1179 results about "Feature engineering" patented technology

The invention relates to the technical field of power equipment operation and maintenance management, in particular to a power equipment asset health management and predictive maintenance service system which comprises a data acquisition and integration module, a feature engineering module, a health assessment and prediction engine maintenance decision and early warning module and a service interface module. The data acquisition and integration module acquires equipment operation parameters through multiple types of sensors, and associates pre-stored equipment asset information to generate an equipment comprehensive data stream; the feature engineering module cleans and standardizes the equipment comprehensive data stream, and constructs a space-time correlation feature matrix; the health assessment and prediction engine comprises a health state assessment unit and a fault prediction unit, the health state assessment unit outputs a health index HI by using a gradient boosting decision tree, and the fault prediction unit outputs a fault probability and a remaining service life RUL in a future preset time period; and the maintenance decision and early warning module generates a grading early warning signal and a maintenance strategy scheme. The intelligent level of operation and maintenance of power equipment is improved, reliable operation of the equipment is guaranteed, and the operation and maintenance cost is reduced.

The invention discloses an employment information matching method and system based on data analysis, and particularly relates to the field of employment matching, and the method comprises the steps: collecting structured and unstructured data, and carrying out the semantic feature extraction through employing a BERT model and BiLSTM-CRF; in the preprocessing stage, entity standardization is realized through a knowledge graph, and a job seeker portrait and post model including a skill matrix and an occupational development trajectory is constructed; in the feature engineering stage, extracting four core features of skill matching degree, salary expectation integrating degree, commuting tolerance and occupational development goodness of fit; the salary integrating degree is quantitatively evaluated through a bidirectional tolerance model, a random forest model with time decay is used for dynamic weight, feature weight is generated based on historical successful cases, and a real-time feedback mechanism is introduced to adjust a weight coefficient; finally, the matching degree function fuses the weighted features and the industry trend factors, and the model is continuously optimized through a three-level updating mechanism.

The invention discloses a fault determination method and device for a micro-service system, a product and electronic equipment. Relates to the technical field of artificial intelligence. The method comprises the following steps: determining an abnormal event flow according to abnormal monitoring information; an abnormal event causal graph is determined based on an abnormal event flow, and then a fault point, a fault chain and a fault classification are determined by utilizing an intelligent agent driven by a pre-training model and combining the event causal graph and a fault mode standard. According to the method, anomaly detection, fault classification and root cause positioning of the micro-service system are realized, and closed-loop diagnosis from anomaly discovery to root cause positioning is realized; secondly, performing depth feature engineering and semantic abstraction on the refined data from the perspective of events and causal relationships, fusing system behaviors and dependency relationships dispersed in different modal data into a unified event causal graph through a graph modeling technology, and providing comprehensive and high-dimensional input for fault reasoning of an intelligent agent; and the accuracy of micro-service fault determination is improved.

The invention relates to the technical field of network flow detection, in particular to a real-time network flow anomaly detection method based on deep learning, and the system comprises the following steps: S1, carrying out the real-time collection and preprocessing of multi-modal data; s2, performing dynamic feature engineering and sliding window statistics; s3, carrying out online adaptive threshold initialization; s4, multi-modal deep learning model reasoning is carried out; s5, updating the adaptive threshold in real time; s6, abnormal decision making and confidence coefficient calibration; s7, generating interpretability analysis; and S8, performing real-time feedback and online learning. According to the scheme, the capability of detecting hidden and complex attacks is remarkably improved through multi-modal data fusion and dynamic feature engineering, network traffic, system logs, user behavior data and external threat intelligence are synchronously collected, and traffic statistical features, time sequence change features, frequency domain features and distribution features are extracted in real time by using a sliding window mechanism.

Some embodiments improve the security of service principals, service accounts, and other application identity accounts by detecting compromise of account credentials. Application identity accounts provide computational services with access to resources, as opposed to human identity accounts which operate on behalf of a particular person. Authentication attempt access data is submitted to a machine learning model which is trained specifically to detect application identity account anomalies. Heuristic rules are applied to the anomaly detection result to reduce false positives, yielding a compromise assessment suitable for access control mechanism usage. Embodiments reflect differences between application identity accounts and human identity accounts, in order to avoid inadvertent service interruptions, improve compromise detection for application identity accounts, and facilitate compromise containment and recovery efforts by focusing on credentials individually. Aspects of familiarity measurement, model feature selection, and a model feature engineering pipeline are also described.

Systems and processes are disclosed for enhancing cybersecurity and optimizing software repositories through integration of web crawling, web scraping, feature engineering, and advanced machine learning algorithms to detect phishing attempts, prevent account takeover fraud, and identify unused code in repositories. The system collects and refines data from various sources, including transaction logs, customer databases, device details, external data sources, and historical fraud data, to build comprehensive datasets. Feature engineering creates new, meaningful features from the refined data, which are used to train and evaluate machine learning models. The best-performing models are deployed in production to monitor incoming communications and transactions in real-time, flagging suspicious activities and optimizing codebases. This processing ensures timely detection and prevention of security threats while maintaining efficient software development processes. Robust protection is provided against evolving cyber threats and enhances software performance and security through continuous learning and adaptation.

The invention proposes a smart park energy consumption management system and method based on artificial intelligence, and relates to the technical field of smart park energy consumption management, and the system comprises a data collection module which collects original time sequence energy consumption data and equipment state information; the data preprocessing module is used for performing data quality processing on the original time sequence energy consumption data to obtain preprocessed time sequence energy consumption data; the topology management module is used for constructing an energy consumption relation graph and generating node topology representation characteristics; the feature engineering module is used for obtaining a space-time-business joint feature tensor; the anomaly monitoring module is used for obtaining an anomaly score and outputting anomaly positioning information based on an interpretable analysis method; and the visual display module is used for generating an alarm report and performing visual display. According to the invention, accurate anomaly identification and intelligent positioning analysis of multi-level and multi-energy-type energy consumption data of the park can be realized, and the automation level and the operation and maintenance efficiency of park energy management are improved.

The invention provides an ultra-high performance concrete multi-performance prediction method based on machine learning. The ultra-high performance concrete multi-performance prediction method comprises the following steps: Step 1, establishing a data set; step 2, data preprocessing is carried out; step 3, establishing an optimal prediction model: based on the feature subset, adopting a plurality of different machine learning algorithms for training, and selecting the machine learning algorithm with the best training effect as the optimal prediction model; step 4, selecting an optimal feature subset; step 5, explaining the influence of the features on model prediction: calculating the contribution degree of each feature to a prediction result based on the optimal prediction model and the optimal feature subset, and helping to understand the decision process of the model; and Step 6, performance prediction of the ultra-high performance concrete: inputting parameters of the to-be-predicted ultra-high performance concrete into the optimal prediction model to obtain a predicted value of the performance. The technical problems that an existing UHPC performance prediction method is incomplete in data set, insufficient in consideration of data processing and feature engineering and poor in model interpretation can be solved.

The invention relates to the technical field of credit risk assessment, in particular to a credit risk dynamic feature extraction method based on hierarchical reinforcement learning. The layered reinforcement learning-based credit risk dynamic feature extraction method comprises the following steps of S1, obtaining credit risk data, and constructing a knowledge graph; s2, extracting candidate feature attributes, constructing an initial risk tag, and generating first-time multi-dimensional vector sample data; s3, performing dynamic feature engineering processing, and performing re-calibration and iterative updating on the initial risk label of the first-time multi-dimensional vector sample data; s4, performing spatio-temporal feature fusion processing, performing risk dynamic assessment, and calculating a risk prediction value corresponding to the time sequence feature; and S5, constructing a layered DQN framework, and dynamically outputting an interpretable risk feature set in combination with SHAP value quantification contribution verification. According to the method, high-precision and high-interpretability credit risk assessment is realized through multi-source data deep integration, self-attention driven feature engineering and time-space fusion risk quantification.

The invention relates to a computer security protection system based on artificial intelligence, which comprises a data acquisition layer, a data processing and feature engineering layer, an AI model layer, a real-time detection and response layer, a feedback and self-adaption layer and a management visualization layer. According to the method, multiple learning paradigms are fused, the problems of model outdated, attack bypassing and alarm fatigue can be solved, the robustness and the optimization accuracy can be improved, a decision-making basis is generated by adopting tools such as SHAP, the interpretability is improved, man-machine cooperation is achieved, key decisions are reserved for manual auditing, and the safety is improved.

The embodiment of the invention provides a network security validity verification and quantitative evaluation method and system, and relates to the technical field of network security, and the method comprises the steps: obtaining global dynamic threat intelligence and a multi-dimensional global network security risk data source, and carrying out the preprocessing; constructing a global feature engineering system based on heterogeneous information network atlas and sequence analysis, forming a feature vector matrix, and mapping the feature vector matrix into an index state vector; inputting the feature vector matrix, the index state vector and the external environment information vector into an evaluation model, dynamically adjusting the weight of the feature vector matrix of each dimension, and outputting the validity score of each safety control point; based on the score, calculating a safety effectiveness index based on a time decay factor; identifying a weak link based on the index, and performing simulation verification to obtain a simulation attack actual measurement result; and an error vector is constructed based on the result and the validity score, and parameter adjustment and weight calibration are carried out. According to the scheme, the accuracy and the real-time performance of network security evaluation are improved.

The embodiment of the invention provides a financial credit intelligent marketing system and method. Comprising a multi-source data acquisition and integration module, a data preprocessing and fusion module, a multi-modal data fusion and feature engineering module, a user portrait construction and subdivision module, an intelligent marketing strategy making module, a marketing execution and real-time monitoring module and an effect evaluation and strategy iteration module. The financial credit intelligent marketing system comprises a plurality of functional modules such as data acquisition, data processing, model training, strategy making, real-time marketing, batch marketing, execution monitoring and the like. A comprehensive and multi-dimensional user portrait is formed by integrating enterprise internal data and an external third-party authorization data source. And machine learning algorithms such as XGBoost, LightGBM and the like are utilized to carry out deep mining and analysis on user behaviors, and purchase intention and demand change of the user are predicted. Meanwhile, based on the Flink real-time data processing technology, quick response and dynamic adjustment of marketing activities are achieved.

The invention discloses a multi-priori knowledge fused cerebral apoplexy rehabilitation map convolutional network evaluation method, and belongs to the technical field of cerebral apoplexy rehabilitation evaluation. Firstly, a high-precision prior information matrix is automatically generated through a collaborative and causal relationship automatic reasoning method based on Riemannian manifold geometry and transfer entropy, the problem that in the prior art, engineering depends on artificial features is effectively solved, and interpretable physical prior guidance is provided for a model. Then, through a multi-relation graph construction and attention weighting multi-modal adaptive fusion method, spatio-temporal features and priori knowledge are adaptively fused, an optimized graph structure is constructed, and the representation ability and interpretability of the model to complex joint interaction are improved. And finally, through a space-time diagram convolutional network guided by prior information and a comparative learning collaborative optimization method, the generalization ability and evaluation precision of the model in a small sample scene are remarkably improved through data enhancement and loss function optimization.

The invention relates to the technical field of chip scheduling and optimization, in particular to a neural network-based main control chip task scheduling and dynamic performance optimization method, which comprises the steps of data acquisition and feature engineering, neural network model design, simulation environment training, model compression and deployment preparation, real-time state monitoring, dynamic decision reasoning, scheduling strategy execution and performance optimization. The data acquisition and feature engineering comprises the following steps: S1, hardware index acquisition; analyzing task attributes (calculation-intensive / IO-intensive), a dependency relationship (DAG), deadline (Deadline) and a resource demand (CPU / GPU occupancy rate); collecting data during chip operation through a performance counter (IPC, cache hit rate and branch prediction error rate), a temperature sensor and a power consumption monitoring unit (PMU); the neural network scheduler can achieve the energy efficiency ratio which is 20%-40% higher than that of a traditional method (such as a CFS scheduler), meanwhile, the neural network scheduler adapts to sudden load changes, and the practicability and the application range of a main control chip are wider.

Hierarchical modelling and advanced feature engineering discover abnormalities in time series data with irregular trends. Data is collected in real time to ensure temporal integrity in the invention. Extraction filters and isolates useful data. Data cleansing removes noise and extraneous data after preliminary analysis identifies patterns and abnormalities. Feature engineering organizes cleansed data for machine learning algorithms. Primary storage stores this data for fast retrieval and extensive trend analysis. Holidays and weekends provide unique patterns in trend analysis. These trends are used to cluster data and create hierarchical predictive models, starting with a first-order model for general trends and increasing in order to refine residuals. Serializing these models improves storage and retrieval. Trend clusters are created from new data points, and algorithms detect pattern deviations. Statistical tests and machine learning classifiers identify anomalies and create alerts and remedial measures. The system monitors and analyzes incoming data to detect anomalies.

The invention provides a cognitive ability decline detection method and system based on physiological indexes of wearable equipment, and relates to the technical field of feature selection and machine learning. Comprising the following steps of multi-dimensional physiological data acquisition, data preprocessing and time alignment, cognitive ability state label definition, feature engineering and data balance, cognitive ability decline detection model training and optimization, and output of cognitive ability state prediction. Multi-dimensional physiological indexes and time information of a user are collected in real time through a wearable device, and the physiological indexes comprise heart rate fluctuation features, heart rate statistical features, body temperature features, blood oxygen saturation features, motion data, electroencephalogram state features, skin electrical features, near infrared spectrum features and the like. The wearable device is used for integrating multiple types of physiological signal sensors, and continuous and non-inductive collection of multi-dimensional physiological data such as heart rate variability, electrodermal response and oxyhemoglobin saturation is achieved.

The invention belongs to the technical field of traffic management and control, and particularly relates to a congestion feed-forward intervention method based on traffic flow phase change critical point recognition, which comprises the following steps: collecting and preprocessing multi-source heterogeneous traffic data; carrying out multi-scale traffic flow feature engineering; identifying a traffic flow phase change critical point based on a space-time dynamic graph neural network and critical moderation effect analysis; generating a multi-objective optimization congestion feedforward intervention strategy; and performing intervention, evaluating the effect and performing adaptive learning. According to the technical scheme, accurate prevention and early intervention can be performed before congestion occurs, and the operation efficiency and reliability of an urban traffic system are remarkably improved.

The invention belongs to the technical field of network security, and provides a network threat real-time detection and defense method and system based on artificial intelligence. The method comprises the steps of multi-modal data acquisition and preprocessing, dynamic graph feature engineering and knowledge graph collaborative fusion, dual-adaptive model training and optimization, streaming real-time detection and anomaly scoring, DRL-driven hierarchical defense response and automatic disposal, and feedback-driven model adaptive updating and block chain auditing. According to the method, a mixed model of OS-ELM + dual-adaptive ridge regression + federated learning is designed, the training speed is higher than that of CNN, and over-fitting / under-fitting is avoided by dynamically adjusting a regularization coefficient; the federal learning realizes data local training and parameter uploading, and solves the problem of privacy disclosure; knowledge distillation enables the model volume to be reduced, edge equipment deployment is adapted while the accuracy is maintained, and the generalization ability is obviously superior to that of a traditional static model.

Systems and methods are disclosed for real-time anomaly prediction using near real-time data. The invention addresses delays in telemetry data collection from infrastructure components, by collecting metrics and logging this data in real-time. Extracted logged data undergoes initial analysis to identify patterns and anomalies, followed by cleaning to remove noise and errors. Feature engineering enhances the data, creating or modifying features to improve machine learning model performance. The system calculates weighted means of previous data values and computes first and second-order differences to capture immediate changes and trends. These calculations adjust the extrapolated value to accurately reflect current conditions. The adjusted data is integrated into the dataset and validated. The validated data trains and tests a machine learning model, which is then finalized and deployed for real-time anomaly detection. This system ensures accurate and timely anomaly prediction, enabling automated incident response to maintain the reliability and performance of infrastructure components.

The invention provides a discrete manufacturing capacity prediction method based on AI multi-agent collaboration, a medium and a system, and belongs to the technical field of AI multi-agent collaboration manufacturing. Equipment material human resources are abstracted into agents by constructing a distributed multi-agent collaboration architecture, and a time synchronization mechanism is configured; an intelligent agent state sensing layer is established, various resource states are monitored in real time by applying an artificial intelligence technology, an intelligent agent collaborative decision network based on a graph neural network is constructed, and stable convergence is realized by adopting a game theory and a consistency algorithm; a historical data preprocessing module is established, key production features are extracted through data cleaning and feature engineering, a processing strategy is selected according to a data missing rate, a productivity prediction result output and feedback optimization mechanism is established, and model parameters are adaptively adjusted according to prediction errors; the technical problems of low productivity prediction precision and incapability of real-time dynamic adjustment caused by isolated and dispersed heterogeneous resource state information in a discrete manufacturing system are solved.

The invention discloses an abnormal data monitoring method and device based on artificial intelligence, and the method comprises the steps: 1, dividing an original data stream through a sliding window, extracting statistics, time sequence and change rate features, and dynamically screening features adaptive to data distribution based on an SHAP value; 2, constructing a double-flow model, capturing a global isolated mode by adopting an improved isolated forest in a static flow, capturing time sequence dependence on the basis of LSTM-AE in a dynamic flow, and fusing two-flow scores through performance-driven dynamic weight distribution; 3, combining a density peak value algorithm with historical density attenuation weighting, and dynamically adjusting an abnormal threshold value; 4, realizing low-delay incremental learning through a double-trigger mechanism and experience playback; 5, multi-granularity interpretation is generated, manual annotation feedback is supported, feature engineering and model training are integrated, and a'detection-interpretation-feedback-optimization 'closed loop is formed; high-adaptability anomaly monitoring is realized through dynamic feature screening, double-flow fusion detection, threshold value self-adaption and man-machine collaborative optimization.

The invention relates to the technical field of artificial intelligence, in particular to an electrical connection safety management and control method based on intelligent monitoring, which comprises the steps of data acquisition, time synchronization, quality evaluation, feature engineering and the like. According to the method, electric, thermal, mechanical, acoustic and other channels are placed on the same time axis through unified alignment and quality marking of multi-modal data and two-stage time synchronization and resampling, explicit marking is carried out on missing, noise and abnormal points, the weight of a subsequent algorithm can be reduced or skipped according to marks, and misjudgment caused by phase errors and dirty data is reduced.

The invention relates to the technical field of financial risk assessment, and discloses a financial risk assessment system based on artificial intelligence, which comprises a financial risk assessment system. The financial risk assessment system comprises a multi-source heterogeneous data intelligent acquisition layer, a dynamic feature engineering and knowledge graph layer, a hierarchical AI risk assessment engine, a real-time risk monitoring and dynamic early warning layer and a decision support and compliance management layer. According to the financial risk assessment system based on artificial intelligence, by constructing a multi-source heterogeneous data intelligent acquisition network, internal data and external unstructured data (such as public opinion dynamics, supply chain information and the like) of financial institutions are integrated, and cross-institution collaboration of data availability and invisibility is realized by adopting federal learning; according to the method, a hierarchical AI evaluation engine is fused with causal reasoning and an interpretability algorithm (such as SHAP / LIME), so that the evaluation precision is improved, the risk cause and the contribution degree of each feature can be clarified, and the requirement of financial supervision on the interpretability of an AI model is met.

The invention discloses a cross-border e-commerce information risk analysis method combined with cloud computing, and relates to the technical field of e-commerce information security. The method comprises the following steps: accessing multi-source data by adopting a cloud edge collaborative architecture, and carrying out data fingerprint identification to generate a cross-border unique feature code; performing dynamic time warping on the transaction behavior, and capturing a time sequence abnormal mode of the cross-border transaction; constructing a transaction space-time diagram, and capturing an abnormal space-time mode in the cross-border transaction by the space-time feature code; constructing a risk assessment screening model to carry out anomaly detection screening; constructing a risk assessment analysis model, and scoring the credit of the cross-border commercial tenants; and establishing a grading response mechanism, and automatically generating a qualified report. Through distributed data acquisition, real-time feature engineering and a self-adaptive deep learning model, a cloud-edge-end three-level risk perception system is utilized, a cross-border feature cross validation algorithm is provided, the problem that data standards of multiple countries are not uniform is solved, and cross-border e-commerce information processing efficiency is improved.

The invention relates to the technical field of communication production management, and discloses a communication equipment production intelligent management system based on machine learning. The system comprises a production data acquisition module, a feature engineering construction module, a dynamic clustering analysis module, an anomaly detection engine module and a production decision optimization module. The production data acquisition module acquires multi-source sensor data in real time and converts the multi-source sensor data into a standardized sequence with a unified timestamp; the feature engineering module extracts a time domain statistical feature, a frequency domain energy feature and an equipment state association feature to generate a high-dimensional feature vector set; the dynamic clustering module adopts an incremental algorithm to divide clusters online; the anomaly detection module establishes a multi-level Gaussian mixture model based on a clustering label, and quantifies an anomaly probability through a mahalanobis distance; and the production decision module integrates the results to generate an equipment maintenance priority sequence and a production takt adjustment instruction. According to the system, intelligent monitoring and dynamic optimization of the whole production process of the communication equipment are realized, and the real-time change requirement of a complex production environment is met.

The invention discloses an electronic product defect AI intelligent detection method. According to the electronic product defect AI intelligent detection method, a multi-mode collaborative perception and space-time alignment system, a data depth preprocessing and multi-scale dynamic feature engineering module and an artificial intelligence fusion analysis and self-adaptive intelligent decision engine are included. A system operation state monitoring and self-adaptive tuning maintenance module; and a product digital twin auxiliary verification and defect refined modeling module. According to the electronic product defect AI intelligent detection method, efficient, high-precision and high-robustness automatic detection, pixel-level positioning and intelligent decision support can be achieved for various complex, hidden and composite defects.

The invention belongs to the technical field of unmanned aerial vehicle remote sensing and agriculture combination, and discloses an intelligent decision-making system and method for corn fertilization based on mechanism-data dual-drive fusion. The system comprises a mechanism simulation module, a data preprocessing module, a feature engineering module, a modeling module, a visualization module, a decision support module, a dynamic feedback correction module and a report generation and push module. According to the mechanism-data double-drive fusion-based intelligent decision-making system and method for corn fertilization, a large-area corn field block image is obtained in a short time through an unmanned aerial vehicle multispectral system, and the nitrogen diagnosis efficiency is improved; through a lightweight machine learning agent model, corn canopy leaf nitrogen nutrition parameters are accurately predicted, and a basis is provided for accurate fertilization; the system monitors the nitrogen nutrition status of the corn in real time, and provides possibility for dynamically adjusting a fertilization strategy; the multispectral remote sensing technology can perform nitrogen nutrition diagnosis under the condition of not damaging corn plants, and the corn growth environment is protected.

The invention relates to the technical field of power utilization monitoring, and discloses a photovoltaic user power utilization abnormity monitoring method and system based on artificial intelligence. The photovoltaic user electricity consumption abnormity monitoring system based on artificial intelligence comprises a data acquisition module which is used for acquiring photovoltaic power generation data, electricity consumption data and environment data of a user; the data preprocessing and feature engineering module is used for cleaning, aligning and normalizing the original data acquired by the data acquisition module and constructing a feature data set for model training and reasoning; and the artificial intelligence analysis engine module comprises an unsupervised learning unit, a supervised learning unit and a deep learning unit. According to the invention, the false alarm rate and the missing report rate can be effectively reduced, the accurate diagnosis of the abnormal type can be realized, and the intelligent and accurate operation and maintenance requirements of power grid enterprises on the power utilization monitoring of photovoltaic users are met.

The invention belongs to the technical field of education data mining and personalized learning, discloses a learner knowledge cognition level diagnosis method and system based on cross-scale learning performance dynamic modeling, and has higher accuracy in the aspects of learner cognition state prediction and knowledge point difficulty assessment. Through a selective state space modeling mechanism and cross-scale historical learning income feature engineering, cognitive change tracks of students in various learning scenes can be accurately captured; and the robustness, convergence efficiency and long sequence processing capability of the model in learner performance prediction are improved. The method can be widely applied to a personalized education platform, a self-adaptive learning system and an intelligent teaching auxiliary tool, provides accurate student learning state analysis for teachers, optimizes learning path design, and improves the teaching effect.

The invention discloses a vehicle collision accident severity prediction method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting the multi-modal data of a vehicle collision accident, and carrying out the preprocessing of the multi-modal data; converting the pre-processed structured numerical data into a first feature map through a Grubrum angle field method; converting the preprocessed unstructured text data into a text semantic vector through a pre-training language model, and converting the text semantic vector into a second feature map; performing size alignment on the first feature map and the second feature map, and performing splicing on a channel dimension to obtain a multi-channel fusion image; and inputting the multi-channel fusion image into a first deep learning model, and outputting an accident severity prediction result. According to the method, the recognition sensitivity and the prediction recall rate of serious injury accidents are effectively improved, meanwhile, complex artificial feature engineering is avoided, and the generalization ability and the interpretability of the model are enhanced.