47results about How to "Achieve estimates" patented technology

The invention relates to a micro-grid intelligent safety secondary voltage control method based on reinforcement learning, and belongs to the technical field of power grids. The method comprises the following steps: firstly, establishing a micro-grid secondary control model under hybrid network attack; a DoS attack defense network based on deep learning is designed, a self-adaptive FDI attack compensation state observer is designed, compensation for unknown FDI attacks is established, a self-adaptive law of compensation signals is designed, and estimation of the system state under network attacks is achieved. An evaluation-execution structure is constructed based on reinforcement learning, a virtual optimal controller and an actual optimal controller are designed in combination with a backstepping method, unknown parts in a system model, an evaluation network and an execution network are approximated by using a neural network, and a weight adjustment law is given. And introducing a log type barrier function into the performance index function, and constraining the output in a fixed constant interval to meet the constraint condition. The method can cope with DoS attacks and FDI attacks, and ensures stable control and safe operation of the secondary voltage of the micro-grid.

The invention provides a RIS-assisted location awareness-based communication and sensing integration method, which is used for further improving the calculation efficiency of atomic norms and improving the accuracy of parameter estimation by utilizing priori knowledge between a base station and a reconfigurable intelligent surface. By incorporating priori knowledge into the scheme design, the calculation complexity can be significantly reduced, and the accuracy of channel estimation and positioning can be ensured at the same time. The method has the advantages that super-resolution channel estimation can be achieved, and the performance of the method is superior to that of an existing orthogonal matching pursuit method. The comparison of various performance indexes shows that the method provided by the invention has obvious advantages. The advantage is crucial for realizing accurate positioning service in a complex environment.

The invention discloses a three-dimensional millimeter wave time-varying channel estimation method and device, and belongs to the field of wireless communication, and the method comprises the steps: building a millimeter wave communication system containing array motion; a three-dimensional time-varying channel model considering the Doppler effect is established, composite signals, caused by array motion, in a three-dimensional time-varying channel are analyzed, each column of a direction matrix after synthesis is consistent with the array manifold of a planar array, and it is indicated that a moving linear array can form a virtual planar array; sparse representation of a three-dimensional time-varying channel is realized through linear array motion; a three-dimensional time-varying channel is recovered based on an iterative meshless weighting algorithm of array motion; comprising AoAs estimation and path gain estimation. According to the method, the negative influence of Doppler frequency shift in the time-varying channel on channel estimation is overcome, the virtual array element is constructed, channel estimation of the three-dimensional millimeter wave time-varying channel is realized through linear array motion, the channel estimation precision is improved, and the pilot frequency overhead and the algorithm complexity are greatly reduced.

The invention relates to the technical field of rotary table control, in particular to a high-precision rotary table intelligent control method and device based on improved ESO, and the method comprises the steps: constructing a mathematical model of a rotary table motor, and deriving a transfer function of the motor; respectively acquiring current and angle data of a turntable motor; building a turntable control system, wherein the three-axis turntable control system is composed of a current loop, a position loop and an ESO-based virtual control law; designing a virtual control law based on ESO; and superposing the designed ESO virtual control law output to the turntable current loop and position loop output, and embedding a control algorithm into a processing module of a turntable upper industrial personal computer to complete the intelligent control of the turntable. According to the method, a transfer function of a motor is established, a virtual control law based on improved ESO is designed on the basis of an extended state observation theory, cooperative control over the rotary table is achieved with PID control, and high-precision pointing of the rotary table is achieved.

The invention discloses a power load prediction method and device based on a hybrid ELM algorithm, electronic equipment and a storage medium, and the method achieves the prediction of a power load through an ELM prediction model (MT-ELM) mixed with MINRES and hidden layer traversal, can achieve the estimation of the number of optimal hidden layer nodes, and eliminates the overfitting. Therefore, the prediction level of the power load is effectively improved. The invention discloses a power load prediction method based on a hybrid ELM algorithm. The method comprises the following steps: S1, performing variation on input and output training sets to construct a training set suitable for an ELM prediction model; s2, calculating a hidden layer output matrix H of the ELM neural network through a calculation mode of a classic ELM model; and S3, traversing and estimating the number of optimal hidden points by using the input and output test sets, eliminating ELM over-fitting through a mixed MINRES method, learning through data over the years and generating an ELM mixed model, and predicting the future short-term power load condition through the generated model.

The invention relates to the technical field of aircraft system fault detection, in particular to an aircraft system fault detection method based on an unknown input observer. According to the method, joint estimation of the state variable and the fault signal of the nonlinear aircraft system can be realized, and the influence of external disturbance on the estimation performance is effectively reduced. According to the method, the defects of an existing fault detection method based on residual signals can be overcome, the fault occurrence time can be provided, the estimated value of the fault amplitude can also be provided, the fault can be further processed in the later period, and the method has wider and profound theoretical significance and practical application value; estimated values of state variables and fault signals of the system can be provided at the same time, faults can be detected in time, and a certain foundation can be laid for monitoring dynamic behaviors of the system in real time and completing specific control tasks.

The invention relates to a space camera on-orbit point spread function estimation method, a storage medium and equipment, and the method comprises the following steps: obtaining a blurred image collected by a camera, and obtaining a corresponding reference image based on the blurred image; taking the blurred image and the reference image as input of a trained PSF estimation model to obtain an estimated point spread function (PSF); the PSF estimation model comprises a first feature extraction network, a second feature extraction network and a PSF reconstruction network, the first feature extraction network takes a blurred image as input and is used for extracting a blurred image feature map, and the second feature extraction network takes a reference image as input and is used for extracting a reference image feature map; wiener filtering operation is carried out on the blurred image feature map and the reference image feature map to obtain a PSF feature map, and a PSF reconstruction network reconstructs and obtains the estimated PSF based on the PSF feature map. Compared with the prior art, the method has the advantages of high accuracy, simplicity in operation and the like.

The present application provides a method and device for estimating the DOA of a low-orbit satellite based on a phased array. The method provided by the present application includes: adjusting the calibrated fully connected multi-beam phased array based on multi-channel phase weighting to reconstruct a steering matrix; the steering matrix characterizes the response of the antenna unit of the phased array in multiple directions; wherein the phase weight corresponding to each channel of the antenna unit of the phased array is changed based on the multi-channel phase weighting, and the steering vector of the received signal in the antenna unit is reconstructed based on the phase weight; the steering matrix is ​​determined based on the steering vector; the steering matrix is ​​eigen-decomposed, and the DOA estimation result is determined based on the decomposition result; the motion trajectory of the low-orbit satellite is determined based on the DOA estimation result, and the DOA estimation result is updated based on the target tracking algorithm. The method and device provided by the present application are used to realize the DOA estimation of low-orbit satellites in maritime communication scenarios based on multi-beam phased arrays.

The invention discloses an air target threat degree comprehensive evaluation method, and adopts an air target threat degree comprehensive evaluation method based on minimum violation ranking. Through the method, the problem of evaluating the threat degree of the aerial target under the condition that observation information has uncertainty and fuzziness is solved. According to the method, the threat degrees of the targets in the air are reasonably ranked, good error interference resistance is achieved, and when the attributes of the targets calculated by a detection system have errors, the threat degree ranking of the targets does not change.

The present application relates to the field of active sonar target detection, and relates to a kind of adaptive target detection method and system suitable for reverberation edge environment.The method of the present application comprises: selecting the data of unit to be detected as main data, and selecting the received data of multiple distance units on both sides of the unit to be detected as overall auxiliary data;Overall auxiliary data is distributed in the three uniform reverberation areas formed by the left reverberation edge and the right reverberation edge of the unit to be detected, and is divided into three auxiliary data subsets accordingly;Modeling target adaptive detection as a binary hypothesis testing problem including no-target hypothesis and target hypothesis;Obtain the joint probability density function of main data and overall auxiliary data under no-target hypothesis and target hypothesis;Based on the modified two-step generalized likelihood ratio test criterion, the joint probability density function and the probability density function of main data and auxiliary data subsets are processed in turn, and the final detection expression is obtained to realize target adaptive detection.

The application discloses a kind of even circle array one-dimensional spatial spectrum direction finding signal processing device, including data processing and covariance matrix module, MUSIC algorithm processing module, Capon algorithm processing module, processing result fusion module and DML algorithm processing module;The function of data processing and covariance matrix module is received signal pre-processing, and covariance matrix is obtained by calculation;MUSIC algorithm processing module is calculated using input covariance matrix to obtain the MUSIC spatial spectrum of receiving array;Capon algorithm processing module is calculated from covariance matrix to obtain the Capon spatial spectrum of receiving array;Processing result fusion module analyzes and judges the calculation result of MUSIC spatial spectrum and Capon spatial spectrum, whether MUSIC spatial spectrum needs iterative search, obtains the direction finding result of incoherent signal, and judges whether it contains coherent signal;DML algorithm processing module processes a pair of coherent signal DOA calculation, and outputs spatial signal direction finding result.This device can accurately calculate the DOA of incoherent signal;And on the basis of incoherent signal direction finding, a pair of coherent signal direction finding is completed.

This invention discloses a soft-constraint target tracking method and system based on genetic resampling, belonging to the field of target tracking technology. The method includes: constructing a target tracking model based on the target being tracked; generating multiple particles required for particle filtering based on the initial state value of the target being tracked; updating each particle over time based on the target tracking model to predict the state and weight of each particle; resampling each particle using a genetic algorithm; calculating the corrected likelihood function of the resampled particles based on the measurement equation; correcting the predicted weights based on the corrected likelihood function; and updating the predicted state based on the corrected weights to obtain the final estimated state of the target being tracked. This invention can integrate known nonlinear inequality soft constraints in the tracking of ground moving targets, improving the tracking accuracy of ground targets under such soft constraints.

A coherent and incoherent signal mixed DOA estimation method based on vector space projection comprises the following steps: step S1, constructing a first pseudo-array manifold model Ac (theta) of a coherent signal and an incoherent signal of a target through a signal vector of an antenna signal; s2, solving a unitary matrix T for constructing a second pseudo-array manifold model A'c (theta) according to the signal subspace value Us and the total signal value X of all antenna signals; s3, according to the unitary matrix T, establishing a second pseudo array flow pattern model A'c (theta), # imgabs0 # A'c (theta) = [ac1,..., acp,..., acP]; acp represents a steering vector for linearly representing coupling of the pth group of the second pseudo-array manifold model through a correlation coefficient; s4, according to a beam space construction spectrum function, separating the coherent signal from the incoherent signal to realize incoherent signal DOA estimation; s5, a sparse reconstruction method is adopted, and the projection value omega pgt is obtained; and performing recovery of correlation coefficients according to the acp corresponding to the correlation coefficient 0, thereby realizing DOA estimation of the coherent signal. The method has the advantage that the DOA estimation is realized by distinguishing the incoherent signal sources under the condition that the coherent signals and the incoherent signals are mixed.

The invention belongs to the technical field of signal filtering, and discloses a roller internal defect ultrasonic signal filtering method, which adopts improved median filtering as a primary filter, can effectively eliminate high-frequency noise in ultrasonic signals, and greatly improves the parameter optimization convergence speed of a subsequent secondary filter. According to the method, the multi-stage filter is constructed as the second-stage filter, the intelligent optimization algorithm is adopted to replace a traditional least square algorithm to obtain the parameters of the filter, operation of a related parameter matrix and an inverse matrix of the related parameter matrix is not needed, and the filter parameter solving process is greatly simplified. The two-stage filter designed by the invention is simple in structure and easy to realize, does not need complex matrix operation, can effectively filter background clutters and interference signals in the roller ultrasonic signals, and provides a basis for subsequent roller internal defect automatic identification research.

The invention discloses an event-assisted unmanned aerial vehicle night precise landing system and method. The system comprises a high-frequency dynamic infrared sign array module, an event camera module, a pose resolving module and a landing control module. The system takes a high-frequency dynamic infrared mark array as a positioning reference, an event camera carried on the unmanned aerial vehicle captures an optical event flow with unique time sequence characteristics by accurately controlling the independent flicker frequency of each infrared light source, then the pose of the unmanned aerial vehicle is calculated by using an N-point perspective method, and finally accurate landing is realized by a landing control module. According to the invention, the event camera and the high-frequency dynamic infrared sign array are innovatively combined, the cooperative working mechanism of the event camera and the high-frequency dynamic infrared sign array is utilized, sub-millisecond-level positioning feedback is realized in a complex environment, the limitation of a traditional static optical sign is broken through, the positioning precision and reliability are improved, and the method is suitable for accurate take-off, landing and positioning of the unmanned aerial vehicle at night and in the complex environment.

The present disclosure relates to the technical field of machine learning, and specifically to a double-layer evidence fusion learning method and device, and a classification evaluation method and device of an evidence deep learning model. The method includes: obtaining a multimodal data set including training data of multiple sample objects, each sample object corresponding to multiple training data of multiple modalities; using an evidence deep learning model for the multimodal data set, adopting a double-layer evidence fusion strategy for each sample object, and obtaining the classification reliability result and classification uncertainty result of the sample object; constructing a loss function under the Dirichlet distribution according to the classification reliability result and the true category label of each sample object in the multiple sample objects, adjusting the model parameters of the evidence deep learning model, and obtaining an optimized double-layer evidence fusion evidence deep learning model. The present disclosure comprehensively considers the generalization of the model and the ability to estimate uncertainty, and improves the accuracy and confidence of data classification.

The invention discloses a robust control method suitable for four-rotor load lifting and delivery, is applied to the field of automatic control of aircrafts, and aims to solve the problem of contradiction between transient response and steady-state performance of an interference estimator of conventional parameters. The invention provides control based on a time-varying uncertainty and interference estimator (TVUDE), compared with a classical uncertainty and interference estimator (UDE), the control has a time-varying interference estimation bandwidth, and due to complementary constraints, the bandwidth is optimized for performance tradeoff between interference suppression and measurement noise amplification; the time-varying interference estimator estimates and compensates time-varying interference faced by the system in the transportation process in real time; the trajectory tracking controller obtains a control signal based on an interference signal matrix output by the time-varying interference estimator, and the control signal acts on an attitude controller in the bottom layer controller to generate a torque signal required by four rotors, so that accurate trajectory tracking of a target track by the four-rotor unmanned aerial vehicle is realized. And the stability of the quad-rotor unmanned aerial vehicle at the moment of throwing the load is ensured.

The invention relates to the technical field of motor control, and particularly discloses a high-precision rotating speed prediction control system and method for a permanent magnet synchronous motor, and the system comprises a rotating speed control module which is used for converting a given rotating speed signal of the permanent magnet synchronous motor into a q-axis component of a stator current given signal, the rotating speed control module is internally provided with a linear super-spiral sliding-mode observer which is used for realizing real-time observation of various torque disturbances and is based on a linear improved super-spiral algorithm; the current control module is used for receiving a three-phase current signal of the permanent magnet synchronous motor and a stator current given signal in the rotating speed control module, outputting a voltage given signal, and generating a PWM signal after SVPWM modulation; and the inverter receives the PWM signal and converts the DC voltage of the power circuit into a voltage signal for actually controlling the operation of the permanent magnet synchronous motor, so as to solve the problem of rotating speed fluctuation caused by load disturbance of the traditional permanent magnet synchronous motor.

The invention discloses a centralized photovoltaic power station force regulation electric charge standard exceeding solving method, which comprises the following steps of: obtaining a mathematical model of a power transmission line L based on historical data obtained by a centralized photovoltaic power station grid-connected measurement meter and the mathematical model of the power transmission line L; calculating to obtain monthly four-quadrant calculated active electric quantity and monthly four-quadrant calculated reactive electric quantity of a photovoltaic power station metering gateway meter in front of a grid-connected bus G of a superior substation; calculating an estimated value of the charging power of the power transmission line by using a weighted least square method according to monthly four-quadrant calculation active electric quantity, monthly four-quadrant calculation reactive electric quantity and actual values of the monthly four-quadrant calculation active electric quantity and the monthly four-quadrant calculation reactive electric quantity of the photovoltaic power station metering gateway meter; according to the estimated value of the charging power of the power transmission line, reactive power is given out by a reactive power compensation device in the centralized photovoltaic power station, and it is guaranteed that the force adjustment electric charge of the centralized photovoltaic power station does not exceed the standard. The invention further discloses a system for solving the problem that the force regulation electric charge of the centralized photovoltaic power station exceeds the standard. According to the invention, the operation economy of the centralized photovoltaic power station is effectively improved.

The present invention relates to the technical field of Internet of Things, and discloses an intelligent medical diagnostic bed control system and method based on the Internet of Things. The method comprises: performing multi-dimensional physiological data collection on a target patient based on a sensor group of the intelligent medical diagnostic bed to obtain a first physiological signal data set; performing third-order extended state observation analysis to obtain vital sign state estimation data; performing nonlinear tracking differential processing to obtain smoothed physiological signal data; performing intelligent diagnostic point planning modeling to obtain an optimized diagnostic node sequence; performing adaptive nonlinear control law calculation to obtain a first regulating control instruction; executing the first regulating control instruction and collecting a second physiological signal data set and performing deep learning analysis and dynamic adjustment of control instructions to obtain a second regulating control instruction. The present invention realizes dynamic adjustment of control instructions and can perform refined control according to real-time feedback from patients.

The application discloses a method and device for estimating carbon dioxide concentration in a vehicle and a storage medium, and relates to the technical field of vehicle ventilation. The method comprises the following steps: collecting a multi-modal signal in the vehicle, wherein the multi-modal signal comprises at least an image signal, a sound signal and a ventilation state signal; estimating a carbon dioxide generation rate and a net air exchange flow rate based on the multi-modal signal; and estimating the carbon dioxide concentration in the vehicle based on an air mass conservation equation, the carbon dioxide generation rate and the net air exchange flow rate. The method can estimate the current carbon dioxide concentration in the vehicle without installing an expensive gas concentration detection sensor, and can avoid the risk of driving caused by the increase of the carbon dioxide concentration in the vehicle cabin environment.

The application discloses a fracturing well perforation cluster flow inversion method and system based on distributed optical fiber acoustic vibration, and comprises the following steps: a response calibration model between unit flow rate and an acoustic energy index is established; a distributed optical fiber acoustic vibration system is arranged in a fracturing well wellbore; acoustic vibration signals are acquired and processed; the acoustic energy index is input into the calibration model to calculate volumetric flow; and a perforation cluster flow distribution map is output. According to the method, inversion processing and result output of volumetric flow of multiple perforation clusters in the fracturing well wellbore can be realized, estimation of the volumetric flow of the multiple perforation clusters in the fracturing well wellbore is realized, and technical support is provided for production monitoring, productivity diagnosis and fine regulation and control in the field of oil and gas reservoir development.

The present disclosure provides a control entity for a wireless communication system, the wireless communication system comprising a receiving entity, a transmitting entity, and a digital controllable scatterer (DCS), the DCS comprising a plurality of controllable scattering units. The control entity is configured to: determine a maximum number of training intervals; estimating a direct channel between the transmitting entity and the receiving entity; determining, for each training interval, a configuration set comprising one or more index subsets, each index subset comprising one or more indexes, each index corresponding to one of the plurality of scatter units included in the respective unit subset; an iterative process is performed to determine, for each training interval, an updated phase configuration for a plurality of scattering units of the DCS.

The application discloses a kind of underwater vehicle propeller shaft frequency and blade frequency variational holographic spectrum identification method and device, variational holographic spectrum method utilizes the thought of holographic representation, realizes the robust high-dimensional holographic representation of nonlinear non-stationary signal, obtains variational holographic spectrum diagram, by feature extraction to variational holographic spectrum diagram, realize the identification and estimation of underwater vehicle propeller shaft frequency, blade frequency and blade number;The method introduces variational mode decomposition, effectively solves the mode aliasing problem of EMD in holographic hilbert spectrum analysis method;According to the characteristics that propeller modulation signal in underwater vehicle radiation noise is low frequency slow change, segmented fourier transform is introduced, the problem that there is large error when hilbert spectrum analysis method is processed low frequency slow change signal is solved.The method solves the problem of insufficient representation when the commonly used radiation noise analysis method LOFAR analysis, DEMON analysis and various improved algorithms are analyzed in nonlinear non-stationary signal.

The invention provides a cooperative network connection vehicle control method considering the influence of model uncertainty. According to the invention, longitudinal vehicle tracking and transverse lane keeping control targets are realized by using a sliding mode control method. A longitudinal controller is designed in combination with an RBF neural network and a sliding mode control theory, and uncertain items in a vehicle model are processed by using approximation characteristics of the RBF neural network. By designing an auxiliary sliding mode surface, the chattering phenomenon existing in the transverse lane keeping controller is suppressed. Then, the internal stability and queue stability of the proposed controller are analyzed. Finally, the effectiveness of the algorithm is verified through a SIMULINK simulation experiment and an intelligent network connection vehicle platform experiment.

This invention discloses a controller orientation localization method and system based on the main lobe direction of beamforming, comprising: acquiring and receiving intermediate frequency or baseband signals from a UAV terminal to obtain timestamped IQ data, and dividing it into multiple time slices according to a preset duration; determining a corresponding angle label for each time slice to form an angle-data segment mapping relationship, wherein the angle label is generated by UAV motion scanning; performing spectral or time-frequency analysis on the IQ data of each time slice to determine the target frequency band where the target signal is located; extracting and normalizing intensity indicators; constructing radiation pattern data; performing radiation pattern smoothing and main lobe sector determination; and performing orientation output and confidence calculation. This invention does not require strict array calibration and multi-channel angle measurement, and can achieve controller orientation estimation under single-channel or few-channel UAV reception conditions. This invention does not rely on communication protocol demodulation, and can construct the radiation pattern based on the energy characteristics of the target frequency band, making it applicable to different standards and encryption conditions.

The invention discloses a battery pack capacity estimation method under abnormal charging interference of an electric vehicle. The method comprises a parameter setting module, a charging data selection module, a charging data abnormity detection module, an abnormal charging data processing module and an optimal capacity estimation module. The parameter setting module is used for setting necessary parameters, the charging data selection module is used for selecting signals for capacity estimation, the charging data anomaly detection module is used for finding anomalies in charging data, the abnormal charging data processing module is used for eliminating interference of abnormal data, and the optimal capacity estimation module is used for giving a reasonable capacity value. The method achieves the estimation of the capacity of the battery pack of the electric vehicle under the condition of abnormal charging through the special processing of the abnormal charging data, avoids the interference of the abnormal charging on an estimation result, improves the accuracy of the estimation of the capacity of the battery pack of the electric vehicle, and has a wide market application prospect.

This invention provides a method and apparatus for identifying errors in a multi-beacon long baseline positioning system. The method includes the following steps: acquiring an initial long baseline underwater positioning model; establishing a multi-beacon underwater positioning model based on the long baseline underwater positioning model and the geometric relationship between the underwater target center and the beacons; obtaining a system error identification model based on the multi-beacon underwater positioning model; calculating the time delay residual using the system error identification model and the underwater target positioning process; constructing an optimal test statistic D for the system error identification model by analyzing the time delay residual; obtaining a selection criterion for the system error identification model based on the optimal test statistic D; and selecting the optimal system error identification model based on the selection criterion. The system error identification model and optimal model selection criterion proposed in this invention can provide theoretical and technical support for high-precision measurement and navigation positioning of underwater targets.

The invention discloses an interference incoming direction estimation method and device, equipment and a storage medium, and is applied to the technical field of satellite navigation. The method comprises the following steps: constructing a spherical coordinate system by taking the center of a uniform circular array as a coordinate origin; obtaining a space steering vector of the array according to a phase relation of each array element in the uniform circular array relative to the origin of coordinates; constructing a unitary matrix to transform the array manifold of the uniform circular array to obtain a received data covariance matrix; obtaining an interference signal subspace through eigenvalue decomposition; constructing a generalized eigenvector reconstruction covariance matrix based on the interference signal subspace; defining a new interference signal subspace; and estimating the interference direction by using the new interference signal subspace and the space steering vector. According to the method, unitary matrix transformation array flow patterns are constructed, decorrelation processing is carried out on a covariance matrix after transformation based on the corresponding relation of feature vectors before and after transformation, generalized feature vectors and steering vector space, and estimation of the interference direction is achieved through a subspace algorithm.

The invention provides a target region tissue thermal treatment temperature distribution control method and system based on ultrasonic temperature measurement. The method comprises the following steps: acquiring an ultrasonic image sequence of a target region tissue under the condition that the target region tissue is preheated at a first temperature; calculating ultrasonic thermal strain according to the ultrasonic image sequence to obtain a temperature image of the target region tissue at the first temperature; according to the temperature image, the physical property parameters and the biological heat transfer equation of the target region tissue, determining individual parameters representing the thermal treatment sensitivity; determining target thermal treatment power according to the target temperature, the treatment time and the individual parameters of thermal treatment; and determining the temperature distribution of the tissue plane of the target region along with the time change according to the biological heat transfer equation and the individual parameters under the target thermal treatment power. Individual thermal sensitivity evaluation and target tissue temperature closed-loop control can be achieved by combining individual parameter measurement under the non-invasive condition, and the safety and accuracy of the thermal treatment process are improved.