252 results about "Back propagation neural network" patented technology

The invention, which belongs to the technical field of wireless communication, provides a position-guided channel knowledge graph construction method comprising the following steps: constructing an unmanned aerial vehicle auxiliary communication scene comprising a ground base station, and constructing a channel knowledge graph model; designing a back propagation neural network, training the back propagation neural network by using enough training samples corresponding to a ground base station j so as to learn a recessive nonlinear relationship between the position of the unmanned aerial vehicle and a channel gain, and representing a channel knowledge graph model as a dominant derivable function of the position of the unmanned aerial vehicle and the channel gain; and introducing a transfer learning algorithm, and performing fine tuning on the trained back propagation neural network of the ground base station j by using a limited sample corresponding to the target ground base station j 'to obtain a back propagation neural network of the target ground base station j', thereby obtaining a channel knowledge graph of the target ground base station j '. The method is a classical optimization method suitable for unmanned aerial vehicle trajectory optimization and deployment problems.

The invention relates to the technical field of synchronous reluctance motor structure optimization, in particular to a permanent magnet auxiliary synchronous reluctance motor structure parameter multi-objective optimization method. The method comprises the steps that motor structure optimization parameters are selected, and the change range of the parameters is determined; carrying out comprehensive sensitivity analysis according to the selected optimization target; performing hierarchical processing on the optimization variables according to the comprehensive sensitivity index, dividing the optimization variables into a strong sensitive layer and a weak sensitive layer, and taking parameters in the strong sensitive layer as to-be-optimized parameters of the next algorithm; comparing the prediction precision of the back propagation neural network, the radial basis, the extreme learning machine, the support vector machine and the kernel extreme learning machine, and selecting an agent model with an optimal prediction effect; combining the established high-precision agent model with a fast non-dominated sorting genetic algorithm, and searching an optimal combination of to-be-optimized parameters; and finally, carrying out single parameterization scanning determination on parameters in the weak sensitive layer by utilizing finite element simulation so as to obtain optimal structural parameters of the permanent magnet auxiliary synchronous reluctance motor.

The invention relates to the technical field of structural engineering, and provides a surface crack fatigue propagation morphology and life prediction method based on machine learning, which comprises the following steps: establishing a finite element simulation model containing a surface crack, calculating a stress intensity factor of the front edge of the surface crack, and generating a finite element simulation data set; performing dimensionless processing on parameters in the simulation data set to generate a machine learning data set; optimizing the back propagation neural network through a hybrid optimization algorithm, and constructing a surface crack stress intensity factor prediction model; the surface crack stress intensity factor prediction model is combined with a fatigue crack growth rate formula to cyclically predict the surface crack growth morphology and the service life of the load bearing structure; establishing a visual interaction interface of parameter input and output and image output based on the surface crack stress intensity factor prediction model and the fatigue crack growth rate formula; a user can quickly predict the propagation morphology of the surface crack and calculate the service life without mathematical analysis and code writing.

The invention belongs to the technical field of microwave remote sensing, and provides an earth surface soil humidity GNSS-R downscaling inversion method based on multi-model fusion. Comprising the following steps: firstly, based on CYGNSS observation data, SMAP SM and auxiliary element data, constructing a training data set of a 36km grid and a test data set of a 3km grid through quality control; a limit gradient enhancement algorithm, a lightweight gradient elevator, a back propagation neural network and a random forest are adopted as base models, the training data set is divided randomly according to the proportion of 7: 3, and all the base models are constructed for training and verification; and finally, deeply learning a test result of each base model through a meta-learner (linear regression model), and automatically adjusting the weight of the test result to obtain a multi-model fused soil humidity downscaling inversion result. According to the method, the advantages of a plurality of base models are fully fused, and the soil humidity downscaling inversion precision can be effectively improved.

The invention discloses a WOA-BP-based SiC MOSFET life prediction method and device and a storage medium, and relates to the technical field of reliability prediction of power electronic equipment. The method comprises the following steps: selecting drain-source electrode voltage as an aging characteristic parameter of the SiC MOSFET, and acquiring voltage data in real time in an aging test process by using a sensor; the collected voltage data are preprocessed, abnormal values are eliminated, missing values are supplemented, and the data are smoothed; parameters of a back propagation neural network BP are optimized through a whale optimization algorithm WOA, and a WOA-BP prediction model is constructed; and initializing a WOA-BP model and inputting voltage data for training, and establishing a mapping relation between the service life of the SiC MOSFET and the voltage to realize prediction of the residual service life of the SiC MOSFET. The SiC MOSFET service life prediction model has the advantages of being high in adaptability, low in calculation complexity, high in prediction precision and the like, can be widely applied to the fields of the semiconductor industry, new energy automobiles, industrial automation and the like, and provides powerful support for health management and maintenance of equipment.

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 combines the advantages of priori knowledge fusion, provides an accidental explosion concrete penetration depth prediction method fused with experience algorithm knowledge, and aims to improve the prediction precision and physical rationality in a small sample scene. The method comprises the following steps: firstly, constructing a combined parameter Z through a physical parameter, a target attribute and a Forrest formula, and taking the combined parameter Z as an input feature of a model to represent a physical rule; then, a multi-objective loss function is designed, the multi-objective loss function comprises a data fitting item, a physical constraint item and a boundary constraint item, the physical constraint item verifies the physical consistency of predicted values through trace disturbance input parameters, and the boundary constraint item ensures that the penetration depth is non-negative. And further constructing a dynamic weight adjustment mechanism, and automatically balancing the optimal weight of the physical law and data fitting according to the training process. And based on a back propagation neural network framework, performing model training in combination with the constraints. According to the method, the mean square error is remarkably reduced in a concrete penetration task, and meanwhile, the physical interpretability of a data-driven model is expanded.

The invention relates to a silicon photonic interlayer coupling structure design method based on an artificial neural network, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring a plurality of interlayer coupling structure data sets, performing normalization processing on the interlayer coupling structure data sets, and dividing the data sets; constructing a back propagation neural network; in the design process, a self-adaptive genetic algorithm is used for optimizing the weight and the threshold value of the back-propagation neural network; using a training set to train the back propagation neural network model; coupling structure parameters are input into the trained model, a coupling efficiency value is output, and whether the coupling structure meets design requirements or not is judged according to the coupling efficiency value; and continuously adjusting the structural parameters until the design requirements are met. According to the method, the design time of the interlayer coupling structure is shortened, the design difficulty of the interlayer coupling structure is reduced, and automation and intelligence of the design of the interlayer coupling structure are realized.

The invention relates to the technical field of grass carp nondestructive testing, in particular to a grass carp muscle unsaturated fatty acid content nondestructive testing method based on scale characteristic wavelength. The method comprises the following steps: constructing a back propagation neural network, and training the network by using a plurality of samples containing scale wavelength information and unsaturated fatty acid information to obtain a prediction model; and the fresh grass carp scale wavelength information is input into the prediction model to obtain the unsaturated fatty acid content of the living grass carp muscle, so that rapid nondestructive detection can be realized, and the detection accuracy is relatively high.

The invention relates to an indoor cross-scene multi-band sub-terahertz channel prediction method, a storage medium and software, and belongs to the technical field of wireless communication. In order to solve the problems of poor adaptability to nonlinear features, high calculation complexity and insufficient generalization ability in traditional channel modeling, a channel prediction model is constructed based on a back propagation neural network, and a particle swarm optimization algorithm is utilized to optimize a network initial weight and a threshold value. According to the method, system parameters and environment characteristics are fused, an indoor sub-terahertz channel simulation data set is constructed, and the indoor sub-terahertz channel simulation data set is subjected to normalization processing and then used for model training and verification. And finally, evaluating the prediction performance according to indexes such as a root-mean-square error, a mean absolute error and a decision coefficient. The method can improve the accuracy and generalization ability of channel characteristic prediction, is suitable for various indoor scenes, and provides technical support for 6G communication system deployment.

The invention provides an oil field productivity monitoring method using a carbon quantum dot tracer agent, and the method comprises the steps: S100, obtaining the convection term and diffusion term characteristics of an oil field through the injection of the carbon quantum dot tracer agent and an inert tracer agent, and building an oil field monitoring characteristic set; step S200, selecting main characteristics of oil field monitoring from the oil field monitoring characteristic set by using a Spearman correlation coefficient and a random forest tree model method; and S300, inputting the main features into a neural network to monitor the productivity of the oil field, wherein the neural network is a particle swarm optimization-back propagation neural network for Wiener process optimization.

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 discloses a battery thermal runaway early warning method, device and equipment for an energy storage power station and a medium, the battery thermal runaway risk of the energy storage power station is predicted by using a neural network, early warning is performed before battery thermal runaway of the energy storage power station, and the safety is improved. Besides, the probabilistic neural network is trained by using a small number of training samples, and after the probabilistic neural network is converged, the back-propagation neural network is trained by using the soft label output by the probabilistic neural network, so that the problems of low learning speed and insufficient training samples in the initial training stage of the back-propagation neural network are solved, and the model training efficiency is improved. Meanwhile, the soft label can solve the problem that the back propagation neural network is prone to falling into a local minimum value in the training process.

The invention provides a lithium ion battery cell data processing method and device, equipment and a medium. The method comprises the following steps: acquiring test data under a plurality of test working conditions; constructing a back-propagation neural network of the lithium ion battery, and positively inputting the test data into the back-propagation neural network to obtain predicted voltages of the lithium ion battery under various test conditions; determining voltage change information of the lithium ion battery according to the predicted voltage; inputting the voltage change information and the test data into an equivalent circuit model for preliminary offline identification to obtain an initial cell internal resistance parameter; reversely inputting the initial cell internal resistance parameter into a back-propagation neural network, forwardly inputting the test data into the back-propagation neural network, and correcting the model parameter by the back-propagation neural network to obtain a target back-propagation neural network; and inputting the battery data of the to-be-identified battery into the target back propagation neural network, and predicting to obtain the cell internal resistance parameter of the to-be-identified battery, so as to improve the identification efficiency and precision of the cell internal resistance parameter.

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 relates to the technical field of power electronics, in particular to an impedance identification method and system for a doubly-fed fan grid-connected system based on data driving, and the method specifically comprises the steps: determining the input and output of the system based on a dq theoretical impedance model of the doubly-fed fan grid-connected system; building a time domain simulation model of the single doubly-fed fan grid-connected system, and performing data collection under various operation conditions by using a frequency sweep method; constructing a single doubly-fed fan impedance identification model based on a back propagation neural network optimized by a genetic algorithm, training the single doubly-fed fan impedance identification model, aggregating the station impedance identification model by using the trained single doubly-fed fan impedance identification model, and then performing impedance identification to obtain equivalent impedance after aggregation of the wind power station; and analyzing the stability of the doubly-fed fan grid-connected system by using the impedance identification model based on the generalized Nyquist criterion. According to the method, the impedance identification precision is ensured, and the impedance identification speed is greatly increased at the same time.

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 Tan mutton producing area identification method based on a back-propagation neural network, the back-propagation neural network is optimized through a particle swarm optimization algorithm, and the method comprises the following steps: extracting lipids in Tan mutton samples from different producing areas; an LC-MS / MS system is adopted to analyze the components and contents of lipids in the Tan mutton samples from different producing areas; screening the components and contents of lipids in all the samples by adopting a random forest model to obtain characteristic lipid data of the beach mutton samples from different producing areas; training a pre-constructed back-propagation neural network optimized based on a particle swarm algorithm by adopting the characteristic lipid data of the Tan mutton samples from different producing areas; and identifying the producing area of the Tan mutton with an unknown producing area by adopting the trained back propagation neural network optimized based on the particle swarm algorithm. According to the method, the characteristic lipids of the Tan mutton in different producing areas are screened out through the random forest algorithm, and accurate identification of the producing areas of the Tan mutton in different areas is realized through the back-propagation neural network optimized by the particle swarm optimization algorithm.

The embodiment of the invention discloses a reverse prediction method and device for a niobium alloy laser welding process and a medium, and relates to the technical field of laser welding. The welding process performance demand information comprises any one or more of a high-temperature oxidation resistance index, a welding depth index, a welding width-depth ratio index, a heat affected zone width index, a tensile strength index and a shear strength index; and according to the welding process performance demand information, welding process parameters corresponding to niobium alloy laser welding are determined by adopting a pre-constructed laser welding process parameter reverse prediction model, and the laser welding process parameter reverse prediction model is a back propagation neural network model based on grey wolf optimization and differential evolution algorithm optimization. According to the method, a large number of repeated tests and cost waste caused by a traditional trial and error method are avoided, and the problem that performance and parameter matching is difficult in the niobium alloy laser welding process is solved.

The application discloses a battery thermal runaway early warning method, device and equipment of energy storage power station and medium, using neural network to predict the battery thermal runaway risk of energy storage power station, early warning before the battery thermal runaway of energy storage power station, improve the safety.In addition, first train the probabilistic neural network with a small amount of training samples, and after the convergence of the probabilistic neural network, train the back propagation neural network with the soft label output by the probabilistic neural network, solve the problem of slow learning speed and insufficient training samples in the initial training of the back propagation neural network, and improve the model training efficiency.At the same time, the soft label can solve the problem that the back propagation neural network is easy to fall into local minimum value in the training process.

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 a wind and light power station energy storage capacity configuration method and system, and the method comprises the steps: carrying out the wind power prediction based on the historical related data of each wind power plant through employing a model based on a convolutional neural network and a long-short-term memory network; based on the historical related data of each photovoltaic power station, using a back propagation neural network model based on a genetic algorithm to carry out photovoltaic generation power prediction, and based on the wind power generation power prediction value of each wind power plant and the photovoltaic generation power prediction value of each photovoltaic power station, obtaining the total prediction power of the wind and light cluster power station. The energy storage capacity optimization configuration is carried out according to the actual load state and the total prediction power of the wind and light cluster power station, so that the accuracy of power prediction of the wind and light cluster power station is effectively improved, then the energy storage capacity optimization configuration is carried out according to the actual load state and the total prediction power of the wind and light cluster power station, and the configuration accuracy and reliability are improved. Therefore, the accuracy and reliability of energy storage capacity configuration are improved.

The invention provides a complex rock mass stability assessment method based on a back propagation neural network, and aims to provide an efficient and accurate assessment means for rock mass stability analysis under complex geological conditions. According to the method, the rock mass numerical model is constructed, influence factors are determined, and the stability of the rock mass is quantitatively analyzed and evaluated by using the back propagation neural network, so that the multi-scale characteristics of the rock mass, the influence of an interlayer staggered belt and external loading conditions can be comprehensively considered, and the stability of the rock mass in different stress states can be accurately predicted; scientific basis is provided for rock engineering design and construction, and engineering risks are reduced.

The invention discloses a slope safety early warning method in geotechnical engineering, and belongs to the technical field of geotechnical engineering, and the method comprises the following steps: obtaining geological conditions and reinforcement measures of a slope geotechnical engineering site, determining a slope model analysis area, and constructing a slope model; selecting a target monitoring point of the slope model; a slope safety early warning coefficient corresponding to slope instability is defined; slope characteristic parameters are obtained; constructing a back propagation neural network between the slope characteristic parameters and the rock-soil response, and performing Bayesian updating to obtain the rock-soil response of each excavation step; and constructing a relation mapping model of the rock-soil response and the slope safety early warning coefficient, obtaining the slope safety early warning coefficient of each excavation step, and performing slope safety early warning. According to the invention, the problem of insufficient early warning accuracy of the slope in geotechnical engineering is solved.

The invention provides a power system intelligent fault diagnosis method and system based on data fusion, and relates to the technical field of intelligent monitoring. The method comprises the following steps: acquiring vibration signals and electric signals of rotating equipment of different fault types in real time by using a sensor, and correspondingly obtaining a vibration matrix and an electric matrix; respectively carrying out weighted combination on the vibration matrix and the electric matrix to obtain a vibration element confidence coefficient and an electric element confidence coefficient; performing normalization processing and fusion processing on the vibration element confidence coefficient and the electric element confidence coefficient to obtain a fusion matrix; performing dimension reduction processing on the fusion matrix by using a principal component analysis method to obtain fusion features; and inputting the fusion features into a back propagation neural network for training to obtain a diagnosis model used for diagnosing the fault type of the rotating equipment. According to the invention, the problems of single characteristic parameter, low mechanical fault diagnosis identifiability and large error of the fault diagnosis method of the rotating equipment are solved.

A variable curvature bending arc control method for a roll bending machine includes specific steps as follows: collecting and pre-processing data; initializing the network structure, and determining the parameters such as a number of input layer nodes, hidden layer nodes and output layer nodes and other parameters of the back propagation neural network according to the learning sample data; encoding a weight value and a threshold value of a back propagation neural network into individuals in a population according to set coding rules, and initializing the population according to a set population size and a random initialization method; GA-PSO iterative operation; initializing the back propagation neural network parameter; training the back propagation neural network; and an application of the control model. The control method accurately controls the variable curvature bending arc torque for the roll bending machine by combining genetic algorithm, back propagation neural network and particle swarm optimization.

The invention provides an atomic stamp-pad ink type identification method based on a hyperspectral imaging technology and a back-propagation neural network, which comprises the following steps: firstly, establishing a hyperspectral data database of atomic stamp-pad ink samples, and then extracting a region of interest of hyperspectral data of each stamp-pad ink sample to obtain a training set and a test set; then establishing a three-layer neural network based on a BP algorithm, and taking the spectral data of the atomic stamp-pad ink sample as nodes of an input layer; when atomic stamp-pad ink type identification needs to be achieved, hyperspectral data of a to-be-detected stamp is collected through a hyperspectral imaging system to serve as data of an input layer, and a result is output through matching. According to the method, a hyperspectral imaging technology is utilized to establish sample libraries of different atomic stamp-pad inks, and different neural network algorithms are compared, so that compared with traditional LSTM and RF algorithms, the BP algorithm is selected to accurately identify different types of atomic stamp-pad inks. The method disclosed by the invention can be used for quickly, accurately and nondestructively identifying the types of the atomic stamp-pad ink on the sample.

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.