129 results about "Support vector regression model" patented technology

The invention relates to the technical field of soil detection and multi-source information fusion, in particular to a soil moisture content cooperative detection method and system.The method comprises the steps that a target area is determined, and a target thermal infrared image of surface soil of the target area is obtained; extracting target characteristic parameters related to the moisture content from the target thermal infrared image, inputting the target characteristic parameters into the trained BP neural network, and predicting to obtain a surface soil moisture content distribution diagram; based on the surface soil moisture content distribution diagram, determining a target range region with abnormal moisture content through threshold comparison; after the air coupling stepping radar is driven to be aligned with the thermal infrared imaging system in a space-time mode, scanning is conducted in a target range area, and a radar reflection coefficient extracted from an obtained radar image and phase difference information serve as radar characteristic parameters; and performing modeling analysis on the radar characteristic parameters based on a support vector regression (SVR) model, and obtaining soil profile moisture content distribution of the moisture content abnormal region by constructing a nonlinear mapping relation and optimizing model hyper-parameter inversion.

The invention relates to the technical field of marine ecological environment monitoring and data analysis, and particularly discloses a sea area phytoplankton biodiversity index prediction method and system based on multi-model integration and feature engineering. After preprocessing, constructing four groups of nonlinear interaction characteristics of a temperature-salt relationship, oxygen-salt balance, chlorophyll chemical oxygen demand coupling and a nitrogen-phosphorus ratio based on environmental factors, combining station characteristics with basic environment and interaction characteristics, carrying out variance threshold screening, inputting a characteristic set into a multi-model integration framework containing models such as linear regression and gradient lifting, and carrying out multi-model integration; training and tuning according to a time sequence segmentation strategy, selecting model output according to a decision coefficient, using a result if the decision coefficient of the support vector regression model is within a preset range, and otherwise, taking a gradient lifting and extreme gradient lifting tree model to predict a mean value. And the prediction accuracy and the model generalization, stability and reliability are improved.

The invention relates to the technical field of artificial intelligence, in particular to an injection molding process parameter optimization method and system based on hybrid algorithm and model fusion, and the method comprises the steps: optimizing a parameter combination of a support vector regression model through a simulated annealing algorithm, building a weighted fusion model based on the optimized support vector regression model and a random forest, and optimizing the model; constructing a hybrid model of an adaptive selection weighted fusion model and an optimized support vector regression model; constructing a three-objective optimization model including buckling deformation, volume shrinkage and production energy consumption, and searching a Pareto optimal solution set in a process parameter space by adopting a multi-objective genetic algorithm by taking the hybrid model as a target value evaluation tool; carrying out local correction on the key process parameters by adopting a gradient descent method until the deviation falls back to be within a preset threshold value, and obtaining optimized process parameters; the defect rate of products can be reduced, and meanwhile production energy consumption is reduced.

The invention relates to the technical field of laser spectrum gas detection, and particularly discloses a TDLAS (tunable diode laser absorption spectroscopy) double-gas concentration synchronous calibration and decoupling method in the presence of spectrum aliasing interference, and the method comprises the following steps: acquiring a multi-band harmonic signal subjected to adaptive optimization while acquiring environmental parameters; an optimal frequency band signal is determined based on the signal quality index; and calculating a spectral aliasing coefficient according to the characteristics of the optimal frequency band signal, dynamically adjusting operation parameters of a search algorithm based on a numerical value interval in which the spectral aliasing coefficient is located, and determining an initial range of dual-gas concentration in a search space. According to the method, the improved genetic algorithm based on the spectral aliasing coefficient dynamic adjustment search strategy is combined with the incremental support vector regression model, stage processing from coarse calibration to fine decoupling is achieved, and the calculation efficiency and the search precision can be automatically balanced when the spectral aliasing degree changes; and online correction is carried out on the model in combination with aging characteristics.

The invention discloses an agricultural load prediction method and system based on multivariate time sequence decoupling multi-modal learning, and belongs to the technical field of agricultural load prediction. Comprising the steps of collecting historical agricultural load and meteorological data; decomposing historical agricultural load and meteorological data by using multivariate variational mode decomposition to obtain cycle, trend and residual mode components; and respectively constructing a time convolutional neural network, a bidirectional gating cycle unit and a support vector regression model for the decomposed period, trend and residual modal component data set, and fully mining feature information of each mode after decomposition, thereby realizing accurate prediction of agricultural load. According to the method, the potential nonlinear space-time coupling relationship between the agricultural load and the meteorological factor is captured, the prediction effect in a seasonal periodic fluctuation scene of the agricultural load and a long-term trend and agricultural load abnormal scene is improved, the agricultural load prediction precision is improved, and a support is provided for reliable and stable operation of a power grid.

The invention relates to the technical field of disease course trend modeling, in particular to a subarachnoid hemorrhage disease course trend modeling system fusing multi-source data. Four kinds of signals of intracranial pressure, blood flow velocity, cerebrospinal fluid pressure and electroencephalogram of a monitored object are synchronously collected, an instantaneous phase is extracted through Hilbert transform, a time window is adaptively adjusted according to the brain blood vessel conduction delay characteristic of an individual, and phase locking indexes among three pairs of signals are calculated. A multivariate coupled oscillator model is established, phase track topology invariant features are extracted, and comprehensive trend indexes are generated through tensor fusion. An individualized four-dimensional phase entropy baseline mode is established, and a double-layer early warning mechanism is adopted: when second derivative continuous symbol overturning occurs in all three phase locking indexes, early warning is directly performed, and when any two phase locking indexes are overturned, a trend index needs to be synthesized for confirmation. And predicting a state level, a trend level and an expected evolution trajectory based on a support vector regression model. According to the invention, precise disease course prediction and early warning are realized, and a basis is provided for clinical decision making.

The invention relates to a cable insulation degradation degree evaluation method based on frequency domain characteristic analysis, and belongs to the field of cable insulation degradation detection.The cable insulation degradation degree evaluation method comprises the steps that cable operation environment data and laboratory accelerated aging data are collected, and a low-frequency impedance characteristic data set with water tree growth stage labels is generated; extracting a dielectric response spectrogram and analyzing a relaxation polarization peak value; when the peak value exceeds a threshold value, extracting harmonic component characteristics and loss factor distribution characteristics by using wavelet transform and principal component analysis; constructing a support vector regression model to predict the length, density and growth stage of the water tree; for middle and later water trees, microstructure evolution characteristic parameters are calculated, and insulation penetration time is predicted; the degradation probability is analyzed through Monte Carlo simulation, and a risk distribution curve is generated; and optimizing the cable replacement time by adopting dynamic planning, and outputting an evaluation report for quantifying the degradation degree and maintenance suggestions. According to the invention, accurate evaluation and prediction of cable insulation degradation are realized, and an important guarantee is provided for safe operation of a power system.

The invention provides a cattle and sheep feed trough residual feed monitoring method based on vision, and relates to the technical field of intelligent livestock breeding, and the method comprises the following six steps: intelligent triggering and multi-modal image acquisition, image preprocessing and fusion, time sequence image segmentation and feature extraction, density adaptive volume calculation, online learning and residual feed estimation, and decision analysis and early warning. According to the method, RGB and near-infrared images are synchronously collected through infrared triggering, after perspective correction and illumination adaptive fusion, an LSTM-U-Net time sequence segmentation model is adopted to solve the dynamic shielding problem, a residual feed area is accurately extracted, the feed type is recognized, density adaptive volume measurement is achieved in combination with monocular depth estimation and texture feature analysis, and the method is suitable for large-scale industrial production. Weight estimation is carried out through a support vector regression model, model parameters are optimized on line based on manual correction data, intelligent early warning of the residual material amount is finally achieved through a cloud platform, and the limitation of a traditional method in the aspects of shielding processing, density adaptation and environment anti-interference is effectively overcome.

The present application pertains to the field of data processing technology, specifically relates to a method, apparatus and device for optimizing a process parameter and a storage medium, which includes: using a first weight parameter of a pre-trained support vector regression model for a manufacturing equipment as an iterative initial value, and calculating a second weight parameter of a support vector regression model using an online proximal gradient algorithm based on training data; obtaining an optimized support vector regression model by updating the first weight parameter of the pre-trained support vector regression model to the second weight parameter; inputting a first process parameter of the manufacturing equipment into the optimized support vector regression model to obtain a first detection parameter output by the optimized support vector regression model; and calculating, according to the first process parameter and the first detection parameter, a target process parameter for the manufacturing equipment.

The invention relates to the technical field of industrial control, in particular to an error compensation method for numerical control machining of an automobile stamping die, which comprises the following steps of: acquiring temperature of a plurality of thermo-sensitive points of a numerical control machine tool and thermal displacement data of a tool nose; the effective thermal driving potential is calculated by considering the time lag effect of the temperature level and the historical temperature change; calculating a thermal phase direction gradient in combination with the temperature difference value and the nonlinear gain; combining the effective thermal driving potential and the thermal phase direction gradient into a feature vector to train a support vector regression model; and inputting the feature vector in real time to obtain a compensation value, and superposing the compensation value to the coordinate instruction by dynamic origin offset. The method can accurately reflect the thermal inertia, capture the dynamic evolution of the thermal state, comprehensively represent the thermal state to improve the adaptability of the model, and correct the thermal error in real time, thereby improving the machining precision and surface quality of the automobile stamping die.

The invention relates to the technical field of coal processing and utilization, and discloses a clean coal yield prediction method based on a support vector machine, which comprises a data acquisition module used for analyzing factors influencing the clean coal yield, acquiring related data and integrating the data into a data set, and a data preprocessing module connected with the data acquisition module and used for preprocessing the data. The data preprocessing module is used for randomly dividing a data set according to a 70% training set and a 30% test set and carrying out standardization and normalization preprocessing, the parameter optimization module is connected with the data preprocessing module and optimizes hyper-parameters of a support vector machine through an improved grey wolf algorithm, and the model building module is connected with the parameter optimization module and is used for building a model. The model building module is used for building and training a support vector regression model based on the optimized hyper-parameters, and the model verification module is connected with the model building module and uses a test set to verify the performance of the model. According to the method, the hyper-parameters of the support vector machine are optimized through the improved grey wolf algorithm, the problem that a traditional optimization method is prone to falling into local optimum is effectively avoided, and the precision of clean coal yield prediction is remarkably improved.

The invention relates to the technical field of building material performance detection, in particular to a concrete coarse aggregate corner angle measurement system based on digital image processing, which comprises a multi-view image acquisition module, a three-dimensional point cloud reconstruction module, a corner angle feature vector extraction module and a corner angle quantitative calculation module. A multi-view imaging system is constructed through a rotary objective table and a binocular camera, surface images of aggregate at different angles are obtained, and high-precision three-dimensional point cloud data are generated in combination with stereo matching and a point cloud dense reconstruction technology. And further constructing an edge feature vector based on the volume compression ratio and the surface roughness, inputting a pre-trained support vector regression model, outputting a normalized edge index, and realizing objective quantitative evaluation of the coarse aggregate edge. The concrete aggregate quality grading and screening device has the advantages of being high in precision, high in automation, wide in adaptability and the like and is suitable for quality grading and screening of concrete aggregates.

The invention relates to the technical field of yaw strategy optimization, in particular to a wind power plant yaw strategy rolling optimization method based on model predictive control. The method comprises the following steps: acquiring real-time and historical wind power plant operation state data of each fan in a wind power plant at a current control moment, and preprocessing the wind power plant operation state data to form a state vector; performing short-term prediction on the wind direction and the wind speed in a future prediction time domain by using a support vector regression model based on the state vector, and generating a wind field evolution prediction sequence for control decision; and coupling the wind field evolution prediction sequence with the yaw angle variable, and constructing a wind power plant power prediction model in a prediction time domain. According to the method, the refined power prediction model integrating wind field short-term prediction, the fan space topological relation and yaw angle variable coupling is constructed, and the influence of yaw operation on the wake flow propagation path, the speed loss and the downstream fan inflow condition can be accurately described.

The application discloses a kind of based on electrochemical impedance spectroscopy battery health state prediction method, this method is by establishing convolutional neural network to eliminate the influence of temperature on prediction result, and by improving particle swarm optimization algorithm to three kernel support vector regression SVR model hyperparameter combination and output weight are optimized, so that the optimized prediction model can guarantee the prediction accuracy, while, the running efficiency and applicability of model are considered, better satisfy the multidimensional requirement of nickel / lithium ion battery state prediction in practical application;The present application makes full use of the rich information of electrochemical impedance spectroscopy data, solves the problem of feature extraction under high-dimensional characteristics, the present application provides a new technical approach for the accurate prediction of nickel / lithium ion battery health state, helps to improve the performance of battery management system, promotes the application and development of battery technology in elevator energy saving and other new energy fields.

The invention discloses a direct-current heat pump system control method based on bus voltage, and particularly relates to the field of direct-current heat pump operation control and power electronic energy management. Collecting a transient change signal of the bus voltage in real time, extracting an amplitude disturbance quantity, a phase offset rate and a zero crossing density, and constructing a bus voltage disturbance feature vector sequence; inputting the feature vector into a support vector regression model for time sequence trend fitting, and generating a bus voltage prediction curve in a future short-time window; according to the local slope extreme point and the trend change rate of the prediction curve, identifying a potential load jump precursor event; constructing a dynamic response regulation and control model, inputting a load prediction result into the model, generating a pre-response control instruction set, and issuing the pre-response control instruction set to an actuator in advance; and monitoring the adjusted bus voltage response, carrying out difference analysis on the adjusted bus voltage response and a prediction curve, and dynamically adjusting parameters of the support vector regression model according to an error trend to realize online self-learning optimization.

The invention provides a radar wave intensity measurement method and system based on multistage filtering amplification, and the method comprises the steps: carrying out the preprocessing of a radar echo signal, and obtaining a preprocessing signal through time domain wavelet packet decomposition and spatial domain wave beam forming collaborative optimization; performing intelligent dynamic gain reconstruction on the preprocessed signal to obtain a gain adjustment signal; performing nonlinear harmonic tracking compensation on the gain adjustment signal to obtain a compensation calibration signal; and performing dynamic threshold intensity extraction on the compensation calibration signal to obtain a final measurement result. According to the method, an environment factor-attenuation compensation mapping relation is established through a support vector regression model, and a dynamic environment compensation coefficient is generated; in combination with wavelet packet multi-scale energy integration and signal-to-noise ratio dynamic weighting, space attenuation is corrected in nonlinear harmonic tracking compensation, and an adaptive threshold and energy estimation are optimized in dynamic threshold intensity extraction; and finally, through Kalman filtering post-processing and spectrum purity monitoring closed-loop verification, the system can perform high-precision measurement in a dynamic environment.

The invention belongs to the technical field of marine environment monitoring and artificial intelligence, and discloses a method for intelligently monitoring coastal water eutrophication. The method comprises the following steps: acquiring and matching spectrum, space and time parameters of a remote sensing image and actually measured data; constructing a multi-modal feature data set; a hybrid prediction model formed by cascading a Transform network and a support vector regression model is constructed and trained, and the Transform network performs unified modeling and deeply fuses multi-source features such as spectrum, space and time through a self-attention mechanism of the Transform network, so that deep intelligent fusion of the multi-source features is realized; performing water quality index prediction and eutrophication state evaluation by using the trained model; and finally, carrying out uncertainty quantization on a prediction result by adopting a Monte Carlo random inactivation technology. According to the invention, the problems of low precision and weak generalization caused by spatial heterogeneity and complex optical characteristics in coastal water monitoring are effectively solved.

The invention provides a plate vulcanizing machine energy consumption prediction method based on DBO-SVR, which comprises the following steps: S1, data acquisition: collecting real-time energy consumption and key parameters in a production process according to the energy consumption characteristics of a plate vulcanizing machine, and constructing a data set; s2, data processing: dividing a data set, removing abnormal values, and filling missing values; s3, constructing an SVR model, and initializing parameters; s4, DBO: updating penalty parameters and kernel function parameters of the support vector regression model by using the DBO; and S5, training and model evaluation: training the model by using the optimal parameters, and predicting the energy consumption of the press vulcanizer by using the optimized model. According to the scheme provided by the invention, core parameters of the support vector machine are optimized by adopting a dung beetle optimization algorithm, the accuracy of model energy consumption prediction is effectively improved, and a reference can be provided for energy-saving optimization of the press vulcanizer through energy consumption prediction of the press vulcanizer.

The invention discloses a method, a system, equipment and a medium suitable for rapid load transfer of a distribution network line of a transformer substation, and belongs to the technical field of the power industry, and the method comprises the steps: determining a target transformer substation, and obtaining all load transfer paths; collecting real-time load transfer data of a target substation, performing prediction by using the trained support vector regression model, and identifying a potential optimal load transfer path; determining initial positions of particles in a particle swarm optimization algorithm according to the potential optimal load transfer path and a random initialization strategy; executing a particle swarm optimization algorithm, and searching an optimal solution in all the load transfer paths; and taking the optimal solution as an optimal load transfer path, and implementing load transfer measures in an actual power system. According to the method, the rapid load transfer path of the distribution network line of the transformer substation is accurately and efficiently determined, the scientificity and accuracy of decision making are improved, it is ensured that the selected scheme can achieve effective load transfer within the shortest time, and the operation efficiency of a whole power system is improved.

The invention provides a terahertz metamaterial resonance enhanced trace thiabendazole detection method and system, and the method comprises the steps: constructing parameter information of a metamaterial sensor based on a single periodic structure and the characteristic peak frequency of thiabendazole, and carrying out modeling simulation according to the parameter information to obtain a terahertz metamaterial with an L-shaped composite double-peak structure; preparing a thiabendazole sample, and performing spectrum acquisition on the thiabendazole sample by using a terahertz metamaterial to obtain corresponding spectrum data; constructing a support vector regression model, fusing a weighted vector average optimization algorithm, and performing hyper-parameter global optimization to construct a corresponding optimization model; and constructing a corresponding detection model according to the spectral data and the optimization model, and realizing nondestructive detection of trace thiabendazole by using the detection model. According to the method, high-sensitivity quantitative analysis on trace amount of thiabendazole residues is realized, and the feasibility and superiority of the INFO optimization algorithm in the field of spectrum detection are also verified.

Aiming at the problem that high-sensitivity and high-accuracy quantitative analysis is difficult to realize when a traditional detection method is used for a complex multi-component mixture, the invention discloses a terahertz spectrum quantitative analysis method for a rubber mixture. Based on a terahertz spectrum technology and in combination with an improved rhodeus ocellatus optimization algorithm (IFBO), accurate detection of the content of two trace anti-aging agents (NBC and 44S) in a five-component rubber mixture is realized. Through systematic experimental design and spectral analysis, differentiation characteristics of different anti-aging agent proportions in a time domain and an absorbance spectrum are determined, and SG preprocessing, PCA and SPXY data set division methods are utilized, so that the signal-to-noise ratio of spectral data and the model generalization ability are effectively improved. A support vector regression (SVR) model is introduced according to small-sample and high-dimensional nonlinear spectral data characteristics, and the significant advantages of IFBO in parameter optimization are verified by comparing the optimization effects of GA, PSO and FBO. Experimental results show that the correlation coefficient (Rp) of the IFBO-SVR model on a prediction set reaches 0.9879, the root mean square error (RMSEP) is reduced to 0.0024, and compared with a traditional algorithm, the method has higher accuracy and stability. The invention not only provides an efficient technical scheme for rapid detection of trace anti-aging agents in complex matrixes, but also lays a theoretical foundation and basis for quality control of rubber products and environmental safety assessment.

This invention provides a method for predicting aging based on metabolite profiles using nonlocal variance enhancement, relating to the fields of medical data analysis and metabolomics. The method acquires LC-MS and / or GC-MS metabolite profile data and the actual age of the subject to be predicted. It performs missing data checks and intra-class normalization on the data, constructs a class-constrained nonlocal variance enhancement feature extraction model, and combines multi-kernel principal component analysis and linear multi-view fusion to obtain fused metabolite profile features. The fused features are then input into a support vector regression model to output predicted age values. The aging degree is determined based on the difference between the predicted and actual age values, which improves the feature expression ability and interpretability of metabolite profile age prediction.

The application discloses a display screen management system and a management control method thereof, and relates to the technical field of intelligent display, which comprises the following steps: starting display screen cluster management software and activating environment sensors to collect environment data; adjusting and evaluating display parameters based on the environment data by using a support vector regression (SVR) model, monitoring user behaviors, obtaining and analyzing behavior data; determining energy consumption management strategies by using the analyzed behavior data, adjusting display parameters and energy consumption; recording the adjusted parameters, entering remote management settings; based on the remote management settings, users will adjust personalized settings through the remote management settings, and update display parameters. Through the collaborative work of the intelligent management software and the environment sensors, the application realizes the automatic adjustment of display screen parameters, dynamically adjusts energy consumption strategies according to the analysis results, realizes energy saving and emission reduction while ensuring display quality, and ensures that the best display effect can be obtained under various environmental conditions.

The application discloses a lithium battery residual service life prediction method based on a fusion data-driven model, and the method comprises the following steps: extracting the constant current charging duration, the constant voltage charging duration, the vertical slope at the corner of the constant current charging curve and the vertical slope at the corner of the constant current discharging curve as health characteristics; taking the four health characteristics as inputs and corresponding battery capacities as outputs to train a chaos sparrow-extreme learning machine model and a least square support vector regression model; obtaining the battery capacity prediction values by using the two trained models respectively, and performing weighted fusion to obtain the final lithium battery capacity prediction value; and finally obtaining the residual service life of the lithium battery in combination with the lithium battery capacity curve. The CSSA-ELM-LSSVR fusion algorithm can fully utilize the CSSA-ELM to extract the overall trend of the lithium battery degradation process, and utilize the LSSVR to obtain the local nonlinear characteristics, so that the accurate prediction of the residual service life of the lithium battery is realized, and the good robustness is also achieved.

This invention discloses a multimodal interactive interface design method for elderly care robots, relating to the field of intelligent elderly care. The method includes: collecting user facial images, voice signals, and touch operation information to form a raw multimodal data stream; processing the raw multimodal data stream to extract standardized user real-time state feature vectors; inputting the user real-time state feature vectors and historical interaction data into a support vector regression model; obtaining the real-time cognitive load level based on the user's real-time state feature vectors in the current session; and analyzing the rate of change of user touch operation accuracy within a time window using historical interaction data, calculating the acceleration value of capability change and the trend of touch operation accuracy, and fusing them to derive the interaction abstraction level. This invention constructs a joint decision matrix sensitive to the acceleration of capability change, dynamically triggering an adaptive interaction strategy when accelerated capability decline is detected, driving the interface engine to adjust interface rendering and multimodal guidance in real time.

The invention belongs to the field of marine acoustics, and discloses a wind speed-based multi-stage modeling regression marine environmental noise model modeling method, which comprises the following steps of: firstly, acquiring and matching marine noise and wind speed data, and extracting a noise power spectrum; fitting the noise spectrum into gamma distribution, and extracting shape and scale parameters as physical statistical characteristics; in the first stage, Gaussian process regression is utilized to establish mapping from wind speed to distribution parameters; in the second stage, the distribution parameters and the wind speed serve as input, a mixed kernel support vector regression model fusing a Wenz empirical physical kernel and an RBF data kernel is adopted, and a full-band noise spectrum is predicted. According to the method, a complex prediction problem is decomposed through a framework of'physical statistical feature guidance ', physical priori and data-driven learning are effectively fused, prediction precision, generalization ability and robustness of the model under the condition of data scarcity are remarkably improved, and a high-precision noise prediction tool is provided for applications such as ocean wind field monitoring and the like.

The invention discloses a coal pillar high-temperature spontaneous combustion fire source positioning inversion method, system and device and a medium, and relates to the technical field of coal mine safety. The method comprises the steps that temperature sensors are arranged on the surface of a coal pillar, coordinates and temperature data of limited monitoring points are collected, and a sample set is constructed; using a support vector regression model to learn a mapping relation between coordinates and temperature; generating a grid based on the geometric boundary of the coal pillar, and inverting a global temperature field through the trained SVR model; the highest-temperature area in the temperature field is extracted after cutting, and the accurate position of a fire source is determined; according to the method, on the premise that the arrangement number of the temperature detectors is reduced and the cost is controlled, positioning and monitoring of the high-temperature fire source in the coal pillar can be accurately achieved, and reliable technical support is provided for coal pillar fire prevention and control.

The application discloses a kind of hollow degree and grain fullness of pepper multi-dimensional acoustic online monitoring method and system, it is related to agricultural product quality detection technical field, method includes: by reticular conveyor belt makes the uniform speed of the pepper monomer to be measured in sound insulation shielding channel movement;Utilize air coupling ultrasonic emission probe to the pepper monomer to be measured directional emission ultrasonic wave, utilize transmission wave receiving probe to capture transmission wave after penetrating the pepper, simultaneously utilize micro sound emission sensor to capture the micro sound emission signal of the pepper;Extract the cavity resonance frequency offset of transmission wave and sound energy attenuation coefficient, carry out band energy extraction to the micro sound emission signal to calculate sound energy entropy;After multi-dimensional acoustic characteristics are standardized respectively, as multi-dimensional feature vector, input into the support vector regression model that is trained in advance, output the hollow and fullness comprehensive evaluation index of the pepper monomer to be measured.This scheme can reduce detection cost, no radiation and can accurately penetrate epidermis to obtain internal grain real state.

The invention discloses a weight calculation method, device and equipment for cigarette cut stems and a storage medium, and relates to the technical field of tobaccos, and the method realizes automatic estimation of the weight of the cigarette cut stems through device image processing and data modeling. Wherein in the step S1, an original image is preprocessed through Gaussian filtering noise reduction, and the image quality basis of subsequent analysis is improved; s2, extracting continuous contours of the cut stems by means of maximum between-class variance method segmentation and edge detection, and defining morphological boundaries; s3, calculating the area of a single piece through a pixel counting method, accumulating the area to obtain the total area, and quantifying the visual features; s4, inputting the total area into a pre-training support vector regression model, obtaining initial weight estimation through nonlinear mapping, and establishing association between the area and the weight; s5, performing normalization processing on the initial estimation, and unifying the data scale; and S6, optimizing the estimation stability by using Kalman filtering dynamic estimation and noise filtering, and finally outputting a weight calculation result. Manual intervention is reduced, and the accuracy and consistency of estimation are improved.