65 results about "Dimensionality reduction algorithm" patented technology

The invention relates to the technical field of textile detection, in particular to a computer-aided textile color fastness prediction method, which comprises the following steps: acquiring multi-source data before and after a textile color fastness test through computer control equipment, including digital images, reflection spectrum data, process parameters and dye characteristic component data, carrying out pretreatment; extracting color difference features and spectrum similarity features, and screening by adopting a dimension reduction algorithm to form a core feature vector; a machine learning algorithm is adopted to construct a color fastness prediction model, parameters are optimized in combination with cross validation and an early stop mechanism, model fine tuning is executed, and a final optimization model is obtained; and outputting a color fastness grade prediction result, and carrying out spectrum similarity secondary verification. According to the method, objective, efficient and high-precision prediction of the color fastness grade of the textile is realized, and the problems of high subjectivity and low efficiency of traditional manual evaluation are solved.

The invention discloses a hydraulic motor abrasion monitoring method and system, and belongs to the technical field of abrasion fault prediction.The method specifically comprises the steps that multi-dimensional data signals of a hydraulic motor are collected in real time and preprocessed; the preprocessed multi-dimensional data of the hydraulic motor are processed, the early wear characteristics of the hydraulic motor are extracted, and the processing comprises decomposition of the preprocessed multi-dimensional data of the hydraulic motor, construction of a decomposition tree, reservation of nodes with preset frequency in the decomposition tree, and reconstruction, difference calculation and window moving average suppression of the reserved nodes; fusing the early wear characteristics of the hydraulic motor, and generating a comprehensive health index through a dimension reduction algorithm; judging whether the hydraulic motor has early-stage fine wear or not according to the comparison of the comprehensive health index and a dynamic threshold value, and giving an alarm according to a judgment result; according to the method, early-stage fine wear signs of the hydraulic motor can be found, a maintenance plan is made in advance, and production interruption caused by sudden equipment failures is avoided.

The invention provides a power battery feature extraction method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining the operation data of a power battery, including time-varying electrical data and thermal data; according to the operation data, multi-modal features at multiple time points are extracted, a multi-modal feature matrix is obtained, and the multi-modal features comprise electrical features and thermal features; analyzing time sequence correlation among different features in the multi-modal feature matrix, constructing a time sequence correlation matrix, performing feature decomposition on the time sequence correlation matrix, and constructing a dimensionality reduction projection matrix; and performing dimension reduction processing on the multi-modal feature matrix according to the dimension reduction projection matrix to obtain a target feature matrix, and completing feature extraction. Through collaborative design of multi-modal feature extraction and a dimension reduction algorithm based on time sequence perception, the precision and efficiency of power battery feature extraction are effectively improved.

The invention discloses a hydrogen fuel cell life prediction method and system, and relates to the field of new energy, and the method comprises the steps: compressing a feature dimension through employing a dimension reduction algorithm, and obtaining a feature vector sequence reflecting the degradation state of a hydrogen fuel cell; constructing a state-of-health index reflecting the performance degradation degree of the hydrogen fuel cell, and constructing a state-of-health curve in combination with the time sequence; based on the change trend of the health state curve, dividing the life process of the hydrogen fuel cell into an early-stage stable stage, a middle-stage slow degradation stage and a later-stage rapid degradation stage by adopting a segmented modeling algorithm; based on the health state index and the feature vector sequence, constructing a time sequence prediction model, and predicting the residual service life of the fuel hydrogen fuel cell; and monitoring a sudden change behavior of the health state curve, and identifying an unexpected degradation condition. According to the method, feature extraction and dimension reduction processing are carried out on the operation data of the hydrogen fuel cell, the health state index and curve are constructed, and the prediction precision of the remaining service life is remarkably improved.

The invention relates to the technical field of computer vision, in particular to a fabric color fastness analysis method and system based on image processing. Comprising the following steps: acquiring a multispectral image of a to-be-detected fabric, and synchronously acquiring multi-factor data; processing the multispectral image to generate an enhanced image; performing image recognition on the gray sample card in the enhanced image to generate a calibration image; performing spectral data projection on the calibration image through a dimension reduction algorithm, and performing color analysis and quantification on color change to generate color difference features; extracting texture features by using a conditional generative adversarial network, carrying out image recognition and calculation on the calibration image through a decoupling algorithm, and generating a region credibility graph; and inputting the chromatic aberration features, the texture features, the multi-factor data and the regional credibility map into an image feature fusion model for mapping, performing analysis through gradient visualization, and outputting an abnormal feature saliency map. According to the method, a color fastness objective analysis closed loop is created, so that the accuracy and the universality of an analysis result are improved.

The invention discloses a cement-based material phase identification and quantitative analysis method combining artificial intelligence and expert knowledge. The method comprises the following steps: acquiring a BSE image, a qualitative element surface spectrum and a quantitative element surface spectrum of a cement-based cementing material sample; synthesizing a color EDS image based on the qualitative element surface spectrum, and performing superpixel division on the image; based on the quantitative element surface spectrum, extracting an average value of element relative contents of all pixels in each superpixel range as an element feature; performing two-dimensional visualization processing on all the element features by adopting a PHATE dimension reduction algorithm to generate derivative element features; performing manual correction and phase label labeling on a clustering result by utilizing a Glue multi-view interaction platform in combination with expert priori knowledge; and fusing the corrected clustering result with the superpixel division result, and regenerating a new phase classification mask with a phase identifier. According to the method, intelligent identification and high-precision analysis of the cement-based material multiphase system can be realized, and the accuracy, reliability and interpretability of phase classification are improved.

A system, method, and computer-program product includes receiving a plurality of vector embeddings having an initial dimensionality and projecting the plurality of vector embeddings into lower-dimensional spaces using at least two different dimension reduction algorithms to generate corresponding sets of projected vector embeddings. Each set of projected embeddings may be quantized and nearest neighbors for the original embeddings and for each quantized set of projected embeddings may be calculated. Additionally, a neighbor preservation metric may be evaluated for each quantized set by comparing its nearest neighbors to those of the original embeddings. Based on the neighbor preservation metrics and a predefined error tolerance, an optimal compression configuration may be selected.

The invention discloses a sea surface small target detection method and system based on a magnetic KNN and an optimized concave packet, and belongs to the technical field of radar signal processing. Comprising the following steps: extracting high-dimensional features of sea clutters and echoes to be detected, and selecting three-dimensional feature points of the clutters and the echoes to be detected according to a dimension reduction algorithm; processing the three-dimensional feature points of the clutters by adopting a constant false alarm algorithm to obtain clutter feature points after constant false alarm; on the basis of the clutter feature points after the constant false alarm, constructing a concave packet by adopting an optimized concave packet construction algorithm; the clutter feature points in the concave packet serve as vertexes of a tetrahedron, and the concave packet is divided into tetrahedral grids; positioning the points in the tetrahedrons by using a pointLocation function, judging the indexes of the tetrahedrons where the points are located, and if the indexes exist, judging that the points are clutters; and if the index does not exist, judging as a target. According to the detection method, the construction efficiency of the concave packet is also improved while the relatively high detection probability is ensured, and the method has relatively good generalization.

This invention relates to the field of wind power generation technology and discloses a method, device, equipment, and medium for fault assessment of wind turbine transmission systems. The method utilizes a data dimensionality reduction algorithm to reduce the dimensionality of multidimensional raw data, retaining core distinguishing features and simplifying calculations. Then, a data clustering algorithm is used to intelligently classify operating states, and a preliminary fault mode mapping is constructed by combining historical faults. Subsequently, the rationality of clustering is verified using signal source correlation coefficients, and long-term historical operating data is filtered through historical data matching rates to reduce the risk of misjudgment. Next, based on time series analysis and degradation path analysis, the coupling relationship between vibration trend slope, temperature accumulation offset, and torque decay cycle is obtained. Finally, core features are extracted through convolutional neural networks to accurately output the probability distribution and specific location of fault occurrence, thereby improving the accuracy of fault identification in the transmission system of offshore wind turbines and the ability to predict component performance degradation.

The invention discloses an identity verification method based on touch sliding gesture features in combination with AI and biological feature recognition technologies. According to the technology, multi-dimensional features of user touch behaviors are extracted through feature engineering, a full-connection neural network is innovatively adopted to deeply mine a data relationship, and a manifold learning theory and multiple dimension reduction algorithms are applied to optimize a data processing flow. In addition, a variational auto-encoder (VAE) is introduced to generate non-owner user data, so that the classification precision and generalization ability of the model are effectively enhanced. Compared with a traditional biological recognition technology (such as fingerprints, faces and irises) and behavior biological feature recognition (such as gaits and keyboard tapping rhythms), the method does not need additional hardware, is higher in privacy protection and is suitable for existing intelligent equipment. Through deep algorithm research and software testing, the system obtains a certain achievement, and an innovative scheme is provided for identity safety verification of intelligent equipment.

The invention discloses a data analysis method and system based on questionnaire investigation, and particularly relates to the technical field of data analysis, and the method comprises the following steps: S1, collecting and preprocessing questionnaire data, and obtaining original questionnaire data; s2, questionnaire feature engineering: extracting core features from the standardized questionnaire data set based on a joint screening mechanism of semantic similarity and information gain, and obtaining low-dimensional feature vectors through a dimension reduction algorithm; s3, subject mining and user group division are carried out on the low-dimensional feature vectors; and S4, outputting a result based on the analysis model, and generating a targeted questionnaire analysis report which comprises topic distribution, group difference and potential demand association conclusions. According to the data analysis method and system based on questionnaire investigation, through a multi-strategy fusion preprocessing mechanism, a combined screening feature engineering method, a fusion modeling analysis strategy and a self-adaptive model optimization module, the accuracy and efficiency of questionnaire data analysis are improved.

This invention relates to a big data topic analysis method based on an embedding model. First, the Sentence-BERT model is used to perform sentence embedding representation on preprocessed Chinese text data. Then, the UMAP projection dimensionality reduction algorithm is used to reduce the dimensionality of the embedded vectors. Next, the HDBSCAN clustering algorithm is used to cluster the dimensionality-reduced vectors. Based on the assignment of each Chinese text in the target Chinese dataset to a corresponding topic class, the Chinese words with the highest c-TF-IDF scores are selected to represent each topic class. Finally, the DSG model is used to perform word embedding representation on the topic words, calculating the similarity between different topic words and between different topic classes, thereby detecting the volatility of newly emerging topic classes. The entire scheme design has higher topic consistency and topic diversity, and can detect new hot topics in a timely and accurate manner, providing early warnings.

The invention discloses a multi-label smell description prediction method, and relates to the field of compound smell prediction, and the method comprises the steps: obtaining compound identification information, molecular structure descriptors and smell label data, and constructing a multi-label smell data set; generating a molecular structure feature vector through a molecular fingerprint coding technology, and extracting a multi-dimensional descriptor reflecting the physicochemical properties of molecules; compressing the molecular fingerprint features to a low-dimensional space through a dimension reduction algorithm; performing unbalanced data processing on the training set, fusing the dimension-reduced molecular fingerprints with the molecular descriptors to form a joint feature matrix, and configuring a class weight balance mechanism and overfitting suppression parameters by adopting a multi-label classification architecture; independently optimizing a probability threshold for each odor label based on the verification set; and outputting a multi-odor label combination prediction result according to the target molecule identification information. According to the scheme, the multi-odor characteristics of the compound can be accurately depicted, and the combined recognition accuracy of the compound odor is remarkably improved.

A system and method of determining efficacy of treatment by at least one processor may include receiving, from at least one camera, images depicting motion of an animal that may be treated with a predetermined substance of interest. Said processor may extract from the images, a plurality of motion features representing motion of at least one specific body part of the animal, and apply a dimensionality reduction algorithm on the plurality of motion features, to obtain a latent vector representing the plurality of motion features in a latent space. The latent vector may include a plurality of latent features. Said processor may subsequently calculate a value of a behavioral indicator, representing a behavior of the animal, based on the latent features of the latent vector, and determine efficacy of the treatment based on the behavioral indicator value.

A large language model-driven unit combination optimization method, relating to the field of power system technology, includes: acquiring basic economic operation data of the power system; constructing and solving a unit combination model with integer relaxed security constraints based on the basic economic operation data; obtaining and solving the unit combination model; constructing variable dimensionality reduction algorithms, constraint dimensionality reduction algorithms, and infeasibility solution repair algorithms; using a large language model to perform single-algorithm evolution on the variable and constraint dimensionality reduction algorithms; using a large language model to perform single-algorithm evolution on the infeasibility solution repair algorithm; solving and calculating the unit combination model with integer relaxed security constraints; calculating the variable constraint dimensionality reduction security constraint unit combination model and obtaining the optimal solution; and using the optimal infeasibility solution repair algorithm to repair the optimal solution of the variable constraint dimensionality reduction security constraint unit combination model, obtaining the result. This method addresses the shortcomings of existing model dimensionality reduction methods in terms of generality, stability, and interpretability.

The invention belongs to the technical field of oil field logging, and discloses an oil field logging data optimization method based on machine learning and a related device. Original oil field logging data are collected through multi-source logging equipment and preprocessed to generate a preprocessed data set; feature correlation analysis and model importance evaluation are carried out on the data set to extract a key feature set through a dimension reduction algorithm, then the key feature set is adopted to train a pre-constructed machine learning model to obtain a logging data optimization model, and finally the model is applied to optimize to-be-processed logging data and output an optimization result. By adopting the method, the effectiveness and representativeness of logging data characteristics can be remarkably improved, the real condition of an underground reservoir can be accurately reflected, the full-well-section high-resolution logging data can be simply, conveniently and quickly obtained at low cost, a reliable basis is provided for a shale oil and gas development scheme, and the rationality of the development scheme and the overall development efficiency are further enhanced.

The invention relates to the technical field of performance detection, and discloses a metering equipment full-performance detection result automatic determination method, equipment and a medium, and the method comprises the steps: collecting multi-dimensional performance parameters of metering equipment under different test conditions, carrying out the preprocessing and preliminary feature extraction of the multi-dimensional performance parameters, and carrying out the calculation of the full-performance detection result of the metering equipment; performing intelligent comprehensive processing by using a nonlinear high-dimensional dimension reduction algorithm to obtain comprehensive performance parameter data; the comprehensive performance parameter data is intelligently detected through a detection algorithm based on machine learning, a detection result is obtained, a judgment result is automatically generated based on the detection result in combination with self-adaptive threshold adjustment, and the method can solve the technical problems that equipment performance parameters are not comprehensively considered, processing is not accurate, and detection and judgment are not accurate.

This invention discloses a rapeseed quality detection method based on feature wavelength optimization and multi-model fusion. The method involves collecting full-band near-infrared diffuse reflectance spectral data of rapeseed samples and simultaneously measuring the true values ​​of physicochemical indicators to construct an original dataset. Based on the data distribution characteristics of each physicochemical indicator, the original dataset is divided into a training set and a test set. Preprocessing algorithms are selected for each physicochemical indicator to process the full-band near-infrared diffuse reflectance spectral data. Dimensionality reduction algorithms are selected for each physicochemical indicator to extract feature wavelengths from the preprocessed spectral data. Predictive models for each physicochemical indicator are established based on the training set, and the performance of the predictive models is verified using the test set to determine the optimal algorithm combination for each physicochemical indicator. The variable importance projection algorithm is used to calculate the comprehensive contribution score of each wavelength point in the full-band spectrum to the four physicochemical indicators. A multi-indicator comprehensive threshold is set, and the core light source wavelength of the portable device is determined from the high-score region.

The invention discloses an unsupervised dimensionality reduction visualization method for cell image data. The method comprises the following steps: acquiring an unlabeled cell image high-dimensional data set; processing the high-dimensional data set by adopting an unsupervised dimension reduction algorithm to generate low-dimensional embedding representation; generating a first visual chart based on the low-dimensional embedded representation to display the distribution of the data in the low-dimensional space; performing unsupervised clustering analysis on the data points in the low-dimensional embedding representation, and identifying at least one clustering center point; generating a second visual chart based on a clustering analysis result, and marking a clustering center point in an identifiable manner; according to the method, the clustering center point can be accurately identified, and a visual data distribution overview is provided for a user.

The invention discloses an intelligent power distribution system optimization control method based on a knowledge graph, and the method comprises the steps: collecting and preprocessing multi-source power distribution operation data, and constructing a power distribution knowledge graph; performing feature extraction and fusion to generate a power distribution high-dimensional state matrix; constructing a neighborhood graph and executing an Isomap manifold learning algorithm to generate a low-dimensional manifold space; constructing an improved Brown bridge diffusion model, and generating a state evolution path; multi-class risk probabilities are calculated, and a power distribution state risk field is generated; and executing a sequential quadratic programming algorithm, and solving an optimal control strategy. By introducing knowledge graph semantic expression, a curvature correction manifold dimension reduction algorithm, an improved Brownian bridge diffusion model and a sequential quadratic programming algorithm, accurate prediction, risk perception and optimal control of the operation state of the power distribution system are realized.

This invention provides a system and method for identifying the categories of Chinese medicinal materials and detecting chlorogenic acid content based on Vis-NIR hyperspectral imaging technology, belonging to the field of plant detection technology. This invention employs hyperspectral imaging technology and multiple dimensionality reduction algorithms to establish models for identifying the categories of Chinese medicinal materials and detecting chlorogenic acid content. Furthermore, this invention optimizes the feature data during model construction using the BO-BOSS algorithm, further improving the accuracy of the model, ultimately achieving non-destructive and accurate detection of the categories of Chinese medicinal materials and their chlorogenic acid content. The detection method described in this invention features high detection accuracy, strong robustness, and non-destructiveness, without causing irreversible damage to the sample interior. It provides reliable technical support for crop / medicinal material harvesting category diagnosis and effective component detection, and offers valuable reference for hyperspectral imaging technology in the category identification and quality detection of other agricultural products, demonstrating excellent practicality.

The invention relates to the technical field of battery safety, and discloses a thermal runaway risk identification method and device, equipment and a medium. The method comprises the following steps: firstly, obtaining multi-dimensional characteristic data such as temperature, voltage and current of battery operation, and obtaining a standardized characteristic data set through difference removal and vacancy supplement preprocessing of abnormal value removal and missing value complementation; secondly, adopting t-SNE and other dimension reduction algorithms to map the high-dimensional features to a low-dimensional space, generating low-dimensional clustering data containing vehicle distribution features, simplifying data complexity, retaining key features, and achieving preliminary separation of risk vehicles and normal vehicles; performing feature space conversion of kernel function dimension raising or coordinate system conversion on the low-dimensional clustering data; and finally, determining a thermal runaway risk vehicle and outputting a risk level through preset threshold judgment or classifier identification. According to the method, the recognition accuracy is remarkably improved, and the method is adaptive to vehicle-mounted and energy storage system real-time early warning scenes.

The invention relates to the field of aluminum casting molds, and discloses a method, a device, equipment, a medium and a program product for determining a temperature acquisition point location of an aluminum casting mold, and the method comprises the following steps: simulating a process of manufacturing a target product by using a target aluminum casting mold to obtain simulated temperature time sequence data of manufacturing the target product by using the target aluminum casting mold; dimensionality reduction is carried out on the simulation temperature data based on a PCA-UMAP-tSNE dimensionality reduction algorithm, and low-dimensional temperature data are obtained; performing clustering analysis on the low-dimensional temperature data to obtain n candidate temperature acquisition points; the n candidate temperature collection points are matched with the structural characteristics of the target aluminum casting mold to obtain N target temperature collection points, N is smaller than or equal to n, and the temperature collection points capable of accurately reflecting the temperature change condition of the aluminum casting mold are provided for the target aluminum casting mold, so that the temperature collection points are adopted for temperature collection and feedback control, and the accuracy of temperature collection is improved. The accuracy and reliability of temperature collection in the casting process can be improved, and therefore the yield of target product casting is improved.

This invention relates to a method for radio signal identification based on multidimensional feature extraction and feature fusion, belonging to the fields of signal recognition and deep learning technology. The invention collects radio signal data, segments the collected radio signals, and converts them into two-dimensional data. Four instantaneous features are extracted from the radio signals. Time-frequency transformation is performed on the radio signal data, and Shannon entropy feature values ​​are extracted. The extracted instantaneous features and time-frequency features are fused, and a linear discriminant analysis (LDA) dimensionality reduction algorithm is used to reduce the dimensionality of the fused feature data. The processed signal data is then input into a deep learning classification network model for signal identification to obtain its corresponding signal category. This invention combines the ideas of multidimensional feature extraction and feature fusion to achieve high-accuracy radio signal identification. Furthermore, by leveraging the advantages of deep learning technology, it can improve the speed and accuracy of radio signal identification.

This application provides a data processing method, apparatus, and electronic device for a risk control system. The data processing method first acquires first data about multiple risk control objects from different target data sources. Then, it determines the data type of the first data, quantifies it according to the data type, and determines the second data to obtain the target data set. Finally, it determines the similarity between each target data set and a preset data set using a pre-defined dimensionality reduction algorithm. This allows the risk control system to determine the source data from the target data sources based on the similarity scores. It effectively identifies data redundancy from each target data source based on the similarity scores, thus determining the source data required by the system. This reduces the workload of processing massive amounts of data when implementing risk control strategies, ensures the effectiveness and timeliness of risk control, effectively reduces maintenance costs, and facilitates data sharing mechanisms.

A unit commitment optimization method driven by a large language model relates to the technical field of power systems and comprises the following steps: acquiring economic operation basic data of a power system; constructing a unit commitment model of the integer relaxation security constraint according to the economic operation basic data, and solving the unit commitment model; obtaining a unit commitment model, and solving the unit commitment model; constructing a variable dimension reduction algorithm, a constraint dimension reduction algorithm and an infeasible solution repair algorithm; sequentially carrying out single algorithm evolution on the variable dimension reduction algorithm and the constraint dimension reduction algorithm by adopting a large language model; carrying out single algorithm evolution on the infeasible solution repair algorithm by adopting a large language model; carrying out solution calculation on the unit commitment model after the integer relaxation security constraint; calculating a security constraint unit commitment model of variable constraint dimensionality reduction to obtain an optimal solution; based on the optimal infeasible solution repairing algorithm, repairing the optimal solution of the security constraint unit commitment model of variable constraint dimensionality reduction to obtain a result, and the method is used for solving the problem that an existing model dimensionality reduction method is insufficient in universality, stability and interpretability.

The invention discloses a method, a system, equipment and a medium for separating multi-source partial discharge signals, and belongs to the technical field of electrical equipment tests.The method comprises the steps that firstly, feature dimension raising processing is conducted on original partial discharge signals, and potential feature information in the partial discharge signals is fully excavated; and the high-dimensional features are compressed to a two-dimensional space through a dimension reduction algorithm, and a visual dimension reduction spectrogram is generated. The spectrogram can clearly present quantity distribution and characteristic distribution modes of different insulation defect types, so that field technicians can quickly identify and distinguish various insulation degradation types. Compared with a traditional method, the method has the advantages that the distinguishing capability of similar partial discharge signals is improved, the accuracy and efficiency of insulation state evaluation are enhanced, and the method is suitable for the field of online monitoring and fault diagnosis of power equipment.

The present invention relates to a flexible dynamic voltage stabilization control method for medium-frequency electric heating energy storage, the method comprising: constructing an impedance model of a medium-frequency electric heating energy storage system; identifying the medium-frequency electric heating energy storage system as an inductive or capacitive operating mode based on the Hope bifurcation theory; converting the 3n+2 dimensional Newton iterative equation of the impedance model into an n+2 dimensional linear equation group through a dimensionality reduction algorithm, and calculating the Hope bifurcation point in real time; determining an optimized trigger angle based on the calculation result of the Hope bifurcation point; and adjusting the operating mode of the medium-frequency electric heating energy storage system to an inductive or capacitive mode based on the optimized trigger angle. The beneficial effect is that the dynamic stability of the medium-frequency heating system is effectively improved, the stable operation of the IGBT full-bridge inverter circuit is ensured, and the output range of the medium-frequency heating system power is increased.

The invention relates to the technical field of wire rod production surface defect detection, in particular to a Litz wire surface defect detection system and method, and the system comprises an image collection module which is used for continuously collecting high-definition images of the surface of a Litz wire; the image processing and analyzing module is used for receiving and processing the image data transmitted by the image acquisition module; and the control and execution module is used for receiving and comparing the data transmitted by the image processing and analysis module and carrying out abnormity alarm, trend early warning and data recording. According to the method, the width, the pitch and the angle of the Litz wire are synthesized into a twisting quality index (TQI) for judgment by adopting a mechanical vision detection matching algorithm, an image is analyzed by utilizing a mode of combining a morphological opening operation and a PCA dimension reduction algorithm, high-robustness and high-precision detection is realized, quantitative detection of the pitch is realized, and the detection precision of the Litz wire is improved. The problems that existing visual detection is poor in stability and low in precision, and the pitch is difficult to quantify are solved.

The invention discloses an apple near infrared spectrum prediction model establishment and grading detection method. The method comprises the following steps: firstly, acquiring a sugar content measured value of an apple sample, and collecting corresponding near infrared spectrum data; next, the collected spectral data is preprocessed, including methods of applying D1, MSC, SNV, Savitzky-Golay smoothing, standardization, maximum and minimum normalization and the like, and an optimal combined preprocessing method is selected from the methods; and carrying out dimension reduction on the preprocessed data through a feature dimension reduction algorithm, such as PCA, ICA and TSVD, so as to screen out an optimal feature dimension reduction method. And finally, predicting the sugar content of the apples by using regression models such as least partial squares regression, support vector regression, ridge regression and kernel ridge regression, and selecting an optimal regression model to establish a final prediction model.