114348 results about "Data set" patented technology

A method for detecting an infrared ship target based on an improved YOLOv7 is provided, including the following steps: obtaining an infrared maritime ship data set; reforming a YOLOv7 network structure based on an MobileNetv3 network and a bidirectional weighted feature pyramid network, and obtaining an infrared ship target detection model by introducing an attention mechanism and an optimized loss function; training and verifying the infrared ship target detection model based on the infrared maritime ship data set to obtain the infrared ship target detection model trained; and detecting a maritime ship based on the infrared ship target detection model trained.

The invention relates to the technical field of intelligent operation and maintenance, and discloses an intelligent operation and maintenance method fusing multi-modal data and active learning, and the method comprises the steps: deploying a hierarchical Internet of Things equipment network in a target operation and maintenance region, collecting a heterogeneous operation and maintenance data set, and generating an operation and maintenance feature data set through employing a unified building operation and maintenance service framework cooperating with a plurality of MCPs; inputting the operation and maintenance feature data set and the user feedback information into a deep learning intention recognition model for intention recognition and demand analysis to obtain structured user demand data and an operation and maintenance task priority sequence; according to the operation and maintenance feature data set and the structured user demand data, performing real-time evaluation on the equipment operation state to obtain fault risk early warning data; autonomous decision analysis is carried out through an agent type artificial intelligence engine, an equipment maintenance scheme and a resource scheduling scheme are generated, and then the problems that in traditional operation and maintenance, fault prediction is single, the model adaptability is poor, and prediction results are difficult to convert into effective decisions are solved.

The invention relates to the technical field of privacy data protection, and discloses a privacy data protection method and system based on homomorphic encryption and federated learning, and the method comprises the steps: processing original health data collected by Internet of Things equipment of a plurality of pension institutions, and obtaining a preprocessing data set and a privacy risk assessment result; encrypting to obtain a double encrypted data set; constructing a decentralized federated learning framework, and calculating global training parameters; performing noise injection processing and model iteration training to obtain a privacy protection model; performing trusted execution environment deployment on the privacy protection model, and performing identity verification through role-based multi-level access control and zero-knowledge proof to obtain a security access strategy; edge node distributed deployment is conducted on the privacy protection model, cross-mechanism health data analysis is conducted through a secure multi-party computing protocol, a collaborative analysis result is obtained, various privacy protection technologies are adopted, and efficient utilization of data is achieved while data privacy is protected.

The invention provides an intelligent power distribution load prediction and adaptive scheduling method, and aims to improve the response capability and operation efficiency of a power distribution system to load fluctuation. According to the method, historical load data, real-time meteorological information and user power consumption behavior data of a target power distribution area are collected, a multi-dimensional input data set is established, a short-term load prediction model is constructed based on the data set, and prediction of future load levels of multiple feeder lines and key nodes is achieved. According to a prediction result, potential voltage deviation, load imbalance and equipment overload risks are identified, and a dynamic scheduling control strategy is generated by adopting a rolling horizon optimization method in combination with a power grid topological structure and operation constraints; the strategy comprises control measures such as transformer tap position adjustment and capacitor bank switching; through issuing a scheduling strategy to an equipment control unit, active adjustment is completed before load change, voltage stabilization, load balancing and energy optimization are realized, and intelligent and adaptive operation guarantee is provided for a power distribution system.

The invention provides an intelligent subway equipment fault diagnosis method and system assisted by a large language model, and relates to the technical field of data processing, and the method comprises the steps: extracting key information through a large language model, constructing a multi-dimensional equipment fault knowledge graph, obtaining a historical fault data set, and extracting key fault monitoring parameters related to the fault, obtaining a sensing state data set, and judging whether the sensor state data is abnormal or not; and calling a pre-constructed sensing distortion correction algorithm, generating a fault monitoring correction parameter and executing parameter correction, inputting multi-dimensional monitoring data into a diagnosis engine driven by a large language model, and outputting a most possible fault type, cause analysis and recommendation processing strategy and a fault identification report. The technical problems that in the prior art, due to the lack of fusion modeling capacity for the unstructured fault text and the structured monitoring data, the intelligent degree of fault diagnosis is low, and accurate recognition and causal analysis are difficult to achieve are solved, and the fault recognition response speed and accuracy are improved.

The invention discloses a power grid dispatching decision-making method based on a graph neural network and a large model, and the method comprises the steps: collecting an original power grid data set, and generating a fusion feature tensor through adaptive distribution mapping and recursive decomposition; importing a fusion feature tensor and a power grid topology matrix, and generating a dynamic graph embedding matrix through spectral decomposition and matrix manifold analysis; performing Markov jump process analysis and optimal transmission fusion on the dynamic graph embedded matrix to generate an integrated feature matrix; a multi-target Lyapunov function is constructed on the basis of the integrated feature matrix, and a robust decision strategy is generated by solving a Hamilton-Jacobi-Bellman equation; and performing multi-dimensional performance monitoring and statistical manifold learning on the robust decision strategy to realize strategy optimization. According to the method, the uncertainty in power grid operation can be effectively processed, and the accuracy and reliability of scheduling decisions are improved.

The invention belongs to the technical field of knowledge retrieval, and discloses a multi-modal large model-based dynamic vector knowledge base construction and retrieval method, which comprises the following steps of: obtaining a multi-source heterogeneous modal data set, and carrying out preprocessing and modal standardization processing on the multi-source heterogeneous modal data set to obtain a standardized multi-modal data set; performing feature extraction and semantic vector representation generation by using the pre-trained multi-modal large model, and constructing a multi-modal knowledge vector set; semantic association analysis and hierarchical clustering are carried out on the multi-modal knowledge vector set, and a structured vector knowledge base is constructed; performing semantic similarity calculation and relation modeling on the vector knowledge base to form a vector relation network; intention analysis and vector representation are performed based on mixed modal query information input by a user, and efficient similarity retrieval is realized in combination with a vector relation network; dynamic optimization is carried out through user feedback, personalized retrieval result adjustment is achieved, and the problem of limitation of a traditional retrieval system during multi-modal data processing is effectively solved.

The present invention discloses an interactive behavior understanding method for posture reconstruction based on skeleton and image features. The steps are as follows: constructing and preprocessing the data set, extracting skeleton and image features, fusing and reconstructing these features, and conducting experimental evaluation and validation. This method retains the purity of skeleton features for human behavior information extraction and uses image features to retain effective environmental information, complementing the model feature information. Skeleton features are extracted using a graph convolution network, enhancing the relevance of input skeleton point information for accurate feature extraction. Effective image features are quickly and accurately extracted through the Vision Transformer network combined with a multi-head attention mechanism.

The invention discloses a ring main unit monitoring method and system based on the Internet of Things, and the method comprises the steps: building a multi-source data collection network of a ring main unit, extracting the multi-dimensional time-frequency domain features of a signal through an edge computing device, generating a multi-source feature data set, and constructing a dynamic digital twinborn model, real-time mapping of the running state of the ring main unit is achieved through a physical-virtual synchronization algorithm, and twin data volumes are output; according to the node influence coefficient of the twin data volume, analyzing a topological structure of a ring network system by adopting a graph neural network, identifying a key fault propagation path and generating a priority monitoring node list; based on the priority monitoring node list and the twin data volume, dynamically generating an adaptive monitoring strategy through a reinforcement learning algorithm, executing the adaptive monitoring strategy and collecting feedback data in real time, and completing closed-loop iteration of the monitoring strategy through an online learning algorithm; through closed-loop control of multi-source perception-digital twinning-intelligent decision, high efficiency, intelligence and reliability of ring main unit monitoring are realized.

The invention relates to the technical field of data processing, and discloses a financial transaction anomaly detection and risk assessment method and device based on artificial intelligence. The method comprises the following steps: collecting and preprocessing multi-source financial transaction related data to obtain a multi-dimensional transaction data set; performing feature engineering and abnormal mode recognition on the multi-dimensional transaction data set to obtain an abnormal transaction recognition result; based on the abnormal transaction identification result and the multi-dimensional transaction data, constructing a financial domain knowledge graph to obtain a relation network between transaction entities; performing multi-dimensional risk assessment on the relation network among the transaction entities to obtain a risk score and risk description data; based on the risk score, performing time sequence analysis and trend extrapolation on the risk description data to obtain a dynamic risk early warning signal; and performing scene simulation and multi-factor analysis on the dynamic risk early warning signal to obtain a risk management strategy suggestion. The efficiency and accuracy of financial transaction anomaly detection and risk assessment based on artificial intelligence are improved.

A method and system are introduced for recognizing cognitive engagement in classrooms by utilizing multimodal data. Associated modalities of cognitive engagement are learned from visual and audio data. This approach involves constructing a multidimensional representation model for cognitive engagement that includes behaviors, emotions, and speech. To identify engagement, three distinct deep learning models are employed: You Only Look Once version 8 (Yolov8) for analyzing body posture, Efficient Network (EfficientNet) for facial expressions, and Text Convolution Neural Network (TextCNN) for speech text. These models are trained and refined with the aid of student engagement surveys, leading to a decision-making process that integrates the results from the different modalities. Additionally, a dataset and a data annotation system are developed for engagement recognition. This innovative method aims to achieve detailed engagement recognition, addressing various perception needs in real-world applications.

Disclosed in the present invention are an adaptive deep transfer fault diagnosis method and system, an apparatus and a medium. The method comprises the following steps: S1: collecting vibration acceleration signals of industrial equipment under different working conditions, and dividing same into a source domain data set and a target domain data set; S2: building a self-tuning universal domain adaptive fault diagnosis model, which comprises a shared feature extractor, a known classifier and a plurality of unknown classifiers; S3: separately calculating a classification loss of known faults of the source domain, a discriminative loss of the plurality of unknown classifiers, a target domain soft consistency regularization loss and an information entropy maximization loss; S4: introducing a dynamic weighting strategy based on model uncertainty assessment to optimize the model parameters; and S5: using the model for diagnosis. The present invention can fully mine valid information in data, can establish reliable class decision boundaries, and in addition, uses the self-tuning dynamic update strategy to adjust weightings corresponding to different loss functions, thus allowing for quick generalization of the model to different industrial diagnosis scenarios.

The invention relates to the technical field of database construction, in particular to a knowledge base question-answering system construction method and device, equipment and a storage medium. The method comprises the following steps: acquiring a user question instruction, and performing part-of-speech tagging and keyword vectorization on a target text to generate a retrieval text keyword vector; performing semantic linear embedding and spatial projection processing on the keyword vectors to obtain keyword semantic vectors; logic mining is conducted on the keywords, a logic relation set among the keywords is obtained, logic recognition is conducted on the target text based on the relations, and retrieval text logic is generated; performing matching retrieval with semantic vectors in a knowledge base to obtain a candidate semantic query data set; and performing coherent logic retrieval on the candidate data set according to the retrieval text logic and the keyword vector to generate a final retrieval target answer. According to the method and the system, more accurate and efficient knowledge base question-answering system construction is realized.

The invention belongs to the technical field of visual special effects, and discloses an AI visual special effect dynamic generation system fusing multi-modal perception. Through cooperative work of six core modules of multi-modal input processing, importance analysis, parameter configuration, parameterized rendering, parameter optimization and rendering and output, deep fusion of special effects and contents is realized. An image sequence, audio data and scene parameters can be processed at the same time, a multi-modal feature data set is constructed, a scene key visual area is scientifically recognized, a visual importance distribution map is generated, and the special effect parameter configuration and rendering process is guided. An improved neural radiation field technology and a physical constraint model are adopted to ensure the consistency and reality of the special effect at different visual angles; and a multi-dimensional quality evaluation and parameter automatic optimization mechanism is introduced, so that the visual expressive force and the artistic value of the special effect are guaranteed. The creation threshold is remarkably reduced, the cooperative expression ability of the special effect and the content is improved, and the special effect becomes a powerful tool for enhancing the narration and enhancing the emotion.

The present invention belongs to the technical field of land change detection. Disclosed are a land change detection method combining matrix decomposition and adaptive propagation, and a system. A deep learning semantic segmentation model combining matrix decomposition and adaptive propagation is used for detecting a land change. The model is of a twin double-branch structure, which can receive both an input of a pre-temporal remote sensing image data set and an input of a post-temporal remote sensing image data set and can share network parameters, and which has the advantage of end-to-end feature learning. In the present invention, an attention method based on matrix decomposition is used to replace a conventional attention method based on manual design; moreover, in view of adaptive propagation technology for pixel offsets of neighboring points, a deep learning twin differential semantic segmentation network is established to realize automatic and precise land utilization / land coverage change detection on the basis of high-resolution optical data.

In embodiments, systems and methods for improving machine-learning systems are disclosed. In embodiments, a system includes a data pool system that is configured to receive data from a plurality of different data sources and maintain a training data set that is used to train a specific machine-learning model based on the data from the plurality of different data sources. In embodiments, the system further includes a data scoring system that determines a data reliability score corresponding to the new data based on a set of intrinsic features of the new data and a data scoring model, wherein the data pool system selectively adds the new data to the training data set based on the reliability score of the new data. The system also includes a machine learning system that trains the specific machine-learning model based on the training data set.

Computer-implemented systems and methods for synchronizing data for dataset execution. The system includes a source database that stores a canonical dataset, a secondary database that stores a processed dataset, and a synchronization server that comprises a processor and a memory. The processor is configured to monitor for a publication of one or more source tables and when the publication is detected, identify the processed tables, corresponding to the source tables, to be updated in the processed dataset. The processor determines a tolerance level corresponding to each processed table and updates the processed tables in the processed dataset. The processor 10 determines whether the processed tables in the processed dataset were successfully updated within the tolerance levels and transmits a notification based on determining whether the processed tables in the processed dataset were successfully updated within the tolerance levels.

The invention provides a meteorological metadata storage method and system based on machine learning, and the method comprises the steps: obtaining a meteorological metadata set of a target region, inputting the meteorological metadata set into a pre-trained time sequence feature extraction model, generating the time sequence distribution features of all meteorological file files, and inputting a spatial topology mapping model, generating spatial correlation topological features of the meteorological elements; based on a cross fusion result of the time sequence distribution features and the space correlation topological features, a hierarchical index structure of meteorological metadata is constructed, a meteorological storage optimization model is called to generate a storage node distribution strategy of a meteorological file, and the meteorological file is stored according to the storage node distribution strategy. And writing each meteorological file file in the meteorological metadata set into the corresponding distributed storage node, and dynamically adjusting the mapping relationship between the physical position and the index of the stored file based on a real-time updating mechanism of the hierarchical index. The meteorological metadata storage efficiency and retrieval accuracy can be improved, and storage resource waste and operation and maintenance cost are reduced.

The invention discloses a water quality prediction method based on graph neural network and spatial-temporal feature fusion, and the method comprises the steps: obtaining hydrological data and water quality data of a plurality of monitoring stations in a preset drainage basin in a preset time period, representing the monitoring stations as nodes of a graph, and representing the adjacency relation between the monitoring stations as edges of the graph, a graph topology network mapping multiple monitoring station pollutant data is constructed, a spatial-temporal feature fusion module is adopted to perform feature extraction and fusion on the graph topology network to obtain water pollution spatial-temporal feature data, and the spatial-temporal feature fusion module is obtained by training a graph convolutional network, a gating circulation unit and a self-attention mechanism. And performing fusion processing on the extracted feature data to form a time sequence data set of a graph topology structure, and finally predicting the data set through a graph time sequence prediction model to obtain water quality prediction results of the plurality of monitoring stations in a target time period. The method can effectively deal with the spatial and temporal change process of water pollution, reduce the water quality prediction cost and improve the water quality prediction precision.

The invention relates to the technical field of deep learning, and discloses an MES digital collaborative management method and system based on deep learning, and the method comprises the steps: collecting multi-source heterogeneous data, and constructing a dimensionless multi-dimensional data set; analyzing a dynamic association relationship among the data items through node feature embedding and edge relationship learning, and generating a production line operation dependency relationship graph; performing collaborative anomaly detection and root cause positioning in combination with the dynamic association weight between the data items to obtain causal association data between the abnormal event and the data parameters; constructing a digital twinborn simulation environment to simulate an influence path of heterogeneous data parameter intervention production disturbance on collaborative anomaly so as to evaluate an anomaly influence result; constructing an iterative scheduling model according to a deep learning framework, and iteratively generating a multi-objective optimized collaborative scheduling strategy to realize dynamic configuration and exception prevention among heterogeneous data; therefore, collaborative optimization management of production operation parameter configuration and abnormity prevention of multi-source heterogeneous data in large-scale production with high dimension, high complexity and dynamic change can be realized.

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention relates to the technical field of meteorological data analysis, and provides an aerosol prediction method and system based on artificial intelligence, which are used for accurately identifying an abnormal aerosol gathering mode in an extreme meteorological event. The method comprises the steps that an original multi-mode meteorological observation data set is acquired, and the original multi-mode meteorological observation data set comprises an earth surface aerosol concentration sequence, three-dimensional atmosphere state variables and boundary layer dynamic parameters; performing space-time heterogeneity correction on the original multi-modal meteorological observation data set to generate a space-time aligned target multi-modal meteorological observation data set; inputting the target multi-modal meteorological observation data set into a spatial-temporal feature fusion network to extract multi-scale spatial distribution features and cross-modal time sequence correlation features, and generating a spatial-temporal feature matrix based on the multi-scale spatial distribution features and the cross-modal time sequence correlation features; and performing multi-stage recursive optimization on the spatial-temporal characteristic matrix through a hierarchical time domain aggregation algorithm to generate an aerosol concentration prediction sequence of the target region.

The invention provides a flowmeter online calibration system and method, and the method comprises the steps: firstly collecting a real-time operation data set of a target flowmeter in a pipeline operation environment, and covering dynamic monitoring data and environment state data; preprocessing the data set to obtain an operation data sequence capable of describing the change trend of the operation state of the flowmeter at multiple continuous time nodes, inputting the operation data sequence into a pre-trained calibration parameter generation model, and generating a calibration parameter set comprising a flow correction coefficient, an environment compensation factor and an error adjustment threshold value; and adjusting and measuring output data in real time based on the calibration parameter set, generating a calibrated flow measurement result, comparing the calibrated flow measurement result with a standard flow reference value for verification, and if the deviation is greater than an error adjustment threshold value, regenerating the calibration parameter set and performing iterative adjustment until the deviation is within a threshold value range, thereby realizing high-precision and self-adaptive online calibration of the flowmeter.

The invention discloses a method, a system and a device for monitoring multifunctional parameters of a direct-current drilling machine, and relates to the technical field of manufacturing of industrial automatic control system devices. The method, system and device for monitoring the multifunctional parameters of the direct-current drilling machine comprises the steps that S1, various data are collected and subjected to standardization and normalization processing, and a standardized working condition data set is constructed; s2, multi-dimensional disturbance characteristics are analyzed, and the stability level of a drilling system is quantified; s3, evaluating a dynamic evolution trend of a working condition, and updating a risk level, a response strategy and a monitoring priority; and S4, identifying an abnormal state based on the key disturbance value and the trend evolution value, and generating a monitoring report. The problems that an existing direct current drilling machine display device is insufficient in key working condition feature extraction capacity, deep understanding and trend analysis of the equipment operation state are difficult to support, and then the early warning timeliness and judgment accuracy of the abnormal state are limited are solved.

The invention relates to the technical field of data processing, and discloses an intelligent inspection and fault positioning method and system for power distribution equipment. The method comprises the following steps: performing multi-type sensor deployment on key points around power distribution equipment to obtain an intelligent sensor network; performing multi-mode acquisition and multi-path transmission processing on data acquired by the intelligent sensor network to obtain an original monitoring data set; cleaning, standardizing and fusing the original monitoring data set to obtain preprocessed structured data; performing multi-dimensional spatial correlation analysis on the structured data to obtain health state evaluation data of the power distribution equipment; performing dynamic threshold calculation and intelligent diagnosis on the power distribution equipment to obtain early warning information and a preliminary diagnosis result; and performing expert knowledge base matching and multi-criterion decision analysis on the early warning information and the preliminary diagnosis result to obtain a maintenance decision suggestion scheme. According to the invention, the accuracy of intelligent inspection and fault positioning of the power distribution equipment is improved.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support generative artificial intelligence (AI)-assisted analytics of structured data sets. For example, a system may receive a prompt that includes information associated with a structured data set which includes at least some numerical data. The system may provide the prompt as input to an agent orchestrator to select one or more generative AI agents to perform analytics tasks corresponding to the information. The agent orchestrator includes a trained AI classifier configured to select the one or more generative AI agents from a plurality of generative AI agents. The system may execute an ensemble model to generate a response to the prompt based on the structured data set. The ensemble model includes the one or more generative AI agents. The system may output a graphical user interface (GUI) that includes one or more elements based on the response.

A composite AI system and method for advanced reasoning and automation that integrates symbolic knowledge graphs and algorithms with non-symbolic, or connectionist, models such as neural embeddings. A hierarchical architecture enables dynamically distributed, cooperative reasoning through layperson and expert-led challenge-based verification, model blending, model fitness and retraining and selection, comprehensive feedback loops at individual model or model blend or process flow with or without supervision, and specialized routing of processing to account for various operational risk, regulatory, legal, privacy, or economic considerations. Models, datasets, knowledge bases, simulations and simulation components, and embeddings are iteratively refined using knowledge graph elements and model, process, simulation or flow / process optimal hyperparameters which are recorded and tracked. Extraction of symbolic representations from connectionist models links them to curated ontologies of facts and principles.

The invention provides a knowledge graph generation method and system for science and technology project risk control, and the method comprises the steps: obtaining a multi-source heterogeneous data set of a target science and technology project, converting structured index data into a standard vector sequence through a heterogeneous data fusion mechanism, and converting unstructured text data into a semantic vector sequence; converting the time sequence behavior data into a behavior pattern vector sequence, inputting the three into a risk quantitative evaluation model, generating a risk entity feature matrix and a risk association strength matrix, and determining a node distribution topology of the knowledge graph according to entity feature vectors in the risk entity feature matrix; and according to association strength values in the risk association strength matrix, determining an entity relationship topology of the knowledge graph, generating a dynamic knowledge graph of the target science and technology project, and identifying a potential risk propagation path in the dynamic knowledge graph. According to the invention, the risk identification result has the dynamic characteristic of real-time updating, and the traceability of the multi-dimensional risk characteristic is maintained.

The invention relates to the technical field of the Internet of Things, and discloses a gas leakage monitoring method, device and equipment based on the Internet of Things and a storage medium, and the method comprises the steps: carrying out the data collection of a gas storage device, and obtaining an initial multi-dimensional data set; performing filtering and edge detection processing to obtain potential abnormal point data; inputting the potential abnormal point data into a mixed deep learning model for gas leakage detection to obtain initial leakage source position data and a leakage intensity value; executing multi-sensor cooperative accurate leakage positioning to obtain target leakage source position data; performing gas diffusion simulation to obtain gas concentration distribution data and risk indexes; based on the gas concentration distribution data and the risk index, dynamic reconstruction of the sensor network is carried out, an emergency response strategy and sensor network configuration information are output, and the method realizes whole-process management from leakage detection and source positioning to risk assessment and emergency response.

The invention provides a decision model construction method based on a big data environment. The method belongs to the economic data decision field. The method comprises the following steps: firstly, acquiring multi-source heterogeneous data including sensor time sequence data, an expert rule base and equipment causal priori knowledge, and constructing a standardized training data set; then, extracting an explicit causal relationship from the historical data by utilizing a causal discovery algorithm, and generating an interpretable knowledge graph in combination with priori knowledge of an equipment manual; thirdly, constructing a neural symbol joint model; neural network parameters and rule confidence coefficients are synchronously adjusted, and collaborative learning of data driving and knowledge driving is realized. And then, carrying out multi-dimensional logic verification on the trained model, including rule conflict detection, anti-factual reasoning and decision path traceability verification. The method can effectively improve the accuracy and interpretability of the decision model, and is suitable for processing decision tasks in a big data environment.