184 results about "Support vector regression model" patented technology

The invention discloses a network security situation awareness and dynamic analysis method based on a graph neural network, and the method comprises the following steps: S1, collecting network related data, and constructing a dynamic security knowledge graph; s2, preprocessing the collected data to generate a node feature matrix; s3, based on the node feature matrix, generating a node embedding representation through an improved graph neural network and a graph attention mechanism, and optimizing the node embedding representation in combination with the graph attention mechanism; s4, establishing a time sequence prediction model based on the improved support vector regression model, and identifying potential abnormal behaviors and security threats; s5, evaluating the identified security threat, and predicting the risk level of the security threat; s6, generating a response strategy according to a security threat assessment result; and S7, executing the response strategy. According to the method, the improved graph neural network and the support vector regression model are utilized to carry out network security situation awareness and dynamic analysis, and the method has efficient and accurate security threat identification and adaptive response capabilities.

The invention provides an industrial internet security situation analysis method and system based on support vector regression, and relates to the technical field of industrial internet security, and the method comprises the steps: collecting alarm data, and recognizing a homologous alarm event through a time window sliding mode; establishing a time sequence propagation matrix, calculating a propagation probability, constructing an attack scene graph and extracting features; calculating a security situation index by using a support vector regression model; and selecting an optimal protection node combination based on the node centrality and the protection decision tree. According to the method, the threat propagation path can be accurately identified, the security situation can be quantified, and the precise protection of the industrial internet environment can be realized.

The invention relates to the technical field of soil detection and multi-source information fusion, in particular to a soil moisture content cooperative detection method and system.The method comprises the steps that a target area is determined, and a target thermal infrared image of surface soil of the target area is obtained; extracting target characteristic parameters related to the moisture content from the target thermal infrared image, inputting the target characteristic parameters into the trained BP neural network, and predicting to obtain a surface soil moisture content distribution diagram; based on the surface soil moisture content distribution diagram, determining a target range region with abnormal moisture content through threshold comparison; after the air coupling stepping radar is driven to be aligned with the thermal infrared imaging system in a space-time mode, scanning is conducted in a target range area, and a radar reflection coefficient extracted from an obtained radar image and phase difference information serve as radar characteristic parameters; and performing modeling analysis on the radar characteristic parameters based on a support vector regression (SVR) model, and obtaining soil profile moisture content distribution of the moisture content abnormal region by constructing a nonlinear mapping relation and optimizing model hyper-parameter inversion.

The invention relates to the technical field of marine ecological environment monitoring and data analysis, and particularly discloses a sea area phytoplankton biodiversity index prediction method and system based on multi-model integration and feature engineering. After preprocessing, constructing four groups of nonlinear interaction characteristics of a temperature-salt relationship, oxygen-salt balance, chlorophyll chemical oxygen demand coupling and a nitrogen-phosphorus ratio based on environmental factors, combining station characteristics with basic environment and interaction characteristics, carrying out variance threshold screening, inputting a characteristic set into a multi-model integration framework containing models such as linear regression and gradient lifting, and carrying out multi-model integration; training and tuning according to a time sequence segmentation strategy, selecting model output according to a decision coefficient, using a result if the decision coefficient of the support vector regression model is within a preset range, and otherwise, taking a gradient lifting and extreme gradient lifting tree model to predict a mean value. And the prediction accuracy and the model generalization, stability and reliability are improved.

The invention discloses a multi-parameter cooperative temperature control system of an energy-saving industrial oven, and relates to the technical field of temperature control of industrial ovens, load data in the oven are collected in real time through a high-precision sensor, and a Kalman filter is used for cleaning, calibrating and filtering environmental noise and drift to obtain smooth and stable data; analyzing a real-time load based on a support vector regression model, capturing a nonlinear relation in combination with a radial basis function kernel, and predicting temperature recovery time and expected energy consumption; a reinforcement learning algorithm is adopted, a high-dimensional state space is analyzed through a deep Q network, the heating power and the airflow speed are dynamically optimized, and optimal control parameters are output; and the control module executes the adjusted setting, monitors the temperature and the energy consumption in real time, feeds back data to a prediction model and a learning strategy, and periodically updates parameters to adapt to load and environment changes. The adaptive capacity and long-term stability are remarkably improved, and the method is suitable for industrial scenes with load change, equipment aging or environment fluctuation.

The invention relates to the technical field of artificial intelligence, in particular to an injection molding process parameter optimization method and system based on hybrid algorithm and model fusion, and the method comprises the steps: optimizing a parameter combination of a support vector regression model through a simulated annealing algorithm, building a weighted fusion model based on the optimized support vector regression model and a random forest, and optimizing the model; constructing a hybrid model of an adaptive selection weighted fusion model and an optimized support vector regression model; constructing a three-objective optimization model including buckling deformation, volume shrinkage and production energy consumption, and searching a Pareto optimal solution set in a process parameter space by adopting a multi-objective genetic algorithm by taking the hybrid model as a target value evaluation tool; carrying out local correction on the key process parameters by adopting a gradient descent method until the deviation falls back to be within a preset threshold value, and obtaining optimized process parameters; the defect rate of products can be reduced, and meanwhile production energy consumption is reduced.

The invention relates to the technical field of laser frequency stabilization control, and discloses a multi-mode laser frequency stabilization error feedback correction system driven by machine learning. The system comprises a multi-modal data acquisition module for acquiring various data to generate a multi-modal time sequence data set; the error feature extraction module is used for extracting an error feature tensor by using a kernel principal component analysis algorithm; the dynamic compensation modeling module is used for constructing a support vector regression model to generate a dynamic compensation strategy matrix; and the feedback control optimization module is used for designing a self-adaptive model prediction control framework to generate a closed-loop correction instruction sequence. In addition, a self-adaptive correction execution module, an error traceability analysis module and an abnormal mode recognition model are further arranged. Through multi-modal data acquisition and analysis and intelligent modeling and control, high-precision laser frequency stabilization is realized, the system can effectively adapt to a complex environment, the stability and reliability of the system are improved, and the system has a wide application prospect in the fields of laser processing, optical communication and the like.

The invention relates to the technical field of laser spectrum gas detection, and particularly discloses a TDLAS (tunable diode laser absorption spectroscopy) double-gas concentration synchronous calibration and decoupling method in the presence of spectrum aliasing interference, and the method comprises the following steps: acquiring a multi-band harmonic signal subjected to adaptive optimization while acquiring environmental parameters; an optimal frequency band signal is determined based on the signal quality index; and calculating a spectral aliasing coefficient according to the characteristics of the optimal frequency band signal, dynamically adjusting operation parameters of a search algorithm based on a numerical value interval in which the spectral aliasing coefficient is located, and determining an initial range of dual-gas concentration in a search space. According to the method, the improved genetic algorithm based on the spectral aliasing coefficient dynamic adjustment search strategy is combined with the incremental support vector regression model, stage processing from coarse calibration to fine decoupling is achieved, and the calculation efficiency and the search precision can be automatically balanced when the spectral aliasing degree changes; and online correction is carried out on the model in combination with aging characteristics.

The invention discloses an agricultural load prediction method and system based on multivariate time sequence decoupling multi-modal learning, and belongs to the technical field of agricultural load prediction. Comprising the steps of collecting historical agricultural load and meteorological data; decomposing historical agricultural load and meteorological data by using multivariate variational mode decomposition to obtain cycle, trend and residual mode components; and respectively constructing a time convolutional neural network, a bidirectional gating cycle unit and a support vector regression model for the decomposed period, trend and residual modal component data set, and fully mining feature information of each mode after decomposition, thereby realizing accurate prediction of agricultural load. According to the method, the potential nonlinear space-time coupling relationship between the agricultural load and the meteorological factor is captured, the prediction effect in a seasonal periodic fluctuation scene of the agricultural load and a long-term trend and agricultural load abnormal scene is improved, the agricultural load prediction precision is improved, and a support is provided for reliable and stable operation of a power grid.

The invention relates to the technical field of data processing, in particular to a fan tower fastening bolt on-line monitoring system based on a sensor, the system comprises a processor and a memory, and the processor executes a computer program of the memory to realize the following steps: acquiring a multi-dimensional monitoring data sequence of any bolt on a fan tower at a current sampling moment; dividing the multi-dimensional monitoring data sequence into subsequences and a current subsequence, and obtaining a temperature and wind pressure influence coefficient between every two subsequences; according to a time interval between a sampling moment and an initial sampling moment of each sub-sequence, obtaining a regression weight of each temperature wind pressure influence coefficient, constructing a weighted loss function, training a support vector regression model to obtain an optimal prediction model, and obtaining a prediction value; and according to the difference between the temperature wind pressure influence coefficient between the current sub-sequence and each sub-sequence and the predicted value of the current sub-sequence, abnormal early warning is performed on any bolt, so that the accuracy of a bolt fastening state monitoring result is improved.

The invention relates to the technical field of disease course trend modeling, in particular to a subarachnoid hemorrhage disease course trend modeling system fusing multi-source data. Four kinds of signals of intracranial pressure, blood flow velocity, cerebrospinal fluid pressure and electroencephalogram of a monitored object are synchronously collected, an instantaneous phase is extracted through Hilbert transform, a time window is adaptively adjusted according to the brain blood vessel conduction delay characteristic of an individual, and phase locking indexes among three pairs of signals are calculated. A multivariate coupled oscillator model is established, phase track topology invariant features are extracted, and comprehensive trend indexes are generated through tensor fusion. An individualized four-dimensional phase entropy baseline mode is established, and a double-layer early warning mechanism is adopted: when second derivative continuous symbol overturning occurs in all three phase locking indexes, early warning is directly performed, and when any two phase locking indexes are overturned, a trend index needs to be synthesized for confirmation. And predicting a state level, a trend level and an expected evolution trajectory based on a support vector regression model. According to the invention, precise disease course prediction and early warning are realized, and a basis is provided for clinical decision making.

The invention relates to a wide-temperature-interval battery health state estimation method based on an electrochemical impedance spectrum, and belongs to the technical field of battery health state monitoring. Aiming at the problem of battery state of health (SOH) estimation misalignment caused by electrochemical impedance (EIS) temperature interference, the invention provides the following scheme: collecting EIS data within 10-30 DEG C, including extracting temperature interference resistance features, principal component dimensionality reduction features and low variance phases, constructing combined features # imgabs0 # and # imgabs1 #, and realizing SOH estimation through a support vector regression (SVR) model. The method breaks through the dependence of a traditional method on a constant temperature condition or a temperature sensor, realizes SOH high-precision estimation in a wide temperature interval, and is suitable for lithium / sodium ion battery on-line health monitoring.

The invention discloses a method for detecting types and concentrations of trace organic matters by combining terahertz spectroscopy with machine learning, which specifically comprises the following steps: preprocessing terahertz time-domain spectroscopy data of a sample, and optimizing model parameters by using an improved particle swarm optimization algorithm; training the optimized model based on 5-fold cross validation, and evaluating the performance of the model; and inputting test set data into the trained model for classification prediction, and outputting a result. According to the method, terahertz signal sensitivity is enhanced through metasurface structure optimization, a support vector classification model and a support vector regression model are optimized in combination with an improved particle swarm algorithm, a qualitative and quantitative double-model collaborative analysis system is established, and sensitive and efficient detection of elementary substance types and concentrations of organic substances in a million fraction concentration (ppm) magnitude is achieved.

The invention relates to the technical field of shield tunneling machine cutter detection, and discloses a shield tunneling machine cutter wear automatic detection system and method, and the system obtains a shield tunneling machine cutter image collected by a camera assembly, and carries out the image preprocessing, noise suppression, cutter region segmentation and region-of-interest extraction to obtain a cutter target ROI image, image segmentation based on a gray threshold is adopted in cutter region segmentation, the gray threshold is determined through the cutter geometric features and the cutter texture features of each sliding window region, and the method can improve the accuracy of image segmentation and the accuracy of the cutter geometric features and the cutter texture features. And finally, performing tool wear feature extraction on the tool target ROI image, and inputting the tool wear feature into an intelligent tool wear detector based on a support vector regression model to obtain a detection result. Therefore, automatic evaluation of the wear degree is realized, human intervention is reduced, and the detection efficiency and accuracy are improved.

The invention discloses a biomass gasification product prediction method based on mechanism and data fusion driving, and the method comprises the steps: constructing a biomass gasification product prediction mechanism model, and inputting biomass gasification operation parameters into the model to obtain a prediction result; calculating a residual vector between a prediction result and actual experimental data; establishing a support vector regression model between the biomass gasification operation parameters and the residual vectors; and taking the output of the support vector regression model as an error compensation item of a biomass gasification product prediction mechanism model, constructing a biomass gasification product prediction model based on mechanism and data fusion driving, and predicting a biomass gasification product by using the model. Through fusion of a mechanism model and data driving, the problem of prediction deviation caused by model simplification hypothesis in a traditional method is solved, high-precision prediction of biomass gasification products is achieved, a theoretical basis is provided for real-time optimization of process parameters, and the method is suitable for industrial control of biomass gasification systems such as fluidized beds and fixed beds.

The invention discloses an SVR combustion instability prediction method and system, and relates to the field of energy power and intelligent control, and the method comprises the steps: obtaining a fault combustion flame chemiluminescence signal, and generating a signal data set comprising a training data set and a test data set; establishing a support vector regression model based on the training data set; evaluating the support vector regression model by using the test data set, and adjusting according to an evaluation result to obtain a prediction model; using the prediction model to predict to-be-predicted sample data; the historical flame chemiluminescence signal data of the gas turbine under different working conditions are adopted as data input and can be used for predicting the working state of the gas turbine after a certain time delay, and the model can perform algorithm processing on the flame chemiluminescence signal measured by the gas turbine and predict the change trend of the future moment. The occurrence time of the unstable combustion can be predicted, and enough time is reserved for the control process of the unstable combustion.

The invention relates to a cable insulation degradation degree evaluation method based on frequency domain characteristic analysis, and belongs to the field of cable insulation degradation detection.The cable insulation degradation degree evaluation method comprises the steps that cable operation environment data and laboratory accelerated aging data are collected, and a low-frequency impedance characteristic data set with water tree growth stage labels is generated; extracting a dielectric response spectrogram and analyzing a relaxation polarization peak value; when the peak value exceeds a threshold value, extracting harmonic component characteristics and loss factor distribution characteristics by using wavelet transform and principal component analysis; constructing a support vector regression model to predict the length, density and growth stage of the water tree; for middle and later water trees, microstructure evolution characteristic parameters are calculated, and insulation penetration time is predicted; the degradation probability is analyzed through Monte Carlo simulation, and a risk distribution curve is generated; and optimizing the cable replacement time by adopting dynamic planning, and outputting an evaluation report for quantifying the degradation degree and maintenance suggestions. According to the invention, accurate evaluation and prediction of cable insulation degradation are realized, and an important guarantee is provided for safe operation of a power system.

The invention provides a cattle and sheep feed trough residual feed monitoring method based on vision, and relates to the technical field of intelligent livestock breeding, and the method comprises the following six steps: intelligent triggering and multi-modal image acquisition, image preprocessing and fusion, time sequence image segmentation and feature extraction, density adaptive volume calculation, online learning and residual feed estimation, and decision analysis and early warning. According to the method, RGB and near-infrared images are synchronously collected through infrared triggering, after perspective correction and illumination adaptive fusion, an LSTM-U-Net time sequence segmentation model is adopted to solve the dynamic shielding problem, a residual feed area is accurately extracted, the feed type is recognized, density adaptive volume measurement is achieved in combination with monocular depth estimation and texture feature analysis, and the method is suitable for large-scale industrial production. Weight estimation is carried out through a support vector regression model, model parameters are optimized on line based on manual correction data, intelligent early warning of the residual material amount is finally achieved through a cloud platform, and the limitation of a traditional method in the aspects of shielding processing, density adaptation and environment anti-interference is effectively overcome.

The invention discloses a screw pump remote control system for multi-parameter adjustment and fault pre-diagnosis, relates to the technical field of operation monitoring, and is used for solving the problems of insufficient prediction precision and poor reliability caused by the fact that the driving capability is not matched with pump body parameters and the requirements of an underground direct-drive system on accurate matching and dynamic monitoring of pump body performance are difficult to meet. In order to solve the problem of monitoring range adjustment lag in the prior art, multi-dimensional data in the operation process of the screw pump are collected, key characteristic values, symmetry information, axis offset and surface wear degree are extracted, a key monitoring area is determined, a stability index is calculated, and an index regression algorithm is utilized to calculate a dynamic adjustment coefficient so as to set an initial monitoring range. The operation quality type is evaluated through a double-threshold mechanism, the distance between the working temperature and the key position is collected in the medium and poor state, the feature vector is constructed to be input into the support vector regression model, the monitoring range is dynamically adjusted, intelligent recognition and optimal control over the operation state are achieved, and the equipment stability and prediction capacity are improved.

The invention relates to the technical field of wearable intelligent equipment, and further relates to an exercise training plan generation method and system and wearable intelligent equipment. The method comprises the following steps: when a user is not in a motion state, determining a first maximum heart rate and a first maximum oxygen uptake of the user based on a support vector regression model and basic information of the user; when the user is in the motion state, photoelectric volume pulse wave signals generated by the user in the motion process are processed through a deep learning model, and a plurality of basic index sequences are obtained; determining a second maximum heart rate and a second maximum oxygen uptake of the user based on the plurality of basic index sequences, the basic information, the maximum heart rate and a maximum oxygen uptake prediction model; and generating an exercise training plan of the user based on the first maximum heart rate and the first maximum oxygen uptake, or generating an exercise training plan of the user based on the second maximum heart rate and the second maximum oxygen uptake. The method reduces the generation cost of the exercise training plan.

The invention relates to the technical field of automatic control, in particular to a binding force adjusting method and system of a binding machine. The method comprises the following steps: acquiring parameter items corresponding to each sample in a binding force test set, wherein the parameter items comprise volume, weight and friction force; calculating the volume weight, the weight weight and the friction force weight of the sample; on the basis of the weighted mean value and the weighted value of each parameter item of the sample, a fluctuation index of the parameter item is obtained; penalty parameters of the parameter items are calculated, wherein the penalty parameters are in negative correlation with fluctuation indexes of the parameter items; and training the support vector regression model based on the binding force of the sample, the weight of each parameter item and the penalty parameter to realize the binding force adjustment of the banding machine, so that the accuracy of the binding force adjustment of the banding machine can be effectively improved.

The present application pertains to the field of data processing technology, specifically relates to a method, apparatus and device for optimizing a process parameter and a storage medium, which includes: using a first weight parameter of a pre-trained support vector regression model for a manufacturing equipment as an iterative initial value, and calculating a second weight parameter of a support vector regression model using an online proximal gradient algorithm based on training data; obtaining an optimized support vector regression model by updating the first weight parameter of the pre-trained support vector regression model to the second weight parameter; inputting a first process parameter of the manufacturing equipment into the optimized support vector regression model to obtain a first detection parameter output by the optimized support vector regression model; and calculating, according to the first process parameter and the first detection parameter, a target process parameter for the manufacturing equipment.

The invention relates to the technical field of industrial control, in particular to an error compensation method for numerical control machining of an automobile stamping die, which comprises the following steps of: acquiring temperature of a plurality of thermo-sensitive points of a numerical control machine tool and thermal displacement data of a tool nose; the effective thermal driving potential is calculated by considering the time lag effect of the temperature level and the historical temperature change; calculating a thermal phase direction gradient in combination with the temperature difference value and the nonlinear gain; combining the effective thermal driving potential and the thermal phase direction gradient into a feature vector to train a support vector regression model; and inputting the feature vector in real time to obtain a compensation value, and superposing the compensation value to the coordinate instruction by dynamic origin offset. The method can accurately reflect the thermal inertia, capture the dynamic evolution of the thermal state, comprehensively represent the thermal state to improve the adaptability of the model, and correct the thermal error in real time, thereby improving the machining precision and surface quality of the automobile stamping die.

The invention relates to the technical field of coal processing and utilization, and discloses a clean coal yield prediction method based on a support vector machine, which comprises a data acquisition module used for analyzing factors influencing the clean coal yield, acquiring related data and integrating the data into a data set, and a data preprocessing module connected with the data acquisition module and used for preprocessing the data. The data preprocessing module is used for randomly dividing a data set according to a 70% training set and a 30% test set and carrying out standardization and normalization preprocessing, the parameter optimization module is connected with the data preprocessing module and optimizes hyper-parameters of a support vector machine through an improved grey wolf algorithm, and the model building module is connected with the parameter optimization module and is used for building a model. The model building module is used for building and training a support vector regression model based on the optimized hyper-parameters, and the model verification module is connected with the model building module and uses a test set to verify the performance of the model. According to the method, the hyper-parameters of the support vector machine are optimized through the improved grey wolf algorithm, the problem that a traditional optimization method is prone to falling into local optimum is effectively avoided, and the precision of clean coal yield prediction is remarkably improved.

The invention provides an air conditioning system energy efficiency optimization method based on load prediction, and the method comprises the steps: collecting air conditioning load related data, building a load prediction model which employs a support vector regression model, and training the support vector regression model through the air conditioning load related data; constructing a model function by adopting a mixed kernel function of a linear kernel and a Gaussian kernel in the support vector regression model, and obtaining an air conditioner load prediction result according to the load prediction model; an energy efficiency optimization model is constructed, the energy efficiency optimization model adopts a mixed integer linear programming model, the mixed integer linear programming model takes the total energy consumption minimization of the air conditioning system as a target function, energy consumption of various devices in the air conditioning system is considered, and an energy consumption function is established; the energy consumption function represents the energy consumption under the given control variable and the air conditioner load prediction result. The operation state and parameters of the air conditioning system are adjusted in real time according to the prediction load and the result of the optimization model, and energy-saving operation of the system is achieved.

The invention relates to an intelligent unmanned aerial vehicle swarm performance test and evaluation method, belongs to the technical field of unmanned aerial vehicle swarms, and solves the problem of insufficient real-time performance and adaptability of unmanned aerial vehicle swarm performance evaluation in the prior art. The method comprises the steps that cooperative operation data, environment disturbance data and task execution data are collected in real time through an airborne sensor; calculating a real-time collaboration index based on the collaboration operation data, calculating a real-time environment index based on the environment disturbance data, and calculating a real-time task index based on the task execution data; constructing a support vector regression model, constructing a training sample set based on historical task data, and optimizing kernel function parameters of the support vector regression model by using an improved particle swarm algorithm based on the training sample set to obtain an efficiency prediction model; inputting a real-time cooperation index, a real-time environment index and a real-time task index into the efficiency prediction model, and outputting a real-time prediction efficiency value; and determining the efficiency level of the unmanned aerial vehicle swarm based on the real-time prediction efficiency value and the dynamic efficiency threshold.

The invention relates to the technical field of cold-chain logistics intelligent temperature control, and particularly discloses a fruit and vegetable multi-temperature-zone cold-chain distribution accurate cold storage and temperature control method, a transportation space is divided into a plurality of independent temperature control zones, each temperature control zone is specially used for storing specific types of fruits and vegetables, and temperature and power supply data in each zone are monitored in real time; a temperature abnormal fluctuation characteristic value and a power supply fluctuation characteristic value are respectively calculated by using fast Fourier transform and Haar wavelet transform, the stability of temperature and power supply is evaluated, and the influence degree of power supply on the temperature stability is further analyzed by using a gradient boosting tree model. And calculating a power supply regulation value based on the support vector regression model, and dynamically adjusting the power supply of the corresponding region to maintain the optimal temperature condition.

The invention relates to the technical field of building material performance detection, in particular to a concrete coarse aggregate corner angle measurement system based on digital image processing, which comprises a multi-view image acquisition module, a three-dimensional point cloud reconstruction module, a corner angle feature vector extraction module and a corner angle quantitative calculation module. A multi-view imaging system is constructed through a rotary objective table and a binocular camera, surface images of aggregate at different angles are obtained, and high-precision three-dimensional point cloud data are generated in combination with stereo matching and a point cloud dense reconstruction technology. And further constructing an edge feature vector based on the volume compression ratio and the surface roughness, inputting a pre-trained support vector regression model, outputting a normalized edge index, and realizing objective quantitative evaluation of the coarse aggregate edge. The concrete aggregate quality grading and screening device has the advantages of being high in precision, high in automation, wide in adaptability and the like and is suitable for quality grading and screening of concrete aggregates.

The invention relates to the technical field of yaw strategy optimization, in particular to a wind power plant yaw strategy rolling optimization method based on model predictive control. The method comprises the following steps: acquiring real-time and historical wind power plant operation state data of each fan in a wind power plant at a current control moment, and preprocessing the wind power plant operation state data to form a state vector; performing short-term prediction on the wind direction and the wind speed in a future prediction time domain by using a support vector regression model based on the state vector, and generating a wind field evolution prediction sequence for control decision; and coupling the wind field evolution prediction sequence with the yaw angle variable, and constructing a wind power plant power prediction model in a prediction time domain. According to the method, the refined power prediction model integrating wind field short-term prediction, the fan space topological relation and yaw angle variable coupling is constructed, and the influence of yaw operation on the wake flow propagation path, the speed loss and the downstream fan inflow condition can be accurately described.

The application discloses a kind of based on electrochemical impedance spectroscopy battery health state prediction method, this method is by establishing convolutional neural network to eliminate the influence of temperature on prediction result, and by improving particle swarm optimization algorithm to three kernel support vector regression SVR model hyperparameter combination and output weight are optimized, so that the optimized prediction model can guarantee the prediction accuracy, while, the running efficiency and applicability of model are considered, better satisfy the multidimensional requirement of nickel / lithium ion battery state prediction in practical application;The present application makes full use of the rich information of electrochemical impedance spectroscopy data, solves the problem of feature extraction under high-dimensional characteristics, the present application provides a new technical approach for the accurate prediction of nickel / lithium ion battery health state, helps to improve the performance of battery management system, promotes the application and development of battery technology in elevator energy saving and other new energy fields.