurality of laser data segments and a plurality of biological reflection data segments respectively; setting a plurality of preset layer numbers, setting the decomposition layer number of a wavelet filtering algorithm as the preset layer number, and carrying out filtering processing on the laser data segment to obtain a denoised data segment; the denoising credibility of the preset number of layers is calculated, and the denoising credibility is positively correlated with the signal-to-noise splitting degree and negatively correlated with the information loss degree; and selecting the denoised data segments obtained by the preset number of layers with the maximum denoising credibility as final denoised data segments, and splicing all the final denoised data segments into a final denoised time sequence. The denoising accuracy is improved by setting a proper number of decomposition layers.

The invention provides a hot rolled steel strip surface defect detection method based on target detection, and aims to solve the problems of difficulty in multi-scale small target recognition, insufficient feature fusion and complex background interference in hot rolled steel strip surface defect detection. Extracting multi-scale defect features by using wavelet convolution through decomposition, convolution and reconstruction processes; multi-scale features are adaptively fused based on channels and space attention through a dynamic feature fusion module (DFF); a dynamic hyperbolic tangent function (DyT) is used to replace a normalization layer to dynamically adjust the input range and compress extreme values to reduce the amount of calculation. The method has the effects of high detection average precision (mAP), low missing report rate and remarkably reduced calculation complexity, effectively improves the efficiency and reliability of identifying the surface defects of the hot rolled steel strip in a complex industrial environment, and is suitable for intelligent steel manufacturing quality control.

The invention provides an attention detection method and system based on a brain-computer interface, and particularly relates to the technical field of brain-computer interfaces, and the system integrates a hardware unit (including TGAM electroencephalogram acquisition, Bluetooth connection and LED illumination control), a software unit (including login, detection, display and data export) and an algorithm unit. According to the method, electroencephalogram collection is carried out, wavelet filtering is utilized to decompose EEG signals, an Attention value and a Meditation value are extracted after calculation, a fusion value AM of the Attention value and the Meditation value is further provided, and control over the LED illumination brightness is achieved according to the fusion value. Besides, the functions of user verification, signal processing, result display and data recording are achieved through the software unit, the problems of extraction and fusion of attention features and relaxation features of electroencephalogram signals in the man-machine interaction process are solved through the algorithm unit, the accuracy and reliability of collection and processing of electroencephalogram data are improved, and the system is suitable for popularization and application. Effective attention feature extraction and feature fusion based on the electroencephalogram signals and adjustment of the LED illumination brightness are achieved, and development of the human-computer interaction technology is promoted.

The invention relates to the technical field of data processing, in particular to an equipment operation state monitoring method and system based on the Internet of Things. The method comprises the following steps: acquiring an oxygen concentration time sequence and a temperature time sequence of equipment; acquiring a plurality of preset local ranges of the oxygen concentration data at each moment in the oxygen concentration time sequence, calculating the noise expression degree of the oxygen concentration data, and calculating the corrected noise expression degree of the oxygen concentration data according to the noise expression degree, the number of decomposition layers in a wavelet filtering algorithm is set according to the corrected noise expression degree, and denoising processing is achieved so as to assist equipment operation state monitoring. Therefore, efficient and accurate de-noising processing is realized through adaptive decomposition of the number of layers.

The invention provides an ultrasonic thickness measuring method and equipment. The method comprises the following steps: transmitting an ultrasonic signal to a test piece by using ultrasonic thickness measurement equipment to obtain an echo signal, carrying out soft threshold-based wavelet filtering processing and Wiener deconvolution filtering processing on the echo signal, then calculating an autoregression coefficient based on a high signal-to-noise ratio signal, reconstructing an original signal through an autoregression spectrum extrapolation model, and finally obtaining the thickness of the test piece. And finally, calculating the thickness of the measured piece according to the time difference of two signals in the reconstructed signals. According to the signal processing method provided by the invention, the thickness of the thin film can be clearly and accurately measured by using the transducer with relatively low frequency, the cost of an ultrasonic thickness measurement system is effectively reduced, and the requirement for measuring the thickness of a thin film material in an industrial environment is met.

The invention discloses an energy efficiency optimization regulation and control method and system combined with energy consumption data analysis, and relates to the technical field of energy efficiency optimization regulation and control. By combining energy consumption data analysis and a support vector machine model, the relation between equipment and regional energy consumption is accurately captured, the future energy efficiency change trend is predicted, global optimization is performed by using a particle swarm optimization algorithm, parameters such as the use duration, the load rate and the power of the equipment are adjusted in real time, energy efficiency management is optimized, and the precision and the flexibility are improved; compared with a traditional static regulation and control method, the energy efficiency requirements under different working conditions can be dynamically responded; the energy consumption data is collected in real time, wavelet filtering and normalization processing are carried out, data accuracy and consistency are ensured, resource waste and operation cost are reduced through particle swarm optimization, and energy consumption and operation cost are reduced; and the PID controller is combined to perform smooth control on the scheduling process, so that the problem of instability of the system is avoided.

The invention belongs to the technical field of new energy automobiles, and particularly relates to a multi-axle hybrid vehicle real-time energy management strategy based on hierarchical control, which specifically comprises the following steps of: (1) calculating load power based on a whole vehicle dynamic balance equation, dividing the load demand power into a high part and a low part by adopting Haar wavelet filtering, high-frequency power and low-frequency power are obtained by decomposing and reconstructing an original signal; (2) distributing high-frequency power to a super capacitor, performing secondary optimization distribution on low-frequency power by taking fuel economy, a battery charge state and bus voltage as optimization targets, and solving an optimal power distribution ratio of an engine-generator set and a power battery pack in real time by utilizing a model prediction control method; and (3) establishing a layered energy management strategy by adopting a mode of combining front wavelet filtering and rear model predictive control. On the premise that the dynamic property of the whole vehicle is met, the fuel economy is improved and the multi-power-source characteristic is exerted by reasonably distributing energy flow between the power assemblies.

The present invention relates to the field of wind turbine monitoring technology, and specifically to a monitoring method, device, equipment and medium for a wind turbine pitch system. The method comprises: collecting image data of the wind turbine pitch system; grayscale processing the image data based on an adaptive grayscale algorithm adjusted by parameters; filtering the grayscale image data using median filtering, Gaussian filtering and wavelet filtering to obtain filtered image data; edge detection and feature and feature point recognition of the filtered image data, wherein the feature point recognition adopts SIFT and SURF fusion algorithms; based on the recognized feature points, the angle of the wind turbine pitch system is calculated according to a geometric relationship algorithm, or, based on the recognized features, the angle of the wind turbine pitch system is calculated according to a template matching algorithm. This method overcomes many drawbacks of traditional sensor monitoring methods, and can timely and accurately grasp the operating status of the pitch system, effectively reducing the probability of wind turbine shutdown due to pitch system failure.

The invention relates to the field of deformation monitoring, in particular to an image deformation monitoring method, device and equipment and a program product. The method comprises the steps that matching errors are remarkably reduced by combining a Zhang's calibration method with epipolar correction, and the signal-to-noise ratio and the contrast ratio of an image are enhanced through median + wavelet filtering and histogram equalization processing. Through multi-scale census transformation and weighted Hamming distance calculation, local and global information can be effectively combined, and the robustness of a cost matrix is enhanced; according to the eight-path aggregation optimization method based on dynamic planning, the precision and the calculation efficiency of parallax calculation are further improved. According to the full-image displacement calculation method based on SIFT feature guidance, the problem of displacement propagation error accumulation is solved through sub-region matching and Gaussian Newton iterative optimization, the displacement measurement precision of a low-texture region is remarkably improved, and good robustness and accuracy are achieved in a complex scene.

The embodiment of the invention provides a self-adaptive target threshold determination method and device, and the method comprises the steps: obtaining a detection cavity sonar signal, and enabling the detection cavity sonar signal to be collected by a detection cavity sonar; performing multi-layer wavelet decomposition on the cavity measurement sonar signal to obtain a low-frequency coefficient of each layer and a high-frequency coefficient of each layer after decomposition; performing threshold processing on the high-frequency coefficient of each layer according to an initial threshold, and performing signal reconstruction based on the low-frequency coefficient of each layer and the high-frequency coefficient of each layer after the threshold processing to obtain a reconstruction signal corresponding to the initial threshold; and according to the noise degrees of the reconstructed signals corresponding to the plurality of initial thresholds, determining a target threshold from the plurality of initial thresholds, the target threshold being used for performing wavelet filtering on a target signal collected by the cavity measurement sonar. The target threshold value obtained through the method can be used for removing periodic noise in signals collected by the cavity measuring sonar, and the quality of the signals is improved.

The invention discloses a cone motion test data processing method based on parallel calculation and harmonic wavelet filtering, and the method comprises the steps: collecting an original voltage signal matrix measured by a wind tunnel force measurement six-component balance through a data collection system when a test model carries out the cone motion in a cone motion wind tunnel test; a principal coefficient matrix and a secondary coefficient matrix are constructed by using a six-component balance formula matrix, and a parallel resolving matrix is constructed by using the principal coefficient matrix and an original voltage signal matrix; iteratively solving a time-domain balance load matrix at each moment in the conical motion wind tunnel test based on the original voltage signal matrix, the principal coefficient matrix, the secondary coefficient matrix and the parallel solution matrix; the method comprises the following steps: determining the center frequency of a harmonic wavelet according to the prior information of a conical motion wind tunnel test, constructing a generalized harmonic wavelet, constructing a comb filter in a frequency domain based on the generalized harmonic wavelet, filtering a frequency domain balance load matrix by using the constructed comb filter, and then converting the frequency domain balance load matrix into a time domain, thereby completing the extraction and reconstruction of load data.

The invention discloses a method for evaluating lag time of influence of blast furnace ironmaking operation parameters on state parameters, which comprises the following steps of: constructing a mapping relation between time delay sequence data of operation variables and blast furnace state parameters, and processing blast furnace state parameter data by adopting modes such as wavelet filtering and difference so as to construct a drift term, a diffusion term and a random process model. And then, evaluating the operation time delay through the established stochastic differential equation simulation result. Constraint conditions are added for optimization of the model according to the blast furnace smelting characteristics and the hierarchical clustering result, and the optimization efficiency and precision are further improved. Finally, the importance of the parameters is evaluated based on the gain or SHAP algorithm, the regulation and control priorities of the operation variables are ranked, and a basis is provided for regulation and control of the blast furnace ironmaking process. The method has the advantages that the delay time of the influence of the operation variables on the state parameters of the blast furnace can be accurately evaluated by fusing the production rule of blast furnace smelting, the production cost is reduced while the quality and the efficiency of the ironmaking process are improved, and the method has a wide application prospect.

The invention belongs to the technical field of inertial navigation, and provides an inertial instrument anti-disturbance alignment method based on sliding window wavelet filtering to solve the problems that measurement information cannot be fully utilized, an alignment result cannot be globally optimal and is seriously influenced by sudden change interference in an existing alignment method. The anti-disturbance alignment method comprises the following steps: extracting a speed error of an inertial instrument; based on a sliding window wavelet filtering method, carrying out noise reduction processing on system noise of the inertial instrument; and carrying out anti-disturbance self-alignment on the inertial instrument according to the speed error and the noise-reduced output data of the inertial instrument. According to the method, the output data of the inertial instrument can be subjected to noise reduction processing, the holding time of the alignment precision of the inertial measurement unit is prolonged, the dispersion of a convergence value is reduced, and the navigation precision is improved.

The application discloses a kind of traffic image segmentation control method and system, it is related to image processing field, wherein the method comprises: the camera of interactive target vehicle is obtained first traffic image;According to wavelet filtering algorithm, first traffic image is carried out denoising processing, and second traffic image is obtained;Based on the second traffic image, ORB feature recognition is carried out, and ORB feature recognition result is obtained;Based on the ORB feature recognition result, clustering is carried out, and ORB feature clustering result is obtained;Based on the ORB feature clustering result, the second traffic image is carried out background isolation, and third traffic image is obtained, and, third traffic image has ORB feature mark;Traffic image segmentation channel is built;Based on the ORB feature mark, according to the traffic image segmentation channel, third traffic image is carried out segmentation control, and target traffic image segmentation result is obtained.The technical problem that present technique is low in accuracy for traffic image segmentation is solved.

The invention relates to the technical field of electrocardiosignal quality evaluation, and discloses an electrocardiosignal quality evaluation method based on an unsupervised cascade adaptive network, which comprises the following steps: S1, preprocessing an obtained original electrocardiosignal: removing power frequency interference through adaptive notch filtering, removing high-frequency myoelectricity noise through soft threshold wavelet filtering, and obtaining a pre-processed electrocardiosignal; keeping a potential pathological characteristic waveform in a filtering process to obtain a preprocessed electrocardiosignal; s2, unsupervised multi-modal feature extraction: an unsupervised feature extraction network is constructed based on an auto-encoder; a normal electrocardiosignal distribution rule is automatically learned through an auto-encoder, so that the dependence on labeling resources of professional doctors is reduced; meanwhile, by means of a two-stage self-adaptive evaluation network, significant noise is filtered first, then pathological signals and noise are accurately distinguished, it is effectively avoided that abnormal waveforms caused by diseases such as myocardial infarction and atrial fibrillation are misjudged as noise, the retention rate of the pathological signals is remarkably increased, and more complete effective data support is provided for clinical diagnosis.

The invention discloses a GPS (Global Positioning System) and inertial navigation fused high-precision navigation method, aiming at the problems that a GPS signal is easily shielded and interfered and an inertial navigation error is accumulated along with time, high-precision navigation is realized through data preprocessing, a self-adaptive fusion algorithm, GPS signal interruption processing and fusion result optimization. In the data preprocessing, multi-path effect detection and correction are performed on GPS data, and zero offset error compensation is performed on inertial navigation data; the adaptive fusion algorithm constructs a model based on a fuzzy logic system, and updates a fusion weight in real time; when a GPS signal is interrupted, map matching is adopted to assist navigation, and the prediction model is utilized to compensate an inertial navigation error; and a fusion result is subjected to wavelet filtering and smoothing processing, and the accuracy is ensured by using consistency check. The method can effectively overcome the limitation of independent use of GPS and inertial navigation, realizes high-precision, continuous and reliable navigation in a complex environment, and improves the adaptability and stability of a navigation system.

The invention provides an easily-confused emotion recognition method based on multi-path feature extraction and gating fusion. The method can be applied to scenes such as human-computer interaction, emotion calculation and psychological health auxiliary diagnosis. According to the method, a feature path is constructed for each group of emotions aiming at three groups of easily-confused emotions of happiness-angry, fear-surprising and sadness-neutral, each path adopts different wavelet parameter sets to generate a multi-scale wavelet filtering kernel, and multi-path time-frequency features are extracted from the same voice signal. And inputting each path output into a gating attention mechanism module, calculating a path weight and performing weighted summation to obtain a fusion feature for easily confused emotion discrimination, and inputting the fusion feature into a classifier to output an emotion category. According to the method, a wavelet filtering kernel group emphasizing different emotion categories is constructed, a multi-path feature extraction structure is generated, and fusion is performed by using a gating mechanism, so that the model can realize easy-to-confusion emotion recognition according to feature contributions of different paths, and the easy-to-confusion emotion recognition accuracy of the model is improved.

The invention belongs to the field of power distribution network governance, and relates to a power distribution network harmonic optimization method and system based on electrochemical energy storage, and the method comprises the steps: collecting initial harmonic data, and carrying out the preprocessing of the initial harmonic data, and obtaining the processed harmonic data; the harmonic data comprises power grid current, energy storage system parameters, PCS power and power grid voltage; the preprocessing comprises wavelet filtering processing and direct current component filtering processing on the initial harmonic data in sequence; based on the processed harmonic data, calculating to obtain a fundamental wave parameter; the fundamental wave parameters comprise fundamental wave active power, fundamental wave reactive power and fundamental wave apparent power; based on the fundamental wave parameter, calculating to obtain a fundamental wave component, and based on the fundamental wave component, determining a compensation current target value; the fundamental component comprises a fundamental active component, a fundamental reactive component and a harmonic component; based on the compensation current target value, a control instruction is generated through cooperation of model predictive control and rapid repetitive control; and the overall power quality and operation reliability of the power distribution network are improved.

The present invention discloses a radar communication modulation recognition method based on hierarchical classification, comprising the following steps: S1, acquiring modulation signal data; S2, normalizing the modulation signal data; S3, performing wavelet filtering on the normalized data; S4, performing feature extraction on the filtered data; S5, using the extracted features as input to a support vector machine, and constructing a hierarchical modulation recognition classifier based on the support vector machine. The present invention utilizes wavelet filtering to reduce the impact of the noise environment on the characteristics of the received signal, and employs time domain statistical features to mitigate frequency bias problems caused by carrier frequency estimation deviations. By hierarchically classifying the signal through a support vector machine trained with corresponding features, the accuracy and stability of signal recognition are improved, and good robustness can be achieved under noise and frequency bias conditions.

The invention relates to the technical field of injection molding machine mold locking return-to-zero control, and discloses an injection molding machine mold locking return-to-zero control system and method. Meanwhile, temperature sensors are arranged on the pull rod, the movable template and the guide rail, and thermal expansion amount of each component is calculated and dynamically compensated in real time; calculating the wear compensation amount based on the accumulated number of cycles of the guide rail; fusing the position of the encoder, temperature compensation and wear compensation to obtain a real position; when approaching a mechanical zero point, monitoring a zero point sensor signal which is subjected to wavelet filtering and self-adaptive threshold processing; and finally, the driving mechanism moves to the zero point in a closed loop mode and automatically stops within the error controllable range.

The application discloses a small drilling rig multi-model drilling speed prediction method and equipment based on stratum lithology classification, considers the important factor of stratum lithology reflecting the drilling environment of the small drilling rig in the role of the drilling speed prediction, introduces stratum lithology information, establishes drilling speed prediction models of multiple lithologies, and is divided into three stages, in the first stage, according to actual engineering application experience, data is screened and resampled, then, the data is processed through wavelet filtering and normalization; in the second stage, three typical lithologies are selected from drilling data, and real-time drilling data is used for lithology classification; in the third stage, the lithology classification result of the second stage is used, drilling data of classification is introduced into the drilling speed model of the corresponding lithology for prediction, a good foundation is laid for intelligent control research in the drilling process of the small drilling rig, the problem that a single drilling speed model has low adaptability to different stratum lithologies is solved, and drilling speed prediction precision is improved.

The invention relates to the technical field of radiation detection, in particular to radiation monitoring equipment and a signal processing method, device and equipment based on combination of wavelet filtering and differential filtering, and the signal processing method based on combination of wavelet filtering and differential filtering comprises the following steps: acquiring a signal to be processed; preprocessing the signal to obtain a processed signal; and processing the processed signal by combining a wavelet filtering algorithm with a delay differential filtering algorithm to obtain a pulse signal peak value. According to the signal processing method based on combination of wavelet filtering and differential filtering, the obtained signal is preprocessed, and then the processed signal is processed by combining a wavelet filtering algorithm and a delay differential filtering algorithm to obtain a pulse signal peak value, so that the signal filtering precision is improved; the technical problem that an existing signal processing filtering mode is low in filtering precision is solved.

The invention discloses a cornea thickness meter and a cornea thickness measuring method, and relates to the technical field of cornea thickness meters, the cornea thickness meter comprises a lower base, an upper base, a thickness meter, a face limiting frame, a transverse adjusting assembly and a face adjusting assembly; an optical transmitting module, an optical receiving module, an ultrasonic auxiliary module and a data processing module are arranged in the thickness meter, a physical positioning mark is formed through high-frequency ultrasonic waves to determine an optical measurement area, and cornea stimulation and measurement deviation are avoided; the transverse and face adjusting assemblies are matched with a distance sensor to achieve automatic and accurate adjustment of the head position of a tester, and in addition, the data processing module is combined with wavelet filtering, an FFT algorithm and a curvature correction formula to achieve real-time processing of multi-channel signals and accurate calculation of the thickness; the problems of measurement deviation and data processing delay caused by low manual adjustment precision and irregular cornea surface of a traditional thickness gauge are effectively solved, and the cornea thickness gauge has the advantages of operation convenience, measurement accuracy and data management convenience.

The invention relates to the technical field of crossing of artificial intelligence and tobacco processing, discloses a cut tobacco feeding moisture regulation and control method and system based on LightGBM, and aims to solve the problems of moisture signal distortion and low regulation and control precision caused by dust interference. The method comprises the following steps: acquiring dust concentration and noise-containing moisture signals of a production line in real time; dynamically generating an optimal wavelet filtering parameter through a nonlinear mapping model based on the dust concentration; performing adaptive wavelet filtering on the moisture signal to reconstruct a pure signal; inputting the pure signal into a pre-trained LightGBM model to predict a moisture value; and generating a regulation and control instruction according to the deviation between the predicted value and the target value to realize closed-loop control. The system comprises a working condition sensing module, a signal acquisition module, a filtering parameter dynamic generation module, an adaptive filtering module, a moisture prediction module and a regulation and control instruction generation module. Through decoupling integration of dynamic filtering and an existing LightGBM model, on the premise that an original model is not changed, the moisture regulation and control precision and the response speed are remarkably improved, and the engineering transformation cost is reduced.

The invention provides an intelligent tire lateral force estimation method and system based on an improved iTransform network, and the method is realized through the following steps: S1, carrying out the acceleration signal collection of a tire, and obtaining the three-axis acceleration data; s2, performing IEEEEMDAN-permutation entropy-wavelet filtering noise reduction on the x-axis acceleration signal and the y-axis acceleration signal, and performing 200Hz low-pass filtering on the z-axis acceleration signal; s3, identifying starting and ending feature points of the z-axis acceleration signal based on an adaptive threshold method, and segmenting three-axis acceleration data; s4, constructing a hybrid network model, and replacing a linear mapping layer and a regression prediction layer in an iTransform model by using a KAN network; and S5, predicting the lateral force of the tire by using the trained model. According to the method, by combining the advantages of the iTransform in time sequence modeling and the KAN in nonlinear representation, the precision and stability of tire lateral force estimation are remarkably improved, and the method is suitable for vehicle dynamics analysis and control scenes and has great significance in improving the maneuverability and safety of vehicle driving.