1005 results about "Early warning model" patented technology

The present invention relates to the technical field of power system equipment and control. The method of the present invention comprises: installing sensors to acquire data for online real-time monitoring, and constructing a comprehensive condition online monitoring model for comprehensive data analysis; comprehensively evaluating the health condition of a generator on the basis of a comprehensive analysis result, and identifying fault causes; and constructing a generator comprehensive data intelligent monitoring and dynamic early warning model to perform insulation degradation trend analysis and prediction on the generator. In the present invention, by comprehensively monitoring the condition of a turbine generator, various key indicators of the generator are captured in real time, and trends and patterns underlying the data are revealed, providing technical support for accurately evaluating the health condition of the generator; and continuous monitoring for the insulation condition of the generator allows for proactive identification of potential risks, thereby providing decision-making support for preventive maintenance, helping operators take measures promptly, preventing faults, improving the reliability and safety of the generator.

The invention belongs to the technical field of coal mine risk early warning, and particularly relates to a coal mine goaf multi-risk comprehensive early warning method and system based on machine learning, and the method comprises the steps: collecting mine pressure, gas and hydrological real-time data in real time through a multi-temporal-spatial-scale sensor, and obtaining a dynamic coupling relation basic data set based on the real-time data; preprocessing noise and missing values according to the dynamic coupling relationship basic data set, and modeling node connection between a geological structure and mine pressure change by adopting a graph neural network to obtain space-time heterogeneous feature representation; non-linear features are analyzed through spatial-temporal heterogeneous feature representation, and a multi-scale dynamic mode is determined; acquiring a risk conduction path in the multi-scale dynamic mode, and acquiring an early recognition signal of a potential disaster chain; based on the early recognition signal, a long-short-term memory network is used for processing a sequential sequence, and the probability of the compound disaster is judged; a high-risk area is extracted from the composite disaster probability, and real-time early warning model parameters are obtained; and generating alarm output according to the real-time early warning model parameters.

The invention belongs to the technical field of power equipment, and discloses a power equipment fault early warning method based on multi-source data fusion, and the method comprises the steps: constructing multi-dimensional feature association through multi-modal data time-space association collection and hierarchical fusion driven by a knowledge graph; a space-time weight matrix is used for correcting sampling deviation, fault mechanism knowledge is combined to strengthen key feature contribution degree, false alarm and missing alarm caused by data isolation are effectively avoided, early recognition of hidden defects of equipment is realized, and global perception capability of early warning is improved. A meta-learning enhanced cross-equipment early warning model and reinforcement learning dynamic threshold decision are adopted, cross-equipment rapid adaptation under a small number of samples is realized through a ''meta-micro'' double-circulation mechanism, and a nonlinear law of fault evolution can be accurately described by combining a three-dimensional dynamic threshold matrix to balance an equipment state, an environment and an operation and maintenance strategy. The model generalization problem of different types of equipment in a complex environment is solved, and the adaptability to scenes such as load fluctuation and environment sudden change is improved.

The invention relates to the technical field of circuit detection, discloses a power transmission line thermochromic wire clamp heating early warning method and system, effectively solves the problem of data acquisition distortion in a strong electromagnetic environment, and improves the accuracy of state evaluation through multi-source data fusion. The dynamically adjusted early warning model reduces the risk of false alarm and missing alarm caused by equipment aging, the intelligent decision support module shortens the fault handling response time, the data closed-loop mechanism ensures the reliability of the system in the whole life cycle, and the reliability of the system in the whole life cycle is improved through multi-sensor cooperative monitoring and edge calculation processing. And the influence of environmental factors on data acquisition is reduced. The two-channel transmission architecture guarantees the data transmission integrity under different network conditions, the CRC verification mechanism effectively recognizes and corrects transmission errors, a high-quality data basis is provided for a subsequent early warning model, the abnormal data recollection mechanism avoids data missing caused by single collection failure, and continuous and stable operation of the monitoring system is ensured.

The invention provides a thermoelectric unit intelligent early warning method and system based on an artificial intelligence model. The method comprises the steps that firstly, multi-source operation monitoring information of different operation dimensions of core components such as a turbine, a boiler and a generator of a thermoelectric unit is obtained; performing time sequence correlation analysis on each modal monitoring data, constructing a dynamic correlation matrix, generating a modal conflict correction coefficient, and obtaining a time sequence dependence feature set; inputting the model into a hierarchical evolutionary early warning model, and obtaining an evolutionary fusion early warning feature vector through modal conflict correction and dynamic feature evolution modeling; generating a working condition self-adaptive dynamic early warning threshold value based on the current operation working condition; through comparison and abnormal traceability of the traceability verification module, an abnormal root component and a propagation path are determined, and a targeted traceability early warning instruction is output, so that the early warning accuracy and adaptability of the thermoelectric unit are improved, and safe and stable operation of the unit is guaranteed.

The invention discloses a project performance evaluation and risk early warning method for multi-source heterogeneous scientific research data, and relates to the technical field of scientific research project management, and the method comprises the steps: collecting the multi-source heterogeneous scientific research data, cleaning, converting, filling missing values, and storing in a unified format; extracting performance and risk features, and fusing to obtain a fused feature set; combining an analytic hierarchy process, an entropy weight method and the like to construct a dynamic weight performance evaluation model; constructing a multi-modal risk early warning model by adopting multiple algorithms and optimizing a threshold value; collecting data in real time to update a feature set, and dynamically adjusting the model; and visually outputting a result, generating an improvement suggestion, and forming closed-loop feedback. According to the method, the multi-source data processing efficiency and evaluation accuracy are improved, the risk early warning timeliness and adaptability are enhanced, a management closed loop is formed, and the problems of difficult data integration, evaluation lagging, insufficient early warning and the like of a traditional method are solved.

The invention relates to the technical field of slope settlement monitoring and early warning, in particular to a visual slope settlement monitoring and early warning method and platform. The method comprises the following steps: acquiring slope settlement monitoring data; preprocessing the acquired slope settlement monitoring data; respectively constructing a multi-scale slope digital twinborn model and a hybrid intelligent prediction model; constructing a multi-level early warning index system; constructing a fuzzy neural network early warning model based on a multi-stage early warning index system through a multi-scale slope digital twinborn model and a hybrid intelligent prediction model; and performing visual slope settlement early warning by using the fuzzy neural network early warning model. According to the space-air-ground integrated monitoring network constructed by the invention, a satellite InSAR, an unmanned aerial vehicle LiDAR and distributed optical fiber sensing are fused, and full-scale monitoring from regional macroscopic deformation to slope surface microcracks and deep soil displacement is realized.

The invention discloses an enteritis patient condition severity risk early warning method, a medium and equipment. The method comprises the following steps: acquiring a borborygmus original signal and a clinical multi-dimensional physiological parameter sequence through a sensing device; constructing a borborygmus dynamic characteristic spectrum based on the borborygmus original signal to generate an acoustic biomarker time sequence; inputting the acoustic biomarker time sequence and the clinical multi-dimensional physiological parameter sequence into a multi-modal fusion early warning model to obtain an intestinal inflammation risk index; executing a signal quality self-evaluation process and generating a data quality warning code when the signal quality is abnormal; triggering a multi-node collaborative monitoring mechanism based on the risk index to generate an intestinal state multi-dimensional situation map; establishing an individualized risk baseline and generating a graded early warning instruction; and finally outputting a comprehensive early warning report. According to the method, multi-modal fusion analysis of the borborygmus signal and the clinical parameters is realized, the accuracy and timeliness of illness state early warning are remarkably improved through dynamic risk assessment and signal quality monitoring, and a reliable basis is provided for clinical decision making.

The invention provides a reinforced soil retaining wall risk early warning method and system based on multi-source heterogeneous monitoring data, and belongs to the technical field of building risk early warning, the method comprises the following steps: inputting first data into a prediction model to obtain second data, the first data being historical monitoring data of a reinforced soil retaining wall to be detected, and the second data being historical monitoring data of the reinforced soil retaining wall to be detected; the second data is monitoring data of the reinforced soil retaining wall to be measured at the target moment; determining a construction data type based on the weather information of the target moment, and constructing data in the second data based on the construction data type to obtain construction data; and inputting the construction data, the second data and the weather information at the target moment into a risk early warning model to obtain a risk early warning result of the to-be-detected reinforced soil retaining wall at the target moment. According to the reinforced soil retaining wall risk early warning method and system based on the multi-source heterogeneous monitoring data, the accuracy and reliability of reinforced soil retaining wall risk early warning can be improved.

The invention relates to the technical field of hydropower station equipment monitoring, in particular to a deep learning modeling and analysis method for hydropower station equipment operation trend early warning. Comprising the following steps: collecting multi-source parameters and dividing dynamic working conditions; mechanism-data driven fusion feature construction is carried out; training a physical informed deep learning model; carrying out meta-learning migration optimization; performing dynamic threshold early warning judgment; and performing mechanism closed-loop verification. The model is built based on a physical informed neural network framework, a differentiable mechanism constraint loss function is introduced, and dual verification is carried out through an equipment simplified simulation model and a historical fault case, so that model output can be ensured to accord with an equipment operation physical rule, and the situation that a pure data driven model possibly deviates from physical common knowledge is avoided; the reliability of the early warning model is improved; according to the method, the basic model is trained by adopting the meta-learning algorithm guided by the fault type label, so that the problems of model over-fitting and high adaptation cost in a small sample scene in the traditional technology are solved.

The invention relates to the technical field of crime abnormity early warning, and particularly provides a crime abnormity early warning method based on a legal knowledge framework and a star graph neural network. The method comprises the steps of obtaining crime determination abnormal data through a judgment document; obtaining normal crime determination data through the legal data set; constructing two types of legal knowledge frameworks of offender name exclusiveness and cross-offender name universality; a Qwen3-14B model is utilized to construct a structured legal element data set with double aligned frames; the method comprises the following steps of: embedding legal elements into dense vectors through a bert-based-history model, and constructing a star graph neural network crime determination model; based on the two crime abnormity early warning mechanisms, performing crime abnormity early warning to obtain a crime abnormity early warning model; according to the method, the problem of the existing AI-assisted law application technology in crime name false complaint recognition is solved, and the recognition accuracy of crime name application errors in judicial scenes is improved.

The invention belongs to the technical field of medical risk early warning, and discloses a medical risk dynamic early warning and intervention decision-making system based on a multi-mode Internet of Things and AI, which comprises a sensing layer, a network layer, a data processing layer, an AI analysis layer and an application layer, the sensing layer comprises a wearable device, an environment sensor, a medical device, a non-contact monitoring device and an implantation device; the network layer constructs a data transmission channel and is provided with an edge computing node; the data processing layer adopts a multi-modal data fusion engine, is responsible for data alignment, data cleaning and feature extraction, and constructs a knowledge graph; the AI analysis layer comprises a dynamic risk early warning model and an intervention decision engine; the application layer is provided with a three-level early warning board, an intelligent intervention terminal and a block chain evidence storage platform. According to the invention, the early warning window is advanced, and the recognition accuracy is improved; the system promotes a medical monitoring mode to be changed from post response to active defense, the medical accident rate is further reduced, and a technical foundation is provided for constructing a new generation of smart hospitals.

The invention discloses a power transmission line forest fire early warning method based on multi-modal image fusion, and aims to solve the problems that when an existing forest fire early warning method is in butt joint with the actual operation condition of a power transmission line, multi-source information complementation is difficult to consider at the same time, false detection and missing detection are often caused by environmental influence, and the early warning timeliness sometimes cannot meet the real-time requirement of power grid operation. According to the method, a power transmission line forest fire early warning model based on multi-modal image fusion is constructed, and the model comprises preprocessing, multi-scale feature extraction, fire point detection and positioning, risk level definition and early warning level division. And training the model according to the visible light image, the infrared image and the radar image at each moment in the process from the beginning to the end of the mountain fire combustion. The invention belongs to the technical field of power transmission line forest fire early warning.

The invention provides a deep coal seam roadway surrounding rock stability intelligent early warning method, and relates to the technical field of coal mine dynamic disaster prevention and control. According to the intelligent early warning method for the stability of the surrounding rock of the deep coal seam roadway, a basic sample library is constructed through a dynamic and static combined loading rock test; synchronously collecting field surrounding rock static stress and micro-seismic data and carrying out time sequence polymerization; constructing an equivalent dynamic and static combined stress index based on a dynamic and static load superposition principle, and forming a seismic-force coupling feature vector; establishing an LSTM early warning model, adopting a transfer learning strategy, pre-training by using laboratory data, and then performing fine tuning by using field data; inputting the real-time feature vector into a model and outputting an instability probability; and verifying and correcting the probability based on actual deformation data of the surrounding rock, and applying a missing report sample to online incremental updating of the model. According to the method, the dynamic and static load coupling effect is quantified, the difficulty of small sample training is overcome, and accurate and self-adaptive early warning of the deep roadway surrounding rock instability risk is realized through a physical feedback closed-loop correction mechanism.

The invention provides a wind turbine state real-time monitoring and diagnosing method, system and device and a medium. The method comprises the steps that state parameters of key components of a wind turbine generator are collected in real time through a distributed monitoring network; performing cleaning, alignment and feature extraction on the data, and constructing a visual monitoring interface; building an early warning model of a whole machine and subsystem level based on the extracted features, and achieving the automatic recognition and positioning of a fault; and automatically matching a preset fault processing scheme according to the fault type. Through combination of multi-source data fusion, the deep learning model and fault tree reasoning, the accuracy and response speed of fault diagnosis are remarkably improved, real-time monitoring, intelligent diagnosis and operation and maintenance decision support of the state of the wind turbine generator are realized, and the operation and maintenance cost and the non-planed downtime are effectively reduced.

The invention provides an industrial furnace temperature adaptive optimization control method and system based on a multivariable chaotic time sequence, and belongs to the technical field of industrial process control, and the method comprises the steps: collecting operation parameters to form a multivariable time sequence, and carrying out the processing verification of chaotic characteristics, and obtaining an analyzable sequence; extracting features through phase-space reconstruction and quantifying variable coupling strength to obtain multivariable chaotic features; a prediction model and an early warning model are constructed based on the above, and a temperature change trend and early warning information are obtained; and finally, setting and optimizing control parameters, generating strategy execution and combining feedback to form closed-loop control. According to the method, the multivariable chaotic characteristics of the industrial furnace are analyzed, the prediction and early warning model is constructed, and self-adaptive optimization control is carried out, so that accurate regulation and control of the temperature are realized, the product quality stability is improved, and the energy consumption and the production cost are reduced.

The invention discloses a monitoring and early warning method for coal rock fracture of an argillaceous weakly-cemented thick top coal roadway, and belongs to the field of top coal roadway coal rock fracture monitoring. The method comprises the following steps: constructing a prediction framework of long-term structural evolution, and judging a potential risk area of a long-term fracture trend; according to the potential risk area of the long-term fracture trend, integrating the feature data of multiple time scales, and obtaining a comprehensive feature vector of the change of the whole coal rock fracture process by applying a time sequence analysis method; dynamically adjusting parameters of an early warning model through an instant risk assessment result in combination with comprehensive data of medium-term stress change and long-term fracture trend, and determining risk grade division under multiple time scales; and according to risk grade division, generating graded early warning information, mapping the graded early warning information to a roadway spatial distribution model, and obtaining a risk visualization layer of a specific area.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an old people falling risk dynamic assessment and real-time early warning system and method based on multi-modal deep learning. The system comprises a multi-modal data acquisition module used for synchronously acquiring behavior data, physiological data and environmental data; the data preprocessing module is used for preprocessing the video data and the sensor data; the multi-modal feature fusion module is used for realizing cross-modal feature interaction of multi-modal data by adopting a hierarchical network architecture and fusing generated comprehensive features; the dynamic risk assessment module is used for realizing long-time-sequence risk prediction based on a prediction model and outputting a risk probability; the real-time early warning module performs real-time early warning based on the predicted risk probability by constructing a multi-level response mechanism; and the model adaptive updating module is used for constructing a closed-loop optimization mechanism and realizing adaptive updating of the model by adopting a transfer learning fine tuning model. The problem of misjudgment caused by one-sided data in the prior art is solved.

The invention provides a karst collapse early warning method and device, electronic equipment and a storage medium. The method provided by the invention comprises the steps of determining a first time sequence feature of surface displacement data, a second time sequence feature of groundwater dynamic data, a spatial feature of geological radar image data and a vibration feature of microseismic signal data, and generating a multi-modal fusion feature vector according to the first time sequence feature, the second time sequence feature, the spatial feature and the vibration feature; according to the multi-modal fusion feature vector and an early warning model, an early warning signal is obtained, and the early warning model is obtained by updating an early warning threshold value through a historical multi-modal fusion feature vector and a Transform-LSTM hybrid network by using a time sequence difference learning strategy; and performing validity judgment on the early warning signal according to the surface displacement data, the groundwater dynamic data and the micro-seismic signal data, and determining whether to perform early warning according to a judgment result. According to the method, the accuracy of karst collapse early warning can be improved.

The invention relates to the technical field of environmental monitoring and atmospheric pollution early warning, in particular to a Yangtze River Delta composite extreme weather ozone pollution early warning model construction method, which comprises the following steps of S1, acquiring high-resolution meteorological data and pollutant concentration data of a Yangtze River Delta region to form an original data set; and S2, carrying out missing value interpolation and abnormal value elimination on the meteorological data and the pollutant data, and carrying out grid alignment according to time and space to generate a unified spatial-temporal characteristic matrix. According to the method, by collecting Yangtze Delta high-resolution weather and pollutant data, performing data cleaning, bimodal feature coding and joint representation modeling, predicting the ozone concentration and generating regional early warning through multi-layer Transform self-adaptive attention, the problems that traditional ozone early warning mostly depends on a single-modal prediction model, and the reliability of the ozone early warning is greatly improved are solved. And due to the lack of multi-modal space-time dependent capture, the problem of early warning information lag is caused.

The invention discloses a movable formwork construction monitoring method and system based on finite element simulation, and relates to the field of bridge construction monitoring and structure health monitoring. The problems that in traditional movable formwork construction monitoring, a model is fixed, early warning lags behind, and it is difficult to dynamically reflect the real state of a structure are solved. According to the method, a digital structure module containing adjustable parameters is established, and working condition simulation is carried out to identify a theoretical high-risk area and optimize sensor layout; performing pre-simulation before each process to output a predicted value, synchronously acquiring actual measurement data during construction, and calculating a standardized deviation sequence; dynamic safety evaluation and early warning are carried out based on time domain feature extraction and multi-index fusion; reverse calibration and online updating are carried out on model parameters through Bayesian variational inference by utilizing historical deviation data, so that closed loop of simulation, monitoring, early warning and model optimization is realized, and the safety monitoring precision and real-time performance in the construction process of the movable formwork are remarkably improved.

The invention belongs to the technical field of compressor maintenance, and discloses a compressor predictive maintenance system and method based on multi-source data fusion, and the method comprises the steps: carrying out the heterogeneous data time-frequency feature analysis of compressor signal data, and generating a multi-dimensional feature spectrum; carrying out load-dependent fault signal separation to generate load decoupling fault feature data; performing environment interference factor elimination analysis to generate a pure fault feature matrix; performing cross-domain signal correlation mapping to generate a multi-source data fusion mode map; constructing a fault feature propagation link, and generating fault evolution path data; carrying out degradation trend prediction under a variable load condition, and generating fault development situation data; constructing a dynamic threshold self-adaptive early warning model, and generating fault early warning critical value data; health state comprehensive evaluation is carried out, a compressor state report is generated, and predictive maintenance implementation is executed; according to the invention, accurate recognition and prediction of the compressor fault are realized, and the equipment reliability and maintenance efficiency are improved.

The invention provides a geological disaster deduction method and system based on a digital twinborn simulation platform, relates to the technical field of geological disaster deduction, and improves the accuracy and robustness of data fusion by adopting a dynamic weight fusion algorithm and combining information entropy and reliability score; constructing a digital twin model based on a multi-source data inversion and three-dimensional geological modeling technology, updating geological parameters in real time, and reflecting the state change of a geological body; a multi-level stepped early warning model is introduced, and the timeliness and foresight of early warning are enhanced by fusing a physical model and deep learning prediction; and in combination with the graded disaster deduction model, the precision is dynamically selected, and the risk thermodynamic diagram is generated, so that the spatial fineness and decision support capability of geological disaster risk identification are improved. Multi-dimensional organic collaboration is realized, the overall accuracy and response speed of the system are enhanced, and efficient and accurate disaster risk management requirements in a complex geological environment are met.

The invention discloses a ship steering engine fault early-warning method based on sparse Bayesian, and the method comprises the steps: collecting the data, such as a rudder angle instruction, an actual rudder angle, steering engine response, motor current, oil pressure, ship speed, ship posture, sea condition grade, in a navigation process, and constructing a time window monitoring sample set through time alignment and normalization; steering engine response features are extracted and spliced with the working condition features to form working condition feature vectors, steering engine state labels are marked in combination with maintenance records to form a training sample set, and an adaptive sparse Bayesian early warning model with working condition related sparse priori is trained; and generating a current working condition feature vector in real-time navigation, inputting the current working condition feature vector into the model to obtain a steering engine fault risk value, and selecting a self-adaptive early warning threshold value according to a sea condition grade to output a fault early warning result. According to the invention, the false alarm rate is reduced and the early fault identification capability is improved under complex sea conditions.

The invention discloses a lithium ion battery thermal runaway multistage early warning system and method and a storage medium, and belongs to the field of battery safety monitoring. The system comprises a data acquisition device, a feature extraction module, an early warning strategy module and an early warning model module. The method comprises the following steps: collecting multi-physical field parameters of heat, electricity, force and gas; screening key feature points by adopting a mutual information method; mining association rules of feature points and early warning levels based on an Apriori algorithm to generate a four-level early warning strategy; and constructing an early warning model integrating grade evaluation and remaining time prediction. According to the method, through multi-parameter coupling analysis and intelligent algorithm mining, the defects that the prior art depends on a single parameter and lacks grading capacity are overcome, early-stage, accurate and grading early warning and remaining time prediction of lithium ion battery thermal runaway are achieved, and the safety early warning precision and emergency disposal capacity of a battery system are remarkably improved.

The invention discloses an equipment fault prediction method in a power transmission and transformation system, and relates to the technical field of power system operation and maintenance, and the fault prediction method comprises the following steps: data collection: collecting the data of power transmission and transformation equipment through a sensor network, and forming a multi-source heterogeneous data set; data preprocessing: performing abnormal value elimination, missing value filling and standardization processing on the multi-source heterogeneous data set; feature extraction: mining associated features and time-space evolution laws of data of different dimensions by adopting a multi-modal fusion network based on an attention mechanism; performing fault prediction, and constructing a dynamic threshold early warning model in combination with an equipment aging curve; and correcting the result to generate a final fault prediction report. The equipment fault prediction method in the power transmission and transformation system solves the problems that traditional prediction depends on single data, the fixed threshold value false alarm is high, and the advance is insufficient, improves the fault prediction accuracy and reliability, and is suitable for operation and maintenance early warning of power transmission and transformation equipment such as transformers and circuit breakers.

The invention, which belongs to the technical field of mine safety monitoring, provides a multi-source information fusion-based roadway surrounding rock roof separation tour-inspection early-warning system comprising a data acquisition module, an edge calculation module, a cloud analysis module and an early-warning feedback module. The data acquisition module acquires multi-source data of surrounding rock of a coal mine tunnel in an automatic mode; the edge calculation module performs spatio-temporal data synchronization on the collected multi-source data and extracts data features; the cloud analysis module performs feature weighted fusion based on the multi-source data to construct a risk early warning model; and the early warning feedback module triggers graded early warning according to prediction data of the risk early warning model, and provides visual feedback, so that a set of automatic, full-coverage and multi-source data fused roadway roof separation monitoring system is constructed.

The invention provides a safety evaluation method for the stability problem of a rock pillar after excavation of a surrounding rock body, and belongs to the field of rock pillar stability analysis, and the method comprises the steps: S1, collecting the stress data, deformation convergence data and micro-fracture data of the rock pillar after excavation of the surrounding rock body in real time; s2, respectively performing feature extraction to obtain a stress evolution feature parameter, a deformation acceleration feature parameter and a damage accumulation feature parameter, and performing coupling analysis to determine a current stability evolution stage of the rock pillar; s3, constructing a current stability state parameter of the rock pillar based on the stress evolution characteristic parameter, the deformation acceleration characteristic parameter and the damage accumulation characteristic parameter; and S4, establishing a dynamic grading early warning model based on the current stability state parameters of the rock pillar, and carrying out rating and early warning on the stability of the rock pillar after the surrounding rock body is excavated by using the dynamic grading early warning model. According to the invention, dynamic evaluation and early warning of the stability of the rock pillar after excavation of the surrounding rock body are realized.

The invention relates to the technical field of power generation, and discloses a wind turbine generator fault early warning method and system based on multi-model combination, and the early warning method comprises the steps: 1, data collection; step 2, establishing an early warning model; 3, real-time monitoring and early warning are carried out; step 4, analyzing a fault source; step 5, estimating fault existence time; and step 6, fault subsequent condition prediction. According to a fault source analysis result, historical fault data and current operation data are combined through the system, the fault existence time is estimated, the prediction model is utilized, the system can not only predict the fault expansion speed, but also predict the influence degree of the fault on the unit performance, and therefore a more effective coping strategy is formulated, and the unit performance is improved. The beneficial effects of early warning the fault of the wind turbine generator and analyzing the fault source are achieved.

The invention provides a lithium ion battery safety valve opening and failure early warning method based on expansive force, and belongs to the technical field of lithium ion batteries. Battery expansive force and cycle data under different pre-tightening force conditions are collected, statistical features are extracted to construct a state feature set, health state groups are divided by adopting a fuzzy clustering algorithm, and the early warning result is obtained. Establishing a segmented nonlinear mapping model of the expansive force and the internal pressure, performing wavelet denoising and robust differential calculation on expansive force signals, and optimizing an initial expansive force derivative threshold value by analyzing time dispersion at different heating rates; a multi-scale feature fusion algorithm based on hierarchical attention aggregation is utilized to construct a state self-adaptive early warning model to correct a threshold value, and a four-stage early warning mechanism is set to monitor the opening and failure states of the safety valve. The technical problem that the opening time of the safety valve cannot be accurately predicted and self-adaptive early warning cannot be realized under different battery health states and pretightening force working conditions is solved.