167 results about "Non linear response" patented technology

The invention relates to the technical field of tunnel engineering, and discloses a tunnel multi-field coupling nonlinear deformation analysis method and system.The method comprises the steps that geological environment information of a tunnel area is collected, a multi-source physical field boundary condition model is built based on collected data, and a heat-seepage-stress-time four-field coupling control model is built based on the collected data; specifying a nonlinear response model for the geological medium to truly reflect the stress-strain behavior of the geotechnical material; solving a coupling equation set, and optimizing the four-field coupling control model; introducing measured data to correct the model; the system comprises a geological data acquisition module, a boundary condition modeling module, a coupling model establishment module, a numerical solution module, a measured data correction module and a visual output and control interface module. According to the method, high-fidelity prediction and evolution analysis of the deformation behavior of the tunnel under the complex geological condition are realized based on comprehensive acquisition of the geological environment, multi-field boundary modeling, control equation construction and numerical calculation.

The invention discloses a river flow missing data reconstruction method, which comprises the following steps of S1, constructing a river network topological structure, and quantifying hydraulic correlation; s2, spatio-temporal feature fusion and multi-source information extraction; s3, performing multi-task cooperative flow reconstruction and confidence coefficient prediction; s4, dynamic weighting and result correction of meteorological factors; and S5, performing anomaly detection and model dynamic updating. According to the method, a river flow missing data reconstruction method is set, the steps are mutually fused and used, and topology-aware space-time diagram convolution is performed: a river network topology structure is encoded into a weighted adjacency matrix for the first time, space correlation features are extracted through a diagram convolution network, and the problem that a traditional method ignores hydraulic connection is solved; a multi-task collaborative learning mechanism: synchronously outputting a flow reconstruction value and confidence, combining topological smooth constraints, and realizing reconstruction reliability quantification while ensuring precision; and meteorological dynamic weighted correction: dynamically adjusting the node weight based on the real-time rainfall intensity, and accurately adapting to the nonlinear response of the water flow in the heavy rainfall period.

The invention relates to the technical field of electronic component testing for investigating or analyzing internal microstructures and physical properties of materials by utilizing an electrical means, and discloses an online testing and analyzing method for physical properties of electronic component materials, which comprises the following steps of: constructing a composite excitation signal formed by linearly superposing a main carrier voltage and a perturbation wave voltage; applying the signal to a to-be-tested electronic component and acquiring response current; and extracting an intermodulation current amplitude at an intermodulation frequency point in the frequency spectrum of which the linear response interval is excluded, calculating a nonlinear response coefficient of the material in combination with the excitation amplitude, and comparing the nonlinear response coefficient with a preset degradation threshold so as to judge whether the nonlinear response coefficient is qualified or not. By extracting intermodulation components which cannot be generated by linear contact resistance, physical isolation of external contact noise in a frequency domain is realized, and the problem that contact resistance fluctuation and material intrinsic aging cannot be distinguished in a traditional linear impedance test is solved.

The invention discloses a liquid hydrogen container heat leakage prediction and boost characteristic detection method and system. The method comprises the following steps: establishing a three-dimensional heat flow field model of a liquid hydrogen container; calculating the heat flow density of a gas-liquid interface based on the three-dimensional heat flow field model so as to adjust the liquid hydrogen evaporation capacity; total heat leakage is obtained through finite element simulation and experimental data collaborative calibration; calculating a static evaporation rate according to the total heat leakage; establishing a nonlinear mapping model of the static evaporation rate and the boost rate through a response surface regression method; the wall temperature distribution of the liquid hydrogen container is monitored in real time, thermophysical parameters are calibrated through the LSTM neural network by adopting a dynamic calibration algorithm, and the output result of the three-dimensional heat flow field model is corrected. By establishing a three-dimensional heat flow field model of the liquid hydrogen container, determining a nonlinear response relationship between the evaporation rate and the pressure rising rate of the liquid hydrogen container, and constructing a cloud-edge collaborative detection system, the heat leakage performance of the liquid hydrogen container is accurately predicted, and efficient pressure rising rate detection is realized.

The invention relates to optical plane correction, and discloses an infrared focal plane array non-uniformity correction method based on integral time adjustment, which comprises the following steps: for each pixel of an infrared focal plane array, respectively establishing a nonlinear response model representing an incidence relation among integral time, radiation flux and detector output response; according to the radiation distribution characteristics of the current scene and the independent response characteristics of each pixel, the optimal integral time is determined for each pixel through an adaptive optimization algorithm; dynamically updating the parameters of the nonlinear response model of each pixel based on the deviation between the actual output of the detector and the output of the nonlinear response model; and compensating the response nonlinear difference between different pixels based on the optimal integral time, and performing further linear compensation on the compensated signal based on a two-point correction formula. With the adoption of the method, the defects of a traditional correction method in the aspects of nonlinear correction, scene adaptation, drift adaptation and the like are overcome, and the imaging quality of the infrared focal plane array is effectively improved.

The invention provides a wind-induced response prediction method and device for a large-span bridge structure and electronic equipment, and the method comprises the steps: obtaining the wind power response data of a bridge in a wind environment; decomposing the wind power response data by adopting a dynamic mode decomposition algorithm to obtain first mode information in the current wind environment; and inputting the mode information into a pre-trained target neural network model, and outputting a wind-induced response prediction result. According to the method, through the synergistic effect of the physical insight of the DMD and the data analysis capability of the neural network, the multi-scale flow mode can be accurately analyzed in real time, meanwhile, the dependence on a large number of computing resources is reduced, the DMD and the neural network model are fused, and the fusion strategy not only solves the limitation of the traditional DMD on nonlinear response prediction, but also improves the prediction accuracy of the DMD. And the defects of the neural network in physical law understanding are overcome, and high efficiency, accuracy and intelligence of wind-induced response prediction are realized.

The invention discloses a laser intelligent management system of a laser particle analyzer, particularly relates to the technical field of laser control, and is used for solving the problem that pollution monitoring is not accurate due to intermittent work of a laser when an existing laser particle analyzer is in an energy-saving mode. By synchronously acquiring a laser working state signal and a background scattered light signal of an optical window, whether the laser is in an initial stage awakened from a low-power-consumption state is judged, and a nonlinear response stage dominated by pollutant state change in the background scattered light signal is identified; analyzing the symmetry of the signal change track at the stage to generate a symmetry breaking index, and synchronously executing Granger causal relationship test on the driving current and background scattered light signal time sequence data to obtain a causal relationship index; and distinguishing signal abnormal sources based on a symmetry breaking index and a causal relationship index so as to evaluate the current pollution level of the optical window, adaptively adjusting pollution monitoring parameters and controlling the working mode of a laser.

The invention discloses a real-time reserve pool calculation method and system based on pulse amplitude information coding, and belongs to the technical field of reserve pool calculation of space-time sequence tasks. The method comprises the steps that modal information in a real environment is received and preprocessed, encoding is carried out according to time steps, and the codes are pulse sequences with different pulse amplitudes; feeding the pulse sequence into an SL end of a volatile memristor array, writing the pulse sequence into the volatile memristor array, and receiving nonlinear response current of dynamic volatile memristor array space projection at a BL end in real time; receiving the response current by the FPGA, sampling at equal intervals to construct a virtual node, converting the response current into a voltage signal through a TIA, and feeding the voltage signal into a pre-trained nonvolatile memristor array for reasoning; and receiving the response current, outputting a maximum value corresponding neuron index through the WTA winner eating circuit, representing a classification or prediction result, and transmitting the result back to an upper computer or performing action decision at a local end.

The invention discloses a method for detecting pollutant concentration based on deep ultraviolet Raman spectrum of deep learning, and belongs to the technical field of spectral analysis. According to the method, aiming at the problems of spectral line overlapping interference, weak trace signals, strong background noise, nonlinear response and the like when a traditional deep ultraviolet Raman spectrum technology is used for detecting pollutants, high-precision quantitative analysis is realized by constructing an SSA-CNN-LSTM-Attention fusion model and combining a deep ultraviolet Raman spectrum pretreatment technology. The model adopts 1D-CNN to extract spectral local features, LSTM to capture sequence long-range dependence, an Attention mechanism to enhance feature peak response weight, SSA to optimize hyper-parameters, and a concentration prediction value is output through a weighted loss function optimization model. And meanwhile, by combining a synthetic data expansion strategy and environment matrix standard sample library training, the adaptability of the model to a complex scene is improved. According to the method, the anti-interference capability and generalization of new pollutant detection are effectively improved, and the method has important application value in the fields of environmental monitoring, industrial safety and the like.

Aspects and embodiments disclosed herein include a surface acoustic wave resonator comprising a multilayer piezoelectric substrate and interdigital transducer (IDT) electrodes disposed on an upper surface of the multilayer piezoelectric substrate, the IDT electrodes having at least partially rounded upper sides.

The invention discloses an intracranial pressure intelligent monitoring system, which relates to the technical field of intracranial pressure monitoring, and comprises a continuous pressure conduction chain construction module for establishing a continuous pressure conduction chain of an intracranial pressure signal, carrying out time sequence segmentation analysis on the intracranial pressure signal acquired in real time, and obtaining a continuous pressure conduction chain of the intracranial pressure signal; identifying a section with lagging and smooth abnormity in the pressure feedback curve, and determining the section as a nonlinear response positioning range; and the elastic pressure buffer window adjusting module introduces an elastic pressure buffer window in the nonlinear response positioning range and adaptively adjusts the width of the elastic pressure buffer window according to the pressure change rate of the adjacent time slices. By constructing a continuous pressure conduction chain and introducing an elastic buffering and layering rearrangement mechanism, it is ensured that an intracranial pressure signal accurately reflects true pressure changes in the abnormal stage. Through dynamic weight band and rhythmicity balance regulation and control, self-adaptive stability of signal energy and a time structure is achieved, and continuity and early warning accuracy of intracranial pressure monitoring are improved.

The invention discloses a 5G microwave amplifier performance test method based on automatic spectrum analysis, and relates to the technical field of data analysis, by introducing a disturbance excitation and recovery behavior comparison mechanism, the short-time dynamic response of an amplifier spectrum is actively induced, the point-by-point difference of a reference spectrum, a disturbed spectrum and a recovery spectrum is analyzed, and the performance of a 5G microwave amplifier is tested. A response-recovery ratio indicator is constructed to identify a local indentation region that is difficult to perceive in the steady-state spectrum. Compared with a traditional method depending on a frequency spectrum smoothing strategy, the method has the advantages that the problem of slight indentation in a high-frequency bandwidth is easy to cover, specific frequency point response is excited through frequency perturbation, power fluctuation or narrow-band interference, and the disturbance response and recovery capability difference of the amplifier become an evaluation basis; therefore, the recognition accuracy of nonlinear response and early failure characteristics is improved, and the stability pre-judgment capability of the 5G amplifier is enhanced.

The invention relates to the technical field of transformer substation operation environment regulation and control, and discloses a transformer substation operation environment low-carbon energy-saving intelligent regulation and control method, system and device and a medium. The method comprises the steps that transformer substation operation environment parameters and device state signals are collected, and a first environment device coupling dynamic representation model is constructed; and in combination with the time sequence prediction and condensation risk assessment sub-model, the nonlinear response relationship of various factors to the heat and humidity distribution and condensation risk of the cabinet is described, and a foundation is laid for accurate regulation and control. And in collaborative optimization, the second multi-target collaborative optimization regulation and control model comprises an upper-layer global energy consumption optimization and lower-layer local environment precise control target function, so that the overall energy consumption is reduced. And low-carbon operation targets and equipment safety constraints are considered, so that low-carbon energy conservation is realized during safe operation of the equipment. The solving strategy is a first improved solving strategy based on model predictive control and particle swarm optimization improvement, the efficiency and precision of processing the complex multi-target collaborative optimization regulation and control problem are improved, and the optimal regulation and control scheme is found quickly and accurately.

The invention belongs to the field of LED display control, particularly relates to a software-definable LED driving chip gray scale display control method, and aims to solve the problems that the refresh rate of each gray scale cannot be flexibly adjusted, the nonlinear response of an LED driving chip and a lamp bead cannot be compensated, first scanning darkness cannot be solved and ghosting cannot be effectively eliminated in the prior art. The method comprises the following steps: determining the number of refresh cycles of one frame of display time and the number of gray scales displayed in each refresh cycle; calculating the initial gray scale weight of each gray scale bit, and adjusting the gray scale weight; scanning the transformation moment line by line according to each refresh cycle, and adjusting the position of the gray scale OE position in the subfield; and when to-be-displayed data is received, controlling the LED driving chip to complete gray scale display of the LED display screen. According to the method and the device, the refresh rate of each gray scale is flexibly adjusted, the nonlinear response is compensated, and the first scanning darkness and the ghosting are effectively eliminated.

A system configured to reduce loudspeaker distortion by performing nonlinear signal processing is provided. A device may include preprocessing component(s) that apply nonlinear signal correction prior to sending a playback audio signal to a driver in order to compensate for a nonlinear response of the driver. While the driver response may be nonlinear, a combination of the preprocessing and the nonlinear driver response results in a combined response that is linear and / or compensates for the nonlinear driver response. For example, applying the nonlinear driver response to a processed audio signal may result in output audio generated by the driver accurately reproducing the playback audio signal input to the preprocessing components. To train the preprocessing components to apply the nonlinear signal correction, a deep neural network (DNN) is trained to model the driver response.

The invention discloses a construction method of a river network hydrodynamic condition visualization system based on digital twinning, and the method comprises the steps: employing a dual gradual change dynamic visual angle switching algorithm, and carrying out the smooth transition to a three-dimensional visualization environment through user interaction in a two-dimensional interface; a semantic deployment modeling mechanism and a three-dimensional river mechanism based on water level interpolation are utilized to automatically generate a building model and a river path in a river-side area, and a water flow material and a flow animation are constructed through a Cesium platform to realize flow effect rendering; water depth and flow velocity data under a continuous time sequence are dynamically acquired, real-time rendering is performed on a Cesium platform, continuity and smoothness of a visual effect are guaranteed by using a nonlinear response interpolation algorithm based on animation driving, and a simulation time step length is controlled through a timer mechanism, so that real-time simulation of the water depth and the flow velocity is realized. And accurate visual expression of the hydrodynamic force change process under different time scales is supported. And users are supported to carry out interactive operation under different visual angles and time dimensions, so that the visual expressive power of the river network hydrodynamic data and the system response efficiency are effectively improved.

The invention provides an active load prediction method and device for a low-voltage distribution transformer area, relates to the field of transformer area load prediction, and solves the technical problem that a prediction error can be sharply increased in a non-linear response facing an air temperature abrupt change influence. The method comprises the following steps: collecting and cleaning multi-source data of a transformer area to obtain electrical measurement data; obtaining weather forecast data of a transformer area location, and performing tensor conversion to obtain a three-dimensional tensor; and obtaining an electric regulation and control policy, performing time sequence alignment on the effective time of the electric regulation and control policy and the three-dimensional tensor to form a bimodal input matrix, inputting the bimodal input matrix and the electric measurement data into the multi-factor coupling probability prediction model, and outputting a load prediction value. The method is used in the active load prediction process of the low-voltage distribution transformer area. According to the method, meteorological parameters, policy texts and other unstructured data are converted into quantifiable features, and high-precision load prediction within 48 hours, 72 hours and other time periods is realized through a multi-factor coupling prediction model.

The invention relates to the technical field of bridge structural members, in particular to a steel-concrete composite bridge UHPC pavement structure finite element model construction method, which comprises the following steps of: acquiring structural parameters and UHPC pavement characteristics of a steel-concrete composite bridge, dividing regions, generating a finite element initial grid set, loading a bridge deck load, calculating stress and displacement response of a UHPC pavement layer in each time step, and constructing a finite element model of the UHPC pavement structure. According to the method, by dividing the structural area and introducing the microfracture factor, finite element grid generation is improved, mechanical behaviors are accurate, stress and displacement response can be obtained by combining dynamic analysis and real-time monitoring, and the method is suitable for the dynamic analysis and real-time monitoring. Potential damage is warned in advance, the stability and durability of the bridge are improved by recognizing a nonlinear response area and optimizing interface mechanical analysis, the design precision and long-term performance prediction of the structure are integrally improved, and the reliability of the bridge under the complex working condition is guaranteed.

The invention relates to the technical field of solid mechanical modeling, and solves the technical problem that an existing unified mechanism modeling strategy neglects geometric nonlinear characteristics of local weak-rigidity parts of an assembly, so that the static response prediction precision of the assembly is not high. The assembly deformation stress cooperative control-oriented aircraft rigid-flexible hybrid structure multi-level analysis method comprises the following steps: carrying out linear and nonlinear response analysis on finite element models of rigid framework parts and weakly rigid thin-wall parts, and establishing a linear mapping model and a nonlinear explicit mapping model; combining the two models to obtain a deformation stress analysis model; and obtaining a clamp holding force-displacement stress mapping model for representing the mapping relationship between the multi-point holding force of the assembly and the node displacement and the node stress based on the deformation stress analysis model. According to the method, the problems of lack of explanation and few mechanisms of a variable stiffness structure model are solved, and efficient prediction and damage early warning of the internal stress of the component are realized.

This application provides a calibration method, an infrared imaging device, and a storage medium for an infrared detector. The method includes: acquiring the original response values ​​of each pixel of the infrared detector; determining the current response value interval of each pixel based on the original response values ​​and the boundaries of multiple pre-calibrated response value intervals of the infrared detector, wherein multiple response value intervals are pre-calibrated based on the characteristics of the response curves of each integration channel of the infrared detector; acquiring the correction coefficient corresponding to the current response value interval of each pixel based on the pre-calibrated correction coefficients corresponding to each pixel in different response value intervals; correcting the original response values ​​of each pixel based on the correction coefficients; and outputting the corrected response values ​​of each pixel. This method effectively improves the non-uniformity correction effect of the image, especially in scenes with large temperature differences, and can significantly suppress horizontal stripe residue caused by nonlinear response.

This invention belongs to the fields of rock mechanics, geological engineering, and geophysical exploration technology. Specifically, it discloses a method and system for constructing a mapping relationship between acoustic wave velocity and surrounding rock stress. The method includes: performing uniaxial graded loading on a standard sandstone sample and simultaneously measuring the P-wave velocity and S-wave velocity under each stress level; then fitting a linear mapping relationship between the P-wave / S-wave velocity ratio and the axial stress of the surrounding rock; introducing joint correction coefficients and water content correction coefficients to optimize the linear mapping relationship and constructing an acoustoelastic model; and using the acoustoelastic model and field-measured wave velocity data to invert the stress state of the actual surrounding rock, thus completing the construction of the acoustic wave velocity-surround rock stress mapping relationship. This invention solves the problems of traditional wave velocity-stress relationships, which, when describing the nonlinear response of rocks at different stress stages, are not sensitive enough to subtle stress changes and lack a complete, high-precision, and interference-resistant stress inversion calculation method, making them difficult to directly apply to complex actual engineering environments.

The invention provides an industrial solid waste intelligent classification method based on big data, and the method comprises the steps: obtaining the porosity of fly ash and steel slag oxide layer data, analyzing the surface microscopic characteristics, and extracting the light scattering intensity and infrared radiation characteristics; according to the non-linear interaction relationship among the humidity, the temperature and the surface characteristics, determining the correlation representation of the environmental parameters and the surface characteristics of the fly ash and the steel slag; extracting classification feature semantics from the association representation of the environmental parameters and the coal ash and steel slag surface characteristics, and constructing a coal ash and steel slag semantics enhanced classification feature set; according to the nonlinear response characteristics of the fly ash and the steel slag extracted from the classification feature set, the recognition accuracy of the classification features is optimized, an optimized classification feature set is obtained, solid waste types are classified, and a preliminary classification model is generated; and obtaining a classification error sample from the preliminary classification model, carrying out iterative optimization on the preliminary classification model to obtain a final classification model, and outputting a solid waste classification result adaptive to environmental change.

The invention provides an automatic Gamma correction method and device, display equipment and a storage medium, and the method comprises the steps: adjusting a register value through a dynamic adaptive step length search algorithm for a plurality of preset binding points, and enabling the current brightness of the binding points to approach the target brightness; based on the register value of the adjusted binding point and the brightness data, calculating a register value of a non-adjusted binding point by adopting a first fitting model; monotonicity check is performed on the calculated register value, the non-monotonic register value is corrected, and polynomial fitting is adopted to replace traditional linear interpolation, so that the nonlinear response characteristics of the panel can be fitted more accurately, the deviation degree of a Gamma curve is remarkably reduced, visual color gradation faults are reduced, and the performance of the panel is improved. Compared with a traditional one-by-one approximation method, the dynamic step length algorithm is accelerated by about 30%, the time consumed for single-machine adjustment on a production line is effectively shortened, a closed-loop feedback mechanism is introduced, an automatic adjustment strategy can be shown in real time according to a panel, and manual adjustment and optimization according to different IC models are not needed.

A noise reduction system for actively compensating background noise in a passenger transport area of a vehicle. The noise reduction system includes a nonlinearity filter unit having a model of a non-linear transfer function of the sound generator, wherein the nonlinearity filter unit is configured to receive the anti-noise signal and to generate a filtered anti-noise signal by applying a non-linear filter function on the anti-noise signal, which is based on the model of the non-linear transfer function in that the non-linear response of the sound generator is at least partially corrected when driven by the filtered anti-noise signal. Wherein the nonlinearity filter unit is further configured to output the filtered anti-noise signal to a sound generator.

A surface acoustic wave resonator comprising a multilayer piezoelectric substrate including a layer of piezoelectric material, and interdigital transducer (IDT) electrodes disposed on the layer of piezoelectric material, the IDT electrodes including a first and second bus bars, a first plurality of electrode fingers extending from the first bus bar toward the second bus bar, and a second plurality of electrode fingers extending from the second bus bar toward the first bus bar and interleaved with the first plurality of electrode fingers in a central region of the resonator, gap regions defined between tips of the first plurality of electrode fingers and the second bus bar and between tips of the second plurality of electrode fingers and the first bus bar, regions of piezoelectric material forming the layer of piezoelectric material in the gap regions having a lesser permittivity and / or piezoelectricity than the piezoelectric material in the central regions.

A method for robotically controlling an endoscope includes navigating an elongate shaft within a patient's body, the elongate shaft including a tip at a distal end; articulating the tip in a first direction using a first pull wire coupled to a dual-wire pulley; reversing the articulation of the tip to a second direction using a second pull wire coupled to the dual-wire pulley based on a nonlinear response region of a kinematic model; determining an end point of the nonlinear response region; and articulating the tip based on the linear response region when the articulation of the tip in the second direction reaches the end point.

The invention discloses a neural network algorithm design method and a neural network operation method based on a resistive memory, and the method comprises the steps: determining the nonlinear response characteristics of the resistive memory, and determining the adjustment mode of the resistive memory according to the response characteristics; determining a parameterization method of back propagation of the neural network, wherein the number and the connection mode of the resistive memories and the inter-layer signal conversion mode of the neural network are determined according to the task of the neural network; according to the adjustment mode of the resistive memories, the number and the connection mode of the resistive memories and the signal conversion mode between layers of the neural network, the forward propagation process of neural network operation is realized; realizing a back propagation process of the neural network according to a parameterization method of back propagation of the neural network; and training the target task to obtain a trained neural network model. The hardware architecture does not need an analog-to-digital converter and the like which occupy a large amount of area and power consumption, so that the power consumption and the design area of storage and calculation hardware can be greatly reduced.

This invention discloses a system and method for monitoring the hydrogen damage state of metals, comprising: a hydrogen charging device for electrochemically charging a plate-shaped sample of the same metal as the target metal that has not undergone hydrogen damage; a hydrogen permeation measuring device for converting hydrogen atoms permeating into the sample into hydrogen ions during the hydrogen charging process and measuring the current generated by the free electrons produced during the conversion process in real time to obtain a hydrogen permeation curve; and an ultrasonic measuring device for emitting ultrasonic guided waves to the sample during the hydrogen charging process and receiving the nonlinear response signal generated by the guided waves exciting ultrasonic waves inside the sample, and then performing cepstral analysis on the received signal to obtain the nonlinear coefficients at different time periods. Based on this, combined with the hydrogen permeation curve, the correlation between the nonlinear response signal and the degree of hydrogen damage is determined, and then the nonlinear response signal of the target metal is substituted into the correlation to obtain the hydrogen damage state inside the metal. This invention can monitor and evaluate the degree of hydrogen damage inside metals in real time.

The application discloses a pile foundation damage identification method based on characteristic curve matching low strain, and belongs to the technical field of pile foundation detection, and specifically comprises the following steps: applying a standardized impact excitation to a target area pile foundation, collecting a time-domain velocity response signal and generating a full-band phase change curve; identifying a directional turning point on the curve, recording a turning direction symbol, a reflection depth and a phase angle change rate absolute value; constructing a three-dimensional coordinate model to cluster the turning points to obtain an aggregated cluster; generating a virtual geological interface in combination with geological stratification data, calculating a spatial overlap degree of the aggregated cluster and the interface to screen a to-be-determined common aggregated cluster; and verifying energy sensitivity, and distinguishing rock layer reflection and damage aggregated clusters according to linear or nonlinear response characteristics. The application distinguishes geological interface reflection and real pile body damage signals in each pile foundation, and realizes accurate evaluation of the damage condition of the pile foundation.

The invention discloses a method and a device for testing the radio frequency direct current conversion efficiency of a diode. The method comprises the following steps: S1, providing input power; s2, adjusting source traction and load traction; s3, adjusting the resistance value of a variable resistor for voltage division; s4, measuring the actual input power of the diode to be tested through the vector network analyzer; s5, obtaining energy efficiency conversion efficiency according to the maximum direct current output power and the actual input power; s6, increasing the input power of the signal source for inputting the diode to be tested, and returning to S2 to obtain the energy efficiency conversion efficiency of the current round; and when the energy efficiency conversion efficiency is not improved any more, the energy efficiency conversion efficiency of the current round serves as the final energy efficiency conversion efficiency. According to the invention, the input power and the load impedance can be adjusted, the conjugate of the source traction and the input impedance can be adjusted according to different input powers, the impedance of the direct current load can be dynamically adjusted through the load traction, the nonlinear response characteristics of the diode under different working conditions are accurately captured, and accurate data support is provided for the design and optimization of a microwave rectification circuit.