261 results about "Non linear response" patented technology

The invention relates to the technical field of agricultural information perception and decision making, in particular to a multi-source perception fused agricultural monitoring method and system. The method comprises the following steps: converting a multi-source heterogeneous agricultural sensing signal into a space-time tensor, constructing a semantic resonance field to simulate nonlinear coupling between modals, and driving multi-modal data to adaptively aggregate by using a gravitational evolution mechanism to form a fused semantic field; calculating non-linear response to generate an agricultural state emergence index, and according to the index, identifying a potential risk area and constructing a binary risk map; for the risk area, semantic disturbance is mapped into an agricultural variable disturbance vector through a modal decoupling matrix, a minimum intervention strategy is generated in combination with sparse optimization of an operation response matrix, and feasible operation suggestions are output after verification of an agricultural knowledge graph; a drift potential energy function is constructed based on strategy execution feedback, strategy parameters are dynamically updated through gradient descent, and closed-loop self-evolution optimization is achieved in combination with trend prediction. According to the invention, full-link adaptive optimization from multi-source sensing to regulation and control decision is realized.

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 relates to the field of liquid crystal display, and discloses a liquid crystal display screen dynamic contrast optimization method and system, and the method comprises the following steps: collecting ambient light intensity and backlight module temperature parameters in real time, and combining with image content entropy analysis to dynamically generate a non-uniform backlight partition grid; a brightness diffusion physical model is constructed based on hardware parameters such as the refractive index of the light guide plate and the LED spacing, and the optimal brightness of each partition is solved by adopting a temperature self-adaptive improved particle swarm algorithm; driving voltage is generated through piecewise nonlinear gamma mapping and inverse function iterative calculation, and the driving voltage is adapted to the nonlinear response characteristics of the backlight module; a hardware accelerator is used for executing a diffusion equation and optimization calculation in parallel, and model parameters are dynamically adjusted in combination with feedback of an optical sensor, so that a closed-loop control link is formed. According to the method, the contrast performance and the dynamic scene adaptability of the display screen are comprehensively improved through dynamic partition adaptive to the environment, an optimization algorithm driven by a physical model and real-time processing accelerated by hardware.

The invention belongs to the technical field of nondestructive testing, and particularly relates to a detection method for quantitatively evaluating fatigue damage in a pipeline through a nonlinear ultrasonic technology. 316 stainless steel and SA335GRP-11 ferrite steel samples are adopted, a welding seam area is formed through V-shaped groove welding, fatigue loading is conducted, and actual pipeline fatigue damage is simulated; for the samples with different fatigue cycles, nonlinear response signals of the samples are obtained; fourier transform is carried out on the obtained signal to obtain an energy spectrum of the signal, and energy changes of the signal on a fundamental frequency band and a second harmonic frequency band are analyzed; and calculating a nonlinear coefficient, and analyzing a change rule of the nonlinear coefficient along with fatigue cycles, so that the evolution process of fatigue damage in the pipeline is evaluated. According to the method, energy is used for replacing the amplitude value, on the basis of the formula principle, integration is used for replacing the amplitude value to improve the intensity of the characterization nonlinear ultrasonic signal, the early stage of damage and the later stage of crack initiation are detected, and reliable guarantee is provided for safe operation of an industrial pipeline system.

The invention provides a diffractive optical element design and manufacturing integration method based on gray level laser direct writing photoetching, which comprises the following steps that: a manufacturing simulator and a wave optical simulator are established, and the manufacturing simulator represents the spatial frequency response of an optical system through a low-pass filtering model; a mapping relation between exposure dose and a developing result is established by combining nonlinear response of photoresist, and the wave optical simulator predicts optical performance by utilizing output of the manufacturing simulator and combining a wave optical theory; by setting initial manufacturing parameters and performing iterative optimization, the surface appearance of the photoresist is dynamically adjusted to be close to the target optical performance, the optimization process is combined with an automatic differential technology, a gradient optimization algorithm and a block optimization strategy, and the calculation efficiency is improved; and the optimized parameters are used for processing gray laser direct writing equipment to manufacture the diffractive optical element. The problem of manufacturing deviation caused by the optical proximity effect in the prior art can be solved, and meanwhile the overall performance of the diffraction optical element is remarkably improved.

The invention discloses a time-space dynamic weight-based building monitoring data accurate registration method, which comprises the following steps of: establishing a response model considering nonlinear behaviors of materials by analyzing deformation characteristics in multi-source monitoring data; constructing a dynamic weight function based on a structural stress state, and realizing self-adaptive distribution of sensor data reliability weight; geometric deformation correction is carried out by using the material nonlinear response model, and geometric distortion caused by material nonlinear behaviors is corrected; space-time registration of multi-source data is realized by adopting a weight fusion and deformation gradient constraint method; and parameter adaptive optimization is carried out through an online learning mechanism. According to the method, the technical problems of low geometric correction precision of a high-stress area, dynamic change of sensor reliability and the like are solved, and submillimeter-level precision building monitoring data registration is realized.

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 discloses a detection method for judging the quality of a cable-stayed pipe, relates to the technical field of cable-stayed pipe quality detection, and is used for solving the problem of poor cable-stayed pipe quality detection precision. The optical fiber sensors are arranged on the surface of the cable-stayed pipe to collect strain data, a continuous space-time strain field is constructed, local gradient changes are extracted, identification of a micro-damage area is achieved, a node response frequency spectrum is obtained in combination with a multi-frequency-band acoustic excitation means, nonlinear energy and frequency coupling characteristics are extracted, and the micro-damage area is identified. Calculating a nonlinear display probability to locate a potential nonlinear response concentration area of the structure, constructing a topological graph model of the structure, reflecting a mechanical connection relation between nodes and monitoring abnormal response, when an abnormal structure behavior is detected, carrying out topological structure state analysis on the area where the abnormal structure behavior is located, extracting key information such as structure coupling and state propagation capability, and carrying out state analysis on the abnormal structure behavior; and high and low risk signals are generated according to a judgment result, so that the accurate identification and intelligent regulation and control of the quality change of the cable-stayed pipe are realized.

The invention relates to the technical field of temperature control, in particular to a heating and ventilation heating power constant-temperature variable-flow control method, and provides the following scheme that circulating pump frequency instructions and water temperature response data of multiple historical regulation and control periods are collected, and a dynamic track of the current water temperature in a temperature inertia field is constructed; calculating an attraction potential energy state between the current water temperature and a target constant temperature value in combination with the disturbance residual matrix; and outputting the frequency bandwidth interval of the circulating pump based on the state to realize dynamic adjustment. An adjacency matrix and a Laplacian matrix are constructed by introducing a hydraulic communication relation, spatial convolution and weighted updating are performed on disturbance residues in combination with a topological diffusion core, and accurate propagation and suppression of disturbance are realized. The method can effectively solve the problems of nonlinear response, delay effect and disturbance coupling of the water temperature to the pump frequency, and the temperature stability and the energy utilization efficiency of a heating system are improved.

The invention discloses a runoff change attribution nonlinear contribution identification method based on segmented hydrological simulation. The method comprises the following steps: dividing an annual runoff sequence into a natural period and a human activity influence period; selecting a watershed hydrological model, and respectively calibrating parameters of the watershed hydrological model in a natural period and a human activity influence period by using hydrometeorological data; analyzing the influence of climate change on runoff change; analyzing the influence of human activities on runoff changes; combining the influence of the climate change and the human activity on the runoff change, and analyzing the synergistic effect of the climate change and the human activity on the runoff change; and quantifying the contribution rate of climatic change, human activity and synergistic effect of the climatic change and the human activity to runoff change. According to the method, on the basis of runoff change attribution linear analysis, the synergistic effect of climatic changes and human activities is recognized in an emphasized manner, the recognition result can objectively reflect the nonlinear response of the hydrological process to environmental elements, and the method is effectively applied to the specific work of watershed water resource evaluation and management.

The invention discloses a mine card simulation method and system based on log data, and relates to unmanned driving. The method comprises the following steps: obtaining operation log data of a mine card under multiple working conditions; extracting nonlinear response feature data according to the running log data; taking the historical control instructions and historical vehicle states of continuous N periods as input characteristics, taking the vehicle state of the current period as an output label, and constructing a training set; using the training set to train an LSTM model, inputting a current control instruction, a historical vehicle state and a current load value, and outputting a predicted vehicle response state; and inputting the predicted vehicle speed, acceleration, front wheel turning angle or hinge angle into a vehicle kinematic model, and calculating the position and attitude of the vehicle under the current load condition. Aiming at the problem that the time delay between a control instruction and an actual response in a super-large inertial system cannot be processed due to the fact that a non-linear dynamic response caused by the fact that mine truck large load change cannot be accurately simulated by adopting fixed dynamic parameters in mine truck simulation, the simulation precision is improved.

The invention discloses a multi-target blending optimization method for refined oil of a refining enterprise, and particularly relates to the technical field of target optimization. Constructing an original physical property feature set by identifying non-linear response sensitive variables in the blending component proportion; respectively constructing an oil product performance prediction model and a blending component interaction influence model based on the original physical property feature set; utilizing an oil product performance prediction model to analyze the influence of non-linear coupling of simultaneous change of a plurality of blending component proportions on the change trend of key performance indexes of the refined oil product; analyzing the influence of the proportion change of a single or individual blending component on the stability of the key performance index of the product oil by utilizing the blending component interaction influence model; and according to the key performance change trend characteristics and the fluctuation sensitivity indexes, constructing a constraint equation, generating a blending solution set, performing target performance achievement degree evaluation and global optimal solution screening, obtaining an optimal blending scheme, improving the blending prediction precision, and realizing stable optimization control of the product oil quality.

The invention discloses a PEDOT sensor nonlinear response compensation optimization system, and relates to the technical field of PEDOT sensors, the system comprises a cloud management center, and the cloud management center is in communication connection with a data acquisition module, a feature extraction module, a response compensation analysis module, a cloud compensation module, a compensation deviation analysis module and a feedback adjustment module. The method combines the neural network model through the cloud, precisely learns the nonlinear characteristics of the sensor, carries out precise compensation, carries out initial linear compensation at the end side, reduces the data amount transmitted to the cloud, improves the real-time performance of the system, carries out the model training and optimization at the cloud through a large amount of historical data, and improves the real-time performance of the system. According to the PEDOT sensor, the error introduced by nonlinear response is remarkably reduced, and the measurement precision and accuracy of the PEDOT sensor are effectively improved in an end-cloud cooperation mode, so that the sensor can provide high-reliability measurement data under different environmental conditions, and the requirement of high-precision measurement is met.

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 relates to the technical field of bridge detection, and discloses a detection data acquisition system and method for bridge support design, and the method comprises the steps: constructing a slippage trend prediction model with a disturbance weight gating structure, fusing historical slippage trajectory data and pressure gradient data, and carrying out the prediction of the slippage trend; the time sequence modeling capability of nonlinear response of the sliding support in a vibration state is effectively enhanced, the perception precision of the model on short-time disturbance and inertia reversal behaviors is improved, then a disturbance change rate sequence is constructed based on a predicted sliding trend sequence, a disturbance scoring factor is calculated by using trend fluctuation amplitude and direction reversal times, and the disturbance change rate sequence is calculated. According to the method, the disturbance time window is dynamically marked, so that the problem of lack of support disturbance behavior dynamic division means in the prior art is solved, finally, different filtering processing modes are adopted for the disturbance time window and the non-disturbance time window, and differentiation processing is performed on real-time strain data. And the availability of the strain data under the low-frequency disturbance background is improved.

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 relates to a fine particulate matter and ozone regulation and control method and system, a storage medium and electronic equipment. The fine particulate matter and ozone regulation and control method comprises the steps that precursor data, maximum emission reduction potential and emission reduction policies of different emission reduction regions and ozone concentration data and fine particulate matter concentration of a receptor region are collected; non-linear responses between the precursors of different emission reduction areas and the ozone concentration of the acceptor area are confirmed; non-linear responses between the precursors of different emission reduction areas and the fine particulate matter concentration of the acceptor area are confirmed; determining the minimum emission reduction of the precursors in different emission reduction areas; confirming constraint conditions of precursors of different emission reduction regions; confirming target constraints of fine particulate matter concentration and ozone concentration in different receptor regions; and calculating the emission reduction proportions of the precursors of all the emission reduction areas. According to the method, the optimal emission reduction scheme of the precursor of the emission reduction area is solved by introducing the joint response relation between PM2.5 and O3 and the precursor thereof and different constraint conditions of different emission reduction cities.

The invention discloses a digital stethoscope system based on a piezoelectric film, and relates to the technical field of medical electronics and biomedical engineering. An existing digital stethoscope based on a piezoelectric film has the challenges of signal noise and interference, limitation of a signal processing algorithm, nonlinear response and time migration, insufficient diagnosis accuracy and automation and the like. A piezoelectric film and microphone dual audio acquisition mode is fused, various physiological signals such as low-frequency vibration and high-quality heart and lung sound are captured, and background noise is removed through a self-adaptive filter and a frequency spectrum subtraction technology; an MFCC feature extraction and dynamic time warping algorithm is utilized to carry out fine feature analysis and time axis alignment on the preprocessed audio signal, and high-precision matching with a storage template is realized; different physiological states are accurately distinguished through intelligent feature processing, real-time and visual diagnosis feedback is achieved through wireless transmission and state indication, and the reliability and efficiency of medical detection are improved.

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 relates to the technical field of equipment calibration, in particular to a multi-channel pressure sensor calibration method and system, and the method comprises the steps: constructing a flexible pressure response platform, carrying out the channel calibration according to the flexible pressure response platform, and carrying out the preliminary deviation modeling; generating a nonlinear response correction factor according to a result output by the preliminary deviation modeling, and performing local regression adjustment on the channel according to the nonlinear response correction factor; in response to completion of local regression adjustment of each channel, performing unified response surface fusion processing; and in response to completion of unified response surface fusion processing, constructing a stability comprehensive criterion function, and generating a calibration result according to the stability comprehensive criterion function. According to the method, the limitation that a traditional method depends on a linear model and static single-point calibration is overcome, and the problem of error accumulation caused by inter-channel interaction interference and delay response is solved.

The invention relates to the technical field of computing power consumption prediction, in particular to a unit computing power energy consumption prediction method based on workload identification and a power regression model, which comprises the following steps of: acquiring a hardware resource time sequence signal and an instruction mixed feature vector of a computing node in real time; inputting the instruction mixed feature vector into a working load fingerprint identifier, and outputting a load type energy influence factor; and inputting the hardware resource time sequence signal and the load type energy influence factor into an adaptive power ridge regression model to generate a unit computing power energy consumption predicted value. According to the method, the problem of large deviation of a traditional fixed coefficient regression model in a voltage fluctuation scene is effectively solved, the power consumption prediction error in a high-voltage scene is reduced, and the method has good nonlinear response capability and platform adaptability.

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.

This application relates to the field of liquid crystal displays, and discloses a method and system for optimizing the dynamic contrast of a liquid crystal display screen, including the following steps: By collecting the ambient light intensity and backlight module temperature parameters in real time, and combining with the analysis of the image content entropy value, a non-uniform backlight partition grid is dynamically generated; Based on hardware parameters such as the refractive index of the light guide plate and the LED spacing, a physical model of brightness diffusion is constructed, and an improved particle swarm algorithm with temperature adaptability is used to solve the optimal brightness of each partition; The driving voltage is generated through piecewise non-linear gamma mapping and inverse function iterative calculation to adapt to the non-linear response characteristics of the backlight module; The diffusion equation and optimization calculation are executed in parallel by a hardware accelerator, and the model parameters are dynamically adjusted in combination with the feedback of the optical sensor to form a closed-loop control link. Through the dynamic partitioning adapted to the environment, the optimization algorithm driven by the physical model, and the real-time processing accelerated by the hardware, the present invention comprehensively improves the contrast performance and dynamic scene adaptability of the display screen.

The invention discloses a multiphoton up-conversion super-resolution microscopic imaging method based on microsphere super-lens enhancement, which utilizes the super-resolution advantage of a microsphere super-lens and the nonlinear response advantage of a lanthanide doped rare earth up-conversion nano material to reduce the saturation excitation power of the rare earth nano material. And super-resolution imaging under the condition of a low-magnification and low-numerical-aperture objective lens is realized. An example verifies that under the condition that system complexity is not increased, excitation powers of 455 nm and 808 nm radiation fluorescence of an up-conversion nano material are respectively reduced by 15.40 times and 23.44 times, fluorescence imaging resolutions of 455 nm and 808 nm can be respectively improved by 3.14 times and 3.6 times under a 10x 0.25 NA objective lens, and corresponding transverse resolution limits can respectively reach 358.20 nm and 480.02 nm, so that the fluorescence imaging resolution of the up-conversion nano material can be improved by 3.14 times and 3.6 times under a 10x 0.25 NA objective lens. The method has the advantages of simple imaging system, low cost and good imaging effect, and is of great significance to biological application research.