2043 results about "Trend analysis" patented technology

The invention discloses a territorial space planning environment influence monitoring method and device. The method comprises the following steps: collecting and preprocessing multi-source environment monitoring data; performing spatio-temporal feature extraction on the preprocessed multi-source environment monitoring data by using a self-supervised spatio-temporal diagram mask transfer attention network to obtain initial spatio-temporal features; performing fusion processing on the initial spatial-temporal characteristics based on a multi-mode diffusion converter to obtain fusion characteristics; calculating an environmental impact evaluation index based on the fusion feature; and based on a preset visualization template and an early warning rule, generating a dynamic monitoring result including a space-time evolution diagram, a trend analysis diagram and early warning information according to the environmental influence evaluation index. Through deep learning and multi-modal data fusion technologies, the precision and efficiency of environmental impact monitoring are improved, and accurate evaluation and timely early warning of environmental impact in the land space planning implementation process are realized.

The invention provides a numerical control machine tool wear automatic detection and compensation method based on artificial intelligence, and the method comprises the steps: collecting the cutting force data of a high-curvature region in real time through multi-sensor fusion, and obtaining the cutting force fluctuation characteristics; cutting temperature data of the high-curvature area are monitored and obtained, the cutting temperature change rate is extracted, whether the temperature exceeds a preset threshold value or not is judged, and if yes, an alarm mechanism is triggered, and cutting parameters are adjusted; predicting the tool wear rate in combination with the co-evolution relationship between wear and temperature, the online monitoring data and the processed time, and generating a wear prediction curve in a preset time period; and performing trend analysis and feature extraction on the wear prediction curve to obtain wear parameter changes of the cutter in a preset time, and if the prediction curve shows that the wear parameter changes at a certain time point in the future exceed a preset critical value, adjusting the cutting parameters and generating a target cutting parameter combination.

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 belongs to the technical field of electromechanical equipment operation and maintenance, and discloses an electromechanical equipment self-adaptive intelligent early warning system based on multi-source sensing data. The system is composed of a multi-source sensing module, an edge data acquisition and preprocessing module, a data cleaning and multi-dimensional feature extraction module, an equipment state dynamic modeling module, an intelligent fault prediction and trend analysis module, a self-adaptive early warning threshold generation and dynamic adjustment module, and an intelligent decision and remote cooperation module. The system is composed of a multi-source sensing module, an intelligent low-carbon operation and maintenance management and control module and a digital twin system integration and full-period mapping module, multiple sensors are deployed through the multi-source sensing module to acquire multi-dimensional data of equipment, cleaning and calibration are performed through the edge data acquisition and preprocessing module, deep processing is performed through the data cleaning and multi-dimensional feature extraction module, and multi-dimensional feature extraction is performed through the multi-source sensing module. The data integrity and accuracy are ensured; and data are quickly transmitted among the modules, so that the monitoring system can accurately present the running state of the equipment in real time.

The invention discloses a territorial space layout adjustment decision method based on planning conflict identification, and belongs to the technical field of territorial space planning and management. Comprising the following steps: constructing a space-time conflict feature vector library containing multi-dimensional attributes; generating a semantic knowledge graph containing a causal relationship, a sequential relationship, a spatial relationship and an affiliation relationship; accurate prediction of influence intensity and duration of the conflict state along with time lapse is realized; trend analysis and risk level evaluation of a cross-scale conflict propagation path are realized; automatically identifying dominant conflicts in the current layout and potential conflicts based on propagation prediction, and intelligently classifying composite conflicts by using fuzzy clustering; a staged differentiation conflict intervention strategy recommendation scheme is automatically generated; solving a territorial space layout adjustment scheme; judging whether the layout adjustment scheme meets a preset conflict relieving standard or not;

The invention discloses an intelligent management method and system for IT equipment asset maintenance, and relates to the technical field of intelligent management. During system operation, multi-dimensional parameters of IT equipment are collected in real time and transmitted to a data processing module, the collected data are preprocessed, the current state of the equipment is comprehensively analyzed, and the health state coefficient Hstate of the equipment is calculated; the method comprises the following steps: acquiring a current equipment operation state, performing baseline establishment on historical operation data of the equipment, comparing the current state with the historical data, analyzing the performance trend and change of the equipment, calculating to obtain a trend analysis coefficient Ttend, and identifying potential problems and performance reduction trend of the equipment through long-term data accumulation. And calculating a final fault prediction probability index Pfault, comparing the final fault prediction probability index Pfault with a set threshold value, obtaining a grade prediction scheme, generating early warning information, prompting a system administrator to sort and distribute according to the urgency and priority of tasks, automatically generating a comprehensive report by the system, and providing decision support for the administrator.

The invention relates to the technical field of the Internet of Things, in particular to an intelligent construction site management method and system based on the Internet of Things. The method comprises the following steps: acquiring construction site environment monitoring data in real time based on multiple Internet of Things sensor nodes; performing real-time environment state change mining on the construction site environment monitoring data, performing global environment perception modeling, and constructing a construction site digital environment perception map; obtaining a construction site omnibearing monitoring video and construction site construction equipment state monitoring data; carrying out target dynamic optical flow tracking on the omnibearing monitoring video of the construction site one by one, carrying out dynamic operation state evolution, and constructing a real-time behavior portrait model of each worker; and performing operation state equipment temperature change trend analysis and dynamic load change evolution according to the state monitoring data of the construction site construction equipment, and generating comprehensive state characteristics of each piece of equipment. The construction site management efficiency and the construction site construction safety are improved.

The invention discloses a self-adaptive control unmanned aerial vehicle flight path correction method and system, and relates to the technical field of unmanned aerial vehicle flight control, and the method comprises the steps: collecting flight environment data through a sensor group carried by an unmanned aerial vehicle, constructing a three-dimensional environment model, and dividing the three-dimensional environment model into space units, the real-time pose of the unmanned aerial vehicle is determined based on point cloud matching and pose estimation technologies, trajectory offset is predicted according to a preset flight path, flight trajectory deviation parameters are generated, and a triaxial compensation strategy of horizontal plane, vertical direction and lateral offset is analyzed through trend analysis of the deviation parameters. And finally performing dynamic compensation correction on the flight path of the unmanned aerial vehicle according to the three-axis adjustment amount. The technical problem that the real-time accurate correction efficiency of the flight path of an existing unmanned aerial vehicle in a complex environment is low is solved, and the technical effect of shortening the dynamic compensation response time of the flight path deviation of the unmanned aerial vehicle is achieved through an industrial automatic control system device manufacturing technology and an intelligent monitoring device.

The invention relates to the technical field of construction progress monitoring, and discloses a construction progress monitoring method and system based on big data. The method comprises the following steps: forming a space-time alignment data set through multi-source data acquisition, filtering and quality evaluation; performing feature extraction and registration to generate a digital model; target detection classification is performed to form a completion state table; progress evaluation is achieved through component-task mapping; trend analysis and risk identification are performed to generate a prediction result; decision reference is provided for personalized information screening and augmented reality display. Through multi-source data acquisition, fusion and intelligent analysis, accurate perception, objective evaluation, scientific prediction and visual presentation of the actual state of the construction site are realized, so that a comprehensive, accurate and prospective construction progress monitoring method is provided, the construction period delay risk is effectively reduced, and the construction management efficiency is improved.

The invention discloses a hydropower station equipment state monitoring method and system, and relates to the technical field of hydropower station equipment monitoring, and the method comprises the steps: collecting the hydropower station equipment state through multi-layer data, and dynamically adjusting the collection frequency according to the change of the equipment state; performing signal quality evaluation on the collected data to generate fusion data; inputting the fused data into a trend analysis module, and generating a trend model of the equipment state in combination with historical data and real-time data; according to an analysis result of the trend model, combining the plurality of abnormal states into an event mode, and comparing the event mode with a known fault mode to determine a risk level; and generating a corresponding early warning signal based on the risk level, and performing corresponding response processing on early warning events of different levels. According to the hydropower station equipment state monitoring method provided by the invention, through multi-level data acquisition and dynamic frequency adjustment, the accuracy of data acquisition and the resource utilization rate are effectively improved, so that the system can adapt to different equipment states.

The invention relates to an optimization method for dynamic monitoring of an underground water level. Comprising the following steps: uniformly arranging a plurality of sensors in an underground water monitoring area, and monitoring multi-dimensional parameters including water pressure, temperature and humidity; the sensors form a gridding monitoring system through a wireless network; inputting multi-dimensional data acquired by the sensor array into a data fusion module, performing cooperative processing on the data by using a data fusion algorithm, and filtering noise and abnormal values; a unified water level change model is constructed, so that data of different parameters complement each other; dynamically adjusting the weight in the fusion algorithm regularly according to the historical data and the new measurement value; in the monitoring area, based on the dimensions of time and space, a space-time analysis model of underground water level changes is constructed, and the periodicity and regularity of water level fluctuation are excavated; and performing trend analysis on the historical water level data by adopting a time sequence analysis algorithm including ARIMA or LSTM to generate short-term and long-term water level prediction.

The invention provides a braid quality detection system and method based on industrial camera detection, and relates to the technical field of data processing. A multi-angle acquisition module; an image fusion module; an optical correction module; a preprocessing module; a preliminary defect detection module; a fine defect analysis module; a self-adaptive filtering module; the dynamic coordinate mapping module is used for mapping the physical position of the defect to obtain actual coordinate data; the data storage and analysis module is used for storing and analyzing historical actual coordinate data and related detection data and generating a quality report and a trend analysis report; the alarm and control module is used for automatically sending out an alarm signal and adjusting production parameters or pausing production when the defect number or the trend change rate in the trend analysis report reaches a preset threshold value; according to the invention, the accuracy and efficiency of braid quality detection are improved.

The invention discloses a retrieval method and system for efficiently collecting global accurate potential customer information, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data from an e-commerce platform, social media, an enterprise official website, exhibition information, an industry database and customs; generating standardized data through spatio-temporal feature extraction and multi-dimensional credibility evaluation; establishing a dynamic topological structure by adopting a characteristic decomposition reconstruction and value network construction method; based on adaptive classification and multi-dimensional trend analysis, customer value prediction and accurate recommendation are realized; and through system quality evaluation and feedback optimization, dynamic updating and optimized display of the result are realized. According to the method, the accuracy and timeliness of customer information retrieval can be effectively improved, the information redundancy is reduced, and the reliability of the recommendation effect is improved.

The invention provides an electric power multi-modal data perception and digital twinborn simulation cooperative interaction method and system, and relates to the field of electric power equipment management and maintenance, and the method comprises the steps: carrying out the electric power multi-modal data perception of data collected by edge equipment, and obtaining the state characteristics of single equipment; constructing a digital twinborn model based on the state features of all devices in the to-be-monitored power system; constructing a power equipment graph knowledge base by using a digital twinborn model, and extracting global state features of the equipment from the power equipment graph knowledge base through a graph neural network so as to construct a global state feature time sequence of the equipment; inputting the global state feature time sequence into the trained time sequence prediction model, and predicting the equipment state at the future moment; according to the multi-modal data, parameters of the digital twin model are adjusted in real time, environmental changes are flexibly adapted, time sequence modeling and fault trend analysis functions are combined, the running state of the equipment is predicted, and the comprehensiveness and foresight of equipment monitoring are further improved.

The invention discloses a special equipment life cycle supervision method and system based on characteristic parameter monitoring, and the method comprises the steps: collecting a multi-dimensional characteristic parameter data flow containing a real-time operation parameter, an accumulated damage parameter and a performance degradation parameter, and carrying out the trend analysis through employing a time sequence prediction model, and generating a trend deterioration early warning signal; an association rule mining algorithm is adopted to carry out association analysis to generate an associated fault early warning signal, then the two early warning signals are fused, inherent attribute data and historical operation and maintenance data are combined, and a real-time dynamic risk score is calculated through a dynamic risk portrait model; and finally mapping to a preset discrete supervision level and automatically executing a corresponding differential supervision instruction set. According to the method, the problems of risk identification lagging and strategy static solidification in traditional supervision are effectively solved, the transformation from passive response to active early warning and from average supervision to accurate strategy implementation is realized, and the foreseeability, pertinence and resource configuration efficiency of special equipment safety supervision are remarkably improved.

The embodiment of the invention provides a smart store data processing method and device based on multi-dimensional data analysis, which innovatively realizes alignment and mapping of spatial-temporal feature sequences by collecting sensor, intelligent terminal and social media data. And performing multi-dimensional data fusion based on the feature weight coefficient, and constructing a commodity recommendation model by using a convolutional neural network and a recurrent neural network. The system continuously optimizes the model performance through incremental learning, and realizes personalized commodity recommendation and store operation trend analysis. And meanwhile, a marketing decision optimization model is introduced, intelligent evaluation and dynamic optimization are performed on a marketing strategy, and the model is continuously updated through feedback data. According to the method, the limitation of traditional single data analysis is broken through, and a comprehensive data processing solution is provided for precision marketing of the smart store.

The invention relates to the technical field of health state analysis, in particular to an intelligent health state analysis system and method based on artificial intelligence, and the method comprises the following steps: S1, collecting multi-modal health data; s2, data preprocessing and anomaly detection: preprocessing the collected multi-modal data; s3, multi-modal data fusion and feature selection: carrying out feature fusion on data of different modals through a deep learning model, and carrying out feature extraction on time sequence data by adopting a convolutional neural network (CNN); s4, health state prediction and trend analysis: performing prediction analysis on the health state of the user based on a long and short term memory network (LSTM), and generating a health state trend chart of the user in combination with historical health data and real-time data; and S5, personalized health intervention suggestions are generated: personalized health management suggestions are automatically generated, a more comprehensive health assessment result is provided, the user can better know the own health condition, and personalized management is carried out.

The invention relates to the technical field of constructional engineering safety monitoring, and discloses a constructional engineering safety monitoring method and system based on machine learning, and the method comprises the steps: obtaining an original detection data set; according to the original detection data set, adopting a noise reduction algorithm to obtain a monitoring data set; according to the monitoring data set, a clustering algorithm is adopted to divide a building structure into a plurality of areas, and a high-risk area is obtained; according to the high-risk area, time sequence data are extracted, a time sequence analysis method is adopted to calculate a quality index change trend, and a trend analysis result is obtained; according to the trend analysis result, analyzing quality index relevance between different areas through a relevance mining algorithm to obtain a potential chain reaction risk assessment report; and inputting the potential chain reaction risk assessment report into a pre-trained building engineering safety monitoring model to obtain a building structure health trend report. According to the method, self-adaptive building quality safety dynamic global analysis can be realized.

The invention discloses a concrete curing device for constructional engineering, and belongs to the technical field of constructional engineering. The device comprises the following modules: a data acquisition and sensing module for acquiring real-time parameters of the interior and surface of concrete through a temperature and humidity sensing network and a plurality of detection devices so as to support subsequent data analysis and decision; the internal temperature control execution module is used for controlling the internal temperature of the concrete through a snake-shaped cooling pipeline and precise flow regulation and maintaining the concrete within a proper curing temperature range; the surface curing execution module is used for continuously curing the concrete surface, and all-directional spraying of concrete surface curing moisture and cyclic utilization of water resources are achieved; the central control module is responsible for data analysis, maintenance decision optimization and execution instruction distribution, and intelligent control and remote monitoring of the maintenance process are achieved; and the performance evaluation module monitors the concrete strength development in real time, evaluates the maintenance effect, provides trend analysis and optimization suggestions, and provides a basis for continuous improvement of a maintenance decision.

The invention provides a current transformer fault early warning method, and relates to the field of fault prediction. The fault early warning method comprises the steps of collecting current transformer operation state information, preprocessing collected operation state information data, constructing a current transformer fault early warning model, and training and testing the current transformer fault early warning model. The current transformer fault early warning model is composed of a long-term trend analysis module, a conditional enhancement modeling module, a bidirectional LSTM module fusing an attention mechanism and a convolutional layer, and a self-adjusting fusion module. The long-term trend analysis module is used for modeling a long-term trend in the time sequence; the conditional enhancement modeling module is used for modeling seasonal components in the time sequence; a bidirectional LSTM module fusing an attention mechanism and a convolutional layer captures short-term and long-term dependency relationships in a time sequence; and the self-adjusting fusion module automatically adjusts the contribution degree of each model output to a final prediction result. The method can accurately predict the future fault condition of the current transformer.

The invention discloses an intelligent early warning diagnosis method based on an intelligent power plant, and the method comprises the steps: detecting an abnormality through employing an unsupervised learning method, and correcting a detection result in combination with a heuristic rule; adjusting the length of a dynamic time window according to the running state of the equipment, ensuring the requirement of data trend analysis, carrying out smoothing processing on trend data captured in the adjusted time window, and extracting fault trend characteristics; and performing abnormal distribution analysis on the fault trend characteristics, performing layered early warning on the faults according to abnormal levels, constructing a step-by-step troubleshooting model based on a Bayesian network, and analyzing fault causes step by step. According to the intelligent early warning diagnosis method, the influence of equipment faults on a power plant system can be effectively reduced, and the overall safety and stability of a power plant are improved.

The invention discloses a petrochemical equipment fault prediction and safety management system and method, and belongs to petrochemical equipment management. The system comprises the following modules: a working condition sensing and classifying module for realizing real-time identification, classification and feature extraction of the equipment operation state; the health state evaluation module is used for calculating equipment health indexes and outputting an equipment performance degradation trend analysis result; the fault prediction and diagnosis module is used for predicting potential fault types and occurrence time, performing diagnosis analysis on fault mechanisms and providing credibility evaluation of diagnosis results; the risk assessment and early warning module comprehensively assesses multi-dimensional risk factors and generates graded early warning information through dynamic threshold optimization and a multi-stage early warning strategy; the maintenance decision support module is used for optimizing a maintenance strategy and providing resource scheduling and maintenance scheme suggestions based on the equipment health state, the fault prediction and the risk assessment result; and the management and optimization module ensures efficient cooperative operation of each module and continuously improves the system performance.

The invention discloses a motor fault detection method and system based on voiceprint recognition. According to the method, an annular microphone array is adopted to collect motor sound signals in a non-contact mode, a three-channel time-frequency data set is constructed through empirical mode decomposition (EMD) and a Mel-frequency cepstral coefficient (MFCC), fault diagnosis is carried out in combination with a CNN + ResNet network, and dynamic time warping (DTW) and CNN fusion matching is supported. The system comprises a preprocessing module, a fault template library and a matching algorithm, integrates wavelet denoising and multi-beam acquisition technologies, covers a frequency band of 50Hz-20kHz, can display a fault type and trend analysis in real time, and triggers secondary verification when the confidence coefficient is insufficient. According to the scheme, the anti-interference capability is improved through array signal processing, model parameters are optimized in combination with transfer learning, non-contact detection is achieved, the real-time performance and accuracy of fault diagnosis are remarkably improved, and the method is suitable for industrial motor health monitoring.

The invention relates to the technical field of dynamic migration, in particular to an edge computing management system supporting dynamic migration of an AI model. The system comprises a state mining module used for identifying available edge computing devices; performing multi-frequency information monitoring on the available edge computing devices, and performing device comprehensive state mining to generate edge device comprehensive state features; the edge topology module is used for carrying out node position positioning one by one based on edge equipment comprehensive state characteristics and carrying out instant state mapping modeling so as to construct an edge computing node topology model; the task flow trend module is used for acquiring an AI model task running log; and performing task queue analysis on the AI model task running log, and then performing model task traffic trend analysis to obtain AI model task traffic trend data. According to the invention, edge computing management with quick response and high processing efficiency is realized.

The invention discloses an electrical switch cabinet detection method based on the Internet of Things, and particularly relates to the technical field of electrical switch cabinets. A sensor is installed inside or outside a switch cabinet, temperature, humidity, current, voltage and gas concentration are collected, data are transmitted to an edge computing node for preprocessing by using a wireless communication technology, the preprocessed data are uploaded to a cloud platform, storage and trend analysis are performed at a cloud end, and the data are analyzed based on big data analysis and an artificial intelligence algorithm. According to the invention, the system can predict potential faults, a user can check the operation state and the prediction result of the equipment in real time through the cloud platform, and carries out the emergency shutdown or equipment scheduling operation remotely according to the early warning information, thereby effectively solving a reliability problem caused by communication interruption or data delay in an existing Internet of Things system, and improving the safety and intelligent level of the monitoring of the switch cabinet.

The invention discloses an equipment monitoring prediction method and system based on industrial middle platform and edge computing, and the system employs a monitoring collection unit to collect the vibration, temperature and current data of equipment in real time, and carries out the analysis through an innovative three-layer prediction architecture: deploying a lightweight threshold rule model at an edge layer to carry out the real-time anomaly detection, an XGBoost model is deployed in the middle layer to execute trend analysis, a time convolution network model is deployed in the cloud layer to realize deep time sequence analysis, and a corresponding grading early warning mechanism is established. The technical problems that an early warning mechanism is not intelligent enough, a prediction model is single, data preprocessing is not perfect enough and the like in traditional equipment monitoring are solved. Through cooperation of three layers of prediction architectures, omnibearing monitoring is realized, system response delay is reduced, and early warning accuracy is improved. Through the combination of a grading early warning mechanism and parameter correlation analysis, the system reliability is enhanced, and the equipment operation and maintenance efficiency is improved; and an edge calculation mode is adopted, so that the network bandwidth requirement is effectively reduced.

The invention relates to the technical field of bridge detection, in particular to a bridge defect automatic identification and inspection method based on deep learning, which comprises the following steps: S1, multi-modal data acquisition: acquiring multi-modal data of the surface and internal structure of a bridge; s2, multi-modal data preprocessing: generating a space-time aligned enhanced data set; s3, feature fusion and extraction: generating a fusion feature spectrum; s4, adaptively optimizing the characteristic spectrum: adjusting the connection weight between network layers according to the real-time environment parameters; s5, defect positioning and type identification: positioning space coordinates of defects and synchronously outputting probability distribution of defect types; and S6, defect analysis and report generation: generating an inspection report including defect evolution trend analysis based on the result of defect positioning and type identification. According to the invention, the bridge inspection efficiency is improved, the labor cost is reduced, and the intelligent upgrading of the bridge management field is further promoted.

The invention relates to the technical field of power systems, in particular to a multi-dimensional network performance index evaluation system and method based on measured data of a new energy station. Comprising a data acquisition preprocessing unit; a multi-dimensional index construction unit; the weight adjustment unit dynamically adjusts the index weight according to the operation condition based on a fusion algorithm of an improved analytic hierarchy process and an entropy weight method; the performance evaluation model unit adopts a comprehensive evaluation model fused with a random forest algorithm to generate multi-dimensional performance scores and risk early warning; and the visual interaction terminal unit is used for displaying the evaluation result in real time and generating a trend analysis report. According to the invention, the three-dimensional evaluation system covering the static electrical parameters, the dynamic stability indexes and the harmonic pollution indexes is constructed, so that the limitation of single-dimensional evaluation in the prior art is broken through, and the extension of the network-related performance of the new energy station from local detection to full-dimensional coverage is realized; and the multi-scene evaluation requirements of the power grid on the steady-state operation, the transient response and the electric energy quality of the station are accurately matched.

The invention discloses a slope structure safety monitoring method and system based on a distributed sensing technology, and the method comprises the steps: carrying out the distributed monitoring of the surface and deep part of a slope, and obtaining monitoring data; data transmission is carried out on the monitoring data through a wireless network; preprocessing and analyzing the transmitted monitoring data to obtain a monitoring data set; according to the monitoring data set, a safety evaluation model of the slope structure is established, and slope surface displacement, deep stress and pore water pressure are comprehensively considered in the safety evaluation model; based on the safety evaluation model, obtaining a quantitative evaluation result of the slope safety state; and establishing a slope disaster early warning model, and performing trend analysis and threshold judgment on the quantitative evaluation result through the slope disaster early warning model to realize early warning and real-time early warning of the slope disaster. The monitoring method provided by the invention has the characteristics of distribution, multiple parameters, long-term stability and high reliability, can comprehensively sense the change of the slope safety state, and provides a reliable basis for disaster early warning.

The invention provides a weather forecast learning system based on an artificial intelligence algorithm, and the system comprises a data access collection module which collects a multi-source data set; the data fusion and processing module is used for carrying out data fusion and processing to obtain a multi-source fusion data set; the AI model design module is used for constructing a multi-model collaborative architecture and carrying out multi-model parallel training and optimization; the model fusion module is used for carrying out multi-model weighted fusion to obtain an AI model; the real-time prediction module is used for updating a prediction result according to the real-time data; the visualization and interpretability module is used for designing a visualization interface and carrying out interpretability verification; and the evaluation and iteration module is used for carrying out comprehensive evaluation and continuous improvement on a prediction result in combination with evaluation indexes. According to the method, accurate and reliable observation data can be obtained, massive meteorological data are efficiently processed by combining an artificial intelligence algorithm, trend analysis and prediction are automatically carried out, and a reliable prediction result is generated.