81 results about "Nonlinear dimension reduction" patented technology

The invention relates to the technical field of optical fiber communication monitoring, in particular to a passive optical fiber multi-parameter digital twin drive abnormal root cause positioning method and system. Collecting temperature, stress, acoustics and polarization signals, and constructing a time domain, frequency domain and energy domain coupling feature tensor and time sequence data set; performing nonlinear dimension reduction and feature decoupling by using a beta-VAE model, and dynamically quantifying contribution of each parameter to anomaly by using an integral gradient to form a contribution degree vector group; constructing a PINN digital twinborn model embedded with heat conduction and elasto-optical effects, and predicting a normal fluctuation range under contribution vector weighting and data-physics dual constraints; modeling measurement and prediction deviations under the guidance of contribution vectors, and outputting an abnormal measurement score, confidence, a position and a time sequence; and a causal graph neural network is constructed, topology and abnormal events are fused for tracing causes, a fault source is identified, and the model is subjected to closed-loop calibration. According to the invention, data driving and a physical mechanism are fused, and accurate detection and root cause positioning of the abnormity of the optical fiber system are realized.

The invention discloses a physical field reconstruction method fusing a reduced-order model and a multi-fidelity model, and belongs to the technical field of data-driven physical field reconstruction. The method comprises the following steps of: firstly, carrying out nonlinear dimension reduction on high-dimensional simulation data by utilizing a deep auto-encoder, extracting a low-dimensional feature vector, training an encoder-decoder network, and establishing bidirectional mapping between high-dimensional data and low-dimensional data; then, a mapping model from the working condition parameters to the low-dimensional features is constructed and used for predicting feature vectors under the new working condition; the prediction features are then reconstructed by a decoder into preliminary physical field data as a low fidelity trend. And finally, on the basis of the trend, in combination with sparse high-fidelity measured data, correction is carried out through a multi-fidelity fusion model, and a high-precision reconstructed physical field is obtained. According to the method, the precision and credibility of physical field prediction are effectively improved by fusing multi-source data, and the method is suitable for the fields of structural health monitoring, digital twinning and the like.

According to the plateau area load prediction method and system based on the signal decomposition and the neural network, an original signal is decomposed into a plurality of intrinsic mode components with physical significance by adopting completely adaptive noise set empirical mode decomposition, mode aliasing is suppressed, and multi-scale features are extracted to effectively suppress load demand oscillation; the problem of non-stationarity of the load of the service area is solved; then, K-means clustering grouping is carried out on the components, similar modes are combined to reduce calculation redundancy, and the problems of heterogeneity and local feature redundancy in complex time series data are effectively solved; performing nonlinear dimension reduction processing on the grouped reconstructed signals by adopting kernel principal component analysis, eliminating noise interference and extracting key features, thereby effectively solving the multiple nonlinear problem; and finally, inputting the dimensionality-reduced features into a long-short-term memory network modeling time sequence dependency relationship, and realizing high-precision prediction through adaptive weighted integration of component prediction results.

The invention discloses a method for constructing an evaluation model for the influence of climate change on biodiversity, and particularly relates to the technical field of ecological modeling and climate response analysis. The method comprises the following steps: generating a high-resolution characteristic grid based on microclimate disturbance data and a remote sensing image, extracting ecological plaques and establishing a heterogeneity spatial index model, constructing a connectivity map and a coupling response path library, extracting a typical species response mode by using nonlinear dimension reduction and spectral clustering, and performing dynamic fitting in combination with biological survey data. And finally, generating a diversity attenuation trend prediction curve and a risk thermodynamic diagram, identifying a biodiversity collapse critical point and outputting an intervention priority, and the method can be widely applied to regional ecological early warning and protection planning.

The invention discloses an MES-based aluminum alloy casting production management method and system, and relates to the technical field of production management, and the method comprises the steps: firstly, synchronously collecting time sequence data streams of core process parameters such as pressure, speed, position and the like, and carrying out the multi-channel data alignment; and a deep learning auto-encoder model is introduced to carry out nonlinear dimension reduction and feature extraction on high-dimensional data, and reconstruction errors are utilized to realize real-time evaluation and anomaly detection of the quality of the casting process. And for the abnormal state exceeding the preset threshold value, key influence factors causing quality fluctuation are further traced through an intelligent diagnosis mechanism, autonomous decision is made based on a diagnosis result, an optimal parameter adjustment strategy is formed, and finally a closed-loop control instruction is generated to drive the production equipment to perform adaptive adjustment. In this way, the problem of unstable quality caused by parameter fluctuation in the casting process can be effectively solved, and the stability and the intelligent level of the aluminum alloy casting process are remarkably improved.

The invention provides an electric power system model important parameter group identification method based on trajectory feature clustering, relates to an electric power system simulation technology, and solves the problem that modeling is not performed due to time domain global error index coarse graining and parameter coupling in traditional electric power system important parameter identification. The parameters are used as to-be-identified high-sensitivity parameters; performing time domain difference on each group of simulation tracks and actually measured tracks to obtain difference tracks, and extracting difference track features; the method comprises the following steps: mapping high-dimensional data to a low-dimensional space through a nonlinear dimension reduction method, and executing K-Means clustering on the data in the low-dimensional space to obtain a cluster label of each sample; sensitivity calculation is carried out on all samples in the clusters, and a parameter combination with the highest occurrence frequency and the maximum contribution to dynamic differences in the clusters is screened out to serve as a final dominant parameter set. According to the scheme, the simulation frequency and the calculation burden can be remarkably reduced, and high-precision and interpretable discrimination of important parameters really influencing the system performance is realized.

The invention belongs to the technical field of autonomous underwater vehicle numerical simulation, and discloses an AUV physical field prediction method based on manifold learning and deep learning, comprising the following steps: performing nonlinear dimension reduction on high-dimensional simulation physical field data based on an Isomap manifold learning algorithm, and extracting low-dimensional manifold features; constructing a deep neural network (DNN) model, and establishing a mapping relation between the working condition parameters and the low-dimensional manifold features; inputting new working condition parameters to the trained deep neural network model, and predicting corresponding low-dimensional manifold features; isomap inverse mapping is realized based on a local linear embedding algorithm, and predicted low-dimensional manifold features are reconstructed into high-dimensional physical field data. Through collaborative prediction of manifold learning and deep learning, the problem that a traditional method is low in calculation efficiency in high-dimensional physical field prediction is solved, prediction precision and real-time performance are remarkably improved, and an efficient tool is provided for AUV design optimization and dynamic control.

The invention discloses a voiceprint feature-based aero-engine abnormal state monitoring method, which comprises the following steps of: acquiring voiceprint signals of an aero-engine under different working conditions, synchronously acquiring rotating speed information, performing signal processing on the voiceprint signals, and constructing voiceprint feature data sets under different rotating speeds and multiple working conditions; carrying out nonlinear dimension reduction processing on the voiceprint feature data set by adopting a stacked auto-encoder; generating an abnormal sample after dimension reduction, performing feature modeling on the abnormal sample after dimension reduction, generating a newly added abnormal sample to expand the training data set, and generating an expanded training data set; constructing a convolutional neural network classification model, and training based on the expanded training data set to obtain an abnormal state recognition model; and carrying out online monitoring on the voiceprint signal collected in real time through the abnormal state recognition model, and recognizing an abnormal state.

The invention provides a backpack multi-sensor data fusion-based motion attitude anomaly real-time detection method, which comprises the following steps of: acquiring multi-channel original data through a backpack-type integrated triaxial accelerometer, a gyroscope, a magnetometer and a barometer, implementing synchronous sampling and time-space alignment, and constructing a standardized time sequence data stream through wavelet denoising and multi-dimensional normalization preprocessing; high-dimensional features representing motion differences are extracted, nonlinear dimensionality reduction is achieved through principal component and Laplacian feature mapping, and a motion mode prototype library is generated; according to the method, a new motion situation can be continuously and adaptively learned, and the motion recognition accuracy, the system robustness and the attitude anomaly detection capability in a complex scene are effectively improved.

The invention provides a medical insurance abnormal data detection method based on an adversarial auto-encoder and a bidirectional LSTM (Long Short Term Memory). A data preprocessing step: cleaning and normalizing the original medical insurance data; a feature engineering step: performing feature extraction and standardization on the preprocessed data to construct a feature set for detection; an unsupervised feature learning step: performing nonlinear dimensionality reduction on the feature set by using an adversarial auto-encoder to obtain low-dimensional potential feature representation; and a time sequence anomaly detection step: inputting the low-dimensional potential feature representation sequence into a bidirectional long-short-term memory network BiLSTM to model a time sequence dependency relationship of the low-dimensional potential feature representation sequence and output an anomaly detection result. AAE and BiLSTM are fused, dimension reduction feature extraction and time sequence modeling are combined, and an AAE-BiLSTM medical insurance anomaly detection model is constructed. The problem of coexistence of high-dimensional heterogeneity and strong time sequence of medical insurance data is effectively solved by fusing the unsupervised feature learning capability of an adversarial auto-encoder (AAE) and the strong time sequence modeling capability of a bidirectional long short-term memory network (BiLSTM).

The invention relates to the technical field of gene editing, and discloses an sgRNA activity prediction method and device based on deep features and automatic machine learning. The method comprises the following steps: acquiring sgRNA training data with editing efficiency labels and discretizing the sgRNA training data into classification labels; after the sgRNA and the PAM sequence are spliced, inputting the spliced sgRNA and PAM sequence into a pre-training depth sequence model to extract high-dimensional features; obtaining a low-dimensional feature vector through nonlinear dimensionality reduction; an automatic machine learning framework is combined with cross validation, and a prediction model is automatically trained and optimized based on low-dimensional features; and finally, carrying out activity prediction and sorting screening on candidate sgRNA by utilizing the model. According to the method, through fusion of deep semantic extraction, nonlinear structure maintenance and a full-automatic modeling process, the accuracy, robustness and development efficiency of sgRNA activity prediction are effectively improved, and an efficient pilot screening tool is provided for a gene editing experiment.

The invention provides a space dimension reduction method for dynamic trajectory analysis, and belongs to the field of data analysis and machine learning. The method takes dynamic trajectory data as Riemannian function type data to carry out nonlinear full dimension reduction, and comprises the following steps of: 1, converting the Riemannian function type dynamic trajectory data into Euclidean space function type dynamic trajectory data; 2, constructing a Gram matrix of time data and a distance matrix of measurement space data for the Euclidean space function type dynamic trajectory data; and step 3, constructing a dimension reduction function to carry out nonlinear dimension reduction on the Euclidean space function type dynamic trajectory data. According to the method, dynamic trajectory data with a complex geometric structure can be effectively processed.

The application provides a rotating machinery state monitoring method based on stack auto-encoding dimension reduction. First, the original vibration signal of the rotating machinery is collected, and multi-domain features of the signal are extracted, including time domain, frequency domain and time-frequency domain features. Then, the extracted multi-domain high-dimensional features are preliminarily screened through a variance selection method, the screened features are subjected to nonlinear dimension reduction by using a stack auto-encoding network, and the dimension-reduced features are divided into a training set and a test set. Next, the training set is input into a fuzzy neural network for training, and after the training is completed, the test set is input, so as to construct a health degree index and a curve capable of reflecting the running state of the rotating machinery. Finally, a fault indication scale is established through a 3 sigma criterion, and once the health degree index exceeds the scale for three times in succession, an early warning is performed, so that the state monitoring of the rotating machinery is realized. The application can discover early faults of the rotating machinery in time, ensures the safe operation of the rotating machinery and reduces the loss caused by the faults.

The invention discloses a multi-working-condition division method and system for nuclear power data based on a TSNE-improved HBBT model and a storage medium, and belongs to the technical field of intelligent operation and maintenance of nuclear power stations. The method comprises the following steps: firstly, acquiring historical operation data of a nuclear power system through a simulation platform, and extracting a mean value, a variance, a slope, an autocorrelation coefficient and an energy characteristic after normalization processing; dividing the feature data into a training set and a test set, and endowing working condition labels; carrying out nonlinear dimension reduction on the training set by adopting T-SNE; and finally, training and classifying by using an improved histogram gradient lifting tree model. The improved HBBT innovatively adopts an adaptive feature binning technology and a sample difficulty and cluster consistency weighting strategy, so that the model precision and robustness are effectively improved. The method solves the problems that an existing unsupervised method is high in randomness and inaccurate in classification, can accurately recognize the steady-state and transition working conditions of the nuclear power system, and provides reliable support for intelligent operation and maintenance.

The invention provides a docking method, device and equipment for a protein surface structure and a drug target, and the method comprises the steps: obtaining the three-dimensional structure data of a protein, generating a surface mesh model based on the three-dimensional structure data, and extracting the multi-modal surface features of the protein; based on the surface mesh model and the surface features of the protein, constructing a multi-scale 3D graph convolutional network, and generating feature codes of the protein; performing nonlinear dimensionality reduction on the feature code of the protein based on an adversarial auto-encoder to generate a low-dimensional feature of the protein; and performing iterative reinforcement learning docking based on the low-dimensional features and ligand positions of the proteins to obtain an optimal docking result. Through the method and the device, the surface features of the protein are extracted more comprehensively and accurately, the calculation amount is greatly reduced, and the problem of poor docking accuracy in the prior art is solved.

The invention provides a brain dynamic mode classification method based on a graph auto-encoder and soft and hard clustering. The brain dynamic mode classification method comprises the steps that S1, data preprocessing and blood oxygen level dependence signal extraction are carried out; s2, constructing a dynamic brain network; s3, nonlinear dimensionality reduction of the graph auto-encoder is carried out; s4, soft and hard clustering conjoint analysis and dynamic mode feature extraction; s5, performing feature screening and validity verification; and S6, classifier training verification and classification result output. According to the method, rs-fMRI data is taken as core input, accurate classification of brain dynamic modes is realized through a whole-process design of data preprocessing, brain network construction, nonlinear dimension reduction, clustering analysis and classification verification, and the problem of low classification accuracy of a traditional magnetic resonance image data classification method is solved.

The application relates to the technical field of network analysis, and discloses a network community discovery system and method through matrix analysis, which comprises the following modules: a network module, which converts a static network topology into an information propagation model, and establishes a node state time sequence dynamic model by defining a node information processing rule and a multi-round propagation mechanism; a phase space reconstruction module, which maps high-dimensional time sequence data to low-dimensional phase space through a nonlinear dimension reduction method to form node trajectory distribution data; a community feature module, which analyzes the convergence, oscillation mode and attractor feature of the node trajectory, and generates community structure feature data through trajectory similarity; and a community division module, which identifies a community boundary through density clustering, and constructs a hierarchical community organization through multi-scale analysis. The application can deeply mine the internal community structure of a network from the perspective of dynamic information propagation, and overcomes the limitation that traditional methods only consider static topology.

The invention discloses a road freight channel identification method and device, computer equipment and a readable storage medium, and relates to the technical field of traffic transportation, and the method comprises the steps: obtaining the original OD travel data of a truck based on high-precision positioning data, extracting the related OD data, and carrying out the cleaning and feature construction; obtaining characteristic variables including district and county-level OD latitude and longitude, travel distance, travel quantity and azimuth angle; a multi-layer perceptron (MLP) is adopted to carry out nonlinear dimension reduction processing on the feature variables, and an optimal low-dimensional embedded vector is obtained; carrying out clustering analysis on the low-dimensional embedded vector by adopting a segmented clustering algorithm and filtering noise; and performing path fitting processing on the filtered line segments to generate a continuous transportation channel, and finally outputting a freight transportation channel identification result after matching with a road network. According to the method, through combination of deep learning dimension reduction and segmented clustering, the spatial distribution characteristics of the freight transport channel are effectively extracted, and the freight transport channel identification precision and efficiency are improved.

The method for evaluating the health of an aero-engine group based on multi-working condition dynamic clustering belongs to the field of aero-engine health state evaluation. Firstly, the set parameters in the engine operation data are subjected to clustering analysis, and small-scale abnormal clusters are subjected to merging processing to obtain working condition category division results. Secondly, a health baseline data set is constructed, the sample data in the working condition category division results are subjected to standardization preprocessing and nonlinear dimension reduction to obtain a low-dimensional feature data set. Thirdly, the low-dimensional feature data set is subjected to clustering analysis by using a Gaussian mixture model, the average Mahalanobis distance of the sample and the health reference center of each clustering category is calculated, and the multi-level health grades corresponding to different clustering categories are obtained. Finally, by fusing the membership soft probability and the sample individual Mahalanobis distance, a continuous health score is constructed, and a health grade judgment interval is obtained. The present application can effectively identify the health state characteristics under different working conditions, support individual difference modeling and group horizontal comparison evaluation, and improve the accuracy.

The invention belongs to the technical field of oil-gas exploration and development, and discloses a complex hidden reservoir characterization method and related equipment, and the method comprises the steps: firstly, extracting layer section attributes from a seismic data attribute body through employing a self-adaptive Gaussian weighting strategy, distributing different weights for a target layer section and a strong reflection layer, thereby suppressing the interference of the strong reflection layer, and obtaining a target layer section; the seismic response of the reservoir is enhanced; then, an equal metric mapping algorithm is used for carrying out fusion analysis on the extracted Gaussian weighted layer section attributes, and the equal metric mapping algorithm is a nonlinear dimension reduction algorithm and can effectively capture a complex nonlinear mapping relation between the layer section attributes and hidden reservoir space distribution features; and therefore, the spatial distribution characteristics of the complex hidden reservoir can be accurately represented.

The application discloses an IGBT residual life prediction method based on multi-feature fusion and KPCA optimization. The method first collects IGBT collector current and voltage and other multi-source signals, extracts time domain, frequency domain, time-frequency domain and derived statistical domain features after pretreatment; then adopts a two-stage strategy of comprehensive evaluation index preliminary screening and mutual information regression fine screening to eliminate redundant features and retain high correlation features. On this basis, nonlinear dimension reduction is carried out by using kernel principal component analysis to construct a high-robustness health index, and the exponential weighted moving average and adaptive gradient detection are combined to accurately divide the degradation into three stages. Finally, the CNN-BiLSTM model is used to deeply mine the time sequence degradation features, and the MC Dropout algorithm is introduced to realize the accurate prediction and uncertainty quantification of the residual life. The application effectively solves the one-sidedness of single-source signal representation and the feature redundancy interference problem, and significantly improves the prediction accuracy and reliability.

The invention provides a GARCH-MIDAS-BiGRU (Gaussian Autoregressive Receiving Channel-MIDAS-BiGRU) Bitcoin fluctuation rate prediction method based on nonlinear dimensionality reduction. According to the method, firstly, a nonlinear dimension reduction technology t-SNE is used for carrying out dimension reduction processing on macroeconomic variables, and low-dimensional features are extracted, so that data redundancy is reduced, and the training efficiency of a model is improved; and then, combining bitcoin price data and the dimension-reduced macroeconomic variables, constructing a multi-factor GARCH-MIDAS model, effectively processing data of different frequencies, and capturing changes of long-term and short-term fluctuation rates. And finally, introducing a BiGRU neural network to further enhance the processing capability of the model on the time sequence data, and better capturing the nonlinear relationship and complex dynamic characteristics in the data. Through the comprehensive method, the accuracy and the reliability of predicting the fluctuation ratio of the Bitcoin can be improved, and a more effective risk management tool is provided for investors.

The application discloses a deposit unsupervised analysis method based on a standardized SHAP value space and a standardized feature space, and relates to the technical field of cross of geology and mineral exploration and artificial intelligence. The application processes in-situ microelement concentration data of measuring points into a standardized data set to represent a standardized feature space, and performs explanatory analysis on a machine learning model trained based on the standardized data set through a SHAP algorithm, and performs mapping processing to obtain a standardized Shapley value matrix to represent a standardized SHAP value space. Then, a strategy of coupling UMAP nonlinear dimension reduction and DBSCAN density clustering is adopted to realize unsupervised structure mining of the standardized Shapley value matrix. Through double-space local topological consistency evaluation and double-space viewpoint divergence anomaly detection, the deviation degree between the double spaces and abnormal measuring points are revealed.

The invention belongs to the field of image super-resolution reconstruction, and particularly discloses an image super-resolution reconstruction method and system based on nonlinear dimensionality reduction of hidden features of a state space model, and the method comprises the steps: achieving the super-resolution reconstruction of a two-dimensional image through a trained super-resolution reconstruction model; the super-resolution reconstruction model comprises a shallow feature extraction module, a deep feature extraction module and a reconstruction module. The shallow feature extraction module extracts shallow features. The deep feature extraction module comprises a plurality of attention enhancement modules which are connected in series, and the large kernel convolution fusion multi-head attention module enhances image detail features by extracting multi-scale spatial features and multi-channel features; the state space model hidden feature nonlinear dimension reduction module establishes a nonlinear mapping relation between compressed hidden features and a feature map, and corrects a linear mapping result through a channel attention mechanism; the reconstruction module obtains a reconstructed image based on the deep features. According to the invention, efficient and accurate two-dimensional image super-resolution reconstruction can be realized.

The application relates to the technical field of battery management systems, in particular to a battery system multi-performance index joint estimation method based on CNN-BiLSTM-SAM. The method collects time sequence data in the battery charging and discharging process, calculates the health state of the battery system, extracts health characteristic parameters, introduces kernel principal component analysis for nonlinear dimension reduction of the health characteristics, constructs a CNN-BiLSTM-SAM hybrid deep learning model of the battery system multi-performance, introduces a multilevel regularization system containing L2 regularization, a Dropout mechanism, an early stopping strategy and data enhancement in model training, effectively inhibits overfitting, uses a genetic algorithm to globally optimize hyperparameters, and finally realizes high-precision joint prediction of the battery system SOC, SOE and SOH. Through nonlinear dimension reduction, a multilevel regularization mechanism and genetic algorithm optimization, the application realizes high-precision joint estimation of the battery system multi-performance index, and has the advantages of superior feature expression capability, high prediction precision and strong generalization capability.

The invention discloses a crack prediction method and device, electronic equipment and a medium. The method can comprise the following steps: optimizing imaging of a complex structure with a breaking joint; crack post-stack attribute description is carried out for different types of breaking joints; fracture post-stack attributes are fused through an LLE nonlinear dimension reduction algorithm, and cracks are comprehensively represented. According to the method, a data basis is tamped through a pre-processing related technology, different parts of the breaking joint reservoir structure are subjected to hierarchical prediction through different attributes, and finally, the crack is subjected to comprehensive characterization through an attribute fusion technology, so that the crack description precision is greatly improved, and a good support is provided for high-quality reservoir prediction.

The invention relates to a ballastless track slab deformation evaluation and prediction method based on track dynamic irregularity, and the method comprises the steps: separating the track slab structure irregularity from the track dynamic irregularity through moving average filtering, wave crest and wave trough recognition and function fitting methods; based on a non-linear dimensionality reduction t-sne and k-means clustering algorithm, evaluating a deformation mode of the track slab in a spatial position; then, in combination with the deformation mode of the track slab, providing a corresponding track slab deformation value evaluation method, and analyzing the evolution rule of the deformation of the track slab along with the time change; and in combination with a track slab deformation evolution rule, training a track slab deformation prediction model by adopting a long-short term memory (LSTM) network. The method has the advantages that detection and monitoring equipment does not need to be additionally installed, low-cost and high-efficiency detection of the service state of the ballastless track plate on the high-speed railway bridge can be achieved, a new tool and a new visual angle are provided for evaluation of the service state of the ballastless track plate on the bridge under complex conditions, and development of intelligent maintenance and management of high-speed railway infrastructures is facilitated.

The invention provides a flow field overpressure distribution nonlinear order reduction prediction method, which is suitable for nonlinear dimension reduction and rapid prediction of flow field overpressure distribution data, and aims to overcome the limitation that high-dimensional complex pneumatic data is difficult to process by a traditional linear dimension reduction method. The method comprises the following steps: firstly, generating a design variable sample set based on an aircraft parameterized shape, and obtaining corresponding high-dimensional overpressure distribution data through CFD simulation; performing nonlinear dimensionality reduction on the overpressure distribution data by using a Gaussian process hidden variable model, extracting hidden space variables and reconstructing a model; and establishing a mapping relation between the design variable and the hidden space variable through an agent model such as Kriging and the like, so as to realize rapid prediction from the design variable to the overpressure distribution. According to the method, a flow control equation does not need to be solved, the reconstruction and prediction of overpressure distribution can be completed within second-level time, the aerodynamic layout design efficiency of the aircraft is remarkably improved, and the method is suitable for rapid design and evaluation of high-performance aircrafts such as low-sonic-boom aircrafts and the like.

The invention discloses a prodrug structure design-oriented prodrug property difference visual analysis method, and relates to the field of prodrug design, and the method comprises the following steps: obtaining structural data containing a prodrug and active drug components corresponding to the prodrug, and carrying out desalination treatment on the structural data to obtain a structural data set; outputting a modification type of the prodrug based on the processed structure data and a skeleton extraction technology; generating prodrug structure difference images classified according to modification types by using a molecular drawing technology; in combination with a molecular chemical information processing technology, obtaining the influence degree of the modification type on the pre-pharmacological chemical property; the method comprises the following steps: acquiring a fingerprint of a prodrug, embedding a high-dimensional fingerprint into a two-dimensional coordinate space by utilizing a nonlinear dimension reduction technology, establishing a structure-property mapping map of the prodrug in combination with an influence degree, and establishing a modification type distribution visual map by taking a modification type as a classification basis. According to the method, hundreds of pairs of structural data can be processed, and property change trends and spatial aggregation distribution characteristics of different modification strategies can be displayed through color coding and two-dimensional embedding maps.

The invention provides a data dimension reduction method based on quantum mechanical characteristics. The method comprises the following steps: S1, performing nonlinear dimension reduction processing on high-dimensional data through quantum kernel principal component analysis; s2, performing linear dimensionality reduction on the output of the S1 based on a quantum neighborhood preserving embedding algorithm of mahalanobis distance; and S3, mapping the output of the S2 to a low-dimensional space by adopting a quantum variational manifold learning algorithm, and generating a final dimension reduction result. According to the method, efficient dimension reduction of high-dimensional data can be realized, the efficiency and precision of quantum machine learning preprocessing are improved, the calculation complexity is reduced, and the accuracy and reliability of a dimension reduction result are improved.