745 results about "Overfitting" patented technology

Systems and methods for computing time series predictions are provided, including a two-stage classifier-and-regressor processor. The classifier is trained on the complete data set while the regressor is trained on a pruned dataset. The classifier includes an Extreme Gradient Boosting classifier. The regressor includes an attention mechanism and a Long Short-Term Memory (LSTM) neural network. For a series of successive time period computations, a current output of the LSTM neural network is recursively fed back as an input to the attention mechanism for a subsequent time period computation. The output of the regressor is scaled by the output of the classifier to adjust for overfitting caused by the pruned training dataset.

The invention discloses an attention mechanism and LSTM fused industrial bearing vibration time sequence signal fault prediction method and system. The method comprises the following steps: collecting a bearing vibration signal and carrying out filtering, noise reduction and normalization preprocessing; constructing a deep learning model combining the bidirectional BiLSTM and a coordinate attention mechanism to extract bidirectional time sequence features and enhance key fault features; carrying out model training by adopting a multi-target composite loss function and an Adam optimizer, and introducing an early stop mechanism to prevent overfitting; performing fault type identification and degree evaluation on the real-time vibration signal by using the trained model, and performing quantitative analysis by fusing multi-scale spectrum kurtosis features and nonlinear kinetic parameters; and finally, outputting a fault diagnosis report, and triggering multi-stage early warning based on an adaptive threshold. The method can realize high-precision and high-reliability bearing fault prediction and health state evaluation, and is suitable for intelligent operation and maintenance of industrial equipment.

The invention discloses a high-resolution remote sensing image accurate classification system and method based on deep learning multi-modal fusion, and the method comprises the steps: S1, carrying out the preprocessing and fusion optimization of multi-modal data; S1.1, carrying out the standardization and normalization: carrying out the standardization and normalization of remote sensing data of different modals, and eliminating the influence caused by the difference between the different modals, the difference between the resolution, the spectral range and the like; for optical images, contrast may be enhanced by histogram equalization. The method aims at solving the problems of data heterogeneity, calculation efficiency, over-fitting, difficulty in labeling, real-time performance, interpretability and the like in an existing method by adopting multi-modal data optimization preprocessing, deep fusion model design, automatic labeling and semi-supervised learning, a lightweight model and hardware acceleration technology and a strategy for enhancing interpretability. Through the optimization, the system can maintain high classification precision, improve the calculation efficiency, reduce manual intervention, enhance the transparency and generalization ability of the model, and meet the actual application requirements.

The invention discloses an EEG-fNIRS deep fusion cognitive state classification method based on feature decoupling, and the method comprises the steps: firstly synchronously collecting EEG data and fNIRS data of a tested mental calculation task, and carrying out the preprocessing of the two collected data, so as to obtain EEG data and hemoglobin concentration change data; secondly, electroencephalogram signal data and hemoglobin concentration change data are taken, data enhancement is carried out through a time window, then the data are sent to a deep fusion network, and public features and private features of EEG and primary features of fNIRS are obtained; and an EEG classification result, an fNIRS classification result and an output fusion classification result are obtained. And finally, designing decoupling loss and cross entropy loss based on a label smoothing strategy, and carrying out training. According to the method, the overfitting problem in fNIRS modal deep learning is effectively relieved, and cognitive state classification can be accurately carried out.

The present disclosure provides a detection method for low-quality fake face images based on domain-invariant feature learning, including the following steps: S1, inputting datasets into a residual network (ResNet)-50, and forward propagating low-quality compressed input images and original images corresponding thereto; S2, obtaining high-frequency information of two datasets of different resolutions; and using the high-frequency information to compensate for features discarded in the compression process; and S3, performing T-distributed stochastic neighbor embedding (T-SNE) operation on outputs from the ResNet-50 at a final stage to obtain feature distributions of a high-quality dataset and a low-quality dataset. Domain invariance is used for inscribing correct artificial artifact traces, alleviating the overfitting on low-quality compressed images.

Systems and methods are for mitigating hindsight bias related to designing and using artificial intelligence models for outlier events. More specifically, systems and methods for the use of synthetic data in the training and / or validation of model predictions in order to prevent overfitting and generate predictions that attempt to predict the unpredictable.

The invention discloses a strategy question-answering system and method based on a vertical domain large model, and the method comprises the steps: S1, receiving the natural language input of a user, and retrieving an open source data set to obtain an original data set with the comprehensive similarity meeting the requirements; s2, performing necessary data cleaning and preprocessing on the original data set to obtain a training set; s3, performing fine tuning on the pre-processed basic large language model by adopting the training data set, and introducing a low-rank structure to modify a weight matrix to obtain a final model; s4, the problem enters a final model for post-processing to generate prediction output; the system takes a large language basic model subjected to fine tuning training as a core, introduces a low-rank structure to perform fine tuning on model parameters, reduces parameter quantity required by training while keeping model performance, reduces model complexity, reduces an overfitting risk, improves generalization ability, supports deep fusion of a specific field knowledge base and universal field data, and has a wide application prospect. And the accuracy and correlation of questions and answers are improved.

The invention discloses a face depth forgery detection method based on an enhanced double-branch fusion model. According to the method, a novel enhanced double-branch fusion model is adopted, so that the robustness and generalization ability of a CNN-based face depth forgery detector are improved. Firstly, RGB high-frequency noise in a face image is enhanced to extract abnormal features of the face image, and then reservation fusion is carried out. Specifically, a depth separable convolution module is used to improve model performance when image features are extracted. A selective kernel module is also used to adaptively extract more representative noise features by dynamically adjusting a convolution kernel. Besides, a multi-scale channel space attention fusion module is designed, and feature information of each part is effectively fused, so that overfitting of the model is reduced, and robustness and generalization ability of the model are enhanced. And finally, comprehensive evaluation on a plurality of reference data sets proves that the method is remarkably improved in the aspects of robustness and generalization.

The invention relates to an adaptive feature weighted fusion fault diagnosis method for multi-source heterogeneous data of a motor, and the method comprises the steps: constructing a multi-head deep convolutional neural network, designing a plurality of parallel convolutional channels in the neural network, deeply mining the abnormal features of original signals of current and vibration, and focusing on the signal features in different dimensions, thereby achieving the fault diagnosis of the multi-source heterogeneous data of the motor. And comprehensive capture of key feature information is ensured. And adaptive weighted fusion is performed on the extracted multi-dimensional features by adopting a correlation analysis method to highlight abnormal key features, so that the feature fusion effect of the multi-source heterogeneous data and the accuracy of anomaly detection are improved. An SELU activation function is adopted and a hyper-parameter optimization strategy of a dynamic attenuation learning rate is combined so that an overfitting problem which is easy to occur in a model training process can be effectively solved and convergence stability of the model and high precision of fault identification can be enhanced. Through adaptive fusion of multi-source heterogeneous data features, key fault features are effectively extracted, and the diagnosis accuracy in an electromechanical coupling fault scene is improved.

The invention discloses a human face sensitive data protection method based on an elastic anti-interference privacy protection mechanism, belongs to the field of information processing, and provides a dynamic self-adaptive sampling strategy based on information entropy in a data preprocessing stage, and the sampling rate is adjusted according to the distribution characteristics of a human face data set and the weight of sensitive information. And the model performance is maximized on the premise of ensuring user privacy. In order to obtain a better privacy protection effect, an entropy adjustment differential privacy mechanism is designed, and Gaussian noise of different degrees is adaptively added according to an information entropy value of face data after a face data set is dynamically and adaptively sampled, so that refined privacy protection of face sensitive data under differential privacy constraint is realized. In a model training stage, an elastic anti-interference training method is provided, diversified adversarial samples are dynamically generated in different stages of model training, and the over-fitting risk of the model is reduced while the robustness of the model to various types of adversarial attacks is enhanced.

The invention provides a hysteresis loop prediction model establishment method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out a hysteresis characteristic experiment on a ferromagnetic material, setting hysteresis loop related characteristics comprising magnetic induction intensity waveform data and magnetic field waveform data under the excitation of preset sine waves under different magnetic induction intensity peak values, establishing a hysteresis loop prediction model comprising an improved convolutional neural network model and a K-nearest neighbor algorithm model, training the improved convolutional neural network model, inputting a magnetic induction intensity peak value and magnetic induction intensity waveform data, and outputting magnetic field waveform data; and adjusting the magnetic field waveform data based on the feature data by using a K-nearest neighbor algorithm model to obtain a final magnetic field waveform result. According to the method, the hysteresis loop prediction model is established based on the improved convolutional neural network in combination with the K nearest neighbor algorithm, the overfitting phenomenon can be prevented, the training speed is higher, the resource consumption is lower, and rapid iteration and adjustment in practical application are facilitated.

The invention discloses a data processing system based on an artificial intelligence algorithm, and relates to the technical field of artificial intelligence and data processing, and the system comprises a data input interface which is used for receiving a multi-source heterogeneous data stream; and the preprocessing engine is connected with the data input interface and comprises a dynamic metadata sensing unit, an incremental quality evaluation unit, a bidirectional verification self-repairing unit and a closed-loop feedback optimization unit. According to the data processing system based on the artificial intelligence algorithm, through dynamic metadata perception and an incremental quality evaluation mechanism, the manual intervention requirement of a multi-source heterogeneous data preprocessing stage is reduced, and the problem of adaptation stiffness caused by the fact that a traditional method depends on a static rule is solved; by combining the bidirectional verification design of the business rule and the model feedback, the dual reliability of the data recovery strategy in logic rationality and algorithm compatibility is ensured, the risk of overfitting or scene mismatching caused by a single verification mechanism is avoided, and the input data quality and decision accuracy of a downstream artificial intelligence model are improved.

The embodiment of the invention provides a fine tuning method and device for a large language model, equipment and a computer readable storage medium. According to the method, the performance of a to-be-fine-tuned large language model is tested, a sample set of prediction errors of the large language model is collected, the samples of the prediction errors are classified on the basis of real categories and error prediction categories of the samples of the prediction errors, namely, the prediction errors of the large language model are classified, and the classification accuracy of the prediction errors of the large language model is improved. Then, a trained pre-training language model is utilized to analyze reasons for generation of the prediction errors, and similar error samples with the same error condition are generated based on the reasons, so that targeted fine adjustment is carried out on the model, and the over-fitting phenomenon is eliminated. According to the method, the performance of the model on different types of errors can be better analyzed, and the overfitting problem of the model in a specific scene can be deeply known and solved, so that the performance of the model in each vertical field is remarkably improved.

The invention discloses an information transmission attack event intelligent prediction model training optimization method, which comprises the steps of a data preparation stage, a feature engineering stage, a model selection and construction stage, a model training stage and a model evaluation and optimization stage, and is characterized in that data are collected from multiple channels, noise is cleaned and removed, missing values and abnormal values are processed, and accurate labeling is carried out; then key features are extracted, redundant irrelevant features are removed through selection, standardized transformation is carried out, a proper model is selected based on data features, an integrated learning combination model is adopted, then a training set, a verification set and a test set are divided, a proper optimizer is selected, parameters are adjusted to be over, training is carried out through the training set, and over-fitting is prevented through the verification set; and finally, using index evaluation of accuracy and recall rate, and adjusting hyper-parameters and continuous optimization of the model structure through cross validation. According to the method, data collection, data imbalance processing and data noise interference processing are adopted, data diversity is improved, and interference of data noise is reduced.

The invention discloses a multi-energy system optimization scheduling method based on a carbon flow theory and deep reinforcement learning. The method comprises the following steps: firstly, carrying out mathematical description on a multi-energy system dynamic optimization problem based on a carbon emission flow theory, and establishing an objective function and constraint conditions of multi-energy system optimization scheduling; then, the scheduling decision problem is expressed as a Markov decision process framework, a reinforcement learning environment is created, and a state space, an action space and a reward function of the multi-energy system are defined; meanwhile, in the depth deterministic strategy gradient algorithm, the training efficiency is improved by introducing an experience replay mechanism based on proportion priority, the overfitting risk in the network training process is reduced by adopting an L2 regularization measure, and the generalization ability of the network in the optimization scheduling decision of the multi-energy system is improved. The method can respond to random fluctuation of a source side and a load side in real time, outputs a coordinated scheduling strategy, ensures low-carbon operation of a multi-energy system, and overcomes the defect that a traditional energy scheduling system cannot quickly adapt to changes.

The invention relates to the technical field of deep learning and small sample learning, in particular to a small sample capacity training method based on deep learning, which comprises the steps of 1, cross-domain data adaptation and feature alignment, 2, meta-knowledge distillation and prototype enhancement, 3, attention-guided small sample fine adjustment, and 4, model uncertainty quantification and iterative optimization. According to the small sample capacity training method based on deep learning, through cross-domain feature alignment, meta-knowledge distillation, prototype enhancement and dynamic iterative optimization, the problems of model overfitting and weak generalization ability in a small sample scene are solved, high-precision model training when the sample size is less than or equal to 50 is realized, and the training efficiency is improved. The method is suitable for data scarce scenes such as medical images and minority language processing.

The invention is suitable for the technical field of machine learning, and provides a feature selection method and system based on domain adaptation and domain adversarial training, and the method comprises the following steps: obtaining source domain data and target domain data, constructing a source domain feature selection target function, and generating a binary feature mask vector; constructing a deep transfer learning framework based on a domain adversarial neural network, training the domain adaptive neural network by using the source domain tagged data and the target domain untagged data in a domain adversarial form, and constructing a cross-domain shared feature representation space of the source domain and the target domain; and feature selection knowledge migration from the source domain to the target domain is realized through decoding conversion. According to the method, the feature distribution difference between the source domain and the target domain is effectively eliminated through the adversarial training strategy driven by the gradient inversion layer, the method has remarkable advantages in a target domain data scarcity scene, the data annotation cost can be reduced, cross-domain potential association can be captured, and redundant features and over-fitting risks are reduced.

The invention discloses a Gaussian splash model training method and device, a medium and a program product, and relates to the technical field of computer graphics, and the method comprises the steps: carrying out the initialization of an original Gaussian splash model based on a sparse input sample image; according to the sparse input sample image and a preset Gaussian densification strategy, multiple rounds of iterative training are carried out on the original Gaussian splash model to obtain a progressive Gaussian splash model, and the Gaussian densification strategy comprises the following steps: when the number of times of iterative training of the original Gaussian splash model meets a preset number range, the number of times of iterative training of the original Gaussian splash model meets the preset number range; the densification threshold value used by the original Gaussian splash model in the iterative training process is inversely proportional to the number of iterative training times. According to the method, a relatively high densification threshold value is used at the initial stage of training, so that an over-fitting risk caused by over-high densification degree at the initial stage of training under a sparse input view angle is reduced.

The invention discloses an integrated wind power prediction method and system based on a multi-source data set. The method comprises the following steps: acquiring historical meteorological factors and fan operation data; preprocessing the historical meteorological factors and the fan operation data to obtain a data set; based on the data set, key features are obtained through a Boruta algorithm, the key features are processed through a sliding window mechanism and a VMD algorithm, and an enhanced feature matrix is obtained; inputting the enhanced feature matrix into a deep learning model for prediction, and obtaining a preliminary prediction value; and carrying out residual error correction and fusion on the preliminary prediction value to obtain a final wind power prediction result. The method effectively improves the capability of processing wind energy intermittency, volatility and randomness, avoids the defects that a physical model is complex in calculation and a statistical model is difficult to process nonlinear and non-stationary features, reduces the over-fitting risk of a single deep learning model, can improve the prediction accuracy and stability, and improves the prediction efficiency. And the method has better generalization ability in practical application.

The invention provides a training method, device and equipment for a synthetic data generation network and a medium, and the method comprises the steps: carrying out the training process of gradually increasing the task difficulty in three stages from the cross-view consistency training to the space-time consistency training, and then carrying out the combined prediction fusion training in combination with a multi-modal large language network. The network is enabled to fully learn at each stage, so that the capability of the synthetic data generation network is gradually enhanced, the problem of overfitting or training possibly caused by one-step training is avoided, and the synthetic data generation network can fuse multi-modal information such as images and texts. More real and diversified synthetic data with dynamic prediction capability are generated, and the training efficiency is improved.

The invention provides a preference alignment optimization method based on reward-driven selective punishment, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining intelligent question and answer training data, and constructing an intelligent question and answer training sample set which comprises a plurality of intelligent question and answer training samples; taking a to-be-optimized large language model as a strategy model and a reference model; and training the strategy model by using the intelligent question and answer training sample set, and measuring the offset amplitude of the strategy model before and after training by using the reference model to obtain an optimized large language model. According to the method, implicit reward signals in the model are introduced, preference data are divided into multiple categories according to implicit reward distribution generated by the model, a dynamic weight function is designed, differential weighted optimization is carried out on different categories of samples, reinforcement learning of high-quality samples and suppression of low-quality samples are achieved, and the method has the advantages of being high in robustness and high in robustness. Weight of low-quality or conflict samples is reduced while high-quality sample learning is enhanced, noise interference is suppressed, and overfitting is prevented.

The invention relates to the technical field of deep learning, provides an optimization method of a defect detection model and defect detection equipment, and can be used for industrial quality inspection. According to the method, at least one to-be-detected product data set in production is used for carrying out multi-round iterative optimization on a defect detection model after pre-training, the defect that traditional model training is isolated from a production line environment is overcome, and closed-loop iterative detection and optimization of the model are achieved. In each optimization process, confidence coefficient learning and active learning are combined, the uncertainty of a model is quantified through confidence coefficient learning, a global confidence coefficient threshold value is determined according to the confidence coefficient corresponding to the defect type of each piece of to-be-detected product data, and the threshold value is dynamically adjusted by using the recall rate and the number of optimization times. And during active learning, sample data screening is carried out by using the adjusted global confidence threshold, so that blind labeling or over-fitting labeling is avoided, the quality of the screened samples is ensured, and the accuracy and generalization of a retraining defect detection model are improved.

The invention particularly relates to an enterprise credit scoring model modeling method and system. The enterprise credit scoring model modeling method comprises the steps of obtaining data, normalizing the data, filtering out abnormal values, performing variable correlation test, and performing standardization processing on variables; preliminarily analyzing and processing unbalanced sample data; screening important features, sorting based on XGBOOST feature importance, identifying category features, realizing category feature coding, expanding to generate new combined features, analyzing feature correlation, and reducing the number of the features to a specified dimension; constructing a base model, searching for optimal model parameters, constructing a hierarchical structure model, determining features and weights of all hierarchies, performing performance evaluation on the model, and continuously performing optimization and improvement. According to the enterprise credit scoring model modeling method and system, the evaluation accuracy of the model on the enterprise credit risk is improved, the over-fitting risk of the model is reduced, the interpretability and credibility of the model are improved, and continuous optimization and iteration of the model are realized.

The invention relates to the technical field of hydropower station equipment monitoring, in particular to a deep learning modeling and analysis method for hydropower station equipment operation trend early warning. Comprising the following steps: collecting multi-source parameters and dividing dynamic working conditions; mechanism-data driven fusion feature construction is carried out; training a physical informed deep learning model; carrying out meta-learning migration optimization; performing dynamic threshold early warning judgment; and performing mechanism closed-loop verification. The model is built based on a physical informed neural network framework, a differentiable mechanism constraint loss function is introduced, and dual verification is carried out through an equipment simplified simulation model and a historical fault case, so that model output can be ensured to accord with an equipment operation physical rule, and the situation that a pure data driven model possibly deviates from physical common knowledge is avoided; the reliability of the early warning model is improved; according to the method, the basic model is trained by adopting the meta-learning algorithm guided by the fault type label, so that the problems of model over-fitting and high adaptation cost in a small sample scene in the traditional technology are solved.

The invention discloses a steel defect detection method based on multi-scale edge enhancement, and relates to the field of industrial detection. The method comprises the following steps: constructing a steel defect detection data set and carrying out preprocessing, and dynamically interacting high and low layer features by constructing a Compute-ConvNeXt and an edge enhanced feature fusion structure (EEFF) so as to enhance semantic expressions of edges and small target defects; according to the method, an improved loss function (Focal-MPDIOU) and a data equalization strategy are combined, the sensitivity of the model to fuzzy edges and tiny defects is improved, meanwhile, the overfitting problem caused by sample distribution unbalance is restrained, and automatic defect detection of production line steel is achieved. A defect detection task with high precision, high robustness and high generalization is realized in a complex industrial scene, and an effective solution is provided for intelligent detection of metal material surface defects.

The invention discloses a visibility regression prediction method based on multi-modal transfer learning and time coding, and relates to the technical field of artificial intelligence. The method comprises the following steps: S1, dividing a data set in different periods according to illumination characteristics, and splitting each period into a training set, a verification set and a test set; s2, preprocessing the data set; s3, constructing an initial model containing a pre-training deep learning network, a time coding module and a multi-layer perceptron regression head; s4, extracting image visual features and time feature vectors; s5, fusing the features and inputting the features into a regression head for prediction; s6, carrying out scheduling training by using layered parameter freezing, an AdamW optimizer and a dual learning rate, and combining with a mixed early stop strategy until convergence; and S7, evaluating the test set to determine a final model. According to the method, complementarity of image and time information is mined, high-precision prediction is realized, generalization is good under different illumination conditions, a layering strategy and an optimization mechanism guarantee stable and efficient training, a multilayer perceptron combination technology enhances expression, and overfitting is effectively prevented.

The invention relates to the technical field of short-term load prediction in a power system, and discloses a short-term load prediction system based on a coder-decoder architecture, and the system is characterized in that a coder is used for extracting local features of a power load mode; and the decoder is used for converting the local features of the power load mode into predicted power load values and outputting the predicted power load values. The encoder is realized by a multi-scale expansion causal convolutional network MSDCC, and the decoder is realized by a bidirectional long short-term memory network BiLSTM. The prediction system construction method comprises the following steps: extracting related data from a historical database, preprocessing and analyzing the data, and constructing a predictor matrix; an MSDCC encoder is constructed; a BiLSTM decoder is constructed; and combining the encoder-decoder architecture to construct a short-term load prediction model. According to the method, the size of the feature map is effectively limited, model parameters are reduced, overfitting is avoided, calculation requirements are controlled, non-linear features are efficiently captured, meanwhile, time keeping complexity is low, and therefore the method has the advantages of being high in prediction efficiency and high in prediction precision.

The invention discloses a knowledge graph completion method based on a topology-aware hybrid convolutional network, which relates to the technical field of knowledge graph completion, and is characterized by comprising the following steps: S1, defining and describing a knowledge graph; s2, designing a message function in the graph neural network; s3, an attention aggregation mechanism in the graph neural network; s4, a hybrid convolution decoder of topology perception; S5, a training strategy; end-to-end training is carried out on the model by adopting a smooth cross entropy loss function with a label, so that the overfitting problem is relieved and the generalization ability of the model is improved. The technical problem to be solved by the invention is to provide the knowledge graph completion method based on the topology perception hybrid convolutional network, so that complementary balance of explicit reasoning and implicit feature interaction is realized, and the adaptability of a model on different relation types is improved.

The invention relates to a cross-bearing single sample intelligent diagnosis method based on cognitive guidance and Riemannian manifold, and belongs to the technical field of rotating machinery fault diagnosis. Aiming at the problems of insufficient global task distribution learning ability, small sample over-fitting, Euclidean modeling limitation and the like of the existing meta learning method in a cross-domain single-sample scene, a cognitive guidance Riemannian meta learning framework is provided. According to the technical scheme, the method comprises the following steps: 1) constructing cognitive prototype learning global task distribution, and guiding a model to extract high-quality general meta-knowledge from multiple tasks; 2) designing a cognitive adaptive factor to dynamically adjust source domain memory, enhancing target domain adaptation and reducing single sample deviation; and 3) introducing a Riemann metric driving strategy, mapping the data to a Grassmann manifold space, and enhancing the non-linear feature discrimination ability by using geodesic distance. According to the method, the average diagnosis accuracy in a cross-bearing single sample task reaches 93.46% and is improved by 10.04% compared with an existing optimal method, and the accuracy and generalization ability under complex working conditions are improved.

The invention relates to the technical field of machine learning optimization, in particular to a machine learning optimization method based on a QUBO model and quantum annealing, and the method comprises the following steps: firstly, discretizing continuous parameters of the machine learning model into binary variables, and constructing a QUBO objective function; then optimizing the QUBO model by using a quantum annealing algorithm; and finally, solving a globally optimal solution by adjusting annealing parameters, wherein the annealing parameters comprise the initial temperature, the cooling coefficient and the number of iterations. The QUBO model can discretize continuous variables (including parameters such as weights and offsets) in models such as AR, SVM and CNN in machine learning into binary variables, so that a nonlinear relation is better processed, and the calculation complexity in the training process is reduced. Through the QUBO model, the regularization item can be better controlled, and the problem of overfitting is avoided. Meanwhile, the QUBO model can be solved in parallel through quantum calculation or a simulated annealing algorithm, and the calculation efficiency under large-scale data is remarkably improved.