83 results about "Tikhonov regularization" patented technology

The invention discloses a self-adaptive InSAR-GNSS high-precision three-dimensional deformation resolving method with regularization introduced twice, and the method comprises the steps: obtaining InSAR deformation data and GNSS three-dimensional deformation data of a to-be-monitored region, and building an InSAR error correction model considering spatial correlation based on the InSAR deformation data and the GNSS three-dimensional deformation data; performing system error correction on the InSAR deformation data by using the corrected error correction model to obtain preliminary correction data; grouping the preliminary correction data according to data precision characteristics and determining an initial weight; iteratively calculating until unit weight variances of each group of observation data are approximately equal to obtain a final weight; and obtaining a high-precision earth surface three-dimensional deformation rate based on the final weight. According to the method, Tikhonov regularization is introduced twice, the weight is dynamically adjusted in combination with the IGGIII equivalent weight function, and various error influences are effectively reduced.

The invention discloses an underway real-time GNSS acoustic underwater positioning method, which belongs to the technical field of underwater positioning, is used for acoustic underwater positioning, and comprises the following steps: constructing an extended Kalman filtering solution model, constructing a constraint equation by taking coordinate prior information of a seabed transponder as a constraint, and solving an initial state parameter and an initial covariance matrix; and processing ill-conditioned problems in the initial state parameter and initial covariance matrix solving process by adopting a Tikhonov regularization method, substituting the initial state parameter and an initial covariance matrix value into an extended Kalman filtering solution model, and carrying out real-time GNSS acoustic underwater positioning on the submarine transponder. The real-time positioning method for the GNSS-acoustic seabed transponder solves the possible ill-conditioned problem in the solving process, has good performance of short convergence time and high precision, can improve the real-time positioning efficiency of the GNSS-acoustic seabed transponder, and is expected to provide powerful technical support for seabed plate movement, geological disasters, disaster early warning and seabed geodetic datum updating.

The invention relates to the technical field of flexible riser motion analysis, in particular to a marine dynamic flexible riser motion response reconstruction method based on monitoring data, which comprises the following steps of: arranging sensors in a flexible riser system by adopting a structure embedded type arrangement method or an external attachment type arrangement method, and optimizing a sensor arrangement mode; a data acquisition and transmission system is arranged on an ocean platform to obtain flexible riser motion response data, the flexible riser motion response data is converted into flexible riser displacement data based on a Timoshenko beam model and Tikhonov regularization, the flexible riser displacement data is subjected to noise reduction processing based on a wavelet threshold noise reduction method to obtain flexible riser displacement noise reduction data, and the flexible riser displacement noise reduction data is subjected to flexible riser displacement noise reduction. And motion response reconstruction is performed on the flexible riser displacement noise reduction data based on variational mode decomposition (VMD). According to the method, the data interpretability, the data reconstruction precision and the real-time performance of motion reconstruction of the flexible riser can be effectively improved.

The invention discloses a method for rapidly evaluating the flexural rigidity of a bridge under vehicle-induced excitation, and belongs to the technical field of flexural rigidity evaluation of variable cross-section beams. The method comprises the following steps of: firstly, acquiring a bridge time-history response by adopting vehicle movement loading, separating a power component by utilizing a VMD method, then identifying a main frequency of an IMF component through FFT, removing the power component higher than a bridge fundamental frequency, and reconstructing a bridge actual measurement quasi-static time-history response; then, an axle effect in bridge actual measurement quasi-static time history response is eliminated, an influence line recognition model is established, and Tikhonov regularization is introduced to further solve a deflection influence line; then deriving an analytic solution of a deflection influence line of the variable cross-section continuous beam based on a virtual work principle, establishing a mathematical relationship between the flexural rigidity and the deflection influence line, and identifying the flexural rigidity of the bridge through the deflection influence line based on the mathematical relationship to obtain flexural rigidity distribution of the full bridge; finally, multiple evaluation indexes are defined and calculated, the bending rigidity distribution of the full bridge is comprehensively evaluated, and the method has the advantages of being good in engineering applicability and high in safety evaluation innovativeness.

The invention discloses an electrolytic tank performance analysis method based on polarization feature recognition. The method comprises the steps that S1, electrolytic tank parameters are set, electrochemical testing is conducted, and EIS impedance data are obtained; s2, preprocessing the EIS impedance data: performing Kramers-Kronig verification on the EIS impedance data, calculating a relative residual error of the data, and selecting a regularization parameter lambda; s3, applying the EIS impedance data to DRT quantitative analysis: converting the EIS impedance data into a DRT spectrogram based on discretization of a radial basis function and a Tikhonov regularization method; and S4, performing DRT spectrogram identification by using a DRT method, establishing an ECM model, and evaluating the influence of metal cation pollution on PEMWE performance degradation by using the ECM model. According to the electrolytic cell performance analysis method based on polarization feature recognition, the core problem that all polarization processes and the evolution rule of the polarization processes in performance degradation are difficult to accurately analyze under the condition of high current density in the prior art is solved.

The invention discloses a non-contact ice friction coefficient measuring method and device, and relates to the tribology testing technology, and the method comprises the following steps: obtaining a motion image of a slide block on an artificial ice rink by using a camera system; performing preprocessing and target detection on the acquired image to obtain coordinate information of the sliding block in a world coordinate system; and based on the coordinate data of the sliding block, fitting and solving the acceleration of the sliding block by adopting primary function expansion in combination with a Tikhonov regularization method, and carrying out inversion in combination with a friction mechanical model and a kinematic model to obtain an ice friction coefficient. Compared with a traditional contact type method, the in-situ high-precision measurement of the ice friction coefficient can be realized under the condition of no sensor attachment or external force interference, the defects of low measurement precision and strong invasiveness in the prior art are effectively overcome, and the method can be applied to the fields of winter road safety detection and ice and snow sports.

The invention belongs to the technical field of boiler heating surface high-temperature corrosion monitoring, and relates to a boiler heating surface temperature monitoring method and system based on dynamic heat flux density monitoring. The method comprises the steps of obtaining temperature field data of a plurality of monitoring areas of a boiler heating surface; constructing an improved Tikhonov regularization algorithm based on the temperature field data, and carrying out heat flow field reverse solution by adopting the improved Tikhonov regularization algorithm to obtain reconstructed heat flow field distribution and a heat conduction nuclear matrix after regularization optimization; and generating a temperature evolution trend by adopting a GRU prediction model based on an attention mechanism based on the reconstructed heat flow field distribution and the regularized and optimized heat conduction nuclear matrix. The method can accurately predict the temperature evolution trend through obtaining the comprehensive temperature field data, facilitates the real-time monitoring of the temperature of the heating surface of the boiler, and improves the safety and reliability of the operation of the boiler. Operation personnel can adjust operation parameters in time according to a prediction result, and the operation state of the boiler is optimized.

The invention relates to the technical field of electromagnetic scattering characteristic analysis and modeling, in particular to a scattering center establishing and correcting method based on a geometric model and reference data. The method comprises the following steps: inputting a target geometric or grid file, and extracting geometric features of a plane, an edge and a curved surface to construct an initial scattering center model; inputting electromagnetic scattering reference data of the same target, solving a scattering center correction coefficient by adopting a pseudo-inverse or Tikhonov regularization method, and optimizing an initial model; and outputting an optimized scattering center model with angle and frequency expansibility. According to the method, the electromagnetic scattering characteristics of metal and coating type complex structure targets in single-station, double-station and full-polarization scenes can be uniformly represented, and the problems of insufficient model precision, incomplete types and high data dependence degree in the prior art are solved; the method achieves the excellent effects that the RCS root-mean-square error is smaller than 2dB and the high-resolution imaging structure similarity exceeds 90% in typical target modeling, and is suitable for the fields of target characteristic simulation and characteristic extraction and recognition.

The invention discloses a bridge model correction method of a frequency and deflection influence line dual-objective function, and belongs to the technical field of bridge finite element model correction. According to the method, the VMD is combined with the Tikhonov regularization influence line identification method, vehicle-induced bridge response power components and multi-axis effect interference are effectively eliminated, the actually-measured deflection time history response of the bridge is restored to the deflection influence line under the unit concentrated load, the effect of identifying the variable-cross-section steel-concrete combined continuous beam bridge influence line is good, and the applicability is wide; according to the model correction method based on combination of a PSO-BP agent model and NSGA-II dual-objective optimization, a complex implicit function relationship between a to-be-corrected parameter and an objective function can be effectively constructed, a Pareto coordinated optimal solution under the dual-objective optimization problem is obtained, then a corrected parameter is obtained, and finite element model correction is achieved.

The invention belongs to the technical field of Internet of Things. The invention provides a large-scale infrastructure network link dynamic prediction method based on nonlinear reserve pool calculation. According to the embodiment of the invention, by combining nonlinear reserve pool calculation and regularization regression technologies, the dynamic, efficient and accurate prediction of a complex network link is realized, and the method is particularly suitable for state inference and stability analysis of a key link in an infrastructure network. A reserve pool calculation framework is adopted, and the core advantage of the method is that only the linear weight of an output layer needs to be trained, and the internal connection of a huge reserve pool is kept random and fixed. A large amount of iterative computation and GPU resources required by back propagation are avoided, and the training complexity and the time cost are remarkably reduced. A Tikhonov regularization item is introduced, so that the overfitting problem under limited training data or noise interference is effectively prevented, and the generalization ability and prediction stability of the model are improved.

The invention discloses a magnetic field inversion current method based on near-field scanning, and the method comprises the steps: obtaining magnetic field data in a circuit, and taking the magnetic field data as input; expanding the measurement plane of the magnetic field to obtain a magnetic field matrix, and enabling the current of the original magnetic field matrix H on the boundary to be continuous; constructing a relationship between the magnetic field and the current in the frequency domain space; a Tikhonov regularization method is used to reconstruct current distribution, and a GCV method is used to optimally select regularization parameters required by the Tikhonov regularization method; extracting structure information of the static current model, taking the structure information as a mask, and acting the structure information on a radio frequency current model result; and outputting the current in the original measurement window as the current reconstructed by the final method. Current artifacts on a boundary are eliminated by expanding a measurement plane, current distribution is automatically solved through regularization, errors caused by empirical selection of filtering parameters are avoided, the precision of reconstructed current is improved, and finally, spatial noise is suppressed through a mask method, and accurate reconstruction of radio-frequency current is achieved.

The invention discloses a dust concentration monitoring method and system based on scattered light measurement, and relates to the field of dust concentration monitoring. The method comprises the following steps: S1, in a range point detection stage, turning off an emitter to implement zero point detection to obtain a zero point value, and generating a range point detection result; s2, collecting an optical signal and a temperature signal, obtaining basic scattering intensity based on demodulation and zero value deduction, and generating an analog output current; s3, on-line correction is carried out on the basic scattering intensity through the correction coefficient and the temperature correction coefficient which are calculated in real time, and the optical scattering intensity is obtained; and S4, calibrating by simulating output current and weighing method concentration, solving a current coefficient vector and converting the current coefficient vector into a regression coefficient, and generating dust concentration by directly using optical scattering intensity and the regression coefficient when equipment runs online. Through real-time correction, intelligent dark baseline compensation, temperature correction and Tikhonov regularization methods, the measurement accuracy and the equipment stability and reliability are improved, and high-precision real-time monitoring is ensured.

The application discloses a kind of light field regulation detection method and device based on computing spectral reconstruction, belong to spectral analysis technical field.The method is by introducing medium-metal composite nanostructure, integrates metal nanoisland array and silicon nitride nanocolumn array on quartz substrate, realizes electric field enhancement using the local surface plasmon resonance effect excited by metal nanoisland array, simultaneously realizes wide spectral range phase coding by medium nanocolumn array;Super surface chip is integrated with single point detector, combines pre-calibrated super surface sensing matrix A and enhancement factor matrix E, and reconstructs spectrum using compressed sensing or Tikhonov regularization algorithm.The application keeps the advantages of miniaturization and no moving parts, realizes directional high sensitivity detection to weak signal, and signal-to-noise ratio is improved.

The invention discloses a method and a device for measuring the motion idle stroke and the overall flexibility of a flapping-wing aircraft mechanism, and relates to the field of miniature aircrafts, and the method comprises the steps: loading an aircraft wing rod on a high-speed camera measurement platform, and recording an image of the wing rod in a motion period; after the recorded image is optimized and denoised, an optical flow method is used for capturing a moving wing rod, a sub-pixel edge detection method is used for recognizing a gray level image of wing rod movement, then the edge is optimized and corrected based on the geometrical characteristics of the wing rod, and edge calibration is carried out on the gray level image of wing rod movement; then, the angular displacement of the wing rod is recognized according to the wing rod motion gray level image after coordinate calibration is completed, meanwhile, the distribution load of the wing rod is reconstructed, and inversion is conducted on the bending moment of the wing rod through recognized coordinate information and a Tikhonov regularization method; and finally, drawing an angular displacement-bending moment curve graph of the wing rod and extracting the idle stroke and the overall flexibility of the aircraft. According to the method, the motion idle stroke and the overall flexibility of the mechanism on the flapping-wing air vehicle are effectively extracted with high precision, and the method has great guiding significance in design, test and function research of the flapping-wing air vehicle.

The invention discloses an intelligent electric energy meter error data processing method based on edge calculation, and belongs to the technical field of error data processing methods. The method comprises the following steps: S1, calculating a mean value and a standard deviation in a total electric energy increment sequence and a sliding window, defining a first-order autoregressive lag correction item, setting an adaptive threshold, defining a state machine system state, and finding out a computer increment sequence of a user in an inactive period by constructing an inactive period criterion; s2, storing the screened effective data increment, calculating the electric energy increment of each sub-meter, defining a continuous zero increment detection function, and constructing an observation matrix X and an observation vector Y by setting an ammeter failure detection window and parameters and collecting effective rearranged data points; s3, calculating a characteristic value and a condition number through an improved genetic optimization algorithm framework, and carrying out iteration to obtain optimal and different individuals; and S4, determining an optimal regularization parameter through an L curve method, and calculating an error coefficient of each electric energy meter through improved Tikhonov regularization.

The invention discloses a cable net structure prestress correction method and system based on multi-experiment sensitivity, and relates to the technical field of structural engineering and structural health monitoring. A sensitivity matrix is approximately constructed through a finite difference method, and construction of the sensitivity matrix is completed in combination with multi-working-condition displacement data; meanwhile, a parameter-aware Tikhonov regularization method is introduced to suppress the ill-conditioned error amplification problem, and self-adaptive step length optimization is realized by adopting left-one-out cross validation (LOOCV), so that the convergence efficiency of prestress correction is effectively improved. The method is not only suitable for a pre-stress identification scene of a cable net antenna, but also can be extensively applied to multiple fields such as large-span space structures, bridge engineering and civil engineering health monitoring, and provides effective technical support for parameter identification and finite element model correction requirements related to complex coupling structures in the fields; and the method has very wide application range and practical value.

The invention relates to the technical field of distortion correction, and discloses a laser imaging wavefront distortion correction system and method based on deep learning, and the system comprises a first module which collects three intensity images; the second module is used for estimating the Zernike coefficient and the amplitude of the target complex amplitude; the third module is used for constructing a loss function; a fourth module that calculates an actuator voltage; the fifth module is used for calculating the Stell ratio and the high-frequency lifting ratio in the modulation transfer function; and the sixth module is used for re-applying the voltage of the actuator when judging that the evaluation condition is not met. According to the method, a full-process correction mechanism is constructed by integrating deep learning and an optical physical rule, and initial correction is realized by stably solving the voltage of the actuator by utilizing Tagunov regularization, so that the high-frequency detail definition in imaging is improved, the correction accuracy, real-time performance and robustness are both considered, and the method is suitable for large-scale popularization and application. And the application suitability of the laser imaging technology in the high-precision field is greatly expanded.

The invention discloses a seamless steel tube intelligent production method based on digital twinning, which comprises the following steps: collecting historical data and real-time monitoring data, and preprocessing to generate a standardized data set; establishing a high-dimensional digital twinning parent model, and reducing the order to obtain a digital twinning reduced-order model; performing parameter calibration on the model to obtain an initial parameter set and a state mapping matrix; inputting real-time monitoring data into the model to obtain updated key parameters, state variables and predictive control quantities; a Tikhonov regularization module is introduced, and a robust correction result is generated; the input model prediction control module generates a smooth control instruction; and collecting feedback data, and inputting the feedback data into the model for online correction and self-learning. By introducing the digital twin reduced-order model and the Tikhonov regularization module, real-time and robust control and high-precision process optimization in the seamless steel tube production process are realized.

The present invention provides a load identification method and system based on hybrid iterative regularization, belonging to the field of load identification technology. A Green kernel function transfer matrix is ​​subjected to TSVD decomposition, and the decomposed singular values ​​are divided into larger singular values ​​and smaller singular values, wherein the larger singular values ​​are singular values ​​greater than a set value, and the smaller singular values ​​are singular values ​​less than or equal to the set value; a Gaussian high-pass filter function is used as a regularization matrix for Tikhonov regularization, and the smaller singular values ​​are corrected to obtain a TSVD-Tikhonov-Gaussian hybrid regularization result; and an LSQR algorithm is used to solve the TSVD-Tikhonov-Gaussian hybrid regularization result to achieve more accurate identification of different types of loads.

The present application relates to the technical field of power system metering, in particular to a method and system for operation error diagnosis and analysis of electric energy meter based on electric energy meter operation error, wherein the method comprises: obtaining electric energy meter measurement value curve data of a target area in a set period from a metering automation system, and generating standardized electric energy meter operation data sequence through preprocessing; using an improved spectral clustering algorithm to carry out curve shape clustering on the data sequence, combining elbow rule to determine the optimal classification number of electric energy meter operation state, and completing the classification of electric energy meters in different operation states; relying on the principle of energy conservation to build a state evaluation model, screening effective data points through a data optimization strategy, reducing the model ill-conditioning by means of an improved Tikhonov regularization method, solving the error estimation equation to obtain the electric energy meter operation error estimation value and complete the state diagnosis. This method is suitable for processing massive electric energy meter time series data, smoothly weakening model operation interference, regularizing the electric energy meter operation error solving process, and optimizing the working condition diagnosis adaptability.

The invention provides an ocean internal wave parameter inversion method and system based on temperature chain buoy data. The method comprises the following steps: preprocessing an acquired multi-depth temperature sequence and then constructing a temperature-time profile; detrending and self-adaptive smoothing are carried out through empirical mode decomposition, and a temperature background field and a pulsating field are separated; extracting a vertical gradient of a temperature background profile by using LOESS local regression, and introducing Tikhonov regularization and threshold gating to guarantee gradient robustness; mapping temperature pulsation into vertical displacement based on isothermal surface conservation, and screening effective data in combination with a credibility index; finally, the amplitude, the period and the wave height of an internal wave are inversed through multi-window wavelet spectrum and Hilbert envelope conjoint analysis, and the reliability of a result is improved through vertical modal consistency verification. According to the method, consistent inversion of ocean internal wave parameters can be completed only by means of a single temperature chain buoy, and the inversion accuracy under the background of weak gradient, weak signal and noise is remarkably improved.

The present application discloses a SAR imaging method and related device based on a hybrid norm deep unfolding network, which relates to the field of remote sensing imaging technology. The method includes: obtaining radar operating parameters; obtaining an echo matrix according to the SAR raw echo signals collected at all sampling times; determining the observation scene of the SAR, and constructing a slant range matrix corresponding to each scene point in the observation scene at each random sampling time; determining the echo signal after range migration correction according to the echo matrix and the slant range matrix; inputting the echo signal after range migration correction into the trained hybrid norm deep unfolding network to obtain a reconstructed scene. In the present application, the optimization equation is transformed into a hybrid norm optimization equation based on the alternating direction multiplier method. The hybrid norm optimization equation includes a Tikhonov regularization optimization equation and an L 1 / 2 regularization optimization equation, and the deep unfolding network approximates the iterative process to reduce the computational complexity of the iterative process.

The invention discloses a section temperature and non-gray absorption coefficient reconstruction method based on multi-camera spectral imaging. The method comprises the following steps: measuring flame monochromatic radiation intensity under multiple wavelengths by using multiple multi-spectral cameras; setting an initial absorption coefficient, and calculating a DRESOR coefficient matrix under each wavelength through a DRESOR method; the DRESOR coefficient matrix and a modified Tikhonov regularization method are combined to solve medium radiation source item distribution, an absorption coefficient is updated through a Newton iteration method, and medium radiation source item distribution under multiple wavelengths is output; simultaneously reconstructing temperature and absorption coefficient distribution from a radiation source item by using an L-M polynomial fitting method; and if the relative error minimization condition is met or the maximum number of iterations is reached, outputting a final flame section temperature and non-ash absorption coefficient distribution result. By means of the scheme, high-precision flame section temperature and absorption coefficient distribution can be obtained based on multispectral image information, the result is reliable, and precision is high.

An underwater bubble group scale distribution acoustic inversion device based on forward and backward scattering belongs to the field of underwater acoustics, and comprises the following steps: with a transmitting transducer as an original point x = 0, laying a 1 # hydrophone at a position x = d1 in the axial direction, and laying a 2 # hydrophone at a position x = d1 + d2 in the axial direction; the transmitting transducer transmits a single-frequency pulse sound signal to the bubble-containing water medium to be detected at the position where x is equal to 0, and the hydrophone collects the signal; measuring an incident sound pressure amplitude, a forward scattering sound pressure amplitude and a backward scattering sound pressure amplitude; the emission frequency is changed for frequency sweeping, and an inversion equation in a matrix form is obtained according to an inversion equation discretized by a forward and backward scattering combined theory; the inversion equation is under positive constraint and finite constraint; tikhonov regularization is introduced into the inversion equation, and bubble group scale distribution parameters of the bubble-containing water medium to be measured are calculated. The method is suitable for a complex environment, can quickly measure the scale distribution of the underwater bubble group, corrects the error of an ideal inversion model, and solves the problem of accurate measurement of off-resonance bubbles.

The invention discloses a transformer three-dimensional temperature field detection method and system based on improved inverse multi-secondary and Tikhonov regularization, and the method comprises the steps: placing a plurality of groups of ultrasonic transducers at the outer side of a transformer oil tank, and obtaining the flight time of N ultrasonic propagation paths through a multichannel temperature detection method; the method comprises the following steps: dividing a three-dimensional area in a tested transformer into M subspaces, taking a geometric center point of each subspace as a center point of a radial basis function, and decomposing flight time of an ultrasonic propagation path according to the subspaces to obtain a time matrix; shape parameters are determined, a reconstruction matrix is solved according to a radial basis function of each subspace, singular value decomposition is introduced to solve a generalized inverse matrix, regularization parameters are determined, a coefficient matrix is solved through a standard Tikhonov regularization method, a reciprocal function of ultrasonic velocity is solved, and finally temperature field distribution of the whole space of the transformer is obtained. Compared with the prior art, nondestructive online detection of temperature distribution in the oil tank is realized.

The invention discloses a method for measuring section temperature and non-uniform absorption coefficient distribution through multispectral imaging. The method comprises the following steps: measuring flame boundary radiation intensity under two wavelengths; setting an initial uniform absorption coefficient, and calculating a DRESOR coefficient matrix under each wavelength through a DRESOR method; solving medium temperature distribution by combining a DRESOR coefficient matrix and a modified Tikhonov regularization method, and updating a uniform absorption coefficient based on a residual minimization principle and a Newton iteration method; after medium temperature distribution is obtained, the temperature distribution is fixed, and non-uniform absorption coefficient distribution is obtained through flame boundary radiation intensity inversion; and when a residual error minimization condition is satisfied or a maximum iteration frequency is reached, outputting a final section temperature distribution and non-uniform absorption coefficient distribution result. By means of the scheme, high-precision section temperature and non-uniform absorption coefficient distribution can be obtained based on multispectral imaging information, the result is reliable, and precision is high.

The application belongs to the technical field of image processing, and discloses an image processing method and device based on an event camera, equipment and a medium. The method comprises the following steps: acquiring event data of a blurred image collected by an event camera; constructing an initial PINN model, embedding an event generation equation in the initial PINN model; inputting the event data into the PINN model to obtain a predicted luminance value, performing self-supervision of a time derivative based on the predicted luminance value and an actual luminance value, introducing a Tikhonov regularization constraint condition to optimize the PINN model, and determining an optimized PINN model; inputting event data of an image to be processed into the optimized PINN model to obtain log luminance values of three color channels of the image to be processed; and converting the log luminance values into a reconstructed image frame by using tone mapping. The method can reduce noise and artifacts of the reconstructed image and improve the quality of the model reconstructed image.

The invention provides a data processing method and device based on a real-time database and electronic equipment, and relates to the field of data processing. In the method, original observation sequences of a plurality of target measurement points in a set time interval are extracted to form a time-value pair set, and after the original observation sequences are input into a kernel transformation unit, a time sensitive kernel function is constructed based on exponential decay to generate a kernel response sequence. And constructing a measuring point mapping function through Tikhonov regularization, mapping the kernel response sequence to a statistical potential energy space independent of a timestamp, and obtaining a statistical potential energy response variable containing a mean value response dimension, a trend response dimension and an offset response dimension. And inputting the statistical potential energy response variable of each target measuring point into a statistical calculation engine to execute a corresponding statistical formula, outputting a statistical result, binding the statistical result with the request path, and caching the kernel response function and the statistical potential energy response variable to support subsequent reuse. By implementing the technical scheme provided by the invention, the statistical calculation efficiency of the real-time database can be improved.

An image reconstruction method for multiphase flow visualization monitoring. First step, ECT source image acquisition: Two-channel ECT source images are obtained respectively through the Tikhonov regularization and the Landweber iterative algorithm. Second step, wavelet image fusion: (1) Perform three-level wavelet decomposition on the two-channel Tikhonov regularization and Landweber iterative source images respectively; (2) Fuse the wavelet coefficients at each scale through Bayesian decision-making and maximum entropy thresholding; (3) Perform wavelet reconstruction on the fused coefficients through inverse wavelet transform. Third step, RCF image reconstruction: Input the wavelet-fused image into the RCF network for training and testing to obtain the ECT reconstructed image. The present invention has the advantages of reducing image artifacts, compensating for source image defects, and improving imaging quality, providing a new technical approach for the realization of multiphase flow visualization monitoring under complex flow patterns.

An acoustic inversion device for underwater bubble group size distribution based on forward and backscattering belongs to the field of underwater acoustics. The device comprises: a first hydrophone is deployed at x = d1 along the axial direction with a transmitting transducer as the origin x = 0, and a second hydrophone is deployed at x = d1 + d2 along the axial direction; the transmitting transducer transmits a single-frequency pulse acoustic signal at x = 0 into the bubble-containing water medium to be measured, and the hydrophone collects the signal; the incident sound pressure amplitude, the forward scattered sound pressure amplitude, and the backscattered sound pressure amplitude are measured; the transmitting frequency is changed to perform a frequency sweep, and an inversion equation in matrix form is obtained based on the discretized inversion equation of the forward and backscattering joint theory; the inversion equation is subjected to positive and finite constraints; and Tikhonov regularization is introduced into the inversion equation to calculate the bubble group size distribution parameters of the bubble-containing water medium to be measured. The invention is applicable to complex environments, can quickly measure the underwater bubble group size distribution, corrects the error of the ideal inversion model, and solves the problem of accurate measurement of non-resonant bubbles.