350 results about "Extreme learning machine" patented technology

The invention relates to the technical field of data processing, in particular to an earthwork balancing method based on discrete elevation points and a free-form surface creation technology, and the method comprises the steps: generating topographic feature partition tags through an improved DBSCAN density clustering algorithm, and constructing a regularized elevation field in combination with octree and R * tree mixed indexes; generating an elevation standard deviation thermodynamic diagram based on Gaussian process regression, and outputting a multi-target digging and filling scheme set in combination with an FEM-DEM coupling model; a multi-layer graph model fused with soil bearing capacity is constructed, a collision-free construction path is optimized through an improved D * Lite algorithm, a positioning track and a planning deviation are fused by using Kalman filtering, and an online sequence extreme learning machine is triggered to realize dynamic updating of the model. According to the method, the complex terrain modeling efficiency and the earthwork volume calculation precision are improved, the resource allocation path is optimized, the construction cost is reduced, and the problem of excavation and filling imbalance caused by data discreteness and modeling staticization in a traditional method is solved.

The invention discloses a lithium battery diagnosis method and system based on multivariable and infrared point cloud. The method comprises the steps that voltage, current, temperature, voiceprint and infrared three-dimensional point cloud data of a lithium battery are collected through multiple sensors; performing mode decomposition on the electric signal by adopting symplectic geometric mode decomposition SGMD, and constructing a two-dimensional input matrix in combination with an original signal; an electric signal is processed by using a Tokenformer model optimized by CBAM, and image data is analyzed by using a YOLOv9 model improved by ECA; integrating the electric signals and the image features through a PTP multi-mode fusion method; and finally, an improved extreme learning machine IELM is adopted to realize fault diagnosis. According to the method, through a multi-modal coupling structure and intelligent algorithm optimization, the diagnosis precision and the calculation efficiency are remarkably improved, meanwhile, the dependence on labeled data is reduced, and the generalization ability and the adaptability of the model are enhanced.

The invention discloses a strain culture monitoring method and system based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the following steps: collecting multi-dimensional original data in a fermentation process, and carrying out the preprocessing to construct a multi-dimensional feature data set; training a bidirectional LSTM model through the multi-dimensional feature data to obtain a high-dimensional feature vector; performing dimension reduction processing on the high-dimensional feature vector through a sparse self-encoding technology to obtain a low-dimensional feature vector, and processing the low-dimensional feature vector through phase mapping and an extreme learning machine to obtain a target classification result; and constructing a Markov decision model based on the low-dimensional feature vector and a target classification result, and solving the Markov decision model through a reinforcement learning algorithm to obtain an optimal adjustment strategy. According to the scheme, intelligent monitoring and precise regulation and control of strain culture based on artificial intelligence are achieved, the defects of a traditional method in data utilization, stage adaptability and model generalization ability are overcome, and the intelligent level and production efficiency of strain culture are improved.

The invention discloses a physical field solving method based on a Bayesian physical information extreme learning machine, and the method comprises the steps: constructing a single-layer full-connection neural network, carrying out the random initialization, and fixing the weight of an input layer; based on a partial differential equation of a physical system and boundary conditions thereof, defining a training loss item containing physical information; a physical system solving problem is converted into a linear least square problem, and a linear equation set is constructed; supposing that an output layer weight parameter obeys Gaussian prior distribution with the mean value being zero, and controlling a covariance matrix by an adjustable hyper-parameter; constructing a Gaussian likelihood function based on the observation data, and calculating posterior distribution of the output weight in combination with the prior distribution; carrying out iterative optimization on the hyper-parameter by adopting an evidence maximization method to obtain a mean value and a covariance of posterior distribution; based on posterior distribution, adopting a Monte Carlo integral method to generate prediction output of the physical system; and performing uncertainty quantization based on the variance of prediction output, and outputting a prediction value and a confidence interval thereof.

The invention discloses a computer network security data processing method and system based on artificial intelligence, and the method comprises the steps: building a multi-channel deep learning fusion model, extracting spatial local features in a traffic sequence through employing a 1D-CNN one-dimensional convolutional neural network, capturing a long-range time sequence dependence relation between log events, and carrying out the recognition of the long-range time sequence dependence relation between log events; modeling the user operation behavior sequence based on an LSTM (Long Short-Term Memory) network, and fusing the feature weight by using an attention mechanism to obtain a fused feature vector; inputting the fusion feature vector into a classifier established based on an OS-ELM online sequence extreme learning machine to perform real-time threat assessment, and outputting a probability index of network attacks occurring in a short time in the future; and generating a cooperative defense decision according to the network attack probability index, and sending the cooperative defense decision to security equipment for execution. Excessive defense or insufficient protection is avoided, and the cooperation efficiency of safety equipment is remarkably improved.

The invention discloses a self-adaptive ultrasonic measurement method and system based on multi-channel cooperation, and relates to the technical field of ultrasonic measurement. The method comprises the following steps: collecting original flight time signals of each channel; meanwhile, environment data are collected in real time; original flight time signals are processed, effective signal arrival pre-flight time is extracted, and a fusion data set is constructed; constructing a two-dimensional sound channel spectrogram based on the multi-sound channel time sequence of the pre-flight time, and performing feature extraction based on a lightweight convolutional neural network to generate an abnormal confidence vector; analyzing the sound channel state based on the abnormal confidence vector; constructing a physical information neural network, and analyzing the corrected sound velocity value of each sound channel and the two-dimensional sound velocity field distribution on the section of the whole pipeline; training an online sequence extreme learning machine model in combination with historical measurement data; and based on the final fusion weight of each sound channel and the corresponding sound channel flow velocity, carrying out weighted fusion to generate a flow velocity optimal estimation value. And the measurement precision and robustness are improved.

The invention provides an intelligent machine tool cutter wear state recognition and machining parameter adjustment method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting the working condition data of a machine tool machining process, carrying out the sliding window segmentation, denoising and normalization processing, extracting a time sequence feature sequence through a bidirectional gating circulation unit, and carrying out the recognition of a machining parameter; the method comprises the following steps: acquiring an intrinsic mode component by combining Hilbert-Huang transform decomposition, extracting a frequency domain feature sequence by utilizing four-layer spatial pyramid pooling, determining a fusion weight based on mutual information entropy to obtain a tool wear state feature vector, and inputting the feature vector into a pre-training extreme learning machine to predict the wear loss and the residual life. And determining an initial adjustment scheme in combination with observation sharing information of a parallel agent structure, predicting a processing quality score through a capsule network and feeding back the score as a reward signal, and obtaining an optimization correction strategy for adjusting working parameters of a machine tool.

The invention discloses an Alzheimer disease risk prediction method based on feature fusion, a medium and equipment. The method comprises the following steps: firstly, acquiring an sMRI image, an FDG-PET image, a biomarker and intelligence score data of a target subject; then, sMRI and FDG-PET image data are respectively input into a three-dimensional convolutional neural network module, and a corresponding first image feature map and a corresponding second image feature map are extracted; secondly, inputting the two feature maps into a cross attention fusion module to obtain image fusion features; and inputting a U-shaped up-sampling network to segment a hippocampus region to obtain hippocampus features. And then, merging the image fusion features, the hippocampus features, the biomarker data and the intelligence score data, and inputting the merged data into an extreme learning machine for calculation, thereby obtaining an onset risk prediction result of the Alzheimer's disease. According to the method, by means of cross-modal data fusion, multi-modal data complementary information is extracted, and the accuracy of onset risk prediction can be effectively improved.

The invention discloses a distributed photovoltaic power prediction method and system, belongs to the technical field of renewable energy prediction, and solves the prediction precision and efficiency bottlenecks of a traditional model under complex meteorological conditions through fusion of deep feature learning and an adaptive optimization mechanism. Firstly, a time sequence sample set is constructed based on a sliding window mechanism, and non-stationary fluctuation characteristics of a power sequence are dynamically captured; eliminating the dimensional difference between the input features and the tags through minimum-maximum normalization; constructing a four-level restricted Boltzmann machine stacking structure, and extracting time-space coupling characteristics of power data by layer-by-layer unsupervised pre-training; and designing an improved extreme learning machine dynamic analysis architecture, optimizing the number of neurons in a hidden layer in combination with grid search, and realizing global optimal balance between model complexity and prediction precision. According to the method, an effective dynamic mode and noise interference are distinguished by using hierarchical feature abstraction capability of the RBM, prediction robustness in strong fluctuation scenes such as cloudy and rainy scenes is remarkably improved through analytical solution and parameter adaptive adjustment of the ELM, and efficient technical support is provided for intelligent scheduling and energy storage optimization in a high-proportion photovoltaic grid-connected background.

The invention discloses a tool fracture and fatigue intelligent detection method based on vibration signal analysis, and the method comprises the following steps: S1, installing a vibration sensor, and collecting the vibration signal of a tool in real time; s2, the collected tool vibration signals are preprocessed, and noise in the signals is removed; s3, performing time-frequency analysis on the preprocessed vibration signals, and extracting time-frequency features in the signals; s4, performing deep feature learning on the extracted time-frequency features to form deep features; s5, the depth features are classified and analyzed, and the health state of the cutter is output; s6, according to the health state optimization feature extraction and prediction result of the cutter, generating learning output; s7, evaluating the health state of the cutter in real time according to the learning output, and pushing alarm information; and S8, according to the alarm information, predicting the service life of the cutter and optimizing a cutter replacement and maintenance strategy. According to the method, short-time Fourier transform and Hough transform are combined, and the extreme learning machine is applied, so that intelligent detection on the fracture and fatigue of the cutter is realized.

The invention provides a hot rolled strip convexity prediction method based on weight distribution strategy hybrid modeling, and relates to the technical field of metallurgical machinery and automation. The method comprises the following steps: acquiring historical production data in the rolling process of a hot-rolled strip, and establishing a convexity mechanism model of the hot-rolled strip; an improved ant colony algorithm is adopted to optimize the extreme learning machine, and a hot rolled strip convexity data driving model is constructed; calculating the information entropy of the mechanical model and the information entropy of the data driving model by adopting an entropy weight method; according to the model information entropy, calculating an initial weight coefficient of a mechanical model and an initial weight coefficient of a data-driven model, and establishing a hybrid model through a weighted summation method; and in hot-rolled strip production of different rolling units, the weight is dynamically adjusted according to the deviation value between the predicted value and the actual value of the mechanism model and the data driving model, and a final hot-rolled strip convexity prediction result is output. By adopting the method, the accuracy of convexity prediction of the hot-rolled strip can be improved.

The invention discloses a power distribution network fault positioning method and positioning system, and relates to the technical field of power distribution network fault positioning, and the method comprises the steps: collecting multiple groups of historical data of different faults of a power distribution network, obtaining the missing intervals of voltage and current sequences in each group of historical data, and positioning the missing intervals to the front, middle and back of the whole sequence; different interpolation methods of different missing intervals are obtained by combining the length of the missing interval, the distance from the starting point position of the missing interval to the previous nearest missing interval and the distance from the ending point position of the missing interval to the next nearest missing interval, an abnormal value is marked for a sequence after interpolation is completed, and the sequence is equally divided into S < front > and S < back >; the abnormal values are repaired by utilizing ELM, a standard data set belonging to the fault is obtained, clustering processing is carried out on data in the standard data set, irrelevant clusters are removed, and a specific position is positioned according to the similarity between the fault and each group of faults in the target cluster. The power distribution network fault positioning method effectively improves the power distribution network fault positioning precision.

The invention provides an intelligent safety helmet operation fatigue risk early warning classification algorithm based on a fractional order depth extreme learning machine. Belongs to the technical field of intelligent wearable equipment and artificial intelligence. The classification algorithm is executed through the following method, and the method comprises the steps that original data are obtained in real time through multiple sensors integrated on the intelligent safety helmet, and the original data comprise personnel physiological data and environment data; the method comprises the following steps: preprocessing collected original data, performing fractional differential or integral processing on the data by using a fractional calculus theory, and extracting deeper information features; by means of multiple sensors (such as heart rate, brain wave, acceleration and environment sensors) integrated on the intelligent safety helmet, physiological data and environment data of an operator can be obtained in real time.

The invention relates to the technical field of internet big data, in particular to an electric power high-altitude worker working state detection method based on image feature fusion, which comprises the following steps: S1, acquiring a to-be-detected original image; s2, inputting the original image into an OpenPose network model to perform human body posture feature extraction to obtain human body posture key point features of each person target in the original image; s3, inputting the original image into a YOLACT model to perform power equipment feature extraction to obtain power equipment features; s4, performing feature fusion on the human body posture key point features and the power equipment features of each personnel target to obtain fusion features of each personnel target; and S5, inputting the fusion features of each personnel target into a trained online sequential extreme learning machine for classification, judging whether each personnel target is performing high-altitude electric power operation or not, and outputting the personnel target performing high-altitude electric power operation in the original image. The detection precision of the working state of the electric power high-altitude worker can be improved.

The invention discloses a DC motor fault diagnosis method based on improved local mean decomposition and composite multi-scale bubble entropy fusion, and relates to the technical field of motor fault diagnosis. In order to solve the technical defect of insufficient fault feature extraction of the existing motor fault diagnosis technology in the prior art, the technical scheme provided by the invention comprises the following steps: collecting a sound signal in a running state of a direct current motor as an original signal; decomposing the original signal by adopting an improved local mean decomposition method to obtain a plurality of product components, and screening out effective feature components with high correlation with the original signal; performing composite multi-scale bubble entropy calculation on the effective feature component, and extracting a multi-dimensional entropy feature vector representing signal complexity; and inputting the multi-dimensional entropy feature vector into a particle swarm optimization extreme learning machine model for classification so as to obtain a fault diagnosis result of the DC motor. The method can be widely applied to real-time monitoring and intelligent fault identification of the running state of the direct-current motor in industrial production.

The invention discloses a data security management method and system based on a cloud computing platform, and relates to the technical field of data security management, and the method comprises the steps: collecting original data of the cloud computing platform in real time, and carrying out the preprocessing; constructing a discrimination model based on a generative adversarial network (GAN) to generate a forged data sample, taking the forged data sample and a real data sample as input data, optimizing the discrimination model by using the input data based on an extreme learning machine, and discriminating attack data; and constructing a security situation model by using a hidden Markov model, optimizing parameters through a cluster particle algorithm, and outputting a predicted security situation based on real-time attack data. A discrimination model is optimized through a generative adversarial network and an extreme learning machine, the discrimination precision of attack data is effectively improved, a security situation model is established through a hidden Markov model, model parameters are optimized in combination with a cluster particle algorithm, a predicted attack path is combined with a deep Q learning network, and the security risk of attack data is improved. And attack path prediction is carried out through the dual deep Q network, and a defense strategy is adjusted in real time.

The invention provides an online sequential preposed interference layer extreme learning machine, a classification method, equipment and a medium, and relates to the technical field of machine learning. The online sequential preposed interference layer extreme learning machine comprises an input layer, an interference layer, a hidden layer and an output layer, the input layer is used for receiving original sample data, the interference layer reduces the sample distribution complexity through nonlinear kernel mapping, the hidden layer is located between the interference layer and the output layer, and the output layer is located between the interference layer and the output layer. And the output layer is used for further extracting features of the samples mapped by the interference layer and calculating a final classification result based on the output of the hidden layer. According to the method, the performance of the extreme learning machine in classification and regression tasks can be remarkably improved, particularly, higher generalization ability and stability are shown when nonlinear distribution samples are processed, meanwhile, dependence on parameter selection is reduced, and the method is suitable for real-time data processing scenes such as intelligent medical treatment, fault detection and financial prediction.

The invention relates to the technical field of ship power control, in particular to a control method for predicting and optimizing power output by a neural network control algorithm, which comprises the following steps of: acquiring sensor data of a ship turbine in real time, extracting time sequence characteristics through a dynamic sliding window, and eliminating noise by adopting adaptive filtering; constructing a power system prediction model by applying an extreme learning machine (ELM), inputting the preprocessed feature data, and outputting a power output prediction value in a future time period; and when the prediction error exceeds a dynamic threshold value, network parameters are adjusted on line through an incremental weight updating algorithm. The data processing capability is broken through: a multi-objective cost function including fuel efficiency, emission indexes and mechanical wear is constructed in combination with a prediction result and a ship navigation state, and a Pareto optimal control solution set is searched by using an evolutionary algorithm; wavelet packet decomposition and Kalman filtering are fused, the signal-to-noise ratio is increased, and the sudden working condition feature capture speed is obviously increased through a dynamic sliding window.

The invention relates to the technical field of gas concentration detection of power equipment, and discloses a method and system for detecting the concentration of gas dissolved in oil based on an improved extreme learning machine, and the method comprises the steps: improving the extreme learning machine into a kernel extreme learning machine and a depth extreme learning machine, and respectively combining with a seagull optimization algorithm to obtain the concentration of gas dissolved in oil; constructing a model for predicting the concentration of the dissolved gas in the transformer oil, and training the model; and based on the transformer data, inputting the transformer data into the model for predicting the concentration of the dissolved gas in the transformer oil, and outputting a prediction result of the dissolved gas in the transformer oil and a fault type. According to the method, an intelligent optimization algorithm and a data processing method are combined, an extreme learning machine with high learning speed and good generalization performance is improved to form a deep extreme learning machine and kernel extreme learning machine model, an improved seagull algorithm is used for optimization, and a prediction model is formed for prediction. And the detection precision of the equipment gas concentration in the power system is improved.

The invention belongs to the field of non-intrusive power load monitoring, and discloses a non-intrusive power load identification method, which comprises the following steps of: constructing a load identification framework utilizing frequency domain characteristics, and aims to solve the problem that transient characteristics cannot be effectively applied in non-linear load identification in non-intrusive load monitoring. A current feature extraction technology based on fast Fourier transform and Hilbert-Huang transform and a classification method based on an extreme learning machine as a main body are used for training and testing a public data set to verify that the method is used for extracting transient load features and identifying nonlinear loads.

The invention provides a soil ammonium nitrogen content hyperspectral prediction method based on an improved extreme learning machine, and relates to the technical field of hyperspectral prediction. The method comprises the following steps: firstly, collecting and treating a soil sample for soil spectral measurement and NH4 < + >-N content determination; measuring soil spectral reflectivity data; preprocessing the soil spectral reflectivity data to form a spectral reflectivity data set; then carrying out characteristic wave band selection by adopting a sequential forward selection algorithm; an improved butterfly optimization algorithm IBOA is adopted to optimize model parameters of an extreme learning machine ELM, and then a hyperspectral prediction model used for predicting the NH4 < + >-N content of the soil is constructed; and finally, the ELM model after parameter optimization is selected to construct a hyperspectral prediction model to predict the NH4 < + >-N content. The method not only provides theoretical and technical support for soil ammonium nitrogen content monitoring, but also provides important reference and guidance for soil nitrogen cycle research and soil management.

The invention discloses an SOH prediction method and system based on an internal resistance-temperature correlation model. The method comprises the following steps: acquiring a pre-established battery internal resistance-temperature correlation model; operating temperature data and a current internal resistance value of a to-be-measured battery are collected in real time, and the operating temperature data are input into the internal resistance-temperature correlation model to obtain a reference internal resistance predicted value at the temperature; calculating the relative deviation between the current internal resistance value and the reference internal resistance predicted value, and constructing a multi-dimensional health state feature vector in combination with the cycle index of the battery; and inputting the multi-dimensional health state feature vector into a pre-trained extreme learning machine (ELM) prediction model, and outputting a health state quantitative index and a residual life prediction result of the battery. According to the embodiment of the invention, the accuracy and reliability of battery health state estimation under different temperature working conditions can be improved, and more accurate battery life management is realized.

The invention discloses a grounding line selection method fusing transient energy and waveform correlation, and the method comprises the steps: extracting a high-frequency component in a zero-sequence current through employing an anti-mode aliasing effect VMD, and obtaining the energy of the high-frequency component through Hilbert transformation; then, correlation analysis is carried out on the zero-sequence current to extract a transient waveform comprehensive correlation coefficient; and forming a fault feature vector by the high-frequency component energy and the transient waveform comprehensive correlation coefficient, inputting the fault feature vector to an extreme learning machine without threshold setting, training ELM by using simulation fault data, and obtaining a fault line selection model based on the ELM to realize grounding fault line selection. According to the method, the endpoint effect and the mode aliasing phenomenon occurring in EMD can be effectively avoided, the transient current comprehensive correlation coefficient is extracted, and the line selection accuracy during high-resistance grounding is guaranteed.

The invention discloses a power distribution network construction material demand analysis and prediction method, which comprises the following analysis and prediction steps: S1, collecting historical material data of a power distribution network construction project, the historical material data comprising project attribute data and material usage data; s2, preprocessing the material consumption data, including data cleaning, standardization and normalization processing; s3, carrying out dimension reduction processing on the material data based on a principal component analysis method; s4, performing clustering analysis on the material project attributes based on a clustering analysis algorithm; s5, parameters of the extreme learning machine are optimized based on a particle swarm algorithm, the optimized extreme learning machine is adopted to construct a power distribution network construction material demand prediction model, and the number and amount of power distribution network construction material demands are predicted; the system can effectively improve the material management level of a power enterprise, and improves the efficiency and quality of a material demand plan.

The invention provides a method, a system and a device for predicting the residual service life of a proton exchange membrane fuel cell. The method comprises the following steps: step 1, collecting monitoring data of a charge-discharge process of the fuel cell; 2, carrying out feature selection on the monitoring data of the charging and discharging process; step 3, using robust local mean decomposition based on dynamic time warping improvement to decompose the monitoring data of the charging and discharging process of the fuel cell; 4, reconstructing a sub-sequence by adopting an entropy control principal component polymerization method; 5, establishing a dynamic adaptive weighted extreme learning machine of the life prediction model and the residual service life prediction model; step 6, improving a projection iterative optimization algorithm; and 7, optimizing the dynamic adaptive weighted extreme learning machine of the residual service life prediction model to obtain a final prediction result. The problem that the residual service life of the fuel cell cannot be accurately predicted is solved, and the prediction accuracy of the residual service life of the cell is improved.

The invention discloses a foundation pit horizontal displacement probability prediction method and system based on a sparse Bayesian extreme learning machine, and belongs to the technical field of civil engineering monitoring and artificial intelligence crossing. According to the method, a probability model containing input and output noise is constructed, feature selection and uncertainty quantification are automatically carried out by using a sparse Bayesian framework, and probability prediction of horizontal displacement at the position where a sensor is not arranged is realized. The method can output the prediction mean value and the confidence interval, effectively solves the problems of data sparsity and noise, and improves the prediction reliability and the engineering decision support capability. The method has the advantages of being high in automation degree, high in anti-interference capacity, suitable for actual engineering monitoring and the like.

The invention relates to the technical field of offshore wind power prediction, and discloses an offshore wind power prediction method and system. An anomaly detection threshold value is dynamically adjusted based on the wind speed change rate, abnormal value cleaning is carried out on historical data, and preprocessed data is obtained; establishing an extreme learning machine model, and initializing network structure parameters of the extreme learning machine model; an improved electric eel foraging optimization algorithm is adopted to optimize an input layer weight and a hidden layer threshold in the network structure parameters, and an optimized extreme learning machine model is generated; inputting the preprocessed data into the optimized extreme learning machine model for training, and constructing an offshore wind power prediction model; and predicting the real-time offshore wind power of the target port. The generalization performance of the model under the complex sea condition is effectively improved, a more accurate power prediction result is provided for power grid dispatching, and meanwhile the engineering timeliness requirement for real-time prediction of an offshore wind plant is met.

The invention provides an anti-saturation fault-tolerant control method and device for a double-flexible-arm space robot, relates to the field of robot control, and solves the multiple challenges that in the prior art, a flexible mechanical arm faces continuous and difficult vibration suppression, an actuator is prone to failure, torque is limited, task time is constrained and the like. The dynamic state of the system is unstable; and the control capability is limited. The method comprises the steps that a hypothesis modal method and a momentum conservation theorem are combined to derive a double-flexible-arm space robot kinetic equation in a Lagrange form; a flexible system singular perturbation technology is utilized to obtain a slow-varying subsystem for representing rigid motion characteristics of a mechanical body and a quick-varying subsystem for representing residual vibration characteristics of a rod piece; for the slow-varying subsystem, designing a fixed-time anti-saturation fault-tolerant controller based on an extreme learning machine and a torque output function; and designing an anti-saturation controller based on state variable negative feedback for the quick change subsystem. The method is used in the robot control process.

The invention discloses a synthetic aperture radar online trajectory planning method based on an adaptive network. The method comprises the following steps: S1, establishing a bistatic SAR system model and a task coordinate system; s2, dividing a task space; s3, establishing a multi-objective optimization model; s4, performing independent evolution in each task block by using a joint multi-objective evolutionary algorithm of a block propagation strategy, and performing joint optimization through an inter-block elite individual sharing mechanism; s5, generating a trajectory sample data set; s6, introducing a cooperative differential evolution optimization mechanism based on a regularization extreme learning machine algorithm, and constructing a large-scale adaptive network; s7, carrying out real-time track prediction and online updating; and S8, outputting an optimal trajectory result meeting the constraint. According to the method, the problems of long time consumption, high calculation complexity and insufficient network generalization performance of a multi-objective evolutionary algorithm in real-time trajectory planning are solved, and the real-time performance and precision of trajectory planning are improved.

The invention discloses a wheat grain moisture content lossless prediction method based on hyperspectral imaging and Wasserstein generative adversarial network data enhancement, which realizes accurate moisture prediction by fusing spectral feature data enhancement and deep learning modeling. Collecting spectral image data of the wheat grains by using a visible light-near infrared and short wave infrared hyperspectral imaging system; a Wasserstein generative adversarial network is adopted to generate synthetic spectrum and moisture data in a differentiated mode, data distribution consistency is verified through t-SNE, and a triple training set is expanded; noise is eliminated in combination with first-order derivative preprocessing, key characteristic wavelengths are screened by using SPA and ReliefF algorithms, and a convolutional neural network regression model is constructed; experiments show that the method achieves # imgabs0 # imgabs1 # RMSEP = 0.5717 in the visible light-near infrared band and # imgabs2 # RMSEP = 1.0009 in the short wave infrared band, and the precision is remarkably improved compared with a traditional extreme learning machine and a back propagation neural network.