31 results about "Svm regression" patented technology

The invention relates to the technical field of battery management, and discloses a battery fault diagnosis method and system for a CCS module, and the method comprises the steps: obtaining a multi-dimensional data sequence of the voltage, current and temperature of a battery pack, carrying out the abnormality detection, and obtaining a potential abnormal time period and a multi-dimensional data subset; performing clustering analysis on the multi-dimensional data subset to obtain an abnormal feature clustering center, and performing dynamic association analysis to obtain an association mode vector set; calculating the similarity of each single data and a fault propagation path, and matching the propagation path with a preset mode to obtain a fault propagation score; fusing the clustering center and the propagation score, positioning a fault monomer through a support vector machine regression model, and determining a fault monomer identifier; and carrying out segmented clustering and logic judgment on the data corresponding to the identifier, and determining a fault type. According to the invention, accurate positioning and type determination of fault monomers can be realized.

The invention provides a video picture and virtual information projection registration and fusion method and system based on a digital twinning scene, and relates to the technical field of digital twinning, and the method comprises the steps: obtaining a virtual three-dimensional coordinate in the digital twinning scene, and a time sequence video stream and an actual three-dimensional coordinate sequence synchronously collected by multiple cameras; then, space pose calculation is carried out through a Perspective-n-Point algorithm, and an initial mapping relation is obtained; performing deviation registration on the relation by using a support vector machine regression model of time sequence perception to obtain an accurate target mapping relation; determining a target fusion region and extracting visual features of the target fusion region; and finally, inputting the visual features and the image features of the virtual information into a random forest model, generating fusion control parameters in combination with a dynamic change index, and then dynamically projecting the virtual information and fusing the virtual information into a video picture, so that the accuracy of virtual and real projection registration and the naturalness of dynamic fusion can be improved.

The application provides a video picture and virtual information projection registration and fusion method and system based on a digital twin scene, relates to the technical field of digital twinning, and obtains a virtual three-dimensional coordinate in a digital twin scene and a time sequence video stream and an actual three-dimensional coordinate sequence synchronously collected by multiple cameras; then, a space pose solution is performed through a Perspective-n-Point algorithm to obtain an initial mapping relationship; subsequently, a time sequence sensing support vector machine regression model is used to perform deviation registration on the relationship to obtain an accurate target mapping relationship; then, a target fusion region is determined and a visual feature thereof is extracted; finally, the visual feature and an image feature of virtual information are input into a random forest model, a fusion control parameter is generated by combining a dynamic change index, and then the virtual information is dynamically projected and fused into the video picture, which can improve the accuracy of virtual-real projection registration and the naturalness of dynamic fusion.

The present application relates to the technical field of complex equipment component repair, in particular to a device component performance and unit body maintenance level relationship mining method capable of effectively improving the use efficiency of an aero-engine, which first carries out expansion processing of repair samples, and then selects a support vector machine regression method which is better in the condition of small sample problems to solve the mapping relationship between the component performance before repair, unit body maintenance level and component performance after repair. Since a component is generally composed of multiple unit bodies, each component has multiple maintenance levels, and the mapping relationship between the component performance before repair, unit body maintenance level and component performance after repair is a many-to-one mapping relationship. In order to improve the accuracy of support vector machine regression, a hybrid kernel function method is used to optimize it, and a particle swarm algorithm is used to optimize the related parameters.

The invention discloses a chlorogenic acid component detection method fused with deep learning, and relates to the technical field of chlorogenic acid. The method comprises the following steps: acquiring a chlorogenic acid hyperspectral image; clustering the chlorogenic acid hyperspectral image through a fuzzy C-means clustering algorithm based on a plurality of subclass centers to obtain a chlorogenic acid hyperspectral cluster, and calculating a chlorogenic acid spectral cluster feature vector; the method comprises the following steps: calculating mutual information of chlorogenic acid hyperspectral cluster feature vectors to obtain hyperspectral cluster feature weights; obtaining a chlorogenic acid hyperspectral cluster weighted vector by combining the chlorogenic acid hyperspectral cluster feature vector and the hyperspectral cluster feature weight; inputting the chlorogenic acid hyperspectral cluster weighted vector into a support vector machine regression model based on high-dimensional multiple scales, and outputting to obtain a chlorogenic acid concentration predicted value; and constructing a chlorogenic acid concentration diagram according to the chlorogenic acid concentration predicted value, calculating a concentration abnormal value through a sliding window method, and performing early warning if the concentration abnormal value is greater than a preset threshold value.

The application discloses a wafer polishing laser interference closed-loop pressure control method, and relates to the technical field of semiconductor manufacturing, and comprises the following steps: S1: a ceramic plate is triggered to automatically measure a program after being turned over with a wafer; S2: a laser interference measurement head is driven by a six-axis mechanical arm positioning system to non-contact scan T2 / T4 points of the wafer, and thickness distribution data is acquired; S3: a Zernike polynomial is adopted to fit the thickness distribution data to generate a three-dimensional distribution graph, and a pressure correction amount is calculated based on a pressure prediction model constructed by a process kinetics model and a support vector machine regression algorithm; S4: an abnormal processing module is used to detect data outliers in real time, and a device protection strategy is triggered when a deviation exceeds a set threshold; and S5: a pressure control instruction is transmitted to a polishing head by a PLC controller through a PROFINET bus. By means of non-contact laser interference measurement, the laser interference technology is applied to wafer thickness detection after polishing for the first time, and single measurement time is less than 20 seconds, so that the detection efficiency is significantly improved.

The invention belongs to the technical field of power equipment insulation state detection, and particularly relates to an XLPE cable water branch quantitative evaluation method and system based on frequency conversion pseudo trapezoidal wave excitation, and the method comprises the steps: applying pseudo trapezoidal wave excitation, and collecting voltage and current signals; wavelet and adaptive filtering are combined for denoising; establishing a Wiener-ANN nonlinear dielectric response model, and fitting cable response through the combination of a dynamic linear neural network module and a static neural network module; obtaining a frequency domain dielectric spectrum and extracting a multi-harmonic characteristic parameter, a loss slope, a hysteresis angle and a time domain nonlinearity; and finally, outputting a water treeing aging comprehensive index WTI through the support vector machine regression model to realize quantitative evaluation of the aging degree. The system comprises a pseudo trapezoidal wave excitation unit, a high-voltage amplification unit, a micro-current acquisition unit, a signal processing unit and an aging evaluation unit. The broadband harmonic response can be obtained through a single test, the test efficiency is improved by more than 75%, the anti-interference performance is high, the evaluation precision is high, and the method is suitable for on-site rapid nondestructive diagnosis.

The invention relates to the technical field of software testing, in particular to a software testing data generation method and system based on a multi-chain doliolaria goblaria swarm algorithm, and the method comprises the steps: initializing a population; randomly generating an initial population, and randomly dividing the population into a plurality of sub-chains; performing fitness sorting on individuals in each sub-chain, and dynamically adjusting the proportion of a leader to a follower in the population; updating the position of each sub-chain leader, and obtaining the actual fitness value of the updated leader individual by actually executing an instrumentation program; updating the position of the follower in the sub-chain based on the adaptive inertia factor, and predicting the fitness value of the updated follower individual by using a support vector machine regression model; updating the optimal fitness value and the corresponding position of each sub-chain and the whole population; and judging whether the current iteration reaches the maximum number of iterations or the solving precision requirement. According to the method, the efficiency and coverage rate of software test data generation are remarkably improved, and the calculation overhead is effectively reduced.

The invention discloses a polyurethane ultraviolet aging hardness prediction method based on support vector machine regression, and the method comprises the following steps: collecting hardness data of polyurethane changing with time in an ultraviolet aging process, constructing a data set, and carrying out the preprocessing; establishing a prediction model taking the aging time as input and the hardness value as output based on a support vector machine regression algorithm; the generalization ability of the model is improved through cross validation and hyper-parameter tuning; a plurality of indexes such as mean square error, mean absolute error, decision coefficient and the like are adopted to comprehensively evaluate the model prediction precision; and predicting the hardness of an unknown aging time point by using the trained model to realize reliable estimation of the aging hardness of the polyurethane. The method is high in prediction precision and high in interpretability, and an effective tool is provided for aging performance evaluation and service life prediction of the polyurethane material.

The invention relates to the technical field of bridge engineering, and provides a multi-objective intelligent optimization method for finished cable force of a cable-stayed bridge. The multi-objective intelligent optimization method comprises the following steps: establishing a finite element model of the cable-stayed bridge, and primarily homogenizing the cable force by adopting a minimum bending energy method according to a principle that the cable force of a long cable is large and the cable force of a short cable is small; constructing a support vector machine regression small sample proxy model based on a radial basis kernel function, carrying out cross validation and tuning on hyper-parameters by adopting grid search and a leave-one-out method, and representing a mapping relation between cable force and structural responses such as bending moment and displacement; establishing a multi-objective optimization model taking bending strain energy and resultant displacement as optimization objectives; according to the multi-target intelligent optimization method based on the double-optimal strategy, comprehensive optimal cable force and partial optimal cable force are used as guidance, common cable force is guided to approach to two types of excellent cable force, iterative optimization is adjusted through a direction factor and a strength factor, a Pareto optimal cable force candidate set is updated through non-dominated sorting, hypercube grids and a congestion degree strategy, and the optimal cable force is obtained. And finally obtaining a comprehensive optimal cable force as a finished cable force of the cable-stayed bridge.

The present application belongs to the technical field of photovoltaic power generation, and particularly relates to a photovoltaic power station power generation capacity prediction method based on artificial intelligence. First, relevant power generation influence data is collected, historical power generation capacity data and meteorological data of the collected inverters are combined to obtain inverter power generation capacity data with meteorological data factors, and data cleaning and repair and other pretreatments are performed; a prediction model is constructed by using a multi-scale adaptive gated LSTM, multi-scale input sequences are divided, first power generation capacity prediction data is obtained through LSTM network modeling and attention mechanism feature fusion; a correction coefficient is calculated according to inverter aging and temperature data; the preliminary corrected prediction data is secondarily corrected through support vector machine regression to obtain final prediction data. The method improves data quality, solves the problem of insufficient generalization ability of traditional models, reduces long-term prediction deviation, and improves prediction accuracy and reliability in complex scenarios.

The application discloses a kind of liquor wisdom distillation process parameter optimization method, belong to liquor brewing technical field.The traditional distillation process is dependent on artificial experience, control precision is low, parameter correlation is difficult to quantify, and the problems such as poor optimization efficiency, the steps of the present application are as follows: build full-process industrial internet-of-things platform, collect 20 variable characteristics and 24 base liquor related index characteristics;According to the sampling time, the data is summarized, the abnormal values are removed by principle / box chart, the missing values are filled by random forest to complete data cleaning;Variable and index correlation is mined using algorithm, and modeling variable set is obtained by combining mutual information maximization dimension reduction;Based on support vector machine regression training prediction model, the process optimization parameter manual is generated by analyzing variable contribution degree.The present application can improve the control precision of distillation, quantify parameter correlation, improve the process optimization efficiency, realize standardized production, and is suitable for liquor distillation production guidance.

The present application relates to the technical field of bridge engineering, and provides a cable-stayed bridge completion cable force multi-objective intelligent optimization method: a finite element model of the cable-stayed bridge is established, the minimum bending energy method is adopted, and the cable force is initially uniform according to the principle of "long cable force is large, and short cable force is small"; a small sample support vector machine regression proxy model based on a radial basis kernel function is constructed, grid search and leave-one-out cross-validation are adopted to optimize the super parameters, and the mapping relationship between the cable force and the bending moment, displacement and other structural responses is represented; a multi-objective optimization model with bending strain energy and combined displacement as optimization objectives is established; and a multi-objective intelligent optimization method based on a double optimization strategy is used to guide the general cable force to approach the two types of excellent cable forces, the iteration optimization is adjusted through a direction factor and a strength factor, the Pareto optimal cable force candidate set is updated through a non-dominated sorting, a hypercube grid and a crowding degree strategy, and finally a comprehensive optimal cable force is obtained as the cable-stayed bridge completion cable force.

The application provides a switch cabinet wye contact thermal fault surface temperature measuring method, comprising the following steps: step one, establishing a switch cabinet temperature fluid field simulation model; step two, based on the switch cabinet temperature fluid field simulation model established in step one, conducting streamline analysis of the temperature fluid field to obtain temperature measuring points on the switch cabinet surface corresponding to internal hot spots; step three, obtaining the relationship between the switch cabinet load current, the surface measuring point temperature and the internal wye contact hot spot temperature under different working conditions and defects through orthogonal calculation as an inversion calculation sample, and then based on a support vector machine regression method, completing sample training to form an inversion calculation model, and using the inversion calculation model to realize internal hot spot temperature inversion calculation from the outside. The application can realize temperature measuring point selection at the switch cabinet shell, can realize the combination of various temperature measuring schemes such as temperature sensors, infrared imaging probes and the like, and can monitor the thermal fault of the switch cabinet circuit breaker part without damaging the original structure of the switch cabinet.

The submarine direct-current power supply system fault positioning method based on the multi-element fault current time domain characteristics provided by the application first acquires the current signal in real time by the protection device, judges whether overcurrent exists at the branch unit, marks the current time as the fault time when overcurrent exists, and acquires the current data within 5 ms after the fault by each protection device; then the initial value of the fault current slope, the initial value of the curvature, the initial peak time and the initial peak size at each branch unit are acquired; then the initial value of the fault current slope, the initial value of the curvature, the initial peak time and the initial peak size are input into the support vector machine regression algorithm trained in advance, and the distance y from the fault point to each branch unit is obtained respectively n ; finally, the fault position is determined, the fault position is sent to the shore base station, and the monopolar grounding fault positioning is completed. The application can quickly and accurately distinguish the monopolar grounding fault line and the fault distance of the submarine observation network direct-current power supply system, the application has a small amount of transmission information, and has a low requirement for the transmission information synchronization.

The invention relates to the technical field of target tracking, in particular to a power transmission line hidden danger target tracking method and system based on spectral image processing, and the method comprises the following steps: collecting a multiband spectral image, extracting an initial spectral stable point group, obtaining a spectral reflection peak value and an absorption valley value, constructing a spectral point template, fitting a reflection spectrum curve, and calculating a feature difference. The method comprises the following steps: judging spectral shift through a feature difference, screening abnormal frames, calculating similarity, generating a trajectory matching score, obtaining a score abnormal region, predicting a target position based on a multi-frame velocity vector, and updating trajectory node information. According to the method, abnormal areas are extracted through multiband spectral image analysis, a spectral feature vector set is constructed, a support vector machine regression model is utilized to stabilize a spectral curve, target spectral feature extraction precision is enhanced, spectral offset is calculated, abnormal frames are screened, a matching trajectory is scored, interference is estimated through a velocity vector, and tracking precision is enhanced through a dynamic adjustment mechanism. And the influence of environmental interference on the tracking process is reduced.

Embodiments of the present application disclose a grain moisture detection method, system, device and storage medium. In a specific embodiment, the method comprises: acquiring grain kernel images at multiple angles; inputting the grain kernel images at multiple angles into a sparse point cloud reconstruction platform to obtain three-dimensional sparse point clouds; inputting the multiple three-dimensional sparse point clouds and the multiple grain kernel images into a Vis-MVSNet three-dimensional reconstruction model respectively to obtain multiple depth maps; fusing the multiple depth maps to obtain a three-dimensional dense point cloud; extracting a scale-free correlation feature of the three-dimensional dense point cloud, inputting the feature into a trained support vector machine regression model, and obtaining a grain kernel predicted moisture content. The embodiment constructs a three-dimensional model of the grain kernel through a three-dimensional point cloud model, and realizes nondestructive detection of the wheat kernel through a support vector machine regression technology, especially realizes nondestructive detection of the high-moisture wheat kernel, and is not affected by external factors such as environmental temperature and humidity, grain type and bulk density, and has high stability.

The invention relates to an intelligent identification method for main control factors of surface mine slope deformation instability, and the method comprises the following steps: S1, data acquisition and preprocessing: obtaining surface mine slope geological data, deformation data and environmental data, and removing and filling abnormal values and missing values; s2, determining candidate control factors: extracting the candidate control factors from the maximum vibration rate in the environment data and the rainfall data; s3, key control factor identification based on a neighborhood rough set: calculating the importance degree of each control factor to the mine slope deformation data based on a neighborhood rough set method, and identifying the key control factors; and S4, key control factor verification based on support vector regression: establishing an SVR model of the screened key control factors and deformation data, and evaluating the accuracy of the key factors based on an SVR prediction effect. According to the method, the SVR model is established based on the neighborhood rough set and the support vector machine regression method, and the accuracy of the key factors can be evaluated based on the SVR prediction effect.

The application discloses an open-pit mine transport equipment multi-source information fusion monitoring method based on automatic driving, which comprises the following steps: step one, collecting the position signal, the speed signal and the steering wheel rotation angle of the transport equipment according to the sampling period; step two, performing data preprocessing on the to-be-fused data of the multi-source information; step three, constructing a support vector machine regression model; step four, establishing a particle swarm optimization algorithm model, solving the optimal solution of the penalty coefficient and the kernel function parameter of the support vector machine regression model, and obtaining the optimal parameter combination; and step five, bringing the optimal parameter combination into the support vector machine regression model, so that the monitoring result is more accurate, and the transport equipment progress route monitoring result is obtained. The application has the characteristics of greatly shortening the operation time, improving the monitoring precision and improving the accuracy.

This application discloses a method and system for single-tree noise reduction prediction based on three-dimensional vegetation structure, belonging to the field of single-tree noise reduction prediction technology. The method includes: collecting point cloud data and noise attenuation data of single trees and classifying them; extracting vegetation structure parameters of the target point cloud from the classified point cloud data; calculating the correlation between the extracted vegetation structure parameters and the collected noise attenuation data, and obtaining the most significant influencing factor in the correlation as a model variable; constructing a single-tree noise reduction prediction model based on a support vector machine regression algorithm, and training the constructed single-tree noise reduction prediction model using different kernel functions; evaluating the prediction accuracy of the single-tree noise reduction prediction model under different kernel functions, and selecting the optimal kernel function for single-tree noise reduction prediction; wherein, the constraint calculation of the prediction model is converted into dual calculation through Lagrange multiplication to obtain the optimal kernel function. Addressing the low quantitative accuracy of single-tree noise attenuation effect prediction in existing technologies, this application improves the accuracy of quantitative prediction.

The invention discloses an intelligent drainage method for construction, relates to the related technical field of underground engineering construction, and aims at solving the technical problem that the water inflow is difficult to predict. The method comprises the steps that multi-source historical data of underground engineering in a set historical period is collected; establishing a water inflow prediction model through a support vector machine regression algorithm, taking geological parameters, construction progress parameters and environmental parameters as inputs, and taking a water inflow water level prediction value and a water inflow flow prediction value in a future set duration as outputs; inputting the current geological parameters, the current construction progress parameters and the current environmental parameters into a water inflow prediction model, and calculating to obtain a water inflow level and a water inflow prediction value in a future set time length; and the controller calculates the target water discharge according to the water gushing level and the water gushing flow predicted value in the future set duration, and generates a drainage pump control signal. According to the method, the accuracy of future gushing water level and flow prediction can be improved, and a reliable basis is provided for drainage control.

The invention discloses a method for predicting the convexity of a hot continuous rolling strip steel plate based on PCA-DWT-SVR, and the method comprises the following steps: collecting rolling process parameters of the hot continuous rolling strip steel, and selecting key characteristic variables according to a rolling mechanism and process optimization experience; carrying out abnormal value removal processing on the selected key feature variables through a Pauta criterion; carrying out dimension reduction on the data subjected to abnormal value processing by adopting principal component analysis in combination with discrete wavelet transform, and screening out key variables of the data subjected to dimension reduction by utilizing a load matrix; establishing a strip steel plate convexity support vector regression prediction model by using the key variables, and performing optimal parameter selection on the strip steel plate convexity support vector regression prediction model through grid parameter optimization; and predicting the convexity of the strip steel plate through the strip steel plate convexity support vector machine regression prediction model after grid parameter optimization to obtain a final prediction result. According to the method, the prediction precision of the plate convexity is improved, the modeling prediction time is greatly shortened, and support is provided for plate convexity quality control and research.

The invention relates to the technical field of heat treatment processes, and discloses a heat treatment system selection method based on machine learning and particle swarm intelligent optimization, and relates to the technical field of heat treatment process.The method comprises the steps that a heat treatment process feasible region is determined according to material characteristics, small samples are prepared through orthogonal experiment design, the hardness is tested, and a data set is constructed; training a Gaussian process regression or support vector machine regression model, establishing a mapping relation between process parameters and hardness, adopting a particle swarm algorithm to carry out reverse search by taking a target hardness value as an optimization target, generating a candidate heat treatment system, carrying out pre-estimation sorting on an empirical relation of plasticity based on tempering temperature and time, according to the method, the number of tensile tests is remarkably reduced, the research and development cost is reduced, and the research and development period is shortened.

This invention discloses a display spectral characterization method based on support vector machines, belonging to the field of color science and technology. The method includes analyzing the principal components of the display's emission spectrum, further determining the number of principal components, scaling the principal component transformation matrix to obtain the principal component decomposition matrix and reconstruction matrix; using the principal component decomposition matrix and driving value spectra to obtain spectral principal component representations, establishing a regression theoretical model of color driving values ​​and spectral principal components; applying the driving values ​​and spectral principal component training set to design a parameter optimization algorithm to obtain a prediction model; based on the model's prediction of the corresponding emission spectrum principal components, applying the spectral principal component reconstruction matrix to perform inverse principal component transformation for spectral reconstruction; and finally, evaluating the spectral reconstruction accuracy. This invention utilizes support vector machine regression and principal component dimensionality reduction methods, exhibiting good prediction accuracy, robustness, and low redundancy, providing an effective method for display spectral characterization.

The invention provides a container terminal equipment dynamic load prediction system and method based on artificial intelligence, and the system comprises a data collection and input module which is used for obtaining multidimensional input data of port operation, and carrying out the standardization processing of the multidimensional input data; the big data statistical analysis module is used for performing space-time alignment on the standardized multi-dimensional input data, and performing feature learning on the space-time aligned multi-dimensional input data through a constructed support vector machine regression model to obtain ship task priori estimation; and the artificial intelligence dynamic evaluation module is used for carrying out space-time coding and feature fusion on the standardized multi-dimensional input data and ship task priori estimation by adopting a neural network based on a Transform architecture to obtain a load prediction result. According to the technical scheme, dynamic load evaluation and optimal management of various devices and space resources of a wharf are realized, so that the intellectualization and refinement level of port operation scheduling is effectively improved.

This invention discloses an analysis, optimization, and control system based on wastewater treatment data, belonging to the field of wastewater treatment technology. It includes a data sensing and acquisition module, a data storage module, a data preprocessing module, a multi-factor water quality prediction module, a multi-objective optimization module, and a system control module. The system constructs time-series data through data preprocessing and employs a long short-term memory network model with an attention mechanism to achieve accurate and advanced prediction of influent water quality, flow rate, and composition. Based on the prediction results, the system constructs a multi-objective optimization function with treatment efficiency, effluent quality, and operating cost as its core. It introduces a dynamic weight adjustment mechanism and a deviation risk quantification model based on logistic regression-SVM regression, using optimization algorithms to solve for the optimal control parameters in real time and then issuing the control for execution. This invention achieves closed-loop intelligent control from "post-event correction" to "pre-event optimization," significantly improving the stability of effluent compliance and effectively reducing overall operating costs.

The application relates to a PGC light intensity and phase mapping demodulation method and device based on machine learning. The method comprises the following steps: a single-frequency laser is injected into an interference type optical fiber sensor, a data acquisition card is used to apply a PGC high-frequency carrier through internal / external modulation, and interference light carrying a to-be-measured physical quantity is output by the sensor; an optoelectronic detector is used to convert the interference light into an electrical signal, and a normalized long-time-domain light intensity digital signal is obtained through the data acquisition card. The demodulation device divides the signal according to a single PGC cycle, performs Fourier transform, inputs frequency domain features into a random forest regression model, outputs a modulation depth, and feeds back a stable modulation voltage. The stable signal is input into a support vector machine regression model optimized by a particle swarm, an initial phase is solved, splicing is performed, direct current is eliminated, and a phase time delay is eliminated, and finally, a to-be-measured physical quantity is demodulated, so that the system demodulation cost can be reduced.

The invention relates to a method and device for predicting water permeability resistance of recycled concrete, and the method comprises the steps: building a corresponding support vector regression equation through real data obtained through a test, generating a training model based on the support vector regression equation, carrying out the preprocessing of the real data, obtaining expanded training data, and carrying out the prediction of the water permeability resistance of the recycled concrete. Hyperparameters of the training model are sampled and selected in a limited search space through a sampling method, and training optimization is performed on the model in combination with training data until an end condition is met; finally, the trained support vector machine regression model can predict a relative permeability coefficient according to the replacement rate of the recycled aggregate, the mixing amount of the fly ash and the mixing amount of the slag, and the relative permeability coefficient is used as an evaluation index to realize accurate prediction of the water permeability resistance of the recycled concrete. The problem that the water penetration resistance of the recycled concrete in practical engineering application is difficult to stably predict is solved.

The application relates to the technical field of wireless power transmission, and discloses a constant-current constant-voltage charging regulation method and system based on wireless power transmission, which comprises the following steps: acquiring an electrical parameter signal, determining a change trend estimation value of a coupling coefficient in combination with a support vector machine, weighting and processing measurement residuals through a Kalman gain matrix, and determining a load real-time adjustment value; when a preset impedance threshold is exceeded, judging a charging stage through fuzzy logic reasoning; collecting transmission efficiency correlation indexes, determining an accurate coupling coefficient and a load joint value through a support vector machine regression model; deducing and correcting a prior state estimation value, and generating a transmission power parameter; evaluating a final power regulation instruction through a charging stability model; refining a transmission state in combination with a distance position offset, and outputting a stable charging result. Through multi-algorithm cooperation and multi-dimensional parameter optimization, the application realizes accurate switching and whole-process stable regulation and control in the constant-current constant-voltage stage, and improves the efficiency, stability and safety of wireless power transmission.

The invention discloses a method for optimizing technological parameters of intelligent distillation of baijiu, and belongs to the technical field of baijiu brewing. Aiming at the problems that a traditional distillation process depends on artificial experience, the control precision is low, parameter association is difficult to quantify and the optimization efficiency is poor, the method comprises the following steps: constructing a whole-process industrial Internet of Things platform, and collecting 20 variable characteristics and 24 raw wine related index characteristics; summarizing data according to sampling time, eliminating abnormal values through a principle / box diagram, and filling missing values through a random forest to complete data cleaning; mining variables and index association by adopting an algorithm, and obtaining a modeling variable set in combination with mutual information maximization dimension reduction; and on the basis of a support vector machine regression training prediction model, a process optimization parameter manual is generated by analyzing the variable contribution degree. The method can improve the distillation control precision, quantify the parameter association, improve the process optimization efficiency and realize standardized production, and is suitable for white spirit distillation production guidance.