116 results about "Back propagation neural network" patented technology

The invention relates to the technical field of data processing, and provides a resistance line-associated underwater bench blasting numerical simulation error correction method, which is characterized in that land and underwater measured data are matched through the same resistance line and similar material parameters, and the resistance line and the water depth are used as basic input of an error prediction model; a reference basis is provided for error correction of subsequent numerical simulation; calculating a target result in combination with a prediction error after initial simulation, comparing a simulation result with the target result during iterative simulation to generate an iterative error, and analyzing and determining sensitivity coefficients of different error classifications in a resistance line and the iterative error so as to adjust material parameters to reduce an invalid iterative process; simulation results are processed according to stress areas in a zoning mode through an equidistant slicing method, adjustment precision is verified in combination with the root rate and the damage area, and the reliability of the simulation results is improved; and meanwhile, parameter data passing verification are input into a back propagation neural network, a correction parameter mapping library is constructed, and the design efficiency of an underwater blasting scheme is improved.

The invention provides a gear peeling time-varying meshing stiffness prediction method and system based on a back propagation neural network, and the method comprises the steps: considering a tooth surface peeling fault based on a gear tooth bearing contact analysis method, and constructing a helical gear pair time-varying meshing stiffness calculation model; a real irregular tooth surface peeling area is fitted by adopting a least square ellipse fitting method to obtain an ellipse appearance representation, any peeling position is completely described through six key geometric parameters, the geometric parameters of the ellipse appearance are systematically traversed and fitted, and diversified peeling appearance samples are generated. Introducing a tooth profile deviation matrix corresponding to the peeling morphology sample into a tooth surface bearing contact analysis model, and constructing a training data set; and constructing a back propagation neural network model of multiple hidden layers, and performing end-to-end training by using the training data set, so that the back propagation neural network model learns a nonlinear mapping relationship from geometric parameters to a time-varying meshing stiffness curve, thereby predicting the time-varying meshing stiffness under any peeling morphology.

The invention provides an intelligent risk screening system and method based on distribution network load prediction, and relates to the technical field of intelligent power grids, and the method comprises the steps: carrying out the multi-scale decomposition processing and bidirectional feature coding of historical load data of a distribution network, and obtaining a time sequence coding feature; graph structure features are extracted from the multi-node load graph of the distribution network, feature fusion is carried out on the graph structure features and the time sequence coding features, and a multi-dimensional prediction matrix is obtained; performing a back propagation neural network model optimization operation based on genetic algorithm learning on the multi-dimensional prediction matrix through the environmental influence data to obtain a load prediction curve and a prediction confidence coefficient, and determining overload risk early warning of the distribution network load according to the load prediction curve and a preset risk threshold value; according to the method and the device, accurate screening of the overload risk of the distribution network based on space-time modeling and a prediction confidence mechanism can be realized, so that the accuracy of load early warning is improved.

The invention provides a gearbox variable working condition mechanical loss torque efficiency correction test method based on a neural network. The method comprises the following steps: S1, determining a to-be-tested gearbox and basic parameters; s2, building a gearbox variable working condition test system; s3, acquiring basic operation data of the gearbox; s4, calculating theoretical mechanical loss torque and efficiency of the gear box; s5, the actually-measured mechanical loss torque and efficiency of the gearbox are collected, and an error data set is generated; s6, constructing and training an error correction model fusing the back propagation neural network and support vector regression; s7, performing mechanical loss torque and efficiency correction based on the fusion model; and S8, verification and feedback optimization of a correction result. According to the method, on the basis of all-working-condition data, the defect of a single model is overcome through double-algorithm fusion, precise correction of the mechanical loss torque and efficiency of the gearbox under the variable working conditions is achieved, and reliable data support is provided for gearbox design optimization, working condition matching and energy efficiency evaluation.

The invention provides an airport noise prediction method based on a back propagation neural network and a support vector machine, and belongs to the technical field of air traffic transportation noise, and the method comprises the steps: collecting airport noise historical data, obtaining flight historical data of airplanes with different powers, and obtaining a prediction input variable and a prediction output variable; according to the input and output variables and an elastic back propagation algorithm, in combination with a gradient time sequence dynamic rarefaction method and an adaptive structure adjustment method, establishing a back propagation neural network prediction model; constructing an airport noise kernel function, and establishing a support vector machine prediction model in combination with a cross validation method; predicting by using a back propagation neural network prediction model and a support vector machine prediction model, comparing and analyzing prediction results in combination with evaluation indexes, and selecting an optimal prediction result as an airport noise prediction result; the problems that an existing noise prediction algorithm depends on a large amount of historical data, the calculated amount is huge, and the noise prediction efficiency is low are solved.

The invention discloses a time sequence reconstruction and machine learning coupled chlorophyll monitoring data breakpoint restoration method, and belongs to the technical field of water quality monitoring. The invention discloses a chlorophyll monitoring data breakpoint restoration method based on coupling of time sequence reconstruction and machine learning, and the method comprises the following steps: S1, collecting water quality monitoring data, and cleaning the monitoring data to obtain preprocessed data; s2, performing time sequence reconstruction on the preprocessed data to obtain a weekly average 1 data set; s3, respectively constructing a radial basis function neural network model and a back propagation neural network model by taking the chlorophyll concentration as a response variable and the conventional water quality parameter as a predictive variable; s4, performing performance evaluation on each model by taking a root mean square error, an average absolute percentage error, goodness of fit and relative error distribution statistics as evaluation indexes, and screening out an optimal model; and S5, applying the conventional water quality parameters in the breakpoint interval of the chlorophyll monitoring data in the water body to the optimal model, and outputting the restored chlorophyll concentration value to complete the dynamic restoration of the breakpoint.

The invention discloses an agricultural machinery braking performance detector based on a Beidou satellite single-point positioning mode, and belongs to the technical field of braking performance detection. The agricultural machinery braking performance detector comprises a signal collector and a host, and the signal collector is internally provided with a satellite signal receiving module compatible with a BDS and a GPS; a sequence parameter extraction module, an integral distance measurement module, an error correction module, a slope calculation module and a performance evaluation module are arranged in the host; the method comprises the following steps: collecting three-dimensional motion data of a detected agricultural machine, calculating an uncorrected braking distance by using a complex Simpson integral algorithm, obtaining an error correction distance caused by single-point positioning signal drift based on a back propagation neural network, calculating the braking distance of the agricultural machine in combination with a slope correction value, and finally judging that the braking performance of the detected agricultural machine is qualified. According to the method, a ground differential base station and an external supporting assembly do not need to be erected, the problem that field detection deployment is tedious is solved, signal drifting and road slope interference are eliminated, and detection convenience and accuracy are improved.

The invention discloses a multi-input multi-output fuzzy neural network control method and system for a pseudo-ginseng planting greenhouse, and the method comprises the steps: obtaining original parameters including greenhouse temperature, humidity, illumination, water and fertilizer concentration, pseudo-ginseng growth state and water flow velocity, and carrying out the standardization of the original parameters, and obtaining input parameters; inputting the parameter into a three-layer back propagation neural network pre-training model, and outputting initial adjustment coefficients of six control quantities such as the opening degree of the sunshade; constructing a fuzzy rule base based on a parameter coupling scene, inputting a preliminary adjustment coefficient to obtain a fuzzy output quantity, and performing defuzzification through a gravity center method to obtain a corrected adjustment coefficient; a control instruction is calculated in combination with an equipment rated range, and closed-loop control is realized through dynamic adjustment. The system automatically operates through a multi-parameter acquisition module, an equipment control output module and the like. The application breaks through the traditional single-input single-output limitation, reduces the parameter fluctuation by more than 50%, adapts to the whole growth cycle of pseudo-ginseng, improves the utilization rate and yield of water and fertilizer, reduces the labor cost, and has remarkable economic benefits.

The invention provides a material constitutive parameter inversion method which can be applied to the technical field of artificial intelligence. The material constitutive parameter inversion method comprises the following steps: acquiring an elastic modulus and an actually measured load depth curve of a to-be-tested material in a single nanoindentation test; performing extrapolation translation preprocessing on the actually measured load depth curve to obtain an equivalent ideal curve; carrying out characterization processing on the equivalent ideal curve to extract input characteristic parameters which comprise press-in rigidity, unloading rigidity and plastic work proportion; the indentation rigidity, the unloading rigidity, the plastic work ratio and the elastic modulus are input into a pre-trained indentation constitutive mapping model to predict the yield strength and the strain hardening index of the to-be-tested material, and the indentation constitutive mapping model is a full-connection back-propagation neural network pre-trained through a finite element virtual test sample. The invention further provides a material constitutive parameter inversion system and device, a storage medium and a program product.

The invention provides a multi-source adaptive fusion positioning method and system and a medium, and belongs to the technical field of automatic driving and navigation positioning, and the method comprises the steps: receiving original data synchronously collected by each positioning module, extracting an input feature vector, and carrying out the standardization; inputting the standardized input feature vector into a pre-trained K-nearest neighbor model, and calculating to obtain an initial confidence coefficient corresponding to each positioning module; jointly inputting the initial confidence coefficient and the input feature vector into a back propagation neural network model to obtain a corrected sensor confidence coefficient vector, and generating a positioning mode recommendation classification; according to the positioning mode recommendation classification, adaptively selecting a working mode of the positioning system; based on an extended Kalman filter algorithm, the sensor confidence vector is used to perform state update, and parameters for vehicle navigation control are output. According to the method, GNSS false fixation is effectively detected, multi-source data are fused, the confidence coefficient is dynamically adjusted, and the positioning reliability is improved.

The invention discloses a borrowing type successive approximation analog-to-digital converter adopting a back propagation neural network for calibration. The borrowing type successive approximation analog-to-digital converter comprises a system composed of a positive and negative capacitance digital-to-analog converter adopting a three-stage split bridge type capacitor array, a bootstrap sampling switch, an automatic return-to-zero comparator, an SAR logic control unit, an original-to-binary module and a BPNN calibration engine. Bootstrap sampling switches are respectively arranged at the tail ends of the positive and negative capacitance digital-to-analog converters, a comparator is connected with the output ends of the two, and the output is connected with an SAR logic control unit; the original-to-binary module converts the code generated by the SAR logic control unit into a binary code; and a BPNN calibration engine receives the code and performs calibration calculation by using a trained back propagation neural network model. By implementing the converter disclosed by the invention, hardware implementation can be simplified while high performance is ensured, so that the ADC realizes efficient and accurate signal conversion under a 180-nanometer BCD process, and power consumption and cost are reduced.

The application relates to a method for predicting the amount of water flowing in front of a dam of the Three Gorges Reservoir based on an improved neural network model, which comprises the following steps: collecting the inflow and outflow data of the Three Gorges Reservoir and the amount of water flowing in front of the dam and dividing the data into training and test sets; setting the particle swarm algorithm, the improved neural network structure and parameters; training the improved neural network by using the training set, optimizing and solving the weight and threshold of the improved neural network by using the improved particle swarm algorithm during the training; calculating the prediction error and judging whether the error is qualified; and using the trained improved neural network for real-time prediction of the amount of water flowing in front of the dam of the Three Gorges Reservoir. After the training process of the error back propagation neural network is optimized and improved by using the particle swarm algorithm, the improved error back propagation neural network is used as the prediction model of the amount of water flowing in front of the dam of the Three Gorges Reservoir, the connection weight of the prediction model is closer to the optimal ideal value, the prediction precision of the amount of water flowing is improved, and the adaptability to the uncertainty of the data information of the reservoir dam is improved.

The invention discloses a weight function method for calculating a stress intensity factor of a circumferential weld surface crack of a pipeline. The method comprises the following steps: constructing a point load weight function for calculating the stress intensity factor of the circumferential weld surface crack of a pipeline structure; establishing a pipeline structure finite element model containing the circumferential weld surface cracks, and calculating a stress intensity factor reference solution under a reference load based on an M integral method; solving a weight coefficient of a calculation point of the front edge of the surface crack of the circumferential weld in the point load weight function, and optimizing a back propagation neural network through a genetic algorithm to establish a weight coefficient prediction model; and carrying out double integral operation on the product of the point load weight function and the stress distribution load on the circumferential weld surface crack surface to realize calculation of a stress intensity factor. The method solves the problems that an existing method is only suitable for the situation that the stress distribution load changes unidirectionally along the crack depth and is not suitable for stress distribution which changes bidirectionally along the crack depth and the crack length frequently occurring in an actual structure, the calculation cost is increased, and the calculation precision is reduced.

The application relates to the technical field of tunnel engineering disaster prediction, and discloses a soft rock tunnel large deformation prediction method based on a particle swarm optimization neural network, which comprises the following steps: selecting geological construction characteristic parameters such as water content of surrounding rock and construction methods as input factors, and establishing a qualitative index and quantitative value mapping; based on orthogonal test design and numerical simulation, engineering sample data sets are constructed and normalized; subsequently, the initial connection weight and bias of an error back propagation neural network are globally iteratively optimized by using a particle swarm optimization algorithm, so that optimal initial values are obtained by taking network errors as fitness values; finally, data training is carried out to generate a mature model, and a predicted deformation value is output and combined with a double check mechanism to carry out engineering early warning. The application solves the problems that traditional neural networks are slow in convergence and prone to falling into local optimization due to random parameter initialization, and effectively improves the prediction accuracy of soft rock tunnel large deformation and the reliability of engineering early warning.

The invention discloses a radar track target classification method based on statistical knowledge and a neural network, and the method comprises the steps: collecting original track data, and carrying out the marking and cleaning processing, and obtaining a standard track data set; extracting kinematics, bistatic radar cross section and track morphological features based on the standard track data set, and constructing an original feature set; performing feature weight learning through a neighborhood component analysis method, and screening according to a preset weight threshold to obtain an effective feature subset; performing statistical distribution analysis on the effective feature subsets according to target categories, determining significant features of each target category, and setting a threshold interval; performing preliminary classification based on a statistical knowledge classifier, and determining whether the to-be-classified track is an easily-classified sample or a difficultly-classified sample through a category discrimination function; and classifying the samples difficult to classify based on a back propagation neural network classifier, outputting the probability value of each target category through a neural network model, and taking the target category with the maximum probability value as a final classification result. According to the invention, the target classification accuracy and real-time performance can be improved.

The application discloses a stiffness and damping optimization method, system, device, medium and product of a vehicle suspension based on a back propagation neural network, and relates to the technical field of vehicles. The method comprises the following steps: training a back propagation neural network based on a training set to obtain a trained back propagation neural network; extracting the trained back propagation neural network by adopting a Taylor expansion method and a finite difference method to obtain a matrix; constructing a nonlinear multivariate inter-axle stiffness-damping objective function based on the matrix; obtaining an input variable sequence and an output variable sequence of a vehicle; and optimizing the stiffness and damping of the vehicle suspension under a constraint condition by taking the function value of the nonlinear multivariate inter-axle stiffness-damping objective function as the target, so as to obtain an optimal stiffness-damping matrix and complete the stiffness and damping optimization of the vehicle suspension. The application can improve the driving smoothness and steering stability of the vehicle.

According to the multi-objective optimization method for the working parameters of the sprocket type residual film recycling machine, the sprocket type residual film recycling machine is used as a research object, and the angular velocity of a picking device, the angular velocity of a film and impurity separating device and the working angle of the film and impurity separating device are set as key variables to be optimized. In order to obtain the optimal residual film impurity rate and picking-up rate, discrete element simulation is performed through an EDEM, a back propagation neural network is trained by using simulation data, and a prediction model of the residual film impurity rate and picking-up rate is established. A prediction result of the neural network is optimized by adopting a non-dominated multi-objective genetic algorithm, and optimal working parameters are given in a Pareto frontier form. When the angular speed of the picking device is 58.80 rad / s, the angular speed of the film and impurity separating device is 40 rad / s, and the angle of the film and impurity separating device is 35.51, the impurity content of the residual film is 24.26%, and the picking rate of the residual film is 83.67%. And working parameters are selected in the Pareto front, so that the impurity rate of the residual film can be effectively reduced on the basis of keeping a relatively high residual film picking-up rate.

The application provides a building hourly indoor temperature prediction method and system based on a double driving mechanism, and belongs to the technical field of building energy consumption management and control. The method comprises the following steps: selecting a heat balance differential equation with a building indoor temperature as an interface variable as a benchmark, and representing a heat transfer process of an envelope structure in a space state, so as to construct a guide module for a physical model; a data driving module is constructed, an indoor temperature change difference caused by a non-principal component item is taken as a target variable, and outdoor meteorological parameters are taken as inputs, so that a back propagation neural network is constructed and trained; the outputs of the physical model guide module and the data driving module are combined, and indoor temperature prediction is carried out based on the back propagation neural network. The application can simultaneously consider the physical authenticity of the hourly indoor temperature prediction and the accuracy and rapidity of the building hourly indoor temperature prediction.

The invention discloses a photovoltaic inverter output power control algorithm based on a predictive control algorithm, which belongs to the predictive control algorithm, and comprises the step of establishing a BP neural network model, and the BP neural network model comprises the steps of determining a grid structure, designing an input layer, designing an output layer, designing a hidden layer, transmitting a function and calculating a predictive error. Historical power generation data and meteorological data of a photovoltaic power station are used as experimental samples, the prediction time is set to be 05: 00 to 19: 00 with good light reception in one day, and the prediction interval time is set to be 1 hour. The global power output by the photovoltaic power generation system is predicted and analyzed by using an algorithm based on a back propagation neural network, a network tracking prediction effect on the global output power is realized, and a distribution network transformer is effectively prevented from being burnt out due to overlarge generated power returned by the resident photovoltaic power generation system.

The invention relates to the technical field of fluid conveying, in particular to a collaborative design optimization method for a guide vane type mixed-flow pump. Preliminarily designing the impeller according to the operating parameters of the design points, acquiring main geometric parameters, parameterizing the axial surface projection shapes of the impeller and the guide vane by adopting two constraints of geometry and size, and associating the axial surface projection parameters of the guide vane with the design parameters of the outlet of the impeller; according to the axial surface projection boundaries of an impeller and a guide vane, in combination with the structural characteristics and constraint conditions of the pumping chamber of the guide vane type mixed-flow pump, two constraints of geometry and size are adopted to carry out collaborative design on the pumping chamber; taking the initial design parameters and the geometric constraint parameters as optimization variables, determining a variable range, sampling by utilizing Latin hypercube sampling, and screening out key parameters by utilizing sensitivity to construct an error back-propagation neural network agent model; and a non-dominated sorting genetic algorithm with a penalty mechanism and an adaptive crossover variation attenuation strategy is introduced to carry out multi-target optimization on design parameters.

The invention provides a ternary protein textured protein prediction method based on machine learning. The method comprises the following steps: acquiring raw material characteristic parameters, process parameters and product characteristic parameters of three protein raw materials, namely vital gluten, yeast protein and soybean meal under a high-moisture extrusion condition; the method comprises the following steps: constructing a ternary protein coupling characteristic and a yeast protein threshold characteristic on the basis of an inhibition effect of yeast protein on wheat gluten cross-linking and a sensitive characteristic of yeast protein solubility on moisture; constructing a back-propagation neural network model, taking the raw material characteristic parameters, the process parameters, the ternary protein coupling features and the yeast protein threshold features as inputs of the back-propagation neural network model, taking the product characteristic parameters as outputs of the back-propagation neural network model, and performing iterative training on the back-propagation neural network model until convergence to obtain a final product. A textured protein prediction model is obtained; and performing forward quality prediction or reverse process derivation on a to-be-detected system by using the textured protein prediction model.

The invention discloses a construction risk evaluation method for a tunnel crossing a strong outburst coal seam group. According to the evaluation method, 15 risk factors including geology, construction technologies and operators are included into a comprehensive evaluation framework; a fuzzy analytic hierarchy process is adopted to obtain global comprehensive weights of 15 risk factors, the weights serve as parameters to be input into a back propagation neural network, dynamic risk prediction is achieved, and an intelligent risk assessment model capable of being updated in real time is constructed. The problems of subjectivity and dependency relationship among indexes in expert judgment are effectively solved, and the risk of the complex system is dynamically simulated and predicted by the aid of powerful nonlinear mapping and learning capabilities of the system. The method significantly improves the tunnel construction risk identification precision and early warning capability in a complex geological environment, and can be widely applied to high-risk underground engineering safety management.

The invention discloses a signal coverage range detection method and device, equipment, a medium and a product. The method comprises the following steps: acquiring a sample data set; wherein the sample data set comprises an RSSI value of at least one detection unit in a detection area; constructing an initial neural network model based on Kriging interpolation and a back propagation neural network; training the initial neural network model by adopting the sample data set to generate a signal coverage detection model; the signal coverage detection model is used for detecting the signal coverage. By adopting the means of the invention, the signal coverage detection is realized by constructing the signal coverage detection model based on the Kriging interpolation and the back propagation neural network, and the efficiency and accuracy of the signal coverage detection are effectively improved.

The application provides a continuous casting round billet subsurface defect detection method and device, neural network parameters of a back propagation neural network are obtained through optimization based on a particle swarm optimization algorithm, and a defect type detection model is obtained by training the back propagation neural network by using training sample frequency domain signals including X frequency domain characteristic values and the true labels of the training sample frequency domain signal data, so that in the actual application process, after obtaining time domain signal data of a target continuous casting round billet, the time domain signal data is converted into frequency domain signal data; the frequency domain signal data is input into the defect type detection model, and the detection result of the subsurface defect of the target continuous casting round billet can be output. By converting the time domain signal to the frequency domain for more detailed analysis, the neural network model learns complex defect characteristics, and compared with manual experience flaw detection, different types of segregation defects can be more accurately identified, and misjudgment and missed detection are reduced.

The invention provides an FDM process energy efficiency modeling and multi-objective optimization method based on a BP neural network and NSGA-II, and relates to the technical field of fused deposition modeling energy efficiency prediction.The FDM process energy efficiency modeling and multi-objective optimization method comprises the steps that an actual measurement processing power curve graph is combined, energy consumption characteristics of the FDM processing process are analyzed, and an energy efficiency function and a printing efficiency function are constructed; designing a five-factor three-level test by using a Box-Behnken method, and researching key factors influencing energy consumption through variance analysis; energy consumption prediction models are respectively established through a back propagation neural network, support vector regression and response surface regression fitting, and an optimal prediction model is determined through analysis and comparison; and on the basis of the optimal prediction model, combining with the actual processing working condition, and comprehensively considering the specific energy consumption, the material deposition rate and the surface roughness, constructing a multi-objective optimization model, and solving the model by adopting an NSGA-II algorithm. According to the method, the energy consumption influence mechanism of the FDM process is researched, relatively accurate energy consumption prediction is realized, and meanwhile, theoretical support and an optimization strategy are provided for parameter optimization of the FDM process.

The invention discloses a distribution line fault detection and classification method based on a back propagation neural network, and the method comprises the steps: respectively obtaining a voltage measurement value and a current measurement value before and after a fault of a distribution line, carrying out the data preprocessing of the voltage measurement value and the current measurement value, and obtaining selected data for data training, and inputting the selected data into a pre-trained back propagation neural network for data training, extracting fault features according to a training result and analyzing a fault rule, and mapping the fault features to corresponding fault types according to a fault rule analysis result to classify the fault features, thereby obtaining a fault classification result. And obtaining a detection and classification result of the current distribution line fault. The method has the advantages that the fault features are accurately mapped to correct fault types or position output, and the accuracy of fault detection and classification is improved.

The application discloses a farm machine braking performance detector based on a Beidou satellite single point positioning mode, and belongs to the technical field of braking performance detection. The signal collector is internally provided with a satellite signal receiving module compatible with BDS and GPS, and the host is internally provided with a sequence parameter extraction module, an integral distance measurement module, an error correction module, a slope solution module and a performance evaluation module. Through the collection of three-dimensional motion data of the measured farm machine, the uncorrected braking distance is calculated by using the complex Simpson integral algorithm, the error correction distance caused by the single point positioning signal drift is obtained based on the back propagation neural network, and the farm machine braking distance is solved in combination with the slope correction value, so that the braking performance qualification condition of the measured farm machine is finally determined. The application does not need to erect a ground differential reference station and external supporting components, solves the problem of complicated field detection deployment, eliminates signal drift and road slope interference, and improves detection convenience and accuracy.

The invention discloses a tobacco sugar spice filling control method and device, electronic equipment and a medium. In the off-line training stage, a historical filling sample list is constructed with filling correlation parameters and filling thresholds as independent variables and dependent variables respectively, a back propagation neural network model is trained in combination with a parameter penalty algorithm and a dynamic learning rate correction mechanism, and a filling threshold initial prediction model is obtained; in the initial model application stage, tobacco sugar spice filling is controlled through the filling threshold initial prediction model, and production filling samples are collected; and in a stack type training stage, updating the historical filling sample list in a stack type by utilizing the production filling samples to obtain a mixed sample list, retraining the filling threshold initial prediction model to obtain a filling threshold target prediction model, and controlling the filling of the tobacco sugar spices through the filling threshold target prediction model. Intelligent control over the tobacco sugar spice filling threshold value is achieved, and high accuracy of filling weight preparation and stability of production time are guaranteed.

The invention relates to the field of metal-based composite materials, and provides a metal-based composite material optimization method based on dynamic reverse design, which comprises the following steps: S1, an early-stage preparation stage of a model: establishing different particle distribution configurations of a particle reinforced metal-based composite material at high flux by utilizing an Abaq us plug-in; s2, a data set construction stage: quantizing the long-range / short-range order degree of an enhancement body through a translation sequence parameter and a radial distribution function, and generating an enhancement body configuration and a random variant thereof; s3, a forward prediction stage: constructing a BPNN-CL model by adopting a back propagation neural network fused with continuous learning, and dynamically mapping the material / structure feature vectors to mechanical properties; and S4, a reverse optimization stage: chaos disturbance, dynamic partition monitoring and an elitism strategy are integrated into an NSGA-I algorithm to form an NSGA-I-PMCP optimizer, and multi-target optimization is carried out by taking BPNN-CL as a real-time agent model. The problem of reasonably designing a matrix, an interface product and a reinforcement of the particle reinforced metal matrix composite to synergistically improve the strength and the toughness of the metal matrix composite is solved.

The invention relates to the field of motor control, in particular to a motor temperature control method, device and equipment and a storage medium, and aims to accurately control the temperature of a joint motor in real time. The method comprises the following steps: acquiring a plurality of temperature signals of a plurality of target devices in a joint motor; a weighted temperature value is obtained according to the multiple temperature signals through a pre-trained neural network, and the neural network is a back propagation neural network optimized through a genetic algorithm; and adjusting the output torque of the joint motor according to the weighted temperature value through a pre-established temperature torque mapping model so as to control the temperature of the joint motor.