57 results about "Linear prediction model" patented technology

The invention relates to the technical field of market trend prediction, and discloses a market trend prediction system driven by macroeconomic indicators. An index acquisition module of the system dynamically acquires core economic indexes such as GDP growth rate, CPI, PMI and currency supply; the data preprocessing module is used for carrying out layered noise reduction processing on the multi-source heterogeneous data; the feature engineering module constructs a market sensitive feature set through spatio-temporal feature fusion; the prediction model building module is used for building a multi-layer nonlinear prediction model based on a deep belief network; the dynamic adjustment module adopts reinforcement learning to optimize a decision threshold value and combines a Markov chain to carry out state transition planning; and the feedback iteration module analyzes and predicts deviation through Bayesian filtering and realizes strategy updating. According to the method, deep learning and reinforcement learning technologies are creatively fused, the prediction precision is remarkably improved through a dynamic calibration mechanism, and the method can be widely applied to the macroeconomic analysis fields of financial investment, industrial planning and the like.

The invention provides a Prony algorithm-based cable segment wave velocity acquisition method, device and system, and a medium, and the method comprises the steps: testing a to-be-tested cable, and obtaining a cable signal transfer function; on the basis of the cable signal transfer function, in combination with cable joint distribution, constructing a cable signal approximation function based on a Prony method; constructing a linear prediction model of the cable signal based on the cable signal approximation function; performing denoising processing on the linear prediction model based on a singular value decomposition method to obtain a denoised linear prediction model; solving the denoised linear prediction model to obtain an attenuation coefficient; and calculating the segmented wave velocity of the cable based on the attenuation coefficient. According to the method, the Prony estimation method is combined with the singular value decomposition noise reduction algorithm, high-precision extraction of attenuation constants and segmented wave velocity decoupling are achieved, and then the electrical distance positioning precision is improved.

The invention discloses a multi-rotor unmanned aerial vehicle and a flight method thereof. The flight method comprises the following steps: collecting disturbance data; obtaining disturbance influence by using the disturbance data, and adding the disturbance influence into a linear prediction model to form a probability-based disturbance influence prediction model; and outputting a control sequence by using the disturbance influence prediction model based on the probability, and realizing accurate control flight of the multi-rotor unmanned aerial vehicle based on the control sequence.

The invention discloses a method for predicting the content of digestible calcium and phosphorus in the whole intestinal tract of a growing pig based on a linear model, and relates to a method for estimating the content of digestible calcium and phosphorus in the whole intestinal tract of calcium and phosphorus, which comprises the following steps: S1, sorting a collected data set through a preprocessing module to obtain an initial data set containing input characteristics and target variables; the target variables comprise the total intestinal digestible content ATTDCa of calcium and the total intestinal digestible content ATTDP of phosphorus, which are measured by a digestion test; s2, performing feature analysis and screening on the initial data set through a feature engineering module to obtain a data sample constructed by a feature subset matched with the target variable; s3, respectively constructing linear prediction models related to prediction of each target variable, and carrying out training, optimization and verification by using a data sample; and S4, importing feature data of a new feed formula to be predicted, completing selection of data samples through S1-S2, and obtaining a predicted value of a target variable through the linear prediction model.

This application relates to a real-time power load forecasting method and system based on a hybrid model. The method includes: acquiring current cycle load data and combining it with historical load data to form a load sequence, and acquiring corresponding weather data; performing timestamp alignment, anomaly processing, and normalization on the load sequence and weather data to obtain a standardized input sequence; determining the order parameters of the linear forecasting model and the set of hyperparameters to be optimized, consisting of the network structure and training parameters of the nonlinear forecasting model, and optimizing them through a combined optimization algorithm to update the training configuration of the two models; outputting the first and second forecast sequences for the next cycle in the current cycle, respectively, and using the second forecast sequence as a trend term to compensate the first forecast sequence to obtain a fused forecast sequence; acquiring the fused forecast sequence for the current cycle from the previous cycle, constructing a residual sequence with the current cycle load data and determining the deviation term, calibrating the current cycle fused forecast sequence online, and outputting the calibrated load forecast result.

This invention discloses a method for calculating needle-free injection depth based on energy analysis, comprising the following steps: Step 1, obtaining the jet process parameters during a single needle-free injection, and calculating the initial peak jet power and the total jet energy of this injection; Step 2, calculating the initial injection depth based on the initial peak jet power using a preset initial depth linear model; Step 3, calculating the diffusion depth increment based on the total jet energy using a preset diffusion depth linear model; Step 4, calculating the predicted maximum injection depth based on the sum of the initial injection depth and the diffusion depth increment. This invention decomposes the injection process into two stages: initial impact and subsequent diffusion, and establishes linear prediction models for each stage based on the jet energy parameters, thereby achieving reliable and accurate calculation and prediction of the injection depth for large-volume needle-free injections.

The invention relates to a model construction method for predicting photovoltaic power generation capacity, which comprises the following steps of: training a linear prediction model for predicting photovoltaic power generation according to a spatial position relationship between a to-be-measured target region and an adjacent region with photovoltaic power generation data, the photovoltaic power generation data of each region and meteorological data of the target region; according to the meteorological data and the photovoltaic power generation data of the target region, training a random forest prediction model used for predicting photovoltaic power generation of the target region; in the training process, according to the deviation between the power generation amount of the target area obtained by the linear prediction model and the power generation amount of the target area obtained by the random forest prediction model, the training parameters of the random forest prediction model are adjusted to complete the training of the random forest prediction model, and a prediction model with high precision and comprehensive analysis is generated.

The invention discloses a circuit breaker actuation time prediction method and system, a storage medium and electronic equipment. The method comprises the following steps: constructing a multi-parameter nonlinear prediction model of a nonlinear relationship between multiple characteristics of a circuit breaker and actuation time; determining a key feature based on a rate of change of each of the plurality of features of the circuit breaker; constructing a grid on a plane based on the key features, and performing prediction by using a multi-parameter nonlinear prediction model based on feature data corresponding to each point on the grid so as to determine an action time prediction value corresponding to each point on the grid; calculating a partial derivative based on the action time predictor to determine a gradient corresponding to each point; and obtaining target key feature data, and based on the target key feature data, the motion time prediction value corresponding to each point on the grid and the gradient corresponding to each point, performing motion time prediction by using a two-point cubic Hermite interpolation algorithm, and obtaining a motion time prediction value corresponding to the target key feature data.

The invention provides a model prediction control method for a paper pulp kappa number in a batch cooking process, and belongs to the technical field of pulping and papermaking, the method comprises the following steps: firstly, collecting multi-dimensional measurable state variables related to delignification in the cooking process to form an original state vector; then, utilizing a Koopman operator and extended dynamic mode decomposition to map the original state vector to a high-dimensional linear space to obtain a dimension raising state vector; a Koopman global linear prediction model is established on the basis of historical data offline training, and a free liquid temperature setting sequence in a future control time domain is optimized in a rolling mode through a model prediction controller; the first control quantity of the temperature sequence is applied to the cooking process in real time after each time of optimization, and rolling optimization is repeatedly executed in combination with feedback correction in the next sampling period, so that the problem of steady-state deviation of an end-point kappa value in batch cooking application is solved, and the adaptability to model mismatch and the robustness of a control system are improved.

This invention relates to the field of intelligent manufacturing and industrial data analysis technology, specifically to a method, system, and storage medium for identifying production bottlenecks based on multi-dimensional data fusion. The method includes: acquiring production data and constructing a multi-dimensional data vector; updating the weights of a linear prediction model based on the multi-dimensional data vector; performing multi-dimensional data fusion using the updated weights to obtain a comprehensive index value; obtaining a production bottleneck severity score based on the comprehensive index value; determining the production bottleneck status based on the production bottleneck severity score and the comprehensive index value; and identifying factors influencing the production bottleneck based on weight changes. This invention dynamically adjusts the weights using a gradient descent method to score the bottleneck, and dynamically updates the judgment threshold using an exponentially weighted moving average. Finally, it performs multi-dimensional cause analysis by comprehensively considering the weight contribution, change trend, and data anomaly, achieving adaptive and accurate location and cause identification of production bottlenecks.

The present invention relates to a method for identifying layered damage of a composite cylindrical shell structure, comprising the following steps: establishing multiple finite element geometric models and setting sensor measuring points, applying a vibration excitation signal, and collecting a vibration acceleration response signal; calculating a frequency response function and constructing a damage characteristic topology map; obtaining a set of damage characteristic topology maps and training a layered position identification model; obtaining a set of linear coefficients between a damage index and a damage area of ​​each layered damage and obtaining a linear coefficient identification model; fixing a composite cylindrical shell to be tested on a vibration table, obtaining an applied experimental vibration excitation signal and collecting an experimental vibration acceleration response signal; calculating an experimental damage characteristic topology map and inputting it into the layered position identification model to obtain a damage position; obtaining a linear coefficient between a damage area and a damage index; establishing a linear prediction model based on the linear coefficient; obtaining an experimental damage index, and inputting the experimental damage index into the linear prediction model to output a damage area.

The application discloses a method for predicting total intestinal digestible calcium and phosphorus content of growing pigs based on a linear model, and relates to a method for estimating total intestinal digestible calcium and phosphorus content, comprising the following steps: S1, arranging collected data sets through a preprocessing module to obtain an initial data set containing input features and target variables; the target variables include total intestinal digestible calcium content ATTD_Ca and total intestinal digestible phosphorus content ATTD_P measured through a digestion test; S2, performing feature analysis and screening on the initial data set through a feature engineering module to obtain a data sample constructed by a feature subset adapted to the target variables; S3, respectively constructing linear prediction models related to the prediction of each target variable, and training, optimizing and verifying the linear prediction models by using the data sample; and S4, importing feature data of a new feed formula to be predicted, selecting the data sample through S1-S2, and obtaining a prediction value of the target variable through the linear prediction model.

The invention provides a method for rapidly predicting the meat content of female procambarus clarkii. The method for rapidly predicting the meat content of the female procambarus clarkii comprises the following steps: S1, placing living female procambarus clarkii on a collection table, and applying broadband sound wave excitation to the female procambarus clarkii by using a micro vibration device to obtain a resonance response signal; s2, performing characteristic measurement on the female procambarus clarkii to obtain morphological parameters, and obtaining a meat content linear prediction model based on the morphological parameters; and S3, carrying out spectrum analysis on the resonance response signal and extracting resonance characteristics. Compared with a traditional method of weighing after slaughtering, the method for rapidly predicting the meat content of the female procambarus clarkia does not need to damage the procambarus clarkia bodies, keeps the integrity of samples, facilitates repeated screening and subsequent observation of genetic breeding, is simple and convenient to operate and short in time consumption, greatly improves the detection efficiency, and is suitable for large-scale high-throughput application.

The application discloses a kind of solubility carbon black electron transfer capacity quantitative prediction method, more than three temperature is selected and is prepared carbon black using biomass, is configured into mixed solution by adding water, mixed solution is cultured by shaker, solution is centrifuged, supernatant is filtered, and solubility carbon black solution is obtained;The concentration of solubility carbon black solution is determined, and the cyclic voltammetry curve of carbon black solution is determined using electrochemical workstation and three electrode system, the peak potential corresponding to each scanning speed in the curve, peak current are obtained, according to scanning rate, peak potential, peak current, solubility carbon black solution concentration, electron transfer rate constant k 0 It is calculated by using Bulter-Volmer equation, linear prediction model is constructed by the logarithmic value of electron transfer rate constant k 0 And pyrolysis temperature, the electron transfer capacity of carbon black at different temperatures is predicted using the model;The method is simple and easy to operate, and the electron transfer capacity of solubility carbon black of known raw material type can be directly predicted.

The invention relates to the technical field of intelligent meteorological early warning, and discloses an intelligent monitoring system based on a rainfall sensor, and the system comprises a sensing collection module which collects signals of the rainfall sensor and synchronizes environmental parameters; the data processing and modeling module is used for receiving the original data, performing preprocessing and feature extraction, and constructing a prediction model fusing rainfall and environmental factors; the model learning and self-adaptive updating module is used for optimizing model parameters according to actual rainfall and prediction errors to realize dynamic updating and continuous optimization; the output and communication module is used for judging the rainfall level and sending prediction and alarm information to a remote terminal through multi-channel communication; and the system control and energy supply module regulates the operation of the module and guarantees the stable and efficient operation of the system in combination with a multi-source power supply and scheduling strategy. According to the method, the moving average preprocessing, the multi-feature linear prediction model, the multi-channel communication and the multi-source energy supply technology are fused, and the prediction precision and the environment adaptive capacity of the rainfall monitoring system are improved.

The invention provides a marine engine operation parameter real-time prediction method, system, medium and device based on a DLinear algorithm, explicit sequence decomposition is replaced by feature decoupling, multivariate prediction is simplified into multiple single-variable prediction through feature decomposition, and complex time sequence modeling is replaced by linear mapping. According to the method, the Dlinear algorithm is simplified and is suitable for parameter time sequence prediction with weak correlation, and an original Dlinear algorithm structure can be used for predicting parameters with stronger correlation. According to the method, the parameter data of the marine engine in different load ranges are collected, and the feature decoupling prediction model and the trend / season bilinear prediction model are established, so that the influence of a full-load test on the service life of the engine can be avoided, and the problem of insufficient effective data samples of modeling is solved; and the nonlinear characteristics of multivariable co-evolution can be quickly captured.

The invention relates to a method for the adaptive observation of a system at a time t, the system comprising:-a set of inputs comprising the control setpoint ut and modeled in the form of vectors: Ut; -a set of output quantities, the set of output quantities being modeled in the form of vectors: Yt; and-a set of quantities representing the state of the system, the set of quantities modeled in the form of vectors: xt; the method is implemented in a computing unit and comprises: a first step E1 of programming a set of linear differential equations relating to the input, the system state and the output; a step E2 of determining a slow-varying parameter; and-an adaptive observation step E3 comprising numerical calculation of the quantity xt of the system state at the moment t by solving the set of linear differential equations.

The invention provides a battery thermal management system control method based on multi-mode switching and predictive optimization, and the method comprises the following steps: building a discrete time state space model based on the physical structure of a battery module; performing first-order Taylor expansion at a nominal working point for a nonlinear coupling term in the discrete time state space model so as to obtain a linear prediction model adapted to online optimization; designing a model prediction controller based on the linear prediction model; a dual-threshold trigger logic based on an instantaneous temperature tracking error and a battery output power demand is defined, so that a battery thermal management mode is adaptively switched. According to the method, the prediction controller based on the linear dynamic state space model is established, and a mode switching mechanism based on temperature error and battery power dual-threshold triggering is introduced, so that the model prediction controller can realize accurate tracking and quick response of the battery temperature, the health state of the battery is improved, and the service life of the battery is prolonged.

The application discloses a system control method and device based on model predictive control and reinforcement learning, comprising: acquiring a training data subset of a controlled system and a total state vector at a current moment, the historical state vector at the current moment comprising a control input vector and a state output vector before the current moment, and a plurality of training data subsets being obtained by unsupervised clustering of training data based on historical state vectors at historical moments; determining an MPC intelligent agent of a local working condition corresponding to a training data subset with the highest similarity according to the similarity between the historical state vector at the current moment and the historical state vector at the historical moment; and generating a control input vector at the current moment according to parameters of the MPC intelligent agent and the total state vector and sending the control input vector to the controlled system, wherein the parameters of the MPC intelligent agent are obtained by training a linear prediction model and reinforcement learning based on the training data subset corresponding to the local working condition. According to the application, the complexity of system control can be reduced, and the interpretability and safety can be improved.

The present invention belongs to the field of robot velocity planning, and more particularly relates to a method and system for time-optimal velocity planning of a robot that considers robot contour error constraints. The method comprises: first, establishing a prediction model for robot joint tracking error based on the robot joint control loop; then, implementing a linear prediction model for the robot end contour error with respect to joint velocity, acceleration, and jerk based on the robot joint tracking error model and the Frenet framework of the robot path; and finally, incorporating a contour error constraint model based on typical constraint equations for robot velocity planning to achieve time-optimal velocity planning that satisfies the robot contour error constraints.

The application provides a wind farm fatigue suppression active control method based on data mechanism hybrid modeling, and belongs to the technical field of wind farms.The method comprises the following steps: a continuous state space model is established, a sampling period is used to discretize the continuous state space model, a discrete state space model is obtained, a threshold value of a switching control strategy of a wind speed region is calculated based on the discrete state space model, and then a prediction model of a main shaft torque and a prediction model of a tower thrust are obtained, which are denoted as a wind turbine linear prediction model; an equivalent fatigue load linearization model is obtained through main shaft torque time series data; after the equivalent fatigue load linearization model and the wind turbine linear prediction model are combined, a data mechanism double-driven fatigue load model is obtained; and the data mechanism double-driven fatigue load model is subjected to active optimization control solving, so that an optimal solution of active power distribution that meets the safety and stability constraints of the wind farm is obtained.

The present invention provides a diaphragm myoelectricity real-time processing method based on linear prediction, a computer and a storage medium. The linear prediction model of the method is used as a diaphragm myoelectricity filter that uses the measured value of the electrocardiogram interference at the historical moment to predict the electrocardiogram interference value at the current and future moments; the diaphragm myoelectricity filter is used to perform a convolution operation on the diaphragm myoelectricity signal interfered by the electrocardiogram, and the convolved signal is subjected to an over-threshold zeroing process to obtain a signal y(k); a diaphragm myoelectricity segment containing the electrocardiogram is obtained, and the linear prediction model coefficient of the segment is calculated, and the diaphragm myoelectricity filter coefficient is adaptively adjusted using the model coefficient; the signal y(k) is sequentially subjected to a second-order high-pass filter and an M filter. conv The diaphragm EMG signal z(k) after noise reduction is obtained by performing low-pass filtering; the envelope of the diaphragm EMG signal z(k) after noise reduction is calculated to obtain the envelope signal z of the diaphragm EMG signal after noise reduction. e (k) The present invention can improve the accuracy of diaphragm electromyographic signal acquisition.

The invention relates to the technical field of agricultural product quality monitoring and risk early warning, and discloses an agricultural product quality risk early warning system and method based on digital twinning, and the method comprises the steps: constructing a metabolic network response digital twinning model under a multi-stress condition, bidirectional data mapping of the physical agricultural product and the digital model is realized; constructing a metabolic toughness potential energy landscape model, and representing the stability and critical transformation characteristics of an agricultural product quality system; extracting an early warning signal based on a critical moderation theory; analyzing an interaction effect among various environmental stress factors, and constructing a toughness map of an agricultural product quality system; integrating analysis results to form an early warning decision and providing an intervention suggestion; according to the method, the limitation of a traditional linear prediction model is broken through, potential risks can be identified in the quality appearance stability stage, early warning several days ahead of time is achieved, and powerful technical support is provided for agricultural product cold-chain logistics and warehouse management.

A braking pressure control method for new energy vehicles based on the Koopman operator includes: establishing the dynamic equilibrium equations and nonlinear model of the braking system; constructing an observation function set, identifying the Koopman operator matrix using an extended dynamic mode decomposition algorithm, constructing a global linear prediction model, and correcting the Koopman operator matrix; constructing an augmented state equation in the discrete-time domain, designing a linear extended state observer based on an improved Kalman filter algorithm, and outputting a lumped disturbance estimate; injecting the lumped disturbance estimate into the prediction equation of the linear model predictive control, and obtaining the optimal control sequence by solving the objective function of a quadratic programming problem; converting the first control increment of the optimal control sequence into a motor drive signal, which is applied to the master cylinder motor of the electro-hydraulic braking system for physical pressure build-up. This invention can solve the problems of low hydraulic pressure control accuracy, poor anti-interference ability, and high computational load in existing technologies.

The present invention relates to a control method, electronic device, computer-readable medium, and computer program product for a DC-DC converter, and belongs to the field of automatic control of DC-DC conversion. The specific control method includes: first establishing an affine nonlinear model containing parasitic parameters of circuit components; constructing a Brunovsky standard form of the model based on a target holographic feedback method, and converting the system into a linear form; establishing a linear prediction model based on the model predictive control principle, and constructing a dynamic semi-elliptical interval control curve. y H and y L The objective function is constrained. Finally, a sequential quadratic programming algorithm is used to solve the objective function and generate a sequence of control variables, U, to dynamically adjust the duty cycle of the switching transistors. This method is primarily used to suppress the cross-effects between branches caused by parasitic parameters and achieve high-precision coordinated control of multiple output voltages.

The present invention discloses an acupuncture stimulation evaluation method based on real-time monitoring of multiple physiological signals fusion, which relates to the field of acupuncture medical technology. Through the correlation analysis of the absolute value of the myoelectric attenuation slope and the standard deviation of the skin temperature phase difference, the "phase lock state" is accurately determined, breaking through the limitation of a single signal dimension, and the determination accuracy is improved to more than 90%; a linear prediction model is constructed based on clinical data, combined with the dynamic correction of the compensation coefficient when the β wave proportion exceeds the standard, to achieve personalized needle retention time prediction, effectively reducing the prediction deviation rate; when the cumulative phase lock time reaches the predicted value, the needle retention adjustment instruction is automatically triggered to form a "monitoring-prediction-execution" closed loop, reducing the ineffective treatment time by about 15%, and improving the efficiency of acupuncture operation; through the real-time correction of model parameters by β wave abnormality monitoring, the efficacy deviation caused by muscle tension or nerve imbalance is suppressed, the system false alarm rate is reduced to below 3%, and the robustness is significantly better than traditional methods.

The invention discloses a superconducting optical fiber preparation method and a high-sensitivity landslide monitoring method, which are applied to the technical field of landslide monitoring. The monitoring method comprises the following steps: constructing a three-dimensional distributed sensing network based on a biological superconducting sensing optical fiber in combination with a multi-source sensor; emitting a detection light signal, and collecting a returned backscattering light signal; demodulating the collected optical signals to obtain strain, temperature or vibration parameters along optical fiber distribution points; in combination with multi-source data about the landslide mass and a landslide geomechanical model, the stable state of the landslide mass is evaluated through a threshold criterion or a machine learning algorithm, a prediction result is obtained by using a linear prediction model, and safety operation is executed based on the prediction result. According to the invention, the sensing of the landslide disaster from the millimeter level to the micro-nano level is realized, the sensitivity is high, the anti-interference capability is strong, and a brand new technical means is provided for the early precise early warning of geological disasters.

The invention discloses a method and a system for constructing an investment cost estimation model of a light-storage integrated station. The method comprises the following steps of: acquiring engineering quantity, equipment composition, energy storage scale, sending-out line parameters and a construction period financial structure in research data of a photovoltaic project; respectively quantifying the data into a direct current side installed capacity, an energy storage configuration proportion, a transmission project line length, a power transmission line voltage grade, a financing interest rate and a construction period; the total investment is divided into basic cost, energy storage increment cost, capitalization interest cost and outgoing line cost, and the four costs are composed of direct current side installed capacity, energy storage configuration proportion, outgoing engineering line length, power transmission line voltage grade, financing interest rate and construction period; forming an overall nonlinear prediction model based on the four costs; and early-stage investment estimation, scheme comparison and selection and power station economy evaluation of the photovoltaic project are carried out based on the overall nonlinear prediction model. According to the invention, accurate prediction of new energy projects of different scales is realized through the overall nonlinear prediction model.

This invention discloses a simulation control method and system based on aircraft servo hydraulics, belonging to the field of aerospace simulation control technology. The method includes preprocessing raw data collected by deploying sensors, constructing a nonlinear state-space equation based on the preprocessed data, performing state estimation and data fusion to obtain a real-time estimated state signal, and further constructing a nonlinear prediction model based on the real-time estimated state signal to output the optimal control input signal. This invention significantly improves the modeling accuracy and dynamic response capability of the simulation by using a Kalman filter to estimate the nonlinear state-space equation in real time. Furthermore, by combining the nonlinear prediction model with the output of the optimal control input signal, low-pass filtering and TD smoothing are used to suppress noise interference, optimizing the smoothness and stability of the control signal. The error correction and rolling optimization strategy based on the feedback function precisely constrains the change in control input, effectively reducing simulation errors and energy consumption.

The application discloses a kind of quantum chemistry-based energetic material electrostatic spark sensitivity prediction method and system, it is related to energetic material performance prediction field, the method includes: new energetic material sample carries out quantum chemistry calculation, obtains the multidimensional influence parameter data set corresponding to the new energetic material sample, and multidimensional influence parameter data set is input into energetic material electrostatic spark sensitivity value prediction model, obtains the electrostatic spark sensitivity value corresponding to new energetic material sample.The present application can solve the technical problems that the existing energetic material electrostatic spark sensitivity linear prediction model is narrow in scope of application, low in prediction efficiency, and it is difficult to realize high-precision prior prediction of electrostatic spark sensitivity of new unsynthesized energetic material.