337 results about "Square error" patented technology

The invention relates to the technical field of model construction, and discloses a neural network-based frozen soil unfrozen water content detection model construction method and system. The method comprises the following steps: carrying out segmented sampling test on a clay sample in a temperature interval of 0 DEG C to-20 DEG C to obtain a multi-dimensional original data set, and carrying out standardization and principal component analysis on the data set to obtain a standardized feature vector; and performing three-stage classification marking on the feature vectors to obtain a training data matrix. A deep neural network is adopted to carry out five-fold cross validation training, and a prediction model of three hidden layers is constructed. And performing correction calculation on model output according to soil mineral composition and porosity parameters, and finally identifying an abnormal value through root-mean-square error analysis and a three-time standard deviation principle to obtain a standardized prediction result. According to the method, the mapping relation between the thermal parameters and the unfrozen water content is established, correction is carried out in combination with the soil body characteristic parameters, and the technical problem that the prediction precision of the unfrozen water content is not high in the prior art can be solved.

The invention provides a multi-platform internet marketing activity effect evaluation and prediction model construction method, and relates to the technical field of data analysis. The method comprises the following steps: collecting marketing activity data from a plurality of internet platforms; performing cleaning, normalization and feature engineering processing on the data; constructing a multi-level evaluation and prediction model based on a machine learning algorithm, wherein the multi-level evaluation and prediction model comprises a data fusion model, an effect evaluation model and an effect prediction model; calculating a click rate, a conversion rate, a user retention rate and a user life cycle value by using an effect evaluation model so as to comprehensively evaluate a marketing activity effect; and performing effect prediction by using the effect prediction model, and evaluating the model through a root-mean-square error, a mean absolute error and a mean absolute percentage error. According to the invention, the accuracy and comprehensiveness of marketing activity effect evaluation and prediction can be improved, and an important basis is provided for optimizing a marketing strategy.

The invention provides an urban water supply pipe network leakage monitoring method, relates to the technical field of urban water supply pipe networks, and aims to reduce dependence on additional equipment and data and improve estimation precision. The method comprises the steps that a dynamic water supply pipe network model is constructed, and leakage flow and the water consumption for metering DMA in a partition mode under the leakage working condition are obtained through simulation based on the constructed dynamic water supply pipe network model; constructing a neural network water consumption prediction model, and predicting normal water consumption when no leakage occurs through training learning; a neural network leakage monitoring model is constructed, and the neural network leakage monitoring model takes normal water consumption and water consumption of DMA subarea metering under the leakage working condition as input parameters to judge whether leakage occurs in each DMA subarea or not; and for the DMA partition with the leakage, establishing a leakage positioning model by taking the minimum square error of the monitoring value and the simulation value of the pressure measuring point when the leakage occurs as an objective function, and solving the leakage positioning model to position the leakage position of the DMA partition.

The invention relates to a communication equipment optimization method and device based on power management. The method comprises the following steps: performing delay power consumption domain transformation and minimum mean square error power equalization processing on power consumption data of communication equipment to obtain power consumption characteristic data; inputting a heterogeneous equipment management neural network to carry out power management prediction to obtain an equipment power consumption prediction parameter; deploying a virtual power supply manager and executing power supply response control on the communication base station and the standby power supply to obtain power supply adjustment parameters; constructing an equipment power consumption scheduling matrix, and performing equalization optimization on the equipment power consumption scheduling matrix by adopting a block iteration mode to obtain a multi-equipment power consumption scheduling scheme; and executing power distribution control according to the multi-device power consumption scheduling scheme, generating an initial power distribution strategy, and dynamically adjusting the initial power distribution strategy through a three-layer Bayesian network model to obtain a target power distribution strategy. According to the invention, the accuracy and adaptive capability of power distribution are improved.

The invention discloses a multi-scale fusion method and system based on satellite sea surface temperature, and relates to the technical field of satellite remote sensing, and the method comprises the steps: obtaining a sea surface temperature data set; performing data preprocessing on the sea surface temperature data set to obtain a processed sea surface temperature data set; based on the actually measured sea surface temperature, performing quality precision evaluation on each data in the processed sea surface temperature data set; the evaluation indexes of quality precision evaluation are composed of average deviation, absolute deviation, root-mean-square error, standard deviation and correlation coefficients; according to the quality precision evaluation result of each piece of data, deviation correction is carried out on each piece of data in the processed sea surface temperature data set based on a segmented regression method, and a corrected sea surface temperature data set is obtained; and according to the corrected sea surface temperature data set, a multi-scale optimal interpolation method is adopted to construct a multi-scale fusion model. According to the invention, sea surface temperature multi-scale information fusion can be realized.

The invention belongs to the field of cable eccentricity detection, and particularly relates to a cable eccentricity detection method and system based on X rays. The method comprises the following steps: calculating a gradient magnitude diagram and a gradient direction diagram through an X-ray image of a cable, screening a point with the local maximum gradient magnitude and low neighborhood divergence as a contour starting point, performing contour tracking to generate a contour point set, and selecting a next contour point based on a tangential prediction direction; performing ellipse fitting on the contour point set to obtain a geometric center, long and short axis parameters and a root-mean-square error of a fitting ellipse, dividing the fitting ellipse into an inner candidate ellipse and an outer candidate ellipse according to a long axis, and matching the inner candidate ellipse meeting the condition for each outer candidate ellipse; and screening out an outer candidate ellipse and an inner candidate ellipse which meet conditions from the candidate pairs, and calculating the eccentricity of the cable to be measured based on geometric center coordinates of the outer candidate ellipse and the inner candidate ellipse. According to the invention, the accuracy and reliability of cable eccentricity measurement results can be improved.

The invention relates to a photovoltaic power generation power prediction method based on an ICSO-SVM model, and the method comprises the following steps: firstly, obtaining historical power generation data and numerical weather prediction (NWP) data of a certain photovoltaic power station, forming an original photovoltaic power generation prediction data set, and then carrying out the preprocessing of the data; then, performing correlation analysis on the features by adopting a Spearman correlation coefficient method, and screening the features with relatively high correlation with the photovoltaic power generation power as input features of model training; in order to further compete for the search and convergence performance of a particle swarm optimization (CSO) algorithm in the SVM hyper-parameter optimization process, the algorithm is improved; on the basis, optimizing hyper-parameters of the SVM by using an improved CSO algorithm (ICSO) so as to construct an ICSO-SVM photovoltaic generation power prediction model; and then, dividing the data set into a training set and a test set according to 8: 2, fully training the prediction model by using the training set, inputting the test set into the trained optimal model for photovoltaic power generation power prediction, and evaluating a model prediction effect by using a root-mean-square error (RMSE) and a mean absolute percentage error (MAPE). Through embodiment analysis, the effectiveness and superiority of the method are verified.

The invention discloses a multi-source observation data dam automatic monitoring and early warning method fused with deep learning, and relates to the technical field of hydraulic engineering, a grey wolf optimization algorithm is used to optimize variational mode decomposition parameters, and an envelope entropy is used as a fitness function to obtain an optimal parameter combination; introducing a multi-scale permutation entropy as a standard for screening signals, determining an effective modal component, and reconstructing the effective modal component to obtain an effective signal; and according to the optimal parameter combination and the effective signal, fusing a deep learning algorithm long-short term neural network to carry out multi-source observation data dam deformation feature learning, taking a mean absolute error and a root-mean-square error as evaluation indexes of GWO-VMD-LSTM prediction accuracy, introducing a decision coefficient to judge the performance of a prediction model, and carrying out multi-source observation data dam deformation feature prediction. And accurate prediction of dam deformation displacement is realized. The prediction result has higher accuracy and precision, and a reliable basis is provided for automatic monitoring and early warning of the dam.

The invention provides a prediction method for the remaining service life of a rolling bearing based on transfer learning, and belongs to the technical field of artificial intelligence and mechanical engineering. Rolling bearing vibration signals are obtained, and a data set is constructed and divided into a source domain and a target domain; constructing a prediction model of the remaining service life of the rolling bearing; the model comprises the following steps: obtaining low-dimensional features and model weights by using a coding layer; the coding layer comprises a multi-head self-attention sub-layer used for obtaining a relationship between features and a model weight; the feedforward mechanism is used for obtaining low-dimensional mapping; predicting the residual life of the bearing by using the feature representation output by the last coding layer through the full connection layer; training the prediction model of the remaining service life of the rolling bearing: calculating the difference between a source domain and a target domain by using the data set, and constructing a loss item; weight parameters are added to the loss item, the weight parameters and the root-mean-square error loss item are added for back propagation, and the model weight is updated. And the generalization ability of predicting the bearing life by the model is improved.

The invention discloses an active noise control method and system of self-adaptive combined weights. The method comprises the following steps: pre-training a control filter; inputting a signal acquired by the reference microphone into each control filter to obtain an output signal; weighted summation is carried out on the combined weight and the output of all the control filters to generate a control signal, so that a loudspeaker is driven to send out an anti-noise signal to cancel interference, and residual noise is collected and fed back by an error microphone; and the instantaneous square error is minimized through an LMS algorithm, the combined weight is iteratively updated, and a quiet area is formed near an error microphone. The method has the characteristic of rapid convergence, achieves good balance between calculation complexity and noise reduction performance, can rapidly adapt to a changing noise environment, and remarkably improves the noise reduction effect and application flexibility of an active noise control system.

The invention belongs to the technical field of battery life prediction, and discloses a system and a method for predicting the residual service life of a lithium battery, and the specific technical scheme is as follows: firstly, selecting a main index with relatively high capacity correlation from battery state indexes such as discharge time, discharge energy and average discharge voltage by using a Pearson's correlation coefficient; carrying out fusion by adopting a convolutional neural network; secondly, an attention mechanism is introduced into the graph convolutional network, and time sequence dependence feature extraction is carried out on the fused data; and finally, combining the output of the convolutional neural network and the output of the graph convolutional network, performing time sequence dependence enhancement by using a bidirectional long-short-term memory network, thereby obtaining a predicted value of the battery capacity, and calculating the remaining service life according to a capacity threshold value. According to the method, feature information extraction in the time dimension is further enhanced, the mean absolute error, the root-mean-square error, the mean absolute percentage error and the absolute error are small, and the prediction precision is improved.

The invention discloses a fog boundary layer parameterization scheme correction method based on a multi-scale physical coupling network, relates to the crossing field of numerical weather forecast and artificial intelligence, and aims to improve the performance of different boundary layer parameterization schemes in a WRF mode, construct a fusion framework of a physical mode and deep learning, adopt a double-branch heterogeneous network structure, and improve the performance of a fog boundary layer parameterization scheme. Establishing a nonlinear mapping relation between different parameterization scheme deviations and large eddy simulation turbulence characteristics by combining a space-time attention mechanism; by correcting systematic deviation existing in a parameterization scheme, the analysis capability of a turbulence structure is improved, and physical description of a boundary layer process is optimized; compared with a traditional boundary layer scheme, the method has the advantages that the fog zone simulation precision is remarkably improved, the fog zone visibility, the liquid water content and the root-mean-square error of a liquid water path are reduced by 66.7%, 50% and 58.3% respectively, the problem that turbulence intermittent characterization is insufficient under a stable boundary layer in a traditional method is effectively solved, and a new normal form is provided for refined forecasting of the fog generation and elimination process.

The invention discloses an improved vibration compensation method suitable for an atom interference gravimeter. The method comprises the following steps: acquiring an atom interference signal and a vibration voltage signal output by a seismometer; initializing population parameters of the improved whale optimization algorithm, wherein the population parameters comprise a search space of a population size, a maximum number of iterations, a gain coefficient K and a delay coefficient tau; the method comprises the following steps: generating an initial whale population by adopting a method of combining Logistic chaotic mapping and Latin hypercube sampling LHS; calculating a phase shift caused by vibration noise and an atomic interference phase delta phi, and calculating the fitness of the whale individual by taking a root-mean-square error of compensated interference fringes as a target function; iteratively updating the population position through an improved whale optimization algorithm, outputting an optimal gain coefficient and an optimal delay coefficient, and obtaining an atomic interference signal after vibration compensation; according to the atomic interference gravimeter vibration compensation method, the global search capability, the local development precision and the stability of a vibration compensation algorithm are improved.

The invention relates to the technical field of photovoltaic power generation modeling and simulation, in particular to a photovoltaic model parameter identification method and system based on an improved symbiont search algorithm. A complex number coding mechanism is introduced on the basis of a traditional SOS algorithm and is expanded from original one-dimensional real number coding to a two-dimensional complex number coding space, so that the search range of a group is expanded, and the optimization capacity and speed of the algorithm are enhanced; establishing a photovoltaic model, and determining a to-be-identified parameter solution vector; adopting a root-mean-square error as a parameter identification objective function of the photovoltaic model; and an improved complex number coding SOS algorithm is operated to identify unknown parameters of the photovoltaic model so as to minimize the difference between actually measured current data and a simulation result, thereby pursuing an optimal fitting effect between the actually measured current data and the simulation result. The improved algorithm provided by the invention has good applicability in the parameter identification process of a single-diode model, a double-diode model and a photovoltaic module model and can quickly optimize, prediction errors are effectively reduced in the aspect of balancing global and local optimal solutions, and the parameter identification precision is improved.

The invention relates to the technical field of weather numerical mode / climate mode prediction and evaluation, in particular to a comprehensive evaluation method and device for climate mode prediction data and a storage medium. The method comprises the following steps: acquiring prediction data and corresponding observation data of different climate modes; calculating evaluation indexes of different climate modes based on the prediction data and the observation data, wherein the evaluation indexes comprise deviation, a spatial correlation coefficient, a root-mean-square error, a time correlation coefficient and a TS score; calculating the weight of each evaluation index according to the information entropy of each evaluation index; and according to the weighted sum of each evaluation index and the corresponding weight, determining a comprehensive evaluation result of the different climate modes. Therefore, according to the method, mode prediction precision, uncertainty estimation, prediction deviation and extreme event prediction capability are evaluated from time and space, one-sidedness of a single index is avoided, and an evaluation result is more objective and credible.

The invention discloses a method for predicting the thermal expansion coefficient of polyimide based on an interpretable machine learning model, which comprises the following steps: collecting the structure and thermal expansion coefficient information of polyimide from a public database and related literatures, then blocking the two ends of a repetitive unit by using hydrogen atoms, generating corresponding molecular descriptors through an SMILES expression of the repetitive unit, and predicting the thermal expansion coefficient of polyimide. A plurality of most important molecular descriptors are selected; constructing a thermal expansion coefficient prediction model, optimizing the model, evaluating the performance of the model by adopting cross validation, and measuring the model by using a decision coefficient, a mean absolute error and a root-mean-square error in the process; the SHAP is used for explaining the model, factors influencing related properties can be explained, experimental synthesis can be rapidly guided, and the method has the advantages of being high in calculation speed and precision and convenient in structural analysis; the method plays a guiding role in synthesis of novel low-thermal-expansion-coefficient polyimide.

The invention discloses a determination method, an application method and a related system of a grain yield prediction model, and relates to the field of grain yield prediction, and the determination method comprises the steps: obtaining time-space dynamic data; preprocessing and normalizing climate data, management data and soil data in the spatial-temporal dynamic data to obtain a spatial-temporal data set; respectively inputting the spatio-temporal data set into a plurality of machine learning models to obtain outputs of the plurality of machine learning models; respectively calculating a root-mean-square error and a decision coefficient of each machine learning model based on grain annual output data corresponding to the spatio-temporal data set and the output of each machine learning model; abandoning the machine learning model with the determination coefficient smaller than a preset value to obtain a screened machine learning model; and based on the screened machine learning model and the corresponding root-mean-square error, constructing a weighted average grain yield prediction model. According to the invention, a scientific basis can be provided for grain production under different climate scenes and management strategies.

The invention discloses an NURBS curve robot trajectory offset point focusing fitting method based on adaptive optimization, which remarkably improves trajectory precision and calculation efficiency through adaptive optimization focused on an error offset point. The method comprises the steps of initial path planning, NURBS curve fitting, error evaluation and offset point extraction, adaptive optimization and real-time feedback adjustment. In error evaluation, double indexes of a root-mean-square error and a maximum absolute error are adopted, an offset point is accurately identified, a control point, a weight and a node vector are focused and optimized by using a genetic algorithm, and local optimum is effectively avoided. A regularization item is introduced into a fitness function, curve complexity and precision are balanced, and smoothness and feasibility of a track are achieved. And finally, real-time adjustment of the trajectory is realized through a multi-feedback mechanism, and the robustness of the robot in a dynamic environment is improved. Compared with the prior art, the precision, the real-time performance and the environmental adaptability of track generation are realized, and particularly, an efficient and accurate track fitting effect is achieved in a complex dynamic environment.

The invention discloses a soil water content inversion method based on improved combination roughness, and relates to the field of remote sensing, and the method comprises the steps: synchronously obtaining a Sentinel-1 radar image and a Sentinel-2 optical image, and extracting different polarization backscattering coefficients, local incident angles and normalized water body indexes through preprocessing; the method comprises the following steps: generating a bare soil simulation backscattering coefficient data set, removing vegetation scattering contribution by using a water cloud model, obtaining a real bare soil backscattering coefficient, constructing a training set and a verification set containing actually measured soil water content, constructing a lookup table based on double models, and calculating the water content of the bare soil by minimizing a root-mean-square error between simulation and the real backscattering coefficient. Global search is carried out in a preset parameter space to determine an optimal earth surface root mean square height and a correlation length, a novel polynomial combination roughness is constructed, a physical correlation between the roughness and a backscattering coefficient is established, a dual-polarization empirical equation set is constructed, and simultaneous solution is carried out after parameters are optimized according to a criterion; according to the method, a more reasonable inversion result of the soil water content in a large range can be obtained.

The invention discloses a battery capacity prediction and state evaluation method and system based on multi-model collaborative learning, and belongs to the technical field of battery management. The method comprises the following steps: constructing a database containing multiple lithium ion battery long-term cycle data, and classifying according to a capacity attenuation trend; cleaning and preprocessing short-term cycle data of the to-be-tested battery; matching the to-be-tested data with the long-term attenuation trend in the database by using a clustering algorithm, and determining an optimal matching trend; distributing weights for the data in the matching trend by adopting a correlation algorithm, and generating initial capacity attenuation prediction; performing sequence correction on the preliminary prediction in combination with meta-learning and a related model, and generating a smooth future attenuation trend conforming to a physical law; and outputting a capacity prediction and health state evaluation result, and evaluating the prediction precision through a root-mean-square error and an average absolute percentage error. The method significantly improves the precision and generalization ability of long-term capacity prediction, and is suitable for various scenes such as electric vehicles, energy storage systems, consumer electronics and the like.

The invention relates to a hydraulic pump motor degradation evaluation method and system based on an LSTM neural network and similarity calculation. The hydraulic pump motor degradation evaluation method comprises the steps that key indexes representing hydraulic pump motor degradation and input parameters influencing hydraulic pump motor working conditions are extracted from test data in different working conditions; selecting and dividing the obtained original test data into a training data set and a test data set; performing normalization and data screening on samples in the training data set to obtain data enhancement samples for model training; samples in the test data set are only subjected to normalization processing and are used for model testing; setting model parameters based on data features, obtaining a volumetric efficiency calculation result, and calculating a root-mean-square error; determining operation conditions of the hydraulic pump motor in the test data, and analyzing change trends of degradation index parameters of the hydraulic pump motor in different time periods; the similarity of the degradation index parameters of the hydraulic pump motor in different time periods is calculated, and the degradation degree is obtained; and respectively multiplying a prediction result of the neural network and a similarity calculation result by a weight coefficient of 0.5 to realize degradation evaluation of the hydraulic pump motor.

The invention relates to the technical field of intelligent agriculture and computer vision, in particular to a small-sample high-density chicken counting framework based on a deep learning Mamba structure, which comprises a feature extraction network, a support-query enhancement module and a decoder. According to the method, a multi-scale feature extraction network based on a residual block (ResNet Block) is introduced, so that local detail information is effectively reserved; and then, a support-query enhancement module is constructed by introducing a Mamba structure, and context interaction between support features and query features is effectively enhanced by utilizing the long sequence modeling capability of linear complexity of the support-query enhancement module, so that the problems of individual overlapping and boundary fuzziness in a high-density chicken flock scene are solved. Experimental results on a PoultryCount real breeding data set show that the mean absolute error (MAE) and the root-mean-square error (RMSE) of the method are reduced compared with those of an existing method, and the chicken counting precision and generalization ability under the condition of a small number of labeled samples are remarkably improved.

The invention discloses a non-contact electrocardiosignal monitoring reconstruction method and system based on time-frequency domain combination, and relates to the technical field of radar signal processing, and the method comprises the following steps: obtaining a chest wall vibration signal S of a to-be-detected person by adopting a frequency-modulated continuous wave radar; separating a heart mechanical motion signal SH from the chest wall vibration signal S; and converting the heart mechanical motion signal SH into an electrocardiosignal ECG through a pre-established ResNet architecture neural network model. Time domain loss and frequency domain loss are used for training a ResNet architecture neural network model, and the error between the calculated heart rate and the real heart rate is quantified through the mean absolute error and the root-mean-square error. According to the method, the neural network capable of being used for ECG signal conversion is designed based on the ResNet architecture, after the neural network is subjected to data training, more detailed signal features can be learned, the fitting accuracy is improved, then the ECG waveform can be accurately predicted, and non-contact electrocardiosignal monitoring and reconstruction are achieved.

The invention discloses an automatic power demand response method based on energy consumption subentry measurement, and the method comprises the steps: obtaining the historical load data of a power grid, carrying out the preprocessing and standardization of the historical load data of the power grid, and constructing a data set. Extracting features with the relevancy with the load value exceeding a first threshold value are selected from the data set through the correlation coefficient, an original feature matrix is constructed based on the extracting features through principal component analysis, a covariance matrix is calculated based on the original feature matrix, and a feature matrix after dimension reduction is obtained. The total square error of different clustering numbers is calculated, the optimal clustering number is determined through an elbow rule, and the clustering center is initialized through a clustering algorithm; and clustering the feature matrix after dimension reduction based on the selected clustering number and the initial clustering center, and updating the iterative clustering center until convergence. And projecting the future feature matrix into the feature space after dimension reduction, calculating the distance from each future feature in the feature space to the center of mass of each cluster, and carrying out distribution to obtain a nearest cluster so as to output a load prediction result.

The invention belongs to the technical field of photoelectric stabilized platform intelligent control, and particularly relates to a photoelectric stabilized platform composite anti-interference method based on improved active-disturbance-rejection control, which realizes multi-band disturbance cooperative suppression through an improved extended state observer (ESO) and non-linear state error feedback (NLSEF) core architecture. For the problems of phase distortion and parameter sensitivity under traditional ADRC high-frequency disturbance, an ALMRBF-ESO observer is constructed by adopting a dynamic regularization constrained radial basis function neural network and an adaptive damping adjustment mechanism, gradient dispersion limitation of a traditional RBF in a high-frequency domain is broken through, and disturbance root-mean-square errors are reduced; by reconstructing a sliding mode gain equation of nonlinear tracking error feedback and implanting a parameter self-correction mechanism, dynamic response of a system is accelerated, overshoot is reduced at the same time, and the parameter drift rate of a controller is stabilized at 0.12. According to the method, on the basis of completely reserving ADRC robustness, the problem of collaborative optimization of ESO broadband disturbance observation precision and NLSEF dynamic tracking performance is solved.

The invention discloses an optimal power flow solving method and device based on a graph neural network and an extreme learning machine, and relates to the field of power grid power flow calculation, and the method comprises the steps: obtaining the node topology of a to-be-solved power system and the historical operation data of each power node, and constructing a system topological graph according to the node topology and the historical operation data; extracting features of a system topological graph based on a multilayer graph convolutional neural network to obtain first node features; taking the minimum total output of the system as a target, screening first node features based on regularization, and fusing the first node features to obtain second node features; performing multiple times of iterative training on the initial extreme learning machine model according to the second node features, optimizing model parameters based on mean square error loss during iteration, completing training until the model parameters meet preset values, and obtaining a first extreme learning machine model; and obtaining real-time operation data of the to-be-solved power system, and obtaining an optimal power flow solving result of the to-be-solved power system according to the real-time operation data and the first extreme learning machine model.

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 discloses a track fusion method and system. The method comprises the following steps: receiving a reported track with a timestamp of a detection target; and estimating the positioning mean square error of each piece of reported track data, and carrying out data fusion on the track according to the estimated positioning mean square error. According to the method, the problem that it is difficult to determine a target track curve with small errors and quantitatively calculate track errors in a traditional multi-base fusion sensing method is solved, and the track accuracy is improved.

The invention provides an abrasive flow polishing device and method for gear tooth surface homogenization. According to the method, tooth-shaped drainage pieces with the same shape as the gear are installed at the two ends of the upper portion and the lower portion of the gear, drainage section design is introduced, an abrasive flow medium path runner model is constructed, main parameters influencing an experiment are sampled and grouped through a Latin hypercube sampling method, abrasive flow polishing simulation is conducted in a grouped mode, and a Gaussian regression process is utilized to obtain an abrasive flow polishing simulation result. Selecting the minimum root-mean-square error of each group of PiVi data, determining the group of experimental parameters, obtaining a group of abrasive flow machining process parameters of optimal inlet pressure, back pressure and drainage section length, and finally guiding the experiment; according to the method, the influence of the inlet effect is relieved, the abrasive flow medium path runner model is constructed, a set of optimal abrasive flow machining process parameter combination is obtained, and the method is suitable for spur gears and bevel gears and high in universality.

The invention discloses a wind power prediction method and system for a region lacking wind measurement data by adopting a BP neural network, and relates to the field of wind power prediction of a wind power plant. The method includes: collecting wind speed, wind direction and other historical feature data and actual wind power of a wind power plant; downloading ERA5 reanalysis data provided by the ECMWF in the wind power plant, interpolating the ERA5 data to each wind turbine generator in the wind power plant by using an inverse distance weight method, and constructing sample data; singular point elimination is carried out on each feature element in the sample data; building a BP neural network model, carrying out normalization processing on the processed sample data, constructing a data set, and grouping and dividing the data set into a training set and a test set; training and outputting the neural network model, and calculating a root-mean-square error output by the model; and outputting the model with the minimum error and testing to obtain the predicted wind power. The method is simple in data acquisition and high in prediction precision, and can provide a reference for predicting the wind power in a region lacking actually measured data.