10194results about How to "Increase diversity" patented technology

The invention discloses a tunnel structure health monitoring and early warning system based on multi-source twin data fusion, which relates to the technical field of tunnel health monitoring and comprises a data acquisition module, a multi-source data fusion module, a health state evaluation module and an early warning module. The data acquisition module can collect various monitoring data of equipment state data, tunnel environment data, traffic condition data and tunnel structure of a monitored tunnel, the multi-source data fusion module integrates and processes the collected multi-source data by using an advanced data fusion technology, and the health state evaluation module evaluates the health state of the monitored tunnel according to the fused data. And the early warning module is used for evaluating the health condition of the tunnel structure by adopting an intelligent evaluation algorithm and identifying potential structure problems, and the early warning module is used for forming an early warning result by applying an early warning algorithm on the basis of an evaluation result. The advanced data fusion method and early warning algorithm are adopted, and the accuracy and reliability of tunnel health monitoring and early warning are improved.

The invention provides a network attack link tracking and threat situation reasoning method based on a knowledge graph, and relates to the technical field of network security, and the method comprises the steps: collecting and processing original data; extracting features and constructing a network attack knowledge graph; performing representation learning by adopting a graph neural network; identifying a time sequence dependency relationship; generating and analyzing candidate attack links; constructing a dynamic Bayesian network model; performing parameter learning by adopting a variational reasoning algorithm; future attack targets and means are predicted. According to the method, the network attack link can be effectively tracked, the future threat situation is accurately predicted, and the network security defense capability is improved.

The invention provides a new material production process parameter optimization method and system based on artificial intelligence, and relates to the technical field of parameter optimization, and the method comprises the steps: constructing a multi-dimensional process parameter feature space, collecting and preprocessing process parameters, extracting correlation features through a deep convolutional neural network, and building a parameter feature space model; generating a technological parameter collaborative optimization model, constructing a multi-objective optimization function, and performing iterative optimization by using reinforcement learning and an improved ant colony algorithm; and performing process parameter adaptive control, constructing a dynamic control interval, collecting parameters in real time and performing adaptive adjustment to realize closed-loop control and model dynamic update.

The invention discloses a method and system for generating enhanced knowledge questions and answers for heterogeneous database mixed retrieval, and the method comprises the following steps: (1) processing text data, and constructing a heterogeneous database; (2) receiving user query, and performing mixed retrieval in the heterogeneous database; the retrieval results are merged, and documents and triple information related to user query are obtained; (3) reordering the retrieved documents and triple information by using a reordering model, and selecting K retrieval results with the highest relevancy with user query; and (4) combining user query and retrieval results, constructing cue words, inputting the cue words into the large language model, and generating coherent and accurate natural language answers. By means of the method and device, more comprehensive and accurate information retrieval can be achieved, and the accuracy and reliability of knowledge question and answer results are improved.

The invention discloses a multi-source remote sensing image semantic segmentation method based on Transform, Mama and a diffusion model, belongs to the technical field of electric digital data processing, graphic data reading and image data processing, and is used for extracting image information of a remote sensing image. Comprising the following steps: acquiring remote sensing images, constructing an image information extraction network based on the remote sensing images, and respectively inputting the remote sensing images in a training set, a verification set and a test set into the constructed remote sensing image information extraction network based on Transform, Mama and diffusion models. According to the method, the RGB features and the depth features are subjected to Transform feature extraction, and a cross Mama method is provided to perform cross enhancement on the RGB features and the depth features; according to the method, a row and column pooling Transform module is used for replacing a traditional Transform module, and detail texture information and large-scale structure information of a feature map in the vertical direction and the horizontal direction are extracted at the same time; according to the invention, a cross Mama attention feature fusion module is used to replace a traditional Mama module, and the modeling capability for complex scenes and diversified targets is enhanced.

The invention relates to the technical field of marine environment monitoring, in particular to a time-space diagram neural network anomaly detection method and system for marine ecology. The method comprises the following steps: acquiring ocean monitoring data; performing data preprocessing on the acquired marine monitoring data; constructing a space-time diagram structure based on the preprocessed marine monitoring data; performing spatio-temporal feature extraction on the constructed spatio-temporal diagram structure by using an anomaly detection model; carrying out feature fusion on the extracted spatial-temporal features; and carrying out local anomaly and global anomaly scoring on the marine ecosystem based on a multi-scale anomaly scoring mechanism. According to the method, a unified data processing framework is designed, multi-source heterogeneous data such as satellite remote sensing data, water quality monitoring data and biological survey data can be processed at the same time, information contained in various kinds of data is fully utilized, and the comprehensiveness and accuracy of anomaly detection are improved. Through the construction of a space-time diagram structure, the incidence relation between different data sources is effectively captured, and more comprehensive feature expression is provided for anomaly detection.

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 field of geological disaster monitoring and early warning, in particular to a slope catastrophe early warning method and system. The method comprises the following steps: establishing a space-time database, storing and managing preprocessed slope data from multiple monitoring sources, and sorting to obtain a slope catastrophe data set; performing feature extraction on the slope catastrophe data set to obtain a key information set reflecting slope catastrophe features; analyzing the key information set to obtain a slope catastrophe value; calculating an early warning threshold value and a real-time displacement prediction value of the side slope based on the side slope data in the space-time database, and fusing the real-time displacement prediction value and the side slope catastrophe value to obtain a fusion value; and evaluating the real-time stable state of each monitored section of the slope according to the obtained fusion value and an early warning threshold value, and judging whether to send out an early warning signal and an alarm signal or not according to an evaluation result. According to the invention, accurate identification of the slope catastrophe value and the potential risk area is realized, and the accuracy and reliability of the early warning system 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 relates to the technical field of Internet of Vehicles, and discloses a holographic road network and Internet of Vehicles fusion method and device, equipment and a storage medium. The method comprises the steps of obtaining multi-source heterogeneous data collected by a target sensor, and performing adaptive multi-modal preprocessing on the multi-source heterogeneous data; performing space-time alignment and hierarchical structure recombination on the preprocessed multi-source heterogeneous data to obtain multi-source traffic recombination data; extracting multi-scale spatio-temporal features in the multi-source traffic recombination data, and performing multi-level semantic association, deep probability fusion and uncertainty modeling on the multi-source traffic recombination data based on the extracted traffic feature vector set and the preliminary dynamic traffic knowledge representation to obtain traffic perception probability distribution; and a high-precision dynamic semantic map is constructed based on traffic perception probability distribution, and fused multi-resolution map representation is obtained. According to the invention, the fusion of the holographic road network and the Internet of Vehicles multi-source data is realized, and the accuracy of the traffic characteristic data in the fusion result is improved.

The invention relates to the technical field of geological exploration, and discloses an advanced geological forecasting method and system based on tunnel geological exploration. The method comprises the following steps: performing data denoising on a multi-source data set pre-collected in a tunnel construction area to obtain an original data set; performing preprocessing and multi-stage data fusion on the original data set to obtain a fused data set; performing three-dimensional space modeling and feature extraction on the fused data set to obtain an intelligent geological prediction result; performing multi-factor risk assessment on the intelligent geological prediction result to obtain a geological risk distribution map in front of the tunnel; performing construction scheme optimization on the geological risk distribution map to obtain a construction guidance scheme; and carrying out comprehensive analysis and dynamic updating on the real-time monitoring data, the construction guidance scheme, the intelligent geological prediction result and the geological risk distribution map to obtain an advanced geological prediction result. According to the method and the system, the defects of insufficient prediction precision, reliability and accuracy of a traditional geological prediction method are overcome, and the safety risk is reduced.

The invention provides a building construction potential safety hazard management method and system, and relates to the technical field of construction safety management, and the method comprises the steps: dividing a plurality of sub-regions, collecting data, classifying known potential safety hazard events, and obtaining a first classification; through comparative analysis of the reference features and the contrast features, corresponding data features and fusion features are selected, and risk assessment models of the first classification are established respectively; obtaining a first risk probability and a comprehensive risk index of each sub-region according to real-time monitoring data and the risk assessment model; according to the first risk probability, the comprehensive risk index and / or the risk change, performing early warning on the sub-region; according to an early warning processing result and field feedback; adjusting an early warning threshold value and / or optimizing the risk assessment model; real-time monitoring, early warning and management of potential safety hazards of the construction site are achieved, the efficiency and accuracy of potential safety hazard management can be improved, the labor cost can be reduced, and the safety level of the construction site can be improved.

The invention relates to the technical field of data processing, and discloses a road infrastructure health monitoring and evaluation method and device, equipment and a medium. The method comprises the following steps: performing multi-mode acquisition and multi-path transmission processing on data acquired by an 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 through a multi-scale time sequence analysis algorithm to obtain health state evaluation data of the road infrastructure; based on the health state evaluation data, performing dynamic threshold calculation and intelligent diagnosis on the road infrastructure 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 and an optimization scheme. The road infrastructure health monitoring and evaluation efficiency and accuracy are 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 provides a multi-level low-altitude air route network construction method in a complex urban environment, and relates to the technical field of low-altitude air route construction, and the method comprises the steps: building a low-altitude flight rule base; performing three-dimensional discretization on the urban space, and dividing the airspace into three-dimensional grids; designing a risk assessment function R; dividing a plurality of height layers, and distributing the height layers for different types of aircrafts; constructing a multi-stage air route network; constructing an airspace three-dimensional airway model; based on the airspace three-dimensional air route model, adopting a hybrid iterative algorithm to optimize the air route planning of each height layer; performing path smoothing on the discrete path points generated by the hybrid iterative algorithm by adopting a B spline curve, and performing flight performance verification on the smoothed path to obtain a continuous flight path; and generating a low-altitude air route network according to the continuous flight path. According to the method, the defects in the aspects of complex urban environment processing, multi-source data integration, multi-level airspace division, path optimization and the like in the prior art can be overcome, so that safe, efficient and flexible low-altitude flight management is realized.

The invention relates to the technical field of data analysis, and discloses a business data analysis method, device and equipment based on big data and a storage medium, the method comprises the following steps: collecting and preprocessing original log data to obtain standardized log data, and performing adaptive multi-modal feature extraction to obtain comprehensive feature representation; inputting a heterogeneous graph neural network to carry out anomaly detection and multi-level root cause analysis to obtain a hierarchical anomaly event; executing task scheduling and resource allocation of reinforcement learning to obtain a task allocation scheme; performing deep learning enhanced root cause reasoning and knowledge graph construction on the task allocation scheme to obtain a system state diagram and a causal relationship; and generating a multi-dimensional task data analysis report. According to the method, comprehensive analysis, anomaly detection, root cause reasoning and intelligent scheduling of log data are realized. The reliability and performance of the system can be improved, and more intuitive and accurate decision support can be provided for operation and maintenance personnel.

The invention discloses a power transmission and transformation equipment fault early warning system based on online monitoring, and the system comprises the following modules: a multi-parameter sensor module which is used for collecting data in real time; the wireless sensor network module is used for transmitting the data to the data processing center for processing; the data fusion module is used for generating a comprehensive health score of the equipment by adopting a multi-modal data fusion algorithm based on a self-evolution nested network; the trend prediction module introduces an adaptive residual module to carry out trend prediction based on a nonlinear time sequence analysis technology of a generative adversarial adaptive residual network; the early warning threshold value adjusting module is used for dynamically adjusting an early warning threshold value by adopting a self-adaptive evolutionary algorithm of meta reinforcement learning; the maintenance suggestion generation module is used for generating maintenance suggestions; and the power grid dispatching linkage module is used for automatically linking a power grid dispatching system and detecting and adjusting an equipment load. According to the invention, a multi-parameter fusion and generative adversarial network method is adopted, and an adaptive residual module is introduced, so that accurate early warning of faults of power transmission and transformation equipment is realized.

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 network security situation early warning method and system based on a knowledge graph, and relates to the technical field of network security, the method establishes a normal behavior reference model of a network node through the combination of the knowledge graph and a deep learning algorithm, and enhances the accurate identification and real-time early warning capability of abnormal behaviors. Through an unsupervised learning algorithm, historical data of each node is learned and modeled, and a normal behavior reference value is formed. In the real-time monitoring process, deviation calculation is carried out on the current access behavior of each node and the reference value of the current access behavior, a deviation degree value is obtained and compared with a set deviation threshold value, and abnormal nodes deviating from normal behaviors are identified and marked. The deviation degree model combines key factors such as a transmission protocol and data transmission duration, and can sensitively detect tiny deviation of the node in communication frequency or authority, so as to quickly adapt to a dynamic network environment.

The invention provides an intelligent retrieval method and system for unstructured asset content based on a large model, and relates to the technical field of data retrieval, and the method comprises the steps: carrying out semantic expansion on retrieval keywords through a knowledge graph enhancement module, obtaining asset feature vectors through a multi-modal feature extraction model, and constructing a hierarchical asset feature index database; the comprehensive similarity is calculated through a weighted fusion algorithm, and retrieval results are optimized and reordered through semantic comprehension and correlation analysis capacity of the pre-trained large model. According to the method, the retrieval precision and efficiency of the unstructured asset content are improved, and intelligent association analysis and semantic understanding of cross-modal assets are realized.

The invention relates to the technical field of process parameter optimization, and discloses a surfactant process parameter analysis method and system based on multi-level factors, and the method comprises the steps: carrying out multi-dimensional process parameter collection and preprocessing on production equipment of a surfactant, and obtaining multi-dimensional process parameter time sequence data; thermodynamic feature extraction is carried out to obtain an equipment temperature distribution feature vector; calculating an equipment performance degradation index; performing process parameter association analysis to obtain a process parameter association probability matrix; executing a double-delay deterministic strategy gradient algorithm to obtain a parameter hierarchical control strategy; according to the technical scheme of the invention, the comprehensive improvement of the product quality, the production efficiency and the equipment reliability is realized through the integration of the equipment state monitoring, the process parameter optimization and the intelligent control strategy. And powerful support is provided for intelligent and fine management of the surfactant production process.

The invention relates to the technical field of data processing, and discloses a power distribution method based on AI artificial intelligence and an intelligent power distribution cabinet. The method comprises the following steps: carrying out real-time data acquisition on key nodes of the power distribution system through an intelligent sensor network to obtain original operation data of the power distribution system, and carrying out cleaning and standardization processing on the original operation data to obtain a standardized data set; performing analysis modeling on the standardized data set through multi-level hybrid model training to obtain an AI analysis model of the power distribution system; processing and analyzing the power distribution data collected in real time to obtain a load prediction result and a dynamic balance strategy; performing abnormal mode identification and multi-path reasoning on the system operation parameters to obtain equipment health state evaluation and maintenance suggestions; and based on the equipment health state assessment and maintenance suggestions, performing multi-objective optimization calculation on renewable energy source supply and power consumption demands to obtain an intelligent energy scheduling scheme. According to the invention, the efficiency and accuracy of the power distribution method based on AI artificial intelligence are improved.

The invention discloses a data quality monitoring method and system based on a knowledge graph. According to the method, data tables and fields in multi-source heterogeneous data are constructed into graph nodes, graph edges are constructed through main and foreign key constraint relationships and field value overlapping analysis, and a data blood relationship knowledge graph is formed; business rules are coded into the atlas to execute data constraint check, and an abnormal data influence range is traversed and tracked through a directed graph; and dynamic monitoring is realized based on the time change of the abnormal score sequence. The invention further provides system implementation including data acquisition and preprocessing, knowledge graph construction, quality monitoring, problem diagnosis and quality evaluation. The method solves the problems that a traditional method is difficult to track the influence range of abnormal data and lacks systematic evaluation, and improves the automation level and accuracy of data quality management.

The invention relates to a health monitoring and fault prediction technology of intelligent equipment, in particular to an equipment fault prediction method based on health degree analysis of the intelligent equipment. According to the method, multiple sensors are embedded in the intelligent device, data of the operation state of the device are continuously collected, and primary processing and abnormal value elimination are carried out through an edge computing node. The processed data is transmitted to a cloud server, and machine learning models such as a deep neural network, a Bayesian classifier and a support vector machine are utilized to analyze the equipment health degree trend, and a potential fault mode is identified. And intelligently generating a fault prediction report according to an analysis result, and notifying an operation and maintenance team to take measures through an operation and maintenance management system. In addition, the system further comprises a historical fault database, a scoring system, an online review group and other functional modules, the accuracy and reliability of prediction are ensured, and the equipment maintenance efficiency and safety are improved.

The invention discloses a mountainous area high pier cast-in-place safety detection method based on multi-source data fusion, and relates to the technical field of intelligent building safety monitoring, and the method comprises the steps: deploying a sensor network, a multi-angle holder video monitor and an unmanned aerial vehicle, covering a mountainous area high pier for comprehensive monitoring, and collecting multi-source data; transmitting the multi-source data to an edge computing node in real time through a wireless network, and preprocessing the multi-source data to form a standardized multi-source data set; applying a spatio-temporal data fusion method, combining historical data and a comprehensive data model to predict a security risk, and generating a risk factor; threshold and rule abnormity identification is carried out on the risk factors, the safety condition is evaluated in real time, early warning is triggered in real time, and disposal suggestions are provided. According to the method, the safety detection efficiency and accuracy in the mountainous area high pier cast-in-place process are remarkably improved through multi-source data fusion and a quantum enhancement multi-modal algorithm, the triggering speed of safety early warning is effectively increased, and the safety guarantee of a construction site is remarkably enhanced.

The invention discloses an intelligent monitoring method and system based on the Internet of Things, a medium and a program product, and the method comprises the steps: abstracting a monitoring region into a dynamic graph structure, and obtaining a self-adaptive monitoring scene graph; fusing at least one piece of video data and at least one kind of data of other sensors at each node to form a multi-mode sensing network; carrying out distributed feature extraction on the multi-modal sensing data to obtain data features, and compressing the data features into low-dimensional vectors; constructing a space-time knowledge graph; executing an anomaly detection algorithm on a preset number of spatial scales; matching a corresponding adjustment mode in a preset adjustment scheme according to the type and the degree of the abnormal event, and adjusting the numerical weights of the nodes and the edges to obtain an adjusted adaptive monitoring scene graph; and according to the adjusted adaptive monitoring scene graph and the numerical value weight corresponding to the node, synchronously adjusting the working parameters of all the cameras of the Internet of Things and other sensors. According to the invention, the integrity and comprehensiveness of the monitoring data acquired by the Internet of Things camera equipment are improved.

The invention provides a collocation recommendation method and system based on association rule mining, and relates to the technical field of association rule mining, and the method comprises the steps: obtaining user historical transaction data, generating a user commodity transaction score matrix, carrying out the standardization processing, and predicting the commodity score through calculating the cosine similarity between users, and obtaining an initial candidate set; carrying out normalization processing on commodity prices, calculating commodity comprehensive similarity in combination with a category hierarchical structure, carrying out clustering grouping, and screening frequent item sets meeting support degree and confidence threshold values; and finally, a commodity price change sequence is extracted to calculate a dynamic price matching degree, a multi-dimensional evaluation index is constructed, a non-dominated sorting genetic algorithm and a deep Q network are adopted for optimization, and scene-based dynamic evaluation and recommendation are realized. According to the invention, the accuracy and timeliness of commodity collocation recommendation are improved, and the interpretability of the recommendation result is enhanced.

The invention provides a marine pollution tracing and monitoring method based on satellite remote sensing and an unmanned aerial vehicle, and relates to the technical field of marine pollution tracing and monitoring, and the method comprises the steps: configuring a pollution diffusion path prediction model and a data driving model based on range pollution data, precise pollution data and deepwater data after multi-modal data fusion, and obtaining a pollution prediction result and pollution error data, carrying out hybrid drive fusion based on the pollution prediction result and the pollution error data, and tracking an initial source of pollutants to obtain pollution source tracing data. Through preprocessing and fusion of multi-modal data, in combination with range pollution data, precise pollution data and deep water data, a pollution diffusion path prediction model and a data driving model are dynamically adjusted, so that a more precise pollution prediction result and pollution error data are generated, and meanwhile, through reverse diffusion simulation and pollution traceability analysis, a more precise pollution prediction result is obtained. The high efficiency of pollution source positioning and decision support is improved, and an accurate and decision basis is provided for coping with marine pollution events.

The invention provides a ship design knowledge retrieval method and system based on a multi-modal knowledge graph. The method comprises the steps of collecting and constructing a ship design set from the ship design field; forming a hierarchical multi-modal knowledge graph for describing a ship design field concept, physical characteristics and design specifications; enhancing association among different modal data through vector similarity calculation; the hierarchical multi-modal knowledge graph is integrated into a ChatGLM model; positioning knowledge nodes related to user demands in the hierarchical multi-modal knowledge graph; matching and positioning the ship design knowledge related to the query content of the user, and feeding back the ship design knowledge obtained by the intelligent semantic retrieval to the user in a hierarchical form. According to the method, ship design knowledge retrieval based on the multi-modal knowledge graph enhanced ChatGLM is realized, and the information acquisition efficiency of engineering designers and the practical application value of the system are improved.

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.