2650 results about "Least squares" patented technology

The invention provides a method for participating in primary frequency modulation control of a power system based on a modular large-scale hydrogen production power supply, and the method comprises the steps: obtaining an original data flow from a high-precision synchronous phasor measurement unit, an electrolytic cell and a submodule, and carrying out the multi-scale decomposition and noise reduction processing after time synchronization and anomaly detection restoration; performing state estimation by using unscented Kalman filtering, extracting frequency disturbance characteristics in combination with empirical mode decomposition and Hilbert transform, and estimating the inertia of the power grid; an electrolytic cell dynamic model is established, and parameter identification and optimization are carried out through a recursive least square method; a control strategy is designed based on a multi-time scale control framework, virtual synchronous generator parameters are optimized through online Gaussian process regression, and frequency modulation control is realized in combination with scene tree random model predictive control and minimum-maximum regret learning; and finally, carrying out coordination control through a distributed consistency algorithm and carrying out multi-target performance evaluation optimization. And the frequency modulation performance and the operation efficiency of the system are improved.

Disclosed is a three-dimensional deformation field modeling method fusing global navigation satellite system (GNSS) / synthetic aperture radar interferometry (InSAR) observation data. The three-dimensional deformation field modeling method includes: five parts of solving an InSAR line-of-sight velocity field, unifying a spatial reference datum of the GNSS data and the InSAR data, verifying precision of a result of the InSAR data, solving a three-dimensional deformation field having high precision based on a least square method, and analyzing and verifying a result. According to the method, the InSAR data is processed according to a high-precision time series method, a long-wavelength error of the InSAR data is corrected on the basis of a difference of two data spatial reference datums, and the accuracy of the result is ensured through verification of internal coincidence precision.

The present invention provides a wind power off-grid hydrogen production power supply topology and control method without a step-down transformer, including: obtaining wind speed data, using a wavelet threshold algorithm for denoising, predicting power based on an LSTM-GRU hybrid neural network, and extracting power fluctuation characteristics through multi-scale entropy analysis; obtaining load data, using Kalman filtering and recursive least squares to establish an impedance model, and extracting dynamic characteristics in combination with Hilbert transform; using a differential evolution algorithm for multi-objective optimization to generate an optimal switching sequence and power allocation strategy; achieving system control through space vector modulation, balanced control, soft switch control and harmonic compensation; achieving state estimation and disturbance suppression based on a sliding mode observer and a nonlinear observer; and achieving fault diagnosis and protection using a deep belief network. The present invention improves system efficiency and reliability and improves dynamic performance.

The invention provides a power grid frequency modulation power energy coordination control method and system based on a super capacitor, and relates to the technical field of power grid frequency modulation, and the method comprises the steps: carrying out the time-frequency analysis of a collected power grid frequency signal, obtaining a frequency disturbance characteristic value, and determining power control data through combining with an electrical parameter value of a super capacitor group; and performing nonlinear programming calculation based on the power control data to obtain a power distribution sequence, dividing the power distribution sequence into a quick response section, a dynamic transition section and a steady state maintenance section according to a preset threshold value, and solving a control value by adopting an augmented Lagrange equation, recursive least square operation and a Lyapunov function respectively. Obtaining a power control instruction; and inputting a power control instruction into the disturbance observer, calculating a dynamic error value, constructing a nonsingular terminal equation, and outputting a power regulation signal in combination with recursive minimum entropy calculation and a superhelix algorithm.

A heat source inversion-based tunnel high-temperature heat hazard advanced geological detection method and system. The method comprises: by means of multi-view joint observation of "rock wall and drilling", extracting two kinds of collected temperature data to form a new sequence, carrying out inversion interpretation on the sequence, constructing a least square-based three-dimensional heat source inversion objective function and an inversion equation, and solving for the inversion equation; and on the basis of a solution result, forming a heat source and temperature field imaging result graph of high-temperature heat hazard detection within a detection area range. The present invention achieves effective positioning and capturing of a high-temperature heat source, high-temperature heat hazard level division and heat hazard risk evaluation, and provides a reference basis for determining a tunnel design and construction scheme.

The invention relates to the technical field of power supply detection, and discloses a fire-fighting equipment power supply detection method and system.The detection method comprises the following steps that voltage fluctuation, current phase and ripple coefficients are collected through a multi-channel sensor group, a feature sequence is generated through sliding window noise reduction, the Euclidean distance is calculated through Z-score standardization, and a calibration instruction is generated when a threshold value is exceeded; kalman filtering adjusts frequency output detection parameters, fast Fourier transform is executed to extract harmonic amplitude to generate a suppression vector, and a least square reconstruction model outputs a state identifier; according to the method, voltage, current and ripples are collected through multiple channels, anti-interference performance and data integrity are enhanced through sliding window noise reduction, a threshold value is adjusted through Z-score and Euclidean distance self-matching, sampling frequency and detection parameters are optimized through Kalman filtering, harmonic characteristics are extracted through FFT, and the state is evaluated through a least square reconstruction model. And a multi-dimensional classification system is constructed by fusing the distortion rate and the phase deviation, so that the detection precision, the sensitivity and the response real-time performance are improved.

The invention relates to the technical field of reaction kettle temperature control, and discloses a fine chemical engineering reaction kettle temperature intelligent control method based on self-adaptive search, which comprises the following steps: S1, data acquisition: acquiring operation data of a reaction kettle, including temperature in the kettle, jacket inlet and outlet temperature, fluid flow and valve opening signals; s2, heat balance calculation: establishing a heat balance equation based on the collected data, and calculating reaction heat release and an equivalent heat transfer coefficient; s3, updating the model; s4, predictive control solution is carried out; s5, performing adaptive search; s6, carrying out security constraint; s7, stage identification; and S8, performing control execution. The reaction heat release amount and the equivalent heat transfer coefficient are calculated in real time through linkage of a heat balance calculation module and a model updating module, online parameter identification is achieved through a recursive least square algorithm, a heat model can be dynamically corrected along with working condition changes, it is ensured that a prediction model is consistent with the actual reaction process, and the prediction efficiency is improved. And the temperature prediction precision and the reliability of control response are obviously improved.

The invention discloses an intelligent multi-gas detection module data processing system and method based on NDIR, and relates to the technical field of gas detection.The method comprises the steps that a multi-wavelength NDIR sensor is used for obtaining light intensity changes of a gas sample under different wavelengths, and original spectral signals are generated; performing denoising processing on the spectral signal by adopting wavelet transform, and performing zero calibration and dynamic baseline deduction; based on the Beer-Lambert law in combination with environmental factors, establishing a relation model between gas absorption and spectral signals; updating model parameters by using a recursive least square algorithm, and continuously optimizing gas concentration prediction; separating gas signals by adopting a non-negative matrix factorization algorithm, and predicting the concentration of each gas; and the edge end operates the lightweight model in real time, and regularly uploads the model to the cloud end for global optimization and federated learning. The method can effectively improve the condition that the precision is insufficient when the model is used for a long time in the prior art.

The invention discloses a micro-grid intelligent scheduling method and system based on an AI large model, and the method comprises the steps: collecting and processing the real-time output data of a photovoltaic power station and a wind power station, and obtaining a standardized micro-grid operation data set; a discrete time micro-grid dynamic model is established and a recursive least square method is adopted to carry out system parameter online estimation so as to obtain a robust scheduling scheme oriented to uncertainty interference; and in combination with real-time operation state monitoring, real-time micro-grid intelligent scheduling is carried out by deploying edge computing nodes. According to the method, the Lyapunov stability theory and the control barrier function are combined, a safety reinforcement learning framework oriented to micro-grid dispatching is constructed, and the absolute safety of system operation in the dispatching process is ensured.

The invention provides an injection molding machine mold opening and closing parameter optimization method and system oriented to energy conservation and efficiency improvement, and relates to the technical field of industrial process intelligent control. The method comprises the steps that mold temperature, hydraulic pressure and vibration signals are collected in real time through a multi-source sensor, and a composite optimization target of a dynamic energy consumption coefficient and a process stability index is constructed; an improved multi-target genetic algorithm is adopted to iteratively generate a candidate parameter set meeting energy consumption and stability constraints, and the algorithm fuses hydraulic system safety boundary dynamic constraints and an adaptive variation strategy; a parameter adjustment effect is rehearsed through a digital twin model, and motion track deviation is calibrated online in combination with a recursive least square method, so that parameter dynamic injection is realized; and triggering abnormal working condition recovery and material self-adaptive optimization based on a closed-loop feedback mechanism. The system comprises a multi-source data acquisition module, a composite optimization calculation module, a dynamic parameter injection module and a closed-loop feedback control module, and the real-time performance and generalization ability are improved through an FPGA hardware acceleration optimization engine and a transfer learning algorithm.

A partial ambiguity resolving method suitable for a complex dynamic environment belongs to the field of high-precision dynamic satellite navigation relative positioning, and comprises the following steps: constructing a single-epoch carrier phase double-difference difference observation equation; and calculating a weight factor corresponding to each satellite through a standardized residual error and an IGG3 method, and substituting the weight factors into the double-difference factor matrix to obtain a corrected observation value. And resolving the updated observation equation through a weighted iterative least square method. And carrying out partial ambiguity fixation on the corrected ambiguity floating point solution to obtain a whole cycle ambiguity fixed solution. And carrying out Ratio-based posteriori consistency traversal detection, monitoring the number of satellites participating in calculation in a traversal search process, and if the number of the satellites is smaller than a threshold value, outputting a floating point solution. And if the number of satellites after traversal search is greater than or equal to And substituting the fixed solution of the integer ambiguity into the updated observation equation to obtain a fixed solution of a three-dimensional relative position vector and a covariance matrix of the three-dimensional relative position vector.

The invention relates to the technical field of noise filtering, in particular to a communication line noise filtering system and method, and the system comprises a temperature coupling module, a voltage response module, a parameter joint debugging module and a link checking module. According to the method, inductance and capacitance node temperature data are collected and input into a piecewise linear regression model, the temperature-to-resonant frequency deviation gradient is calculated, a noise suppression bandwidth threshold value is dynamically matched in combination with a reference table, the frequency stability under temperature fluctuation is improved, Kalman filtering is used for processing voltage sampling, and a fluctuation predicted value is generated. According to the method, interval indexes are extracted based on a parameter mapping table, instant inductance and capacitance parameters are generated through least square fitting, voltage transient fluctuation self-adaption is achieved, noise power spectrum density is calculated through fast Fourier transform, a 3dB attenuation point is compared with a threshold value difference value to trigger parameter rollback, closed-loop control is formed, it is ensured that suppression bandwidth is continuously matched with a noise frequency band, and noise suppression is achieved. The bandwidth offset is reduced to be within 1.2%, and the optimization response time is shortened to 200 ms level.

The invention belongs to the field of industrial automation and mechanical control, and discloses a heavy-load mechanical arm trajectory tracking and control method and system under a dynamic working condition, and the method comprises the steps of kinetic parameter identification, multi-model predictive control and fault-tolerant control, real-time collection of motion state data of a mechanical arm, and real-time control of the mechanical arm. A recursive least square method and a particle swarm optimization algorithm are combined, and dynamic parameters of the mechanical arm are updated and optimized in real time; a dynamic prediction model set under different dynamic load working conditions and motion states is constructed, an optimal model is dynamically selected, and a mechanical arm trajectory tracking strategy is adjusted through a model prediction control algorithm; the motion state of the mechanical arm is monitored in real time, fault detection and diagnosis are synchronously carried out, redundancy control is started once a fault is detected, the control strategy is dynamically adjusted in combination with the redundancy freedom degree of the mechanical arm and the distributed control architecture, normal operation of the system is maintained, the robustness and reliability of the system are improved, and the requirements of complex operation scenes are met.

The invention discloses a lithium battery state estimation method based on relaxation time scale modeling, and the method comprises the steps: 1, carrying out a sudden pulse charging test on a lithium battery, collecting voltage and current data, and calculating the open-circuit voltage, direct-current internal resistance and a time-domain impedance sequence of the lithium battery; and 2, converting the time domain impedance sequence of the lithium battery into a frequency domain impedance sequence after discrete Fourier transform, establishing a fitting equation set of a distribution function of the relaxation time scale DRT of the lithium battery and the frequency domain impedance sequence, and solving by adopting a regularized least square method. And 3, taking the solved DRT model distribution function as the input of a neural network, taking the state parameters of the lithium battery as the output of the neural network, and establishing a prediction model of the state parameters of the lithium battery. According to the method, EIS test required by traditional relaxation time scale modeling is avoided, the control complexity and design cost of test acquisition equipment are effectively reduced, and the method can be effectively applied to occasions such as battery life prediction, battery classification and screening, battery echelon utilization and the like.

The invention relates to the technical field of pollution traceability, in particular to a harbor storage yard dust pollution traceability method and system, and the method comprises the following steps: collecting the real dust concentration and dust diffusion influence factors of different monitoring points in a harbor storage yard, and constructing a dust diffusion evaluation model; when the position of the pollution source is not determined, obtaining the position and the source intensity of the pollution source by using an improved sparrow search algorithm based on the real dust concentration, the dust diffusion influence factors and the dust diffusion evaluation model; obtaining a matrix estimation formula for defining an inverse Gaussian function according to a least square method estimation method, and further defining the inverse Gaussian function according to the dust diffusion evaluation model; when the position of the pollution source is determined, the source intensity of the pollution source is obtained based on an inverse Gaussian function according to the real dust concentration and the dust diffusion influence factors. According to the invention, a large amount of monitoring equipment which is high in cost and complex in installation and maintenance does not need to be installed, and the position and the source intensity of the pollution source of the port storage yard can be traced intelligently, accurately and efficiently.

The invention relates to the technical field of high-precision pressure sensing, and discloses a pressure response data calibration method and device and computer equipment, and the method comprises the steps: collecting the output response data of a pressure sensor array, and constructing a spatial feature matrix, a time feature matrix and an environment feature matrix based on the output response data; performing trilinear least square separation to obtain dynamic response data and time characteristic data; performing symmetric geometric mode decomposition to obtain linear response component data, periodic fluctuation component data and nonlinear characteristic component data; inputting a multi-branch gating circulation unit network model to carry out dynamic calibration strategy prediction to obtain a first dynamic calibration strategy; performing particle swarm iterative optimization to obtain a second dynamic calibration strategy, and generating a progressive calibration parameter set; static and dynamic pressure sensing calibration is performed on the pressure sensor array to obtain a comprehensive pressure sensing calibration result, so that the accuracy of pressure sensing calibration is improved.

The invention discloses a cold station multi-equipment combination energy efficiency optimization method and system, relates to the field of cold station energy efficiency optimization, and aims to solve the problems of low energy efficiency and response lag caused by manual adjustment. The method comprises the following steps: S1, acquiring key operation data (power, state, temperature, flow and frequency) of a water chilling unit, a cooling tower and a water pump and external environment data (temperature and humidity, wind speed and load prediction) in real time; s2, constructing an equipment energy efficiency model based on historical data, and quantifying the change of the equipment efficiency along with the load rate, the temperature difference and the environment temperature; s3, according to the current load and environment prediction, simulating and traversing the energy consumption of the equipment start-stop combination and the water pump frequency combination by applying a Monte Carlo algorithm, and screening a combination strategy with the lowest total energy consumption of the system; s4, converting the optimization strategy into a control instruction, and issuing the control instruction to equipment for execution; and S5, comparing the actual energy consumption with the predicted value, and if the deviation exceeds 5%, correcting model parameters by adopting a least square regression algorithm to realize closed-loop optimization. And the energy efficiency and the response speed of the cold station are obviously improved.

A GNSS positioning slowly-varying fault detection method based on residual-SVR regression comprises the steps that an observation information sequence is acquired based on a Kalman filter, and a covariance matrix of the observation information sequence is calculated; accumulating multi-step information through a sliding window, and constructing chi-square statistics; based on the fault-free data, constructing a training set by taking an innovation sequence as input and chi-square statistics as output, and generating an innovation-statistics mapping function; and fitting a normal slope threshold value based on an SVR predicted value, carrying out least square fitting on an observation statistic curve by sliding a window in real time, and judging whether to start a slow change fault alarm or not. According to the method, the residual error sequence is directly used as model input, and the dynamic chi-square statistical magnitude is used for replacing a traditional dichotomy label, so that the detection delay is reduced; an SVR detection model based on grid search and cross validation collaborative optimization is utilized, and an optimal parameter combination of a minimum mean square error (MSE) is screened through logarithm uniform sampling, interval linear sampling and five-fold cross validation, so that the average absolute error of slowly varying fault detection is reduced.

The invention provides a real-time dynamic harmonic detection method and system for a traction power supply system, and relates to the technical field of harmonic detection, and the detection method comprises the following steps: collecting a voltage signal of the traction power supply system in real time, and dynamically tracking fundamental frequency fluctuation through a phase-locked loop technology; dynamically adjusting the data window length of FFT analysis; dynamically adjusting a frequency spectrum calculation kernel of the FFT; carrying out linear compensation on the detected harmonic phase; taking the preliminarily detected harmonic parameter as an initial value, and iteratively updating the harmonic parameter until convergence by minimizing a signal reconstruction error; dynamically adjusting a sampling interval according to the fundamental frequency fluctuation, and carrying out anti-aliasing processing by adopting a filter with a variable cut-off frequency; and constructing a matrix model containing a fundamental frequency fluctuation parameter, and calculating a real harmonic parameter through a least square method. According to the method, the detection defect of the fixed window FFT during frequency fluctuation can be systematically solved, and high-reliability real-time data support is provided for harmonic resonance suppression of the electrified railway.

The invention discloses a modeling method for inverting surface high-precision three-dimensional deformation through InSAR and GNSS data fusion, and belongs to the technical field of surface deformation monitoring. The method includes the steps of resolving InSAR line-of-sight deformation, unifying InSAR and GNSS data reference systems, establishing a stress model based on an elastic theory, calculating a three-dimensional deformation component for the first time based on a least square theory, obtaining an optimal weight matrix by applying a variance component estimation method, calculating final three-dimensional deformation and the like. According to the method, three-dimensional deformation and strain parameters are estimated by fusing InSAR measurement data and GNSS measurement data based on the elastic theory, the stress relation between adjacent points is considered, and unnecessary spatial interpolation of GNSS measurement values is avoided. The variance component estimation method solves the problem of unreasonable weight ratio of different types of observation values, and according to the adjusted optimal weight matrix, a function model is solved based on the least square method, and high-precision and high-spatial-resolution earth surface three-dimensional deformation information within the research range is obtained.

The invention provides a method and device for determining the horizontal stress difference ratio of a deep coalbed methane reservoir, and relates to the technical field of deep coalbed methane exploration, and the method comprises the steps: obtaining the logging data and azimuth seismic observation data of the deep coalbed methane reservoir, building a reservoir parameter initial model of the deep coalbed methane reservoir based on the logging data, and obtaining the horizontal stress difference ratio of the deep coalbed methane reservoir; constructing a target functional for inverting deep coal bed gas reservoir parameters on the basis of azimuth seismic observation data and an azimuth seismic reflection coefficient equation, and solving the target functional on the basis of a generalized linear inversion theory and an iterative reweighted least square joint algorithm under the constraint of the initial model to obtain a reservoir parameter target model; and calculating the horizontal stress difference ratio of each sampling point in the deep coal bed gas reservoir according to the target model. According to the method, under the constraint of the accurate initial model, accurate estimation of the horizontal stress difference ratio of the deep coal bed gas reservoir is achieved, and the technical problem that an existing horizontal stress difference estimation method is low in accuracy is solved.

The invention provides an intelligent control method and system based on electro-hydraulic linkage. The method comprises the steps that multi-mode environment parameters are collected, a digital twin environment model is constructed, and data are synchronized with an electro-hydraulic servo system in real time. And establishing a nonlinear model, identifying parameters by using a least square method and a recursive least square method, verifying the precision through a step response test, and updating. And constructing an adaptive LSTM neural network inverse model to output a control voltage signal. And the signal is subjected to weighted fusion with the output of a PID controller, and the PID controller dynamically adjusts a gain parameter according to an error integral term to generate a control instruction to drive the system. According to the invention, the LSTM inverse model provides feed-forward compensation, the PID controller realizes feedback correction, and the LSTM inverse model and the PID controller are complementary. Meanwhile, based on a related nonlinear model and a parameter self-adaptive updating mechanism, the influence of working condition change is accurately described, and high-precision control of the electro-hydraulic servo system under the complex working condition is achieved.

The invention relates to the field of intelligent industry, and discloses a visual robot hand-eye calibration method based on a circular hole calibration plate, which takes the circle center of a calibration plate circular hole as a fixed point as a constraint, establishes a hand-eye calibration model combined with robot kinematics parameters, and performs a contrast experiment with a traditional standard ball method under the same experiment platform. After a calibration result is obtained, a certain high-precision plane is scanned with reference to a flatness error evaluation method, a point cloud of the plane is obtained, the distance root-mean-square error between the point cloud and the least square fitting plane is calculated, and the distance root-mean-square error serves as an evaluation parameter of the hand-eye relation calibration precision. According to the method, the position coordinates of the fixed points in the space are collected more accurately under the on-line laser coordinate system, and kinematics parameters of the robot are incorporated into the hand-eye calibration model, so that the root-mean-square error of plane measurement can be reduced, and the superiority of the method is verified.

The invention discloses a self-adaptive battery life calibration method for cloud-side cooperation, and belongs to the crossing field of an energy storage system and cloud-side cooperation calculation. According to the invention, a cloud-edge double-layer collaborative framework is provided; an edge end estimates the health state and the residual life of a battery in real time through a recursive least square extended Kalman filtering model; the error observer calculates a prediction error based on a sliding window, and a dynamic threshold triggers an uploading mechanism; the edge end adopts an auto-encoder to compress original time sequence features into abstract vectors, and the abstract vectors and error statistics are uploaded together; the cloud performs incremental learning by using a deep sequential network, and only finely adjusts tail level parameters of which the gradient sensitivity exceeds a threshold value to generate a correction value; and the correction value is compressed and issued to an edge end, local model parameters are updated through weighted fusion, and a covariance matrix is adjusted. The method realizes high-precision life prediction and dynamic calibration, remarkably reduces the communication load, and is suitable for electric vehicles, power grid energy storage and other scenes.

The invention provides a wind power plant AGC optimization control method and system based on model prediction control, and relates to the technical field of model control, and the method comprises the steps: building an adaptive prediction model, carrying out the online identification of parameters through a recursive least square method, constructing an active power optimization objective function, solving a control instruction sequence, designing a feedback compensator, and correcting a control instruction. And finally, issuing to each fan to execute and realize closed-loop control. According to the method, the AGC control precision of the wind power plant can be improved, the power fluctuation is reduced, and the power grid friendliness and the adaptability of an AGC control system are enhanced.

The invention discloses a high-temperature surface contact thermal resistance elimination method based on double thermocouple compensation, and belongs to the technical field of material thermal property characterization and testing. The method comprises the following steps: synchronously acquiring double-thermocouple temperature, and obtaining a temperature difference signal through filtering calibration and environment correction; calculating the real-time heat flux density according to the corrected temperature difference, the Seebeck coefficient and the dynamic correction factor; establishing a nonlinear dynamic coupling model containing contact thermal resistance parameters, and performing temperature interval division; identifying a contact thermal resistance parameter on line by adopting a self-adaptive recursive least square algorithm, and generating a dynamic thermal resistance compensation value; and applying the compensation value to a double-thermocouple signal according to the weight, and outputting accurate high-temperature surface temperature. According to the method, data acquisition and preprocessing, dynamic heat flow correction, nonlinear segmented modeling, self-adaptive online identification and refined compensation output strategies are adopted, the influence of high-temperature surface contact thermal resistance can be effectively eliminated, and the accuracy and reliability of temperature measurement in the high-temperature environment are remarkably improved.

The invention relates to a lithium battery state-of-charge and state-of-health joint estimation method, and belongs to the technical field of lithium ion batteries. The method comprises the following steps: performing a cyclic aging experiment on a battery under a cyclic working condition; an open-circuit voltage curve is extracted through data analysis, a first-order equivalent circuit model is selected to construct a circuit, and a state equation is written; carrying out parameter identification by adopting a multi-forgetting factor recursive least square algorithm, and constructing an SOC estimator of an unscented Kalman filter based on a state equation and an observation equation to carry out SOC estimation; constructing an SOH estimation observer based on an extended Kalman filter, and estimating the SOH by using the estimated SOC; carrying out segmented dynamic identification on the open-circuit voltage curve by adopting an average moving method in combination with recursive least squares; and coupling the SOC estimator and the SOH estimator, and realizing SOC and SOH joint online estimation by applying the dynamically identified open-circuit voltage curve.

The invention discloses a remote sensing shallow sea water depth inversion method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a satellite remote sensing image and actually measured water depth data of a to-be-measured area; performing correction preprocessing on the satellite remote sensing image to obtain spectral data; dividing the actually measured water depth data based on a preset proportion to obtain a training set and a test set; constructing a partial least squares regression model according to the training set and the spectral data of each wave band corresponding to the training set, predicting the spectral data, obtaining a prediction result, and performing prediction verification; when the prediction verification result does not meet the preset condition, the training set and the test set are divided again; the previous steps are cycled until the prediction verification result meets the preset condition; a support vector machine model is constructed through a parrot optimization algorithm based on the training set and the test set; and performing water depth inversion of the target area by using the support vector machine model. The method can accurately perform shallow sea water inversion, and can be widely applied to the technical field of data inversion.

The invention belongs to the technical field of radar signal processing, and discloses a radar scattering rapid test method based on polarization decomposition. Comprising the following steps: scanning a target 3D model, simulating scattered field distribution in different incident angles and polarization modes, calculating polarization contrast, and identifying a polarization sensitive area; establishing a dynamic coordinate system conversion model, fusing RTK data, IMU data and laser tracking data, and correcting the position of the trolley and the model in combination with a Doppler frequency shift model and a least square method; calculating an optimal test path of the trolley according to the sensitive area by adopting a natural heuristic optimization algorithm; establishing a geometric environment model, obtaining a back scattering signal through beam forming, analyzing a polarization coherence matrix, obtaining a high-entropy region, and decomposing scattering components; according to the method, closed-loop optimization is formed from a data acquisition source to a scattering component analysis tail end, and the test efficiency and the real-time performance are remarkably improved.

The invention discloses a liquid chromatography-tandem mass spectrometry detection method and system, and the method comprises the steps: designing a multi-dimensional gradient elution program according to the physicochemical properties of a target compound, and obtaining a chromatographic separation spectrogram; performing efficient ionization on the target compound according to the chromatographic separation spectrogram to obtain an ionization signal; according to the ionization signal, fragmenting the target ions, and obtaining a fragment ion spectrogram through fragment ion screening; according to the fragment ion spectrogram, a multivariable data analysis algorithm based on machine learning is adopted, a standard curve of a target compound and an interferent database are combined, quantitative analysis is carried out, and a quantitative result is generated through a dynamic interference correction technology in combination with principal component analysis and a partial least square method. By utilizing the embodiment of the invention, the sensitivity, the accuracy and the real-time performance of detection can be improved through multi-dimensional gradient elution, dynamic ionization and fragmentation parameter optimization in combination with an analysis means of machine learning.