129 results about "Linear analysis" patented technology

The invention discloses an intelligent biological reaction monitoring system and method, and belongs to the field of intelligent monitoring, and the system comprises a reaction stage dividing unit which is used for dividing and determining multiple reaction stages; the variable relation mining unit is used for determining a dominant variable and an auxiliary variable, mining a variable linear relation and a collaborative insensitive loss function, and supervising and training the soft monitoring module; the continuous monitoring acquisition unit is used for configuring a monitoring equipment group, performing continuous monitoring and determining reaction monitoring data; the data analyzing and screening unit is used for preprocessing the data, performing linear analysis and abnormal reaction positioning and determining effective monitoring data; the game equilibrium decision-making unit is used for performing game equilibrium decision-making on the abnormal reaction identifier and determining a reaction adjustment strategy; and the reaction monitoring management unit is used for responding to the adjustment strategy, carrying out feedback monitoring analysis and reaction monitoring management, improving the biological reaction monitoring precision, realizing real-time online monitoring and carrying out intelligent regulation and control aiming at abnormal conditions.

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 relates to the technical field of multi-modal fusion, in particular to a sperm quality assessment method and system based on multi-modal fusion, and the method comprises the following steps: obtaining image and parameter data to generate a space-time alignment set, extracting trajectory calculation linearity, analyzing parameter fluctuation to mark an abnormal time window, extracting an image assessment deformity rate to generate a feature matrix, and grading to generate a multi-modal sperm quality evaluation result. According to the method, linear calculation is introduced in the trajectory path extraction process, spatial distribution thermodynamic mapping is combined, the dynamic characteristics of local sperm populations are dominated, an abnormal time window is constructed based on the parameter value change rate and a set threshold value, and motion consistency indexes are combined, so that the dynamic characteristics of the local sperm populations are optimized. The explanation depth of sperm head malformation data in quality judgment is enhanced, the dimension space of sperm quality description is effectively expanded, the consistency ratio between vitality and morphology is introduced, track continuous characteristics are combined, a hierarchical grading system is established, and the accuracy of sperm quality evaluation results is ensured.

The invention discloses a straw coverage monitoring method based on multi-source remote sensing data, and the method comprises the steps: carrying out the interpolation processing of SMAP brightness temperature data and ASCAT backscattering data through a Backus-Gilbert method, and generating down-sampling data with the resolution of 10 km; the method comprises the following steps: up-sampling Sentinel-1 synthetic aperture radar data, MODIS NDVI data and LST data to a resolution ratio of 10km; according to the Sentinel-1 data, the MODIS NDVI data and the LST data after up-sampling, a regression model of the 10 km ASCAT data is constructed; linear analysis is carried out according to the vegetation water content and the total primary productivity, VWC-VOD and GPP-VOD fitting equations are obtained, and intercept values of the fitting equations are extracted; defining the intercept value as the inverse evolution of the straw coverage; and constructing a linear hybrid model based on the VOD feature vectors of the end members, and inverting the abundance of the end members through minimum error measurement to obtain the farmland straw coverage. According to the method, SMAP brightness temperature data with low resolution is improved to 1km resolution through a multi-step downscaling and inversion method, and the spatial detail expression capability of microwave remote sensing data is improved.

The invention discloses an ARX password anti-differential and linear analysis security assessment method and system, and relates to the password technology, and the method comprises the steps: respectively constructing a differential distribution table CDDT of dependent carry of modular addition and a linear approximation table CLAT of dependent carry; respectively sorting the CDDT and the CLAT according to the differential probability and the linear probability; after sorting, searching the optimal differential feature and the linear feature of the ARX password based on a branch and bound algorithm; and after all plaintext differences and masks are traversed, obtaining the maximum difference and linear feature probability as the maximum difference and linear feature probability of the ARX password of the corresponding round number. The invention provides an ARX password anti-differential and linear analysis security assessment method based on a carry-dependent differential distribution table and a linear approximation table, and solves the problem of anti-differential and linear analysis security assessment in an ARX password design process.

The invention discloses a control system for intelligently detecting the lifting process of a tower crane. The control system comprises a data acquisition module, a self-adaptive frequency spectrum filtering module, an impact retention module, a motion time sequence feature construction module, a linear drift monitoring module, a nonlinear disturbance separation module, a threshold statistical decision module and a lifting process control module. The invention belongs to the field of intelligent control, and particularly relates to a control system for intelligently detecting the lifting process of a tower crane, which adopts a self-adaptive sampling method based on gradient triggering to improve the accuracy of fault detection. The control accuracy of the jacking process is improved by applying an optimized wavelet threshold function; extracting linear dynamic characteristics of a load current linear relation through a linear drift monitoring module; residual difference is constructed in the residual subspace, nonlinear energy indexes and errors are calculated, the method is extremely sensitive to nonlinear disturbance and non-Gaussian noise, and a blind area of linear analysis is reinforced; and the control effect of the tower crane lifting process is improved.

The invention provides an under-actuated unmanned ship trajectory tracking control method, which is used for under-actuated unmanned ship trajectory tracking control with input quantization and output limitation based on an event trigger mechanism, and comprises the following steps: acquiring sea condition information of a surrounding environment and other ships, and establishing a trajectory tracking control model of an unmanned ship; a composite quantizer is adopted to quantize control input of a control system, and a linear analysis model is used to describe an input quantization process; designing a barrier Lyapunov function on the basis of an output limited theory, compensating uncertain items and external interference in the under-actuated unmanned ship model by using a neural network system, and obtaining a trajectory tracking controller and an adaptive rate of the under-actuated unmanned ship; an event triggering mechanism is introduced, and the control rate of the under-actuated unmanned ship system is updated based on an event triggering strategy; it is proved that the stability of the designed under-actuated unmanned ship trajectory tracking control system is finally bounded when priori information of parameters does not need to be quantized, and all signals are finally bounded, so that Zeno behaviors are avoided.

The invention discloses a non-linear analysis method in a multi-arch structural plane considering translation and rotation of arch feet. The non-linear analysis method comprises the following steps: 1) deducing a geometric non-linear equilibrium equation in the multi-arch structural plane under the action of a vertical uniformly distributed load; 2) introducing a multi-arch boundary condition, and deducing to obtain a vertical displacement approximate analysis in a balance path considering translation and rotation nonlinearity of an adjacent arch structure; 3) further obtaining a relational expression between the dimensionless load of the multiple arches and the dimensionless load parameters; 4) deducing a multi-arch nonlinear branch point buckling equilibrium differential equation, and obtaining a critical load approximate analysis according to an internal force condition of multi-arch branch point buckling; 5) deducing to obtain approximate analysis of the multi-arch nonlinear jump buckling critical load.By means of the method, complex solving through finite element software can be avoided, and the calculation cost and workload of multi-span arch structure analysis are reduced.

The invention provides a large-span beam formwork three-dimensional parameterization deepening design method, and belongs to the technical field of building construction.The method includes the steps that geometric parameters of a large-span beam three-dimensional parameterization model are obtained, and a multi-scale homogenization mechanical analysis model is established to describe nonlinear characteristics of a concrete material; an arc length control method is combined with a Newton-Raphson iterative improved algorithm to carry out large-deformation nonlinear analysis to calculate deformation control parameters, safety performance is evaluated, support structure parameters are adjusted, and a double-layer game optimization model is established to determine optimal template support system parameters. On the basis of a bending-torsion coupling analysis result, a force transmission path diagram is established through a load transmission path generation function, and an optimal force transmission path is determined by adopting a gradient optimization algorithm, so that the problems that the design of a large-span beam formwork supporting system lacks an accurate mechanical calculation basis, and structural deformation and stress distribution cannot be accurately predicted are solved; and the design of the supporting system is not reasonable.

The invention discloses a cooperative control method for stay cable force and main beam line shape of an extradosed cable-stayed bridge. The cooperative control method comprises the steps that cable force data, main beam line shape data and main beam section internal force monitoring data in the construction stage are collected; based on a preset cable force adjustment judgment criterion, whether cable force adjustment needs to be started or not is judged; when triggering conditions are met, a geometric nonlinear analysis model considering cable-beam-tower cooperative deformation is established, and a cable force-main beam line shape sensitivity influence matrix is generated; screening K stay cables as key adjustment objects by adopting an influence matrix column vector L2 norm sorting method, and performing global optimization based on a simulated annealing algorithm to obtain an optimal displacement mean square error and an optimal cable force adjustment amount; and according to the key adjustment object and the cable force adjustment amount, multi-cable synchronous grading tensioning work is carried out. The invention provides a systematic and intelligent solution for the construction control of the extradosed cable-stayed bridge, and has remarkable economic benefits and social benefits.

A method and system for analyzing a vehicle winch design includes a preprocessor determining contact locations for winch components from a mesh model. The preprocessor determines load vectors at a winch assembly and counter-vectors at a fairlead based on a relative position of a winch wire and a hook retention location based on a winch load rating. A non-linear analysis system determines plastic strains, deflections, and clearances under the winch load rating based on the contact locations, load vector and counter vector, and communicates the plastic strains, deflections, and clearances to a post processing system. The post processing system compares the plastic strains to strain limit at a post processor, compares the deflection to a deflection limit, compares clearances to a clearance limit and generating a display based on comparing the plastic strains, compares the deflection to the deflection limit and compares clearances to the clearance limit.

The invention relates to the field of coal preparation equipment supervision, in particular to a coal preparation equipment operation state monitoring system based on the Internet of Things, and aims to solve the problems that the supervision target of coal preparation equipment is single and independent, and effective operation supervision cannot be carried out on the relevance of multiple equipment among coal preparation production lines. Comprising a coal dressing process division unit, a fault time statistical unit, an operation fault statistical unit, an equipment fault supervision unit and a process correlation analysis unit. According to the method, when the coal preparation equipment operates, classification management is performed on the coal preparation equipment, linear analysis is performed according to the fault occurrence conditions of the equipment in multiple classifications during operation monitoring, and the fault occurrence conditions of the coal preparation equipment in multiple procedures are judged according to the analysis result; therefore, the supervision system of the coal preparation equipment not only can supervise single equipment, but also can maintain and overhaul the equipment in the related working procedures by managers, so that the situation that other equipment is affected and breaks down due to the fact that the equipment in one group of working procedures breaks down is avoided.

The invention discloses a method, a device, equipment, a medium and a program product for monitoring a series capacitor on a direct current link, and the method comprises the steps: carrying out the state control of a power switch, so as to control the charging and discharging states of an electrolytic capacitor, collecting the capacitor voltage data of the electrolytic capacitor in the charging and discharging process, the charging and discharging states comprise the charging state and the discharging state of the electrolytic capacitor, performing linear analysis based on the capacitor voltage data, determining the linear relation between the capacitance value of the electrolytic capacitor and the discharging time, and monitoring the electrolytic capacitor of a direct current link in the power converter according to the linear relation; due to the fact that the capacitor on the direct-current link is monitored on the basis of the linear relation by conducting linear analysis on the capacitance value and the discharge time of the electrolytic capacitor, monitoring complexity is effectively reduced, complex sampling and calculation are not needed, meanwhile, the state of the capacitor is accurately monitored, monitoring cost is effectively reduced, and monitoring efficiency is improved. And the capacitance monitoring efficiency is improved.

The invention discloses an animal blood pressure prediction model establishing method based on smell information. The animal blood pressure prediction model establishing method comprises the following steps that response signals are obtained through response of an electronic nose gas sensor array to feces smell of a hypertensive rat; detecting the blood pressure of the hypertensive rat of which the excrement smell is detected; extracting characteristic response signals from the excrement smell information, wherein the characteristic response signals comprise a response mean value, a steady-state value, an integral and a differential; principal component analysis and discriminant analysis are carried out on the extracted characteristic values, and qualitative discrimination is carried out in combination with the blood pressure states (hypertension and normal blood pressure) of the rats; the extracted characteristic values serve as independent variables, multivariate linear analysis, a multi-layer perceptron neural network and partial least squares regression analysis are carried out, and quantitative prediction is carried out on the blood pressure of the female rats and the male rats. By means of the method, rat blood pressure state judgment and blood pressure detection based on excrement smell are achieved, and the problems that invasive operation difficulty is large, conscious animal individual compliance is poor, external interference factors are large, and a large amount of manpower, material resources and financial resources are consumed are solved. And a basis is provided for rapid and non-invasive evaluation of experimental animals.

A first circuit receives a single-ended signal and a reference voltage. The first circuit shifts a common mode of the single-ended signal based on a level-shifting current. A preamplifier receives the single-ended signal and the reference voltage from the first circuit and amplifies the single-ended signal to obtain an amplified signal. A decision feedback equalizer (DFE) system receives the amplified signal and the reference voltage from the preamplifier. The DFE system generates a first average signal height of the amplified signal above the reference voltage and a second average signal height of the amplified signal below the reference voltage. A second circuit provides the level-shifting current to the first circuit and adjusts the level-shifting current based on a linearity parameter associated with the amplified signal.

The invention relates to the technical field of food engineering, and discloses a colorimetric principle-based fried food Maillard reaction product concentration analysis model and frying equipment, and the colorimetric principle-based fried food Maillard reaction product concentration analysis model and frying equipment are established through three steps of experimental design, data acquisition and model construction. According to the concentration analysis model for the Maillard reaction product of the fried food based on the colorimetric principle and the frying equipment, in order to accurately judge the optimal frying end point of the fried food, improve the percent of pass of the fried food and adapt to industrial production, a linear analysis model based on R, G and B values of mobile phone imaging and the absorbance of the Maillard reaction product is established; the deep-frying end point judgment value is determined in combination with sensory evaluation, the optimal deep-frying end point of the deep-fried food can be judged, data are rapidly obtained through standardized mobile phone imaging parameters and substituted into a preset model for calculation, instant monitoring and judgment of the optimal deep-frying end point can be achieved, the qualified rate of the deep-fried food is increased, and the method is suitable for industrial production.

The invention discloses a precision air conditioner energy-saving optimization method based on improved PSO. The precision air conditioner energy-saving optimization method comprises the steps that collected precision air conditioner historical operation data are preprocessed; performing energy consumption characteristic engineering: performing correlation calculation on the influence factors of the operation energy consumption of the precise air conditioner by adopting a linear analysis method and a nonlinear analysis method respectively; establishing an energy consumption prediction model according to a correlation calculation result, and performing hyper-parameter optimization; determining a target function, a decision variable and a constraint condition, and establishing an energy-saving optimization model; and the energy-saving optimization model is solved based on an improved PSO algorithm for dynamically adjusting the inertia weight and the compression factor, and the optimal operation parameter combination of the precise air conditioner when the precise air conditioner operates with the minimum energy consumption under the constraint condition is obtained. The method is suitable for energy consumption prediction and energy-saving optimization of the precise air conditioner in the multi-working-condition change scene, the operation parameters of the precise air conditioner can be rapidly and accurately adjusted through the solving result of the precise air conditioner energy-saving optimization model, and the time needed for maintaining the environment temperature is shortened.

The invention provides an intelligent monitoring method and system based on an AI suspended pouring bridge, and relates to the technical field of bridge construction.The intelligent monitoring method comprises the steps that the experimental environment temperature and experimental image data are obtained; performing linear analysis on the experimental image data to obtain an experimental elevation and an experimental axis; obtaining a first experiment downwarping trend according to the experiment elevation and a preset experiment formwork erection elevation; according to the experimental environment temperature, the experimental elevation and the experimental axis, trend prediction is carried out through the constructed down-warping trend prediction model, and a predicted down-warping trend is obtained; comparing the first experiment down-warping trend with the predicted down-warping trend to generate a hanging basket construction adjustment strategy so as to optimize and adjust hanging basket construction; and performing concrete pouring construction management through the predicted formwork erecting elevation corresponding to the optimized and adjusted second experiment downwarping trend. According to the monitoring method and system provided by the invention, the accuracy of formwork erecting elevation can be ensured, the linear effective control of the suspended pouring bridge construction is realized, and the construction precision and efficiency are improved.

The invention relates to the technical field of data processing and engineering project process management, in particular to a full-process intelligent management method and system for bridge and tunnel maintenance engineering. The method comprises the following steps: extracting a disease geometric contour based on bridge and tunnel inner wall three-dimensional point cloud data, and calculating a centroid coordinate, a volume and a surface contour area; constructing a space retrieval sphere by using a target disease centroid, screening an adjacent disease set, and calculating a local aggregation density by combining the volume and the centroid distance; calculating the average depth of the target disease, and deducing the overlapping sharing rate of the working plane; constructing a group topology consumption equivalent through linear analysis; and inputting the extreme gradient lifting model to output an optimal repair material dosage table and a material distribution operation instruction book. According to the method, the disease space distribution and aggregation degree can be accurately evaluated, the construction loss surface overlapping probability and the material sharing potential are scientifically evaluated, the material preparation accuracy is improved, waste and cost are reduced, and the whole-process intelligent management of bridge and tunnel maintenance engineering is realized.

The present application relates to the field of data analysis, and in particular to a fetal brain MRI tissue analysis method, apparatus, and electronic device, comprising obtaining multiple post-treatment brain MRI images, each corresponding to a different gestational age; performing brain tissue segmentation on each post-treatment brain MRI image to obtain multiple post-treatment local brain tissue segmentation maps corresponding to multiple local brain tissues; obtaining, for each post-treatment brain MRI image, a pre-treatment local brain tissue segmentation map corresponding to each post-treatment local brain tissue segmentation map; determining, for each post-treatment brain MRI image, a deformation coefficient for each local brain tissue; generating, for each local brain tissue, a linear analysis map corresponding to each local brain tissue based on the deformation coefficients of the local brain tissue corresponding to all gestational ages; and determining final brain tissue analysis information based on the linear analysis maps corresponding to all local brain tissues. The present application facilitates a holistic analysis of the impact of changes before and after treatment on different gestational ages and different brain tissues.

The invention provides a clinical laboratory performance verification intelligent system, and relates to the technical field of medical detection, the system comprises a comparison analysis module, a precision analysis module, a linear analysis module, a dilution verification module and a reference interval verification module; and each module automatically analyzes and verifies according to the data uploaded by the user, and generates an analysis and verification report. By automatically executing analysis and verification, artificial factors are reduced on the basis of providing comprehensive performance verification services, so that the accuracy of clinical laboratory performance verification is improved.

The invention relates to a power transmission network planning method and system considering wind power reliable reserve capacity, and belongs to the technical field of power network planning, the power transmission network planning method considering the wind power reliable reserve capacity comprises the following steps: calculating wind power reliable reserve used for quantifying reliable reserve capacity which can be provided by a wind power plant in different operation modes; the method comprises the following steps: constructing an objective function for minimizing the total planning cost of a power transmission network on the basis of reliable wind power reserve, constructing constraint conditions, and constructing a power transmission network planning model considering the wind power reserve capability on the basis of the objective function and the constraint conditions; and performing linear analysis and solution on the power transmission network planning model to obtain an optimal power transmission network planning scheme. According to the method, on the premise that the system operation safety constraint is met, the standby supply capacity of wind power in different operation modes is quantified, so that stable and controllable standby resources are provided for a power system, and the system scheduling flexibility and the new energy resource consumption level are improved.

The invention discloses a low-frequency oscillation optimization method of an electric power system containing an SDC and a DFIG based on balanced truncation and series control compensation. The low-frequency oscillation optimization method comprises the following steps: 1, establishing a linear analysis model of a wind power grid-connected electric power system containing a synchronous generator SG, the DFIG and an additional damping controller SDC; 2, truncation processing is carried out on a state matrix in the state matrix equation of the main system after balance transformation, and a main system state matrix equation after order reduction is obtained; 3, constructing a time domain matrix of the system containing the series feedback controller to obtain a system state matrix equation containing the series feedback controller; 4, solving a characteristic value and a damping ratio of the system state matrix equation containing the series feedback controller, and constructing a control parameter analytic expression of the characteristic value and the damping ratio to the SDC in a low-frequency oscillation mode; and 5, solving by using the low-frequency oscillation optimization model of the SDC to obtain the optimal adjustment amount of the control parameters of the SDC. According to the invention, the control compensator is added to optimize the characteristic value under low-frequency oscillation and the control parameters of the SDC, so that the stability of the system is optimized.

The present application relates to the field of agricultural information technology and smart agriculture, and in particular to a dominant factor screening and litchi flowering rate prediction method based on a nonlinear model, which comprises the following technical solutions: collecting multi-dimensional original data of Feizixiao litchi in the flowering induction period; performing linear regression test on the original data, and starting a nonlinear analysis process when the fitting degree of the linear model is lower than a threshold value; constructing and training an XGBoost nonlinear regression model; using a SHAP method to interpret the model, calculating the average absolute SHAP value of each feature and sorting, and screening out nonlinear dominant factors; drawing a SHAP dependence graph for the dominant factors, and automatically determining the key threshold value affecting the flowering rate through curvature analysis; inputting the dominant factor data of the target year into the model, and outputting the flowering rate prediction value; the present application can effectively capture the nonlinear relationship between the influencing factors and the flowering rate, realize objective screening of the dominant factors and automatic identification of the key threshold value, significantly improve the flowering rate prediction accuracy, and provide a scientific basis for litchi production precision management.

The present application belongs to the technical field of numerical simulation of underground water in mine, and discloses a GMS cooperated with GIS health risk prediction method for backfilling underground water in open-pit mine. The present application combines with geological data around mining area, carries out fine 3D geological modeling through GCAD, EVS, Tin and Solid module in GMS, and on the basis of analyzing hydrogeological conditions of mining area, generalizes boundary conditions and aquifer, determines source and sink, hydrogeological parameters, establishes relatively accurate 3D underground water seepage model of open-pit mine by GMS, and identifies and verifies the model combining with underground water field monitoring data; determines each parameter of solute transport model through static leaching test, dynamic adsorption test and multi-parameter free energy relationship model, establishes solute transport equation of biological, chemical and seepage mutual action by multiple regression linear analysis, outputs solute transport result of open-pit mine backfilling pollutant in different stress periods of simulation period by MT3DMS, disperses solute transport result by Scatter in GMS and Field Calculator based on Python algorithm, and visually expresses in GIS combining with health risk prediction model, so as to solve the problem that potential health risk of backfilling material to underground water pollution in the process of open-pit mine backfilling mining or backfilling treatment is difficult to predict.

The application discloses a kind of sensor fault detection method and device of structural health monitoring system, it is related to structural health monitoring technical field, comprising: obtaining the nonlinear structure monitoring data of sensor system collected by structural health monitoring system, nonlinear structure monitoring data is handled using improved geometric PNL hybrid model, obtain linear to-be-separated mixed signal, using FastICA model to process to-be-separated mixed signal, obtain multiple independent elements, determine the separation matrix of improved geometric post-nonlinear independent component analysis model according to multiple independent elements, using the processing of improved geometric post-nonlinear independent component analysis to real-time collection nonlinear structure monitoring data, determine whether sensor system exists fault and the sensor of fault occurrence.The method can still complete linearization processing to sensor mixed signal under the condition that prior knowledge is unknown, compared with simple linear ICA analysis algorithm, it is more suitable for complex nonlinear structure.

The invention provides a structural damage positioning method and system based on wavelet-PCA, and relates to the technical field of structural damage positioning. The method comprises the following steps: acquiring a structure vibration response signal through a sensor to obtain an original signal data set; de-noising the signal by adopting wavelet transform, and extracting a principal component characteristic matrix through principal component analysis; performing phase-space reconstruction on the principal component time sequence to generate a high-dimensional power system track; calculating quantization factors such as recursion rate, certainty and average diagonal length based on recursion plot analysis, and forming a recursion quantization feature matrix; generating a spatial correlation distribution diagram by analyzing the correlation between the recursion characteristics of different sensor positions; and realizing damage positioning according to the correlation abnormal region. The early weak damage can be effectively detected, the defect that a traditional linear analysis method is insensitive to nonlinear characteristics is overcome, and the method has the advantages of being high in anti-interference performance and accurate and reliable in positioning.

The invention relates to a visual intelligent troubleshooting method for hidden dangers of an internal structure of a dam based on machine learning, and belongs to the technical field of image data processing, and the method comprises the following steps: data collection: employing an array sensor to obtain original monitoring data; data preprocessing: performing noise reduction and normalization processing on the original monitoring data, and dividing the processed data into a training set and a test set; model training: constructing a deep learning neural network model until the deep learning neural network model converges, and obtaining a trained hidden danger prediction model; and hidden danger prediction: inputting the test set into the trained hidden danger prediction model, and outputting a hidden danger prediction result of the internal structure of the dam. Visual display: performing visual display according to the hidden danger prediction result; the method has the beneficial effects that the linear analysis defects of traditional threshold judgment and empirical formulas are overcome, and the hidden danger recognition precision is remarkably improved.

The application discloses a kind of high-precision digital oscilloscope nonlinear analysis and calibration method, comprising: subsection reorganization optimization process is used to extract the nonlinear error of digital oscilloscope at different working temperatures;Subsection type modeling fitting process is used to fit the nonlinear error, and obtain nonlinear error model;FPGA real-time calibration process, the error model is stored in high-precision digital oscilloscope FPGA, and is stored as calibration data table, and the output digital code of digital oscilloscope is determined by FPGA, and the corresponding error model in FPGA is found out in real time, and the nonlinear error value is obtained, and the nonlinear error value is compensated to real-time output digital code, and calibration compensation is completed;The method proposed in the application can enable high-precision digital oscilloscope to realize nonlinear digital calibration, so as to significantly improve its linearity and dynamic performance, including SFDR and ENOB.