80 results about "Nonlinear calibration" patented technology

The invention relates to the technical field of high temperature sensing and data fusion, in particular to a method for array thermocouple multi-mode compensation and DIC stress field space-time coupling fusion, which comprises the following steps: step 1, in a thermotechnical signal intelligent processing and compensation unit, completing hardware and algorithm collaborative design of an electronic cold junction compensation module; 2, constructing a multi-algorithm fusion compensation module, integrating nonlinear correction, drift compensation and interference suppression functions, and accurately coping with various interference signals through a dynamic weighting strategy; step 3, adopting a sub-pixel-level matching algorithm and a homography matrix calibration technology to realize high-precision space alignment of the temperature and stress measurement units; and establishing a nonlinear incidence relation between the temperature and the stress based on an improved Gaussian process regression model. According to the invention, based on collaborative design of the thermotechnical signal intelligent processing and compensation unit and the DIC vision and temperature data conjoint analysis module, the core precision problem of temperature and stress detection in a high-temperature environment is solved through hardware optimization and algorithm innovation.

A computer-implemented system for multidimensional generative value assessment and applicant selection, consisting of: a data collection unit configured to electronically receive applicant data consisting of structured academic records, work experience records, digital documentation, and unstructured narrative responses generated from generative self-assessment instruments and contextual interviews; a feature extraction unit coupled to the data acquisition unit, configured to apply computer-assisted text processing, semantic analysis, and token-level attribute identification to transform narrative responses and structured data into multidimensional feature vectors that represent generative indicators of innovation, mentoring, collaborative performance, resilience, social contribution, ethical consistency, and predicted institutional impact; a weighting calculation unit configured to assign weight values ​​to the extracted feature vectors based on a digital generative profile definition matrix that includes dimensions, sub-criteria, indicators, documentation requirements and importance coefficients, with the weighting being distributed across the generative dimensions defined in the digital matrix and configurable according to the institutional context; a quantitative rating unit configured to calculate a generative rating score by aggregating weighted feature vectors derived from self-assessment inputs, interview-based ratings, document analyses, and authenticity predictions, with the aggregation including normalization, nonlinearity correction, conflict handling, and artifact frequency balancing to obtain a consolidated score; a proof verification unit configured to electronically validate referenced digital evidence by performing content extraction, metadata verification, pattern matching, and cross-document correlation to determine authenticity, credibility, and contextual relevance with respect to the calculated feature vectors; a classification determination unit configured to assign a classification level to an applicant by comparing the generative assessment score with a set of system-defined calculation thresholds, including at least a lower threshold, a middle threshold and an upper threshold, the classification levels representing different generative maturity states and determining subsequent eligibility for selection; a decision generation unit configured to produce a digital output data set that includes classification level, feature aggregation summaries, evidence validation results, and recommended organizational actions, wherein the decision generation unit encodes the data set in a digitally signed, tamper-proof format and stores it on a non-volatile storage medium; and A system control unit acts as an operational interface to all other units and is configured to orchestrate data flow, scheduling, process state transitions, and event logging to ensure verifiable traceability, consistency, and auditability of the evaluation and selection processes.

The invention relates to the technical field of multi-peak spectrum demodulation, and particularly provides a fiber bragg grating multi-peak spectrum demodulation method and system. The method comprises the following steps: extracting local spectral features based on an experimental reference spectrum to form initial atoms, and performing translation offset and normalization processing to obtain an over-complete spectral atom dictionary; performing global offset preliminary estimation based on the dictionary, and obtaining preliminary estimation values of peak sites of the measurement spectrum and the reference spectrum through cross-correlation calculation; executing constraint orthogonal matching pursuit sparse recovery based on the estimated value, and recovering atomic displacement from the measurement spectrum by using block sparsity, translation consistency and non-negative constraint; and performing wind speed inversion and calibration based on the atomic displacement, and converting the wind speed into a wind speed estimated value through a nonlinear calibration model to obtain a final result. According to the method, a dictionary based on experimental data is constructed, dependence on large-scale labeled data is reduced, a physical mechanism and sparsity prior are fused, and the problems that a traditional method is insufficient in precision and weak in generalization ability in a complex environment are solved.

The invention discloses a nonlinear correction method, device and equipment for excitation characteristics of a mutual inductor, and particularly relates to the technical field of mutual inductors. The method comprises the following steps: acquiring original data corresponding to a mutual inductor, wherein the original data comprises an original current signal, historical magnetic flux and distributed temperature data; performing noise reduction processing on the original current signal to obtain a purified current signal, and determining a magnetic conductivity correction coefficient and a temperature gradient according to the distributed temperature data; inputting the purification current signal and the magnetic conductivity correction coefficient into a hysteresis model to obtain a real-time magnetic flux, and inputting the purification current signal, the distributed temperature data, the temperature gradient and the historical magnetic flux into a magnetic flux correction network to obtain a magnetic flux correction value; and compensating the real-time magnetic flux according to the magnetic flux correction value to obtain a corrected magnetic flux result of the mutual inductor. According to the invention, multi-physical field fusion input can be realized, transient response can be improved, harmonic distortion compensation can be carried out, and high precision of data can be maintained.

The invention relates to the technical field of supplementing missing measurement data of an anemometer tower, in particular to a method for supplementing missing measurement data of an anemometer tower. In the data preparation stage, historical observation data of a target anemometer tower needs to be collected, and missing time periods and missing features are determined, so that subsequent method selection is linked; meanwhile, data of surrounding meteorological stations in the same period are obtained, the spatial correlation between the data and the anemometer tower is verified, and a basis is provided for probability distribution mapping and quantile matching in subsequent CDF-t and QDM methods. The nonlinear correction method based on cumulative distribution function transformation and quantile increment mapping shows unique advantages in the field of climate downscaling, the CDF-t can effectively eliminate system deviation by establishing a probability distribution mapping relation between observation data and mode output, and the system performance is improved. The QDM maintains statistical characteristics of historical sequences while retaining climatic change signals through quantile matching, and new possibility is provided for long-period data reconstruction through combination of the two methods.

The invention relates to the technical field of intelligent security and protection, and discloses an intelligent security and protection alarm method and system based on an intelligent park. The method comprises the following steps: acquiring original security and protection data by using sensing equipment arranged in a smart park; performing standardization and redundancy elimination processing on the original security and protection data, dividing safety areas of the park through a pattern recognition algorithm, calculating a load intensity index of each area, and generating a security and protection situation summary; according to the security situation summary, a heuristic search algorithm is adopted to determine alarm trigger parameter initial values including detection sensitivity and alarm intervals, and a basic alarm parameter group is generated; performing nonlinear correction on the basic alarm parameter group, and outputting an environment perception alarm parameter group; training the historical security data set by adopting a neural network model, and analyzing the interaction between the load and the threat to obtain a threat diffusion model; and comparing the threat diffusion model with the real-time security data points, performing difference measurement, reconstructing alarm trigger logic according to a measurement result, and completing autonomous regulation and control of security and protection of the smart park.

The invention discloses a gas sensor multi-mode adaptive preprocessing method based on a Mama state space model, and relates to the field of gas detection, and the method comprises the following steps: synchronously collecting original signals, environmental parameters and historical time sequence data of a sensor; performing feature extraction on the three types of data to generate a sensor feature vector, an environment feature vector and a historical feature vector; the method comprises the following steps of: obtaining a final fusion feature through processing, mapping the final fusion feature to a 128-dimensional embedding space through a contrast learning encoder, inputting a three-layer residual error connection Mamba time sequence prediction network to output a zero offset, dynamically predicting process noise and observation noise through a neural network, executing adaptive Kalman filtering to output a smooth signal, and outputting a final fusion signal. Adopting a learnable piecewise linear network to carry out nonlinear correction on the filtering signal; the gas concentration value is calculated by integrating the correction signal and the zero offset, and finally a confidence score and a data quality mark are provided; according to the method, high-precision, real-time and robust pretreatment of various gas sensors in a complex environment is realized.

The invention discloses a Rhatish culture VRAR virtual-real fusion interaction method based on eye movement tracking, and particularly relates to the technical field of virtual reality and augmented reality man-machine interaction. The method comprises the following steps: acquiring an eye image of a user through a near-infrared image acquisition unit, and inputting the eye image into a double-branch convolutional neural network model to estimate a two-dimensional fixation point of a screen; performing nonlinear correction on a fixation point based on the user personalized sight line offset correction model established in the offline calibration stage; in the online interaction stage, the corrected fixation point is combined with equipment pose data to be converted into three-dimensional space rays, the distance and angle relation between the three-dimensional space rays and an interaction target is calculated, and an interaction instruction is triggered when a threshold condition is continuously met. According to the method and the system, accurate, stable and natural eye movement interaction of the virtual contents of the Rhatish culture and the entity exhibits in the VR / AR environment is realized, and the user experience and the system robustness are improved.

The invention discloses a rotor turn-to-turn short circuit diagnosis method based on an exciting current-shaft voltage harmonic nonlinear correlation coefficient. The method comprises the following steps: synchronously collecting parameters, and preprocessing the collected parameters through wavelet threshold denoising and temperature normalization; extracting harmonic analysis according to the shaft voltage harmonic characteristics; identifying magnetic saturation, temperature and load nonlinear factors and calculating corresponding coefficients; performing dynamic correction on the Pearson's correlation coefficient through the nonlinear correction coefficient, and combining with fuzzy logic weighting to obtain fuzzy logic weighting; defining a working condition feature vector, and constructing a working condition adaptive dynamic model comprising a linear regression basic model and an LSTM neural network; and calculating a dynamic threshold value based on the basic threshold value and the nonlinear factor, and judging the turn-to-turn short circuit trend of the rotor according to the comparison of the correlation coefficient and the dynamic threshold value and a prediction result. According to the invention, through combination of the nonlinear correction correlation coefficient and the LSTM neural network, dynamic compensation of nonlinear factors is realized, and the accuracy and reliability of rotor turn-to-turn short circuit fault diagnosis are improved.

The invention relates to the technical field of temperature sensor control, in particular to a nonlinear correction optimization method for a miniature semiconductor temperature sensor based on deep learning, which comprises the following steps: acquiring an original temperature sequence of the miniature semiconductor temperature sensor in a preset temperature range, and synchronously acquiring an error related environmental parameter sequence and a sensor aging coefficient; the original temperature sequence is subjected to empirical mode decomposition processing, and main temperature fluctuation mode components including nonlinear errors are extracted, the original temperature signals of the miniature semiconductor temperature sensor are subjected to hierarchical processing through empirical mode decomposition, the main temperature fluctuation mode components carrying the nonlinear errors can be accurately separated and extracted, and the accuracy of temperature fluctuation is improved. In combination with the dual-branch neural network processing data and through the null drift compensation factor and the sensitivity compensation factor, baseline drift and sensitivity attenuation caused by long-term use are corrected in a targeted manner, so that the miniature semiconductor temperature sensor still keeps high precision after long-term use, and the application in a high-precision scene is met.

The invention provides a camera monitoring video real-time identification data processing method and system, and relates to the technical field of computer vision and intelligent monitoring, and the method comprises the steps: extracting a current video frame from a real-time video frame sequence based on a multi-dimensional environment state descriptor, executing adaptive scene partitioning, and obtaining the confidence coefficient weight of each region; taking the confidence coefficient weight of each region as input, and performing joint nonlinear calibration on core parameters of the target detection network through a lightweight neural network model to obtain an optimized detection parameter set; and carrying out multi-target synchronous identification and depth feature abstraction on the current video frame by adopting the optimized detection parameter set to obtain a virtual feature contour set containing personnel and vehicles. Through environment adaptive detection and depth feature abstraction, precise identity verification, abnormal behavior recognition early warning and cross-domain trajectory association and cooperative response, a complete intelligent processing link from complex scene perception to global situation understanding and closed-loop management and control is realized.

The invention relates to the technical field of earphone equipment, in particular to a Bluetooth earphone non-contact type dynamic control system and method based on gesture recognition, and the system comprises an induction signal collection module which obtains the total number of clock cycles and generates an original count value, a signal standardization module which carries out the operation of the original count value to generate a standardization intensity value, and a control module. The spatial feature distinguishing module is used for generating a spatial curvature gradient coefficient based on the standardized intensity numerical value and screening an effective intensity numerical value; and the linear mapping control module is used for performing nonlinear correction mapping on the effective numerical value, generating a vertical distance coordinate numerical value and generating a volume gain control parameter based on a change rate. According to the method, environment interference is eliminated through standardized data differential operation, effective fingertip control gestures and large-area skin contact are distinguished through space curvature gradient coefficients, a polynomial regression model is called to establish the corresponding relation between signal intensity and distance coordinates, non-contact hovering interaction is achieved, and ear canal compression and mechanical abrasion are avoided.

The invention relates to the technical field of blood glucose monitoring, in particular to a CGM sensor calibration method and system based on a non-linear compensation model.The method comprises the steps that in-plant verification sensitivity of a sensor serves as a core input parameter of a non-linear calibration function, and inherent response characteristics of the sensor when the sensor leaves a factory are included into a blood glucose estimation model; the sensitivity reference of each sensor is explicitly quantified, so that the source of individual difference of the sensors is eliminated by verifying sensitivity priori in an embedded factory, and the same current and different blood glucose caused by initial difference of sensor production process and enzyme activity can be avoided; compared with the prior art that the sensitivity difference when different CGM sensors leave a factory is not considered, cross-sensor signals and blood glucose mapping references can be unified from the root of a model only through a unified linear function or depending on SMBG manual calibration adaptation, and the technical problem that cross-sensor consistency and individualized adaptability are poor is solved.

The invention relates to the field of gamma dose rate monitoring, and provides a high-altitude radioactive point source dose rate distribution analysis method and system, and the method comprises the steps: collecting original counting data and energy spectrum data, and obtaining environment parameter data and flight carrier position information; performing statistical calculation on the original counting data to obtain counting statistical parameters; performing nonlinear correction on the original counting data, the energy spectrum data, the environmental parameter data and the counting statistical parameters to obtain corrected detector data and measurement uncertainty; performing fusion calculation on the corrected detector data to obtain a fusion weight, and performing verification processing to obtain fusion dose rate data; performing prediction processing on the fused dose rate data and the environmental parameter data to obtain a range switching signal; and performing reconstruction processing on the fused dose rate data, the range switching signal, the measurement uncertainty and the flight carrier position information to obtain ground dose rate distribution data. According to the invention, the accuracy and reliability of high-altitude radioactive point source dose rate distribution analysis are improved.

The application discloses a nonlinear calibration method and device of a magnetic encoder. The method comprises the following steps: setting multiple inflection points on an angle curve diagram formed based on an angle scanning result to divide an angle period into multiple angle curve segments, wherein each angle curve segment is approximately a first-order curve; acquiring a compensation coefficient of each angle curve segment; acquiring an angle output threshold value of each angle curve segment; and starting calibration after detecting a data valid signal, acquiring a target output angle after calibration based on the corresponding compensation coefficient and the angle output threshold value according to an input angle. The application divides a complete angle period into multiple angle curve segments by setting multiple inflection points, and each angle curve segment is approximately a first-order curve. Then, the slope of each angle curve segment is approximated to 1 through a compensation coefficient, and a target output angle compatible with an angle measurement value of an angle measuring instrument is obtained after calibration. The compensation algorithm is simplified, and resource consumption is reduced.

This invention discloses a system and method for monitoring partial discharge and thermal characteristics of switchgear, comprising: a dual-spectrum imaging module that simultaneously acquires a sequence of ultraviolet pulse images and an infrared temperature field sequence; a spatiotemporal synchronization control module that generates a synchronization control signal and acquires operating condition information; and an edge fusion diagnosis module deployed at a local edge that extracts pixel-by-pixel features from the spatiotemporally aligned dual-spectrum images, constructs a joint feature vector matrix of partial discharge and temperature rise, and performs real-time nonlinear correction based on the operating condition feature vector by retrieving an operating condition feature correction model, then retrieves the spatiotemporal weight allocation rule corresponding to the current defect evolution stage, dynamically allocates spatial weights and temporal weights for weighted fusion, generates a fusion confidence heatmap, and thus achieves defect identification and level classification. This invention achieves accurate diagnosis of insulation defects in switchgear through a collaborative mechanism of adaptive operating condition correction and dynamic weight fusion.

The invention provides a regional integrated energy system material configuration prediction method and system based on a regression algorithm, and relates to the technical field of energy configuration prediction.The method comprises the steps that historical operation data, environment data and equipment operation data of a regional integrated energy system are collected; carrying out time sequence fusion on the transportation volume, the coal yield and the generating capacity data to form a plurality of time sequence vectors; clustering analysis is carried out on the time sequence vectors to extract typical load curve features and generate clustering center feature vectors, and meanwhile key influence factors are determined based on historical data; and in combination with the clustering center feature vector and the key influence factor, generating a preliminary prediction result, finally performing nonlinear correction, and outputting a configuration prediction scheme conforming to the physical constraint of the regional energy network. According to the invention, the accuracy and adaptability of material allocation prediction of the regional integrated energy system are improved.

The application discloses a nonlinear calibration method for high-temperature mechanical property testing, and relates to the technical field of mechanical property testing of key components of high-temperature equipment. The method comprises the following steps: obtaining information data of a target material under different working conditions; the information data comprises half-life cycle load-displacement data; pre-processing the information data; based on the pre-processed information data, performing data conversion based on a polynomial function model to determine strain data; the strain data is used to realize cross-working-condition unified representation of data; the polynomial function model is a relationship model used to represent the strain-displacement calibration relationship, which is constructed based on experimental data by using a least square method; the experimental data is strain-displacement data obtained after a set number of fatigue and creep-fatigue tests are performed based on multiple loading conditions. The application aims to realize displacement and strain conversion and improve conversion accuracy.

The invention provides an infrared measurement system nonlinear correction method based on a convolutional neural network, and the method comprises the steps: S1, experiment data preparation: obtaining a series of input signal standard values and measurement system response values through an experiment, and taking the values as an experiment training data set; s2, model training: training the experimental training data set by using a convolutional neural network algorithm to obtain a convolutional neural network model between the response value of the measurement system and the standard value of the input signal, and training one convolutional neural network model for each wavelength value or each waveband range of the infrared measurement system; and S3, model verification: evaluating each trained convolutional neural network model by using an experimental verification data set to verify the accuracy of the model to predict the input signal standard value. According to the method, the nonlinear correction of the infrared measurement system is realized, the limitation of N-order polynomial or piecewise linear function fitting can be broken through, and the method is suitable for nonlinear correction of various infrared measurement systems.

The invention relates to a nonlinear calibration method and system based on a heuristic optimization algorithm DMD (Digital Micromirror Device), and the method comprises the following steps: for a to-be-optimized DMD light modulation system, taking the micromirror overturning time obtained according to a required display image as a to-be-optimized parameter in advance, loading the to-be-optimized parameter to the DMD, taking a light intensity actual value output by the DMD light modulation system as feedback, and carrying out nonlinear calibration on the to-be-optimized DMD light modulation system; optimizing and calibrating the turning time of the micromirror through a heuristic optimization algorithm; and the DMD light modulation system adopts the optimal micromirror overturning time to carry out gray imaging. Compared with the prior art, the method has the advantages that the turning time of the micromirror is optimized through a heuristic optimization algorithm, and nonlinear high-precision calibration of the DMD gray scale is realized; in simulation and experiments, the method shows the advantages of global optimization and uniform regulation and control, and the linearity of the DMD is improved by 42.2% after calibration.

The invention discloses a matte quality multivariable time sequence prediction method based on digital twinning, and provides an innovative modeling and prediction scheme for the complexity of matte quality prediction in the copper smelting process. According to the method, a multi-scale time sequence modeling framework is constructed, and multi-variable cross-dimension interactive calculation of the copper smelting process is achieved in combination with a digital twinning technology. The method specifically comprises the steps that a multi-head self-attention mechanism of a Transform model is adopted, the long-term dependency relationship of process parameters under a time sequence is captured, and the modeling capacity of complex time sequence data is improved; a Kolmogorov-Arnold Network (KAN) refinement module is introduced, a global feature output by Transform is subjected to nonlinear correction by using an adaptive B-spline basis function, and the adaptability of the model to variable working conditions is enhanced; a physical entity-digital twinning virtual-real interaction mechanism in the copper smelting process is established, a digital twinning model is constructed based on Unity 3D, and virtual-real mapping and matte quality prediction in the copper smelting process are achieved. Experimental results show that the method has high precision and stability in matte quality prediction, can provide effective support for intelligent monitoring and optimization of the copper smelting process, and has important technical advantages and application value.

The invention relates to a nonlinear correction device and method for a spectrum acquisition system, and belongs to the technical field of spectrum detection, the spectrum acquisition system is controlled to start spectrum acquisition from the integration time corresponding to saturated energy, and the integration time is gradually reduced by a preset step length until the full-pixel energy is lower than dark noise, so that the spectrum acquisition efficiency is improved. Obtaining spectral data corresponding to each pixel at different integration times; performing normalization processing on the spectral data corresponding to each pixel at different integration times to obtain a normalization coefficient corresponding to each pixel under different energies; fitting a four-parameter logarithmic function equation based on the energy of each pixel and the normalization coefficient corresponding to the energy, and performing nonlinear correction on the spectrum acquisition system based on the four-parameter logarithmic function equation obtained by fitting; the problem of nonlinearity of absorbance caused by the silicon detector can be systematically corrected; meanwhile, the nonlinear fitting precision is high, the nonlinear correction device is low in cost, and the method is simple, convenient and high in efficiency.

The present disclosure relates to a piecewise broken line non-linear calibration device of an analog-to-digital converter, a driving chip and an electronic device. The device comprises: a first adjusting unit configured to select a target slope from a slope storage unit according to high n bits of an analog-to-digital conversion result, and adjust low bit quantization values corresponding to N-n bits of the analog-to-digital conversion result by using the target slope to obtain adjusted low bit quantization values, wherein an output range of the analog-to-digital converter is divided into a plurality of continuous interval segments, and each interval segment corresponds to a slope; and a second adjusting unit configured to adjust high bit quantization values corresponding to high n bits of the analog-to-digital conversion result according to slopes of a plurality of interval segments before an interval segment corresponding to the target slope to obtain adjusted high bit quantization values, and obtain a calibrated analog-to-digital conversion result by using a sum of the adjusted high bit quantization values and the adjusted low bit quantization values. The embodiments of the present disclosure can greatly reduce the calculation complexity, save the area overhead, improve the energy efficiency, and have a higher analog-to-digital conversion efficiency.

The invention provides a feedback DAC mismatch calibration method and circuit for a delta-sigma type ADC, and the method comprises the steps: measuring the relative mismatch current of a feedback DAC unit in a loop through the characteristics of a negative feedback loop of the delta-sigma type ADC, and carrying out the mismatch calibration of the feedback DAC unit through changing the switch code combination corresponding to the reset and setting of the output end of the DAC unit. The polarity of the output current of the reference DAC unit and the to-be-calibrated DAC unit is forced to be reversed in sequence, the difference value of the accumulated value of the digital code output by the ADC under the condition that the polarity of the output current is reversed is determined, and the difference value reflects the relative mismatch current of the to-be-calibrated DAC unit relative to the reference DAC unit. A control code corresponding to the relative mismatch current is fed back to the to-be-calibrated DAC unit for trimming, so that a nonlinear error caused by the relative mismatch current of the DAC is calibrated and corrected; the DAC nonlinear calibration is realized under the condition of not additionally increasing excessive area and power consumption, so that the overall linearity and quantization precision of the ADC are improved.

This disclosure relates to a single-frame gamma correction method and system based on multi-region response weighted fitting, comprising: acquiring a projected image captured by a camera, the projected image containing four gray levels and horizontally divided into eight regions; selecting multiple pixels at the center of each region, calculating their average gray value as the measured output value of that region, pairing the calibrated gray value with the corresponding measured output gray value, fitting the light intensity relationship between the calibrated gray value and the measured output value through a nonlinear response model to obtain a symmetrical region combination and its initial gamma value; weighting and fusing the initial gamma values ​​of the symmetrical region combination to obtain a fused gamma value, and substituting the fused gamma value into the nonlinear response model to obtain nonlinear correction parameters; obtaining compensation coefficients for image preprocessing based on the nonlinear correction parameters, and preprocessing the sinusoidal fringe image using the compensation coefficients to obtain a corrected ideal sinusoidal fringe image. This method can effectively improve correction efficiency and robustness.

The invention relates to a method for rapidly measuring trace silicate radicals and phosphate radicals. The method comprises the following steps: respectively implementing corresponding linear calibration methods or nonlinear calibration methods under two reaction systems on the basis of determining that a current calibration environment is a low-concentration linear calibration stage or a high-concentration nonlinear calibration stage, and obtaining corresponding calibration data; the reaction systems are respectively a first reaction system and a second reaction system which are distinguished according to a high-pressure value and reaction time of the chemiluminescence instrument; the nonlinear calibration method is realized based on a transition probability P and a quenching parameter K; and rapid measurement of trace silicates and phosphate radicals is carried out based on calibration data and a reaction system. The invention also discloses a system and a device for rapidly measuring the trace silicates and phosphate radicals, the contents of the trace silicates and phosphate radicals in water can be simultaneously detected by utilizing the time difference and the luminous intensity difference of the phosphate radicals and the silicates in reaction, and on the basis of accurately quantifying the phosphate radicals, the detection accuracy is greatly improved. The measurement influence of phosphate radicals on silicate radicals in a chemiluminescence method is eliminated.

The invention discloses a transshipment storage bin discharging adjusting system and adjusting system based on horizontal continuous loading. The transshipment storage bin discharging adjusting system comprises a transshipment machine analysis module, a storage bin collection module and an analysis regulation and control module. The reversed loader analysis module is used for collecting carrying data of a reversed loader and analyzing the carrying data to generate carrying parameters; the carrying parameters are transmitted to the reversed loader and the storage bin, and the reversed loader and the storage bin are controlled in real time; the storage bin acquisition module is used for acquiring operation data in the storage bin; the analysis regulation and control module obtains the internal operation data of the storage bin and the carrying data and carrying parameters of the reversed loader, the real-time impact force of discharging of the storage bin is obtained, and then the discharging weight of the reversed loader is calibrated in real time. The method has the beneficial effects that a nonlinear calibration model is established according to impact force, vibration acceleration and sensor response characteristics, interference of impact vibration on weighing signals is effectively eliminated, and the real blanking weight is output.

The invention discloses a force control method of a subcutaneous soft tissue surgical robot, which relates to the technical field of medical robots and comprises the following steps: acquiring a soft tissue ultrasonic echo data set, executing sound velocity calibration analysis, generating an actual sound velocity value, performing nonlinear correction on the actual sound velocity value by using a tissue viscoelasticity compensation factor, and outputting a real-time corrected sound velocity value. And performing thickness conversion according to the soft tissue ultrasonic image and the real-time corrected sound velocity value to obtain an actual fat thickness value, and performing differential operation on the actual fat thickness value and the zero pressure reference value to form a subcutaneous fat layering deformation quantity. According to the method, sound velocity calibration analysis and nonlinear correction are carried out on the soft tissue ultrasonic echo data set, meanwhile, the depth attenuation compensation model is constructed to carry out accurate modeling on the deep muscle correction deformation quantity, and controllability and safety of complex soft tissue operation in the operation process are improved.

The invention provides a digital signal dynamic adaptation method and system under adaptive modulation coding, and relates to the technical field of transmission, and the method comprises the steps: carrying out the frame synchronization, symbol timing synchronization and carrier frequency offset compensation of a digital baseband signal; performing time domain anomaly detection on the synchronous receiving signal, and performing signal separation according to a time domain anomaly detection result; performing digital pre-distortion processing on the useful signal components based on a nonlinear model of the power amplifier; performing fractional order Fourier transform on the nonlinear correction signal; and performing signal observation embedding on the multi-path suppression signal, performing dual uncertainty analysis based on a signal observation embedding vector, and triggering predictive adjustment of adaptive modulation and coding to realize dynamic adaptation of the digital signal when abnormity exists. According to the method and the device, the problem that signal abnormity is not identified and dealt with in time in the prior art can be solved, real-time abnormity prediction and modulation coding optimization are realized, and the technical effect of improving the anti-interference capability of a communication system is achieved.

The embodiment of the invention discloses a model training method and device, an impedance inversion method and device, electronic equipment and a storage medium. The model training method comprises the steps of inputting first observation seismic data into an original inversion network to obtain a first impedance inversion result, and performing forward modeling of the first impedance inversion result based on physical model constraints to obtain first preliminary simulation seismic data; performing nonlinear correction on the first preliminary simulation seismic data to obtain a first correction value, and obtaining first final simulation seismic data based on the first preliminary simulation seismic data and the first correction value; loss calculation is carried out based on the first observation seismic data and the first final simulation seismic data, parameters of the original inversion network are adjusted according to a first loss calculation result, and an impedance inversion network is obtained. According to the technical scheme of the embodiment of the invention, the impedance inversion precision can be improved.