2206results about How to "Improve interpretability" patented technology

The invention provides a building health monitoring and evaluation method and system based on a physical neural network, and the method comprises the steps: collecting the monitoring data of vibration, strain, displacement and the like of a building through a sensor network, and carrying out the data preprocessing based on wavelet transformation and a neural network; inputting the processed data into a physical constraint encoder, mapping the processed data to a physical space by adopting a Hamilton quantity encoding method, and constructing constraint conditions in combination with physical model parameters; performing feature matching by using a physical constraint neural network, and extracting key features; adopting a principal component analysis method to decompose early warning indexes, and constructing parameter mapping through a deep neural network; generating a hierarchical decision rule based on a decision tree algorithm; and carrying out constraint check and cost calculation on the candidate schemes, and finishing scheme sorting through an analytic hierarchy process. Physical knowledge is embedded into a deep learning framework, and the accuracy and reliability of monitoring evaluation are improved.

The invention discloses a medical diagnosis intelligent decision-making system based on multi-modal data fusion, and the system comprises the steps: obtaining a medical image, biological features and electronic medical record data of a patient through a patient information collection unit, and extracting feature vectors from different modal data through a feature processing unit; the core of the system is a modal fusion engine, and the module combines a deep neural network and a Bayesian inference engine to realize deep learning and probability-dependent modeling of an association relationship between features; carrying out weight optimization on different modal features by adopting an attention mechanism through a self-adaptive feature optimizer, and generating optimized feature representation; the intelligent diagnosis analysis unit calculates the diagnosis probability based on the optimized features, performs auxiliary diagnosis in combination with a case knowledge base when the confidence coefficient is insufficient, and outputs a diagnosis report containing diagnosis categories, basis and suggestions; according to the method, multi-source heterogeneous data are fused, deep learning and probabilistic reasoning are combined, intelligent medical auxiliary diagnosis is achieved, and diagnosis accuracy and interpretability are improved.

The invention provides a cerebral hemorrhage personalized treatment scheme optimization method and system based on big data analysis, and relates to the technical field of data analysis, and the method comprises the steps: constructing a cerebral hemorrhage patient multi-source heterogeneous clinical database, extracting feature subsets, carrying out semantic alignment, carrying out time sequence modeling and fusion, and obtaining a patient panoramic feature representation; constructing a cerebral hemorrhage diagnosis and treatment knowledge graph, adding the cerebral hemorrhage diagnosis and treatment knowledge graph to a preset graph attention network, distributing weights, obtaining knowledge-enhanced patient feature representation and taking the knowledge-enhanced patient feature representation as a state, generating an action space based on an optional treatment scheme, constructing a reward function and determining a candidate treatment scheme; performing medication suitability analysis, performing semantic conflict identification in combination with the patient allergy history and complication, and performing screening to obtain a suitable candidate treatment scheme set; and for each scheme, predicting a multi-dimensional prognosis index, constructing a multi-criterion decision matrix, combining personal influence factors, adjusting the weight of each index, modeling an optimization problem and determining an optimal treatment scheme.

The invention provides a cross-modal similarity-based text mining data query method and system, relates to the technical field of data mining, comprises a cross-modal similarity-based text mining data query method and system, and belongs to the technical field of natural language processing. The method comprises the following steps: constructing a multi-level feature extraction network to respectively obtain text, image and time sequence feature vectors; feature fusion is realized by using an adaptive cross-modal contrast learning network, and a cross-modal similarity matrix is constructed; performing hierarchical screening on the target text by adopting a multi-level screening model to obtain a candidate text set; a text relation reasoning module is constructed based on a graph neural network, and text clustering and relation prediction are completed through a multi-task learning framework. According to the method, the accuracy of cross-modal text data query is improved, the adaptivity of feature fusion is enhanced, and efficient text relationship mining is realized.

The invention discloses a data-driven cross-domain intelligent asset knowledge reasoning and value evaluation method and system, and relates to the technical field of intelligent assets, and the method comprises the steps: extracting multi-domain data features of intelligent assets based on a deep learning model, and constructing a cross-domain intelligent asset knowledge graph; analyzing a user evaluation target based on a pre-trained language model, retrieving related assets in a knowledge graph, and performing knowledge reasoning by using an attention mechanism and a graph reasoning algorithm; and on the basis of the value evaluation model of reinforcement learning training, a final evaluation value and an evaluation report containing a confidence interval are generated in combination with a time sequence prediction algorithm. According to the method, multi-source heterogeneous data can be effectively fused, knowledge reasoning and value evaluation of cross-domain intelligent assets are realized, and the evaluation efficiency and accuracy are improved.

The invention relates to the technical field of electric power short-term load prediction, in particular to an industrial park short-term load prediction method and system based on a Diffusion-Transform-AR model. Acquiring non-stationary historical load data and meteorological data of the industrial park, and performing missing value processing on the data; performing correlation analysis on the data by using the maximum information coefficient, selecting meteorological data with relatively high correlation, and reprocessing time data; performing time sequence decomposition on the load data by using a seasonal trend decomposition method to obtain trend term, seasonal term and residual term features; constructing a diffusion Transform prediction model, and taking the residual term, the meteorological data and the time data as input prediction residual; constructing an autoregression prediction model and taking the trend term as an input prediction trend; according to the method, the advantages of various models are combined, time sequence features are deeply mined, the prediction precision and the model generalization ability are effectively improved, and the method is suitable for complex industrial park load prediction scenes.

The invention provides a crop yield prediction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining continuous historical meteorological data, soil data, crop planting data and remote sensing image data of a designated region for many years, and carrying out the alignment according to a time sequence, and constructing a multi-dimensional spatio-temporal data cube; a fusion prediction model is trained, the fusion prediction model is used for extracting growth feature vectors in the crop growth and development process and carrying out yield prediction, and model parameters of the fusion prediction model are dynamically adjusted through an adaptive learning rate optimization algorithm; the method comprises the following steps: acquiring real-time meteorological data, soil data and remote sensing image data of a specified area, inputting the data into a pre-constructed fusion prediction model, introducing an agricultural knowledge graph into the fusion prediction model to restrain and correct a prediction result of the fusion prediction model, and reasoning to obtain crop growth and estimated yield of each grid of the specified area. And performing spatial interpolation on the estimated yield of each grid to generate a yield distribution diagram of the specified area.

The invention provides an intelligent fault diagnosis method for a numerical control machine tool, which is applied to a control system of the numerical control machine tool. The method comprises the steps of collecting multi-source sensor data in the operation process of the numerical control machine tool, wherein the multi-source sensor data comprises at least two of a vibration signal, a temperature signal, a current signal and a sound signal; performing preprocessing and feature extraction on the multi-source sensor data to obtain preprocessed data; based on the preprocessed data, constructing and training a machine tool dynamic model to obtain a target machine tool dynamic model; when abnormal operation of the numerical control machine tool is detected, fault state estimation is carried out based on the target machine tool dynamic model and a model prediction control method, and a fault estimation result is obtained; and based on the fault estimation result, constructing a fault evolution path by adopting a Monte Carlo tree search algorithm, and outputting a fault diagnosis result. The accuracy and efficiency of fault diagnosis of the numerical control machine tool can be improved.

The invention relates to the technical field of data processing, and discloses a road condition monitoring recognition method, device and equipment based on machine vision and a medium. The method comprises the following steps: carrying out multi-source data acquisition on a road environment to obtain an original data set; performing dynamic time warping and multi-modal feature fusion processing on the original data set to obtain a spatial-temporal feature tensor; performing multi-scale pyramid decomposition and depth feature extraction on the spatial-temporal feature tensor to obtain a road condition feature vector; semantic analysis is carried out on the road condition feature vectors through feature mapping and knowledge graph enhancement, and a road condition attribute set is obtained; performing multi-level risk quantification and space-time propagation simulation on the road condition attribute set to obtain a graded early warning index and a risk distribution map; and performing multi-target tradeoff and causal chain reasoning on the graded early warning indexes and the risk distribution map to obtain a self-adaptive traffic regulation and control instruction set. According to the invention, the efficiency and accuracy of road condition monitoring and identification based on machine vision are improved.

The invention discloses a construction method and device of a multi-component data agent, and the construction method specifically comprises the following steps: obtaining sample data, and constructing a vector knowledge base; extracting a knowledge service flow according to the sample data, determining an ontology through knowledge representation, and forming a knowledge graph; training an initial language model based on the vector knowledge base, and giving a data processing model; and performing modularization arrangement and linking on the knowledge graph and the data processing model in combination with a logic tool chain and a thinking graph of the data intelligent agent, and constructing to obtain the multi-component data intelligent agent. According to the method, the knowledge graph is fused to construct the business rule engine, training data support is provided, the thinking chain process is modeled into the thinking graph of a graph structure, and the data processing model and the knowledge graph associated with the workflow node are executed, so that the accuracy of result reasoning of the data agent is enhanced, and the knowledge boundary is expanded; and particularly for financial auditing data, key information can be better extracted, and the working efficiency of power grid auditing personnel is improved.

The invention discloses a neurology nursing potential safety hazard monitoring system and method, and relates to the technical field of medical information safety monitoring, and the system comprises a first collection processing unit which is used for collecting monitoring data of a target patient, inputting the monitoring data into a time sequence deep network for processing, and obtaining risk feature data; the second collecting and processing unit is used for collecting physiological state data of the target patient, constructing a risk assessment rule base based on a medical knowledge graph to analyze the physiological state data, and generating risk probability data; and the risk early warning unit is used for inputting the risk feature data and the risk probability data into a Bayesian fusion network, generating risk early warning information and sending the risk early warning information to the medical care terminal. According to the invention, collaborative analysis of multi-source information is realized through the Bayesian fusion network, the problem of low early warning accuracy of a traditional single monitoring method is solved, and the reliability of potential safety hazard monitoring is improved.

The invention belongs to the technical field of electric power data security management and intelligent question answering, and particularly relates to an electric power data security policy large model question answering system and method based on relation pooling. The system comprises a power data security policy knowledge base, a language model, a question processing module, an answer generation module and a user output module. Data of the electric power data security policy knowledge base comes from an electric power data security policy file; the language model converts a natural language problem into a structured query for knowledge base retrieval; the question processing module retrieves policy terms related to the natural language question to serve as retrieval results; the answer generation module generates a final answer according to the natural language question and a retrieval result of the knowledge base; by introducing the intelligent question-answering system combining the large language model and the knowledge base, the problems of low efficiency, inaccurate interpretation, incapability of dynamically updating and the like in the existing power data security policy query and management are solved.

The invention discloses a method and a device for constructing a sample efficient world model based on Transform. The method comprises the following steps: separating historical interaction data to obtain historical action sequence data and historical original state data; converting the original state data into multi-scale feature data and carrying out denoising enhancement processing to generate enhanced state data; performing adaptive quantization coding on the enhanced state data to obtain discrete latent variable data; causal structure data are constructed based on historical data, attention weights are designed, and causal enhancement representation data are generated; calculating uncertainty data based on the time sequence association data and generating multi-step prediction sequence data; decomposing the long-term prediction target to obtain layered value data and calculating strategy gradient data; and screening the new interaction data and updating model parameters. Through the methods of multi-scale feature extraction, causal structure learning, hierarchical value evaluation and the like, the prediction accuracy and the sample utilization efficiency of the world model are improved.

The invention relates to the technical field of new energy power generation, and discloses an intelligent panoramic monitoring management system and method for a new energy box transformer substation, and the method comprises the steps: obtaining an original monitoring data set of a target box transformer substation, and carrying out the data cleaning; key features are extracted from the cleaned data, and a weighted feature set is generated; constructing a box transformer substation fault prediction model to obtain a fault risk assessment value; performing graded early warning on potential faults based on the evaluation values; constructing a box transformer fault diagnosis knowledge graph, and performing reasoning and tracing in combination with the knowledge graph during high-risk or medium-risk early warning; through multi-source data fusion and knowledge graph reasoning, comprehensive monitoring of the operation state of the box transformer substation and intelligent fault diagnosis are realized, and the operation reliability and maintenance efficiency of the new energy box transformer substation are improved.

The invention discloses an e-commerce intelligent operation monitoring and collaborative decision-making method based on big data analysis, and the method comprises the following steps: S1, collecting multi-source heterogeneous data, and constructing an original data set; s2, performing data cleaning and characterization processing to generate a feature data set; s3, constructing a knowledge graph and dynamically updating the knowledge graph by using the graph embedding model; s4, generating potential causal paths and weights in combination with the neural relationship reasoning model and the diffusion model, and introducing a microknowledge enhancement layer to generate causal enhancement features; s5, identifying abnormal behaviors in combination with a multi-level anomaly detection mechanism; s6, designing a dynamic game optimization model, determining a collaborative optimal solution under a multi-target conflict through Nash equilibrium, and processing a dominant and subordinate relationship in combination with a Stackelberg game to generate a collaborative optimization strategy; and S7, displaying the monitoring state and the collaborative optimization strategy through a visual interface. According to the invention, big data analysis, a knowledge graph and a dynamic game technology are combined, so that e-commerce intelligent operation monitoring and collaborative decision-making are realized.

The invention provides a DCS system autonomous controllable upgrading and system fault prediction method and system, and relates to the technical field of fault prediction, and the method comprises the steps: obtaining historical operation data and real-time operation data of a DCS system, carrying out the preprocessing, and generating an autonomous controllable upgrading scheme through a deep reinforcement learning algorithm; the method comprises the following steps of: setting specified time for upgrading time, carrying out consistent backup on the state of the DCS, executing software updating, hardware configuration adjustment and system parameter optimization according to upgrading steps, analyzing indexes of the DCS in real time in the process of executing the autonomous controllable upgrading scheme, and recovering the DCS to the state before upgrading when an abnormal condition is detected. And verifying and optimizing the real-time fault prediction result based on a knowledge graph, and generating an explanatory report including a fault prediction and upgrade decision process according to the optimized fault prediction result and the autonomous controllable upgrade scheme.

The invention provides an unstructured data automatic processing method and system based on deep learning, and relates to the technical field of data processing, and the method comprises the steps: obtaining unstructured data through an intelligent data collector, building a data feature fingerprint database, constructing a data quality evaluation model through an incremental learning algorithm, and carrying out the data standardization processing; extracting dominant features and recessive features by using a multi-layer cascade network structure and a self-attention mechanism, and constructing a feature association graph and a bidirectional reasoning channel; and performing rule matching and causal reasoning based on the domain knowledge base, and finally realizing automatic structuring of the unstructured data. According to the method, the automation degree and the accuracy of data processing are improved, the manual intervention cost is reduced, and the method has relatively high universality and expandability.

The invention discloses a concept recommendation method based on large language model structure learning and knowledge representation, and belongs to the field of concept recommendation. The method comprises the following steps: firstly, acquiring learning data and a concept knowledge graph of a student account in a learning process, and preprocessing the learning data and the concept knowledge graph; performing text coding on an enhanced text generated by the large language model by using a pre-training language model to obtain a final hidden vector of each concept, further constructing an enhanced concept knowledge graph, and pre-training a GraphGPS concept encoder based on the enhanced concept knowledge graph and a double-view cross graph contrast learning method; synthesizing education data by a TabDDPM model to pre-train a deep knowledge tracking model; and splicing the concept representation of question embedding, answer embedding and structure perception and the student knowledge state representation to form a final representation, further obtaining a coding sequence, pre-training a sequence coding model and finely adjusting a concept recommendation matrix, and finally forming a concept recommendation model for performing concept recommendation.

The invention provides a wind power plant monitoring image automatic analysis and fault prediction method, system and device and a medium, and the method comprises the following steps: 1, collecting image data, sound data, vibration data, SCADA system data and environment data of a wind power plant, and forming multi-modal data; step 2, carrying out preprocessing on the obtained multi-modal data; 3, extracting features from the preprocessed multi-modal data, and fusing the features to obtain fused features; step 4, performing fan fault diagnosis and prediction based on the fused features to obtain a fault diagnosis and prediction result; according to the invention, end-to-end training of fault type identification and severity evaluation is realized, the accuracy of diagnosis and prediction is improved, and the workload of manual processing is greatly reduced.

The invention relates to the technical field of data processing, and discloses a multi-granularity extraction and enhancement method, device and equipment for multi-source heterogeneous data. The method comprises the following steps: performing multi-level feature extraction on a multi-modal data set and original feature parameters to obtain a multi-modal feature set; performing correlation analysis on the multi-modal feature set to obtain a feature correlation matrix and optimized feature representation; performing cross-modal knowledge migration processing on the feature incidence matrix and the optimized feature representation to obtain migration feature parameters; performing dynamic fusion processing on the migration feature parameters to obtain target fusion features; and performing multi-task segmentation processing according to the target fusion features to obtain a plurality of sub-task sequences, performing time sequence enhancement and multi-task fusion analysis on each sub-task sequence to obtain target extraction data, and performing visual display on the target extraction data. According to the method and the device, the efficiency and the accuracy of multi-granularity extraction and enhancement for multi-source heterogeneous data are improved.

The invention discloses a variable working condition migration bearing fault diagnosis method based on a ViTCapsNet network in a noise environment, and the method comprises the steps: firstly obtaining vibration signal data of a rolling bearing in different fault states under different loads, and adding Gaussian white noises of different degrees; performing continuous wavelet transform on the data added with the noise to obtain a two-dimensional wavelet image, and dividing the two-dimensional wavelet image into a training set and a test set; a ViT Encoder model is established, position information capable of being trained is added by using an Embedded module, position features are embedded into the model, and global information in the features is extracted through a multi-head self-attention mechanism in the Encoder module; constructing a capsule network which comprises a primary capsule layer and a digital capsule layer, further extracting spatial information in the features, and performing accurate fault diagnosis; and finally, performing visual interpretation on the features processed by the Encoder module by using Grad-CAM, and completing efficient migration fault diagnosis on a test data set.

The invention discloses a medical field large model pre-training and fine tuning method and device based on a knowledge graph, and the method comprises the steps: training a reward model through employing collected medical related text data and a quality marking result, and screening out high-quality data from the newly obtained medical related text data through employing the trained reward model; coding the medical knowledge graph into a text format processed by a large language model, and alternately inputting the high-quality data and the coded medical knowledge graph into the large language model in different batches for model pre-training; and designing an instruction fine tuning data format comprising an instruction, a knowledge graph reasoning path and a generated answer, inputting a training sample constructed in the instruction fine tuning data format into the pre-trained large language model for model fine tuning, and outputting a final answer and reasoning path according to the instruction newly input by the user by utilizing the fine-tuned large language model. According to the method, the professionality of the large language model in the medical field can be effectively improved, and AI application in high-risk fields such as medical treatment is promoted.

The invention relates to the field of drug target interaction, and discloses an application of a multi-modal feature fusion model in drug target binding affinity prediction, which comprises the following steps: S1, obtaining an original data set of drug target binding affinity, and taking a binding affinity index in the data set as a label of a training set and a test set; s2, acquiring representation information of other modals by using the drug sequence and target sequence representation provided in the original data set, preprocessing the acquired representation information of drugs and targets in different modals, and processing the representation information into a model acceptable feature vector form; and S3, respectively storing the multi-modal feature vectors of the drug and the target as well as the binding affinity tags in a form of pytorchsensor (. Pt). According to the method, shallow characterization of the drug and protein sequences is learned by applying the pre-training model, and the problem that a drug target binding affinity data set is insufficient is solved.

The invention discloses a photoelectric multi-mode decision fusion method and system for mental disorder recognition, relates to the technical field of mental disorder recognition, and performs decision fusion based on an improved machine learning algorithm by integrating photoelectric multi-mode data and collecting and analyzing various data in real time. Through effective fusion of result data of different modal recognition models, the accuracy and stability of mental disorder recognition are improved, meanwhile, the misjudgment rate of a single modal method is reduced, and the overall diagnosis efficiency is improved. The invention not only provides an innovative method, but also designs a corresponding fusion system, and provides a comprehensive solution for early recognition and diagnosis of mental disorders.

The invention relates to the technical field of AI computing, in particular to a knowledge reinforcement learning method and system based on multiple agents. Modifying parameters of the large language model by using a supervised fine-tuning training method for the pre-trained large language model; constructing a knowledge base of a large language model, and vectorizing, storing and retrieving texts in various file formats; a multi-agent workflow optimization information processing and decision-making process is developed, a real-time problem is decomposed, a logic chain for solving the problem is constructed, and internet texts are obtained, filtered and summarized. Compared with the prior art, the method has the advantages that the knowledge quantity of the pre-trained large language model in the specific field is increased, the knowledge base is high in flexibility, the obtained text is high in real-time performance, certain interpretability is achieved and the like, and then the ability of the large language model to solve problems in the specific field is improved.

The invention belongs to the technical field of natural language processing. According to the zero resource framework-based fine-grained large model illusion processing method and system, multi-dimensional anomaly detection is carried out on a preprocessed text through a large language model, and a multi-dimensional anomaly detection result is obtained; performing anomaly correction priority ranking according to a multi-dimensional anomaly detection result; dynamically adjusting a context window range according to a syntactic analysis result and the generated syntactic dependency tree; performing priority correction on high-risk vocabularies and sentences according to an abnormal correction priority ranking result in combination with the adjusted context window range; and evaluating the corrected content, and obtaining a corrected final text when a termination condition is met. According to the method, the excessive confidence, subtle semantics and grammar deviation illusion detection of the detection content is realized, and the illusion detection processing capability of a large language model, especially the detection processing capability of a fine-grained illusion problem, is improved.

The invention provides a limb nerve rehabilitation state evaluation method based on multi-modal sensor data fusion. Motion inertia data, electromyographic signal data and text data of a corresponding limb nerve rehabilitation action manual of a patient during rehabilitation actions are obtained in real time; inputting the data into a designed and trained multi-modal data feature extraction network to obtain motion inertial data features, electromyographic signal data features and text semantic features; inputting the motion inertia data features, the electromyographic signal data features and the text semantic features into a designed and trained multi-modal data feature fusion network to obtain multi-modal fusion features; and inputting the multi-modal fusion features into a trained prediction output network to obtain a limb nerve rehabilitation state prediction value of the patient. Through multi-modal data fusion and time-dependent feature extraction, the limb nerve rehabilitation state of the patient is evaluated more accurately, and a more reliable diagnosis basis is provided for doctors. Meanwhile, the labor cost is reduced and the evaluation efficiency is improved.

The invention belongs to the field of fault diagnosis, and particularly relates to a main shaft bending common characteristic diagnosis system based on a stacking integration method, and the system comprises the following modules: a sensor module which is used for collecting a vibration signal of a main shaft in real time; the data acquisition and processing module is used for converting an analog signal into a digital signal and preprocessing the signal acquired by the sensor; the feature extraction module is used for extracting features from the preprocessed vibration signals; the machine learning diagnosis module analyzes and classifies the extracted features by using stack integration based on a plurality of trained machine learning models, and judges the health state of the main shaft; and the output module is used for outputting the diagnosis result and sending out an alarm signal when abnormality is detected. Real-time monitoring and automatic diagnosis of the main shaft bending fault are achieved, compared with a traditional method depending on expert experience and manual detection, the fault detection efficiency and accuracy can be greatly improved, and misjudgment and missed judgment risks caused by human factors are reduced.

The invention discloses a lower limb exoskeleton rehabilitation robot joint torque prediction method and device based on an electromyographic signal, and the method comprises the steps: firstly collecting the electromyographic, joint angle, angular velocity and joint torque data of a user, and carrying out the modeling of four muscles related to a knee joint through a neuromusculoskeletal model; and identifying physiological parameters used in the model by using a sparrow optimization algorithm, and outputting an optimal solution to obtain an estimated torque. Then, the real torque and the estimated torque are endowed with the weights of # imgabs0 # respectively, and a fusion torque is obtained; and training a neural network model by using the collected data and the fusion moment, and meanwhile, enabling the neuromusculoskeletal model to participate in the training process of the neural network model by adopting a method of bringing the neuromusculoskeletal model into a loss function, so that the constraint of the model is increased. And finally, storing the trained model for subsequent calling. The method has the advantages of high precision and strong generalization, and model training can converge more quickly under the condition of less data volume.

The invention provides a prognosis risk prediction model construction method based on coexistence of acute myocardial infarction and atrial fibrillation and application. The method comprises the following steps: S1, acquiring historical patient clinical information of a target patient and death time after coexistence of diseases; s2, preprocessing clinical information of historical patients; s3, using an LASSO method and a ten-fold cross validation method to identify related clinical features related to death time after coexistence of diseases; s4, dividing a training set and a verification set; s5, performing training optimization on the four machine models by using the training set, evaluating the prediction performance of the models by using the verification set, and determining a Glmnet model with the optimal prediction performance; and S6, performing model interpretation on the Glmnet model by using an SHAP value, and determining an optimal prediction factor of which the model prediction contribution ranks the first three. The prognosis risk of coexistence of acute myocardial infarction and atrial fibrillation can be accurately predicted, and reference is provided for clinical diagnosis and treatment of patients with coexistence of acute myocardial infarction and atrial fibrillation.