76 results about "Linear prediction model" patented technology

The invention discloses a new energy station intelligent micrometeorological monitoring device suitable for extreme weather and a power prediction method, and relates to the technical field of new energy station meteorological monitoring and power prediction. The device integrates a plurality of meteorological sensors, combines satellite meteorological data and ground meteorological station data, improves meteorological monitoring accuracy through data fusion, and gives 8-15-day long-time scale weather forecast. According to the method, based on meteorological data collected in real time and historical meteorological data of a new energy station, a long-short term memory network model and a segmentation multi-target risk method are adopted to carry out risk assessment on extreme weather, and an extreme weather early warning signal is given. A deep neural network is utilized to establish a power nonlinear prediction model of a new energy station, and a higher-precision power prediction result is given through a random forest dynamic correction algorithm. The meteorological and generated power prediction accuracy of the new energy station is improved, and the operation safety of the new energy station is improved.

The invention discloses an acupuncture stimulation evaluation method based on multi-physiological signal fusion real-time monitoring, relates to the technical field of acupuncture medical treatment, and aims to accurately judge a phase locking state through correlation analysis of an absolute value of a myoelectricity attenuation slope and a skin temperature phase difference standard deviation, break through the limitation of a single signal dimension and improve the judgment accuracy to 90% or above. A linear prediction model is constructed based on clinical data, and dynamic correction is performed in combination with a compensation coefficient when a beta wave ratio exceeds a standard, so that personalized needle retaining time prediction is realized, and the prediction deviation rate is effectively reduced; when the accumulated phase locking time reaches a predicted value, a needle retaining adjustment instruction is triggered automatically, a'monitoring-prediction-execution 'closed loop is formed, invalid treatment time is shortened by about 15%, and acupuncture operation efficiency is improved; model parameters are corrected in real time through beta wave anomaly monitoring, curative effect deviation caused by muscle tension or nerve imbalance is inhibited, the system false alarm rate is reduced to 3% or below, and robustness is remarkably superior to that of a traditional method.

The invention relates to a millimeter-wave radar global vehicle track splicing method based on track similarity, which comprises the following steps of: sensing a moving vehicle on a traffic road by utilizing millimeter-wave radars arranged on the traffic road at certain intervals, and acquiring vehicle track data detected by the millimeter-wave radars; and according to the vehicle type similarity, the distance similarity, the speed similarity and the behavior similarity, vehicle tracks of the two radar monitoring blind areas and the overlapping area are matched, the matching degree of the two target vehicle tracks is judged, and finally, tracks between frame windows are spliced according to the matching degree to obtain a final target track. The method solves the problem of discontinuous cross-device multi-target trajectory tracking, solves the problem of poor splicing effect of a trajectory splicing algorithm based on a linear prediction model, and has the advantages of being independent of moving target feature information, high in precision, good in adaptability and the like.

The invention relates to an intelligent operation and maintenance method for power transmission network equipment based on digital twin dynamic coupling, and belongs to the technical field of intelligent detection of power systems. The method comprises the following steps: dynamically splitting a power grid panoramic frame into an equipment physical entity layer and a static background environment layer, and generating a panoramic display picture through cloud and edge parallel rendering and a terminal HMD picture synthesis technology; establishing real-time bidirectional mapping between the physical entity and the virtual twinborn body of the power grid equipment, and synchronizing sensor data to the digital twinborn model; adopting an improved permutation entropy algorithm to extract equipment operation characteristic quantity, and dynamically distributing rendering pipeline resources in combination with a Bayesian network diagnosis model and an analytic hierarchy process evaluation system; and constructing an equipment state quality index, and quantitatively evaluating the health degree of the current equipment and predicting the future degradation trend by dynamically comparing the characteristic parameters with an expected value and combining health factor correction and a time sequence nonlinear prediction model. According to the invention, full-life-cycle efficient management and control of the operation state of the power grid equipment can be realized.

The invention discloses an AUV (Autonomous Underwater Vehicle) trajectory tracking method and system based on adaptive time-varying optimization control. The method comprises the following steps: configuring a time-varying linear prediction model; defining a multi-constraint condition and a target function; the control input of the AUV is optimized through a model prediction control method, so that the path tracking error is minimized, and the change of the control input is reduced; through rolling optimization, optimal control input meeting multiple constraint conditions in each motion period is solved; applying the optimal control input to a motion model of the AUV, and predicting trajectories at multiple moments in the future; acquiring an actual track of the AUV, comparing the actual track with the predicted track, and calculating an error; according to the error, a correction mechanism is fed back for track information correction; and in each control period, the optimal control input is recalculated by using the track information after feedback correction, and the control precision is continuously improved through rolling optimization until the tracking task is completed. The method has the advantages of real-time performance, robustness and high precision.

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 relates to the technical field of aero-engines, and discloses an anti-surge method and system for an aero-hybrid electric engine, and the method comprises the steps: setting a value interval of the maximum allowable acceleration rate according to the structural integrity limitation and acceleration performance requirements of the engine; compressor performance data are obtained through a compressor component performance test, and a linear prediction model changing along with time is established based on the performance data of adjacent moments; predicting an engine operation state in a future preset time period according to the linear prediction model acceleration rate; and calculating a surge margin according to the predicted operation state, and when the predicted surge margin is lower than a preset safety threshold, dynamically adjusting the maximum allowable acceleration rate. According to different severe environments and working conditions, the variable acceleration rate limit value is set, the surge margin is strictly controlled, surge can be actively restrained, the surge risk is greatly reduced, full-process closed-loop anti-surge control is formed, the method is suitable for complex working conditions and multi-scene application, an engine is effectively protected, and flight safety is guaranteed.

The invention provides a wind power plant fatigue suppression active control method based on data mechanism hybrid modeling, and belongs to the technical field of wind power plants, and the method comprises the steps: building a continuous state space model, carrying out the discretization of the continuous state space model through a sampling period, and obtaining a discrete state space model, calculating a threshold value of a switching control strategy of a wind speed region based on the discrete state space model, and further obtaining a prediction model of main shaft torque and a prediction model of tower thrust, and recording the prediction models as wind turbine generator linear prediction models; an equivalent fatigue load linearization model is obtained through main shaft torque time sequence data, and after the equivalent fatigue load linearization model and a wind turbine generator linear prediction model are combined, a data mechanism dual-drive fatigue load model is obtained; and performing active optimization control solution on the data mechanism dual-drive fatigue load model to obtain an active power distribution optimal solution meeting the safety and stability constraints of the wind power plant.

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 present invention relates to a method for adaptive observation of a system comprising at a time t: - a set of input quantities comprising the control setpoint ut and modeled in the form of a vector: Ut, - a set of output quantities modeled in the form of a vector: Yt, and - a set of quantities representing the state of the system and modeled in the form of a vector: xt; the method being implemented in a calculation unit and comprising: - a first step E1 of programming a set of linear differential equations between the input quantities, the state quantities of the system and the output quantities, - a step E2 of determining the slowly evolving parameters, and - a step E3 of adaptive observation comprising the numerical calculation of the quantities of the state of the system xt at time t by solving the set of linear differential equations. Figure for the abstract: Figure 6.

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.

The invention relates to the technical field of new energy, and discloses an operation control method of a renewable energy water electrolysis hydrogen production device, which comprises the following steps: step 1, collecting renewable energy power data, combining environmental data analysis, and establishing a power prediction model by using a long short-term memory network; step 2, establishing a variational optimal control model of the water electrolysis hydrogen production system, and setting constraint conditions of power input, voltage and current density of an electrolytic bath by taking maximization of hydrogen yield and minimization of electric energy consumption as optimization targets; the power prediction method based on the long-short-term memory network is adopted, accurate prediction of renewable energy power generation fluctuation is achieved, the technical effect of stabilizing the water electrolysis hydrogen production process is achieved, and compared with a simple linear prediction model in the prior art, the power prediction accuracy is remarkably improved, and the power prediction efficiency is improved. The problem that the input power of the electrolytic cell is unstable due to renewable energy fluctuation is solved.

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 relates to the technical field of energy monitoring, in particular to an energy maximum tracking prediction system under vector product meaning, comprising: a data acquisition module for acquiring physical quantity data related to energy, sensors including but not limited to an electric field intensity sensor, a magnetic field intensity sensor, a power sensor and a flow sensor; the vector construction module is connected with the data acquisition module through a data transmission technology, and is used for converting the associated data group into a vector form according to the characteristics of the acquired physical quantity and a physical model preset by the system; and the vector operation module is connected with the vector construction module through a data transmission technology and is used for carrying out vector product operation on the vector from the vector construction module. According to the method, the characteristics of the optimal hyperplane are searched by using support vector regression # imgabs0 #, nonlinear data are accurately fitted, compared with a traditional linear prediction model, the advantages are remarkable, the capability of tracking the energy state in real time is achieved, and the operation cost is reduced.

The invention relates to the technical field of force feedback control, in particular to a mechanical-electrical integration assembly method and system based on force feedback control. The method comprises the following steps: acquiring electromechanical integrated assembly sensing data; carrying out data preprocessing on the obtained electromechanical integrated assembly sensing data; constructing a multivariable nonlinear prediction model, and training the model by using the preprocessed data set; generating a control signal based on adaptive genetic and fuzzy control algorithms; and electromechanical integrated assembly is realized based on a control system. And force feedback control is realized through cooperation of various algorithms such as PID, adaptive heredity and fuzzy, the assembly action can be adjusted in real time according to the assembly force, and the quality problem caused by improper assembly force, part position deviation and the like is effectively avoided.

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.