1370 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 provides a deep learning-based avalanche early warning model construction method and system, and the method comprises the steps: firstly obtaining multi-source environment monitoring data, including meteorological time sequence, topographic space and accumulated snow layer physical data, of a target region, carrying out the time dimension alignment of the meteorological time sequence data to generate a feature sequence, carrying out the meshing of the topographic space data to generate a feature set, and carrying out the construction of an avalanche early warning model; the method comprises the following steps: extracting parameters from accumulated snow layer physical data to generate a state vector, inputting a deep learning network model containing time sequence attention, spatial convolution and cross-modal interaction units, generating a fusion feature vector, constructing a training set based on historical avalanche event annotation data, performing dynamic weight optimization on the fusion feature vector, and generating an avalanche risk prediction model. And finally, receiving current monitoring data in real time, outputting a risk level and an early warning trigger threshold value by the avalanche risk prediction model, and generating a multi-level early warning signal when a real-time risk value exceeds the threshold value, thereby realizing accurate avalanche early warning.

The invention relates to the technical field of road safety, in particular to a road construction safety self-adaptive warning broadcast system and method, and the method comprises the steps: collecting environment parameters of a construction region in real time; detecting dynamic traffic parameters of vehicles around the construction area; tracking a real-time position and a movement track of the construction machinery, and monitoring a safe area border crossing behavior of a constructor; predicting a dynamic risk level in the construction area based on the environmental parameters, the dynamic traffic parameters, the construction machinery motion trail and the personnel border crossing state, and generating a grading response strategy; and outputting a self-adaptive warning signal according to the grading response strategy. According to the method, the problems that a traditional early warning model depends on one-dimensional parameters, the static threshold misjudgment rate is high and the response delay is large are effectively solved, and accurate quantification and real-time early warning of the construction machinery and vehicle conflict risk are achieved through multi-source data fusion and dynamic probability calculation.

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 provides a river region disaster monitoring and pre-warning method and system based on multi-source monitoring data analysis, and the method comprises the steps: obtaining the multi-source monitoring data of a river region, carrying out the data preprocessing of the multi-source monitoring data, eliminating the noise interference in real-time position data, and carrying out the time synchronization alignment of channel image data and environment parameter data, thereby achieving the early warning of the river region disaster. The method comprises the steps of generating a standardized monitoring data set, extracting water flow dynamic characteristics, meteorological anomaly characteristics and channel obstacle distribution characteristics of a river region, generating a multi-dimensional disaster associated characteristic set, inputting the multi-dimensional disaster associated characteristic set into a preset disaster early warning model for dynamic analysis, generating a disaster early warning signal, and determining a disaster type and an influence range. And triggering an autonomous separation mechanism of the dragging airship and a quick start instruction of the unmanned aerial vehicle, broadcasting early warning information to ships in a channel through the unmanned aerial vehicle, and synchronously transmitting the disaster type and the influence range to a command center. According to the invention, the timeliness of disaster early warning and the space adaptation precision of treatment measures can be improved.

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 geological disaster monitoring, and discloses an intelligent early warning method and system for geological disasters in geotechnical engineering, and the system comprises a data collection module, a data processing module, a feature extraction module, an early warning model module, a response execution module and an optimization feedback module. Static geological parameters, dynamic environment parameters and historical disaster data are integrated, a standardized space-time correlation data set is constructed, the limitation of a single data source is broken through, multi-dimensional dynamic response characteristics of a rock-soil body are captured, a reliable data basis is provided for accurate early warning, the rigidity defect of a traditional fixed threshold value is avoided, and the early warning accuracy is improved. The method achieves the self-adaption of the risk early warning sensitivity, reduces the misjudgment and missing judgment caused by environment interference, intercepts a dynamic data stream in real time through a sliding window, calculates the risk mean value and variance, quickly responds to sudden environmental changes such as rainfall sudden change and vibration abnormality, and generates a graded early warning signal.

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 power station equipment state trend prediction method based on deep learning, and the method comprises the following steps: S1, obtaining the historical operation data of power station equipment, and carrying out the preprocessing; s2, constructing a trend early warning model, a temperature time sequence prediction sub-model and a vibration time sequence prediction sub-model based on the historical operation data of the power station equipment by using a deep neural network; s3, sequentially outputting a degradation degree prediction value, a temperature trend prediction value and a vibration trend prediction value through the model; s4, establishing a three-layer evaluation system and generating a state evaluation result; and S5, generating early warning information according to a state evaluation result. According to the method, a multi-level deep network is constructed, and multi-dimensional accurate prediction of equipment degradation, temperature and vibration trend is realized; technologies of seasonal coding, adaptive attention and the like are introduced, so that the perception and learning capabilities under complex working conditions are enhanced; a three-layer dynamic evaluation system is innovated, scientific health state evaluation from parts to the whole is provided, and a more comprehensive and intelligent solution is provided for early warning of a power station.

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 discloses a relay state prediction and fault early warning method and system based on deep learning, and the method comprises the steps: S1, building a constraint condition of a generative adversarial network based on a relay physical model, and forming an enhanced fault waveform signal according with a physical rule through adversarial training; s2, receiving a real-time current and voltage signal and a mechanical vibration signal, and extracting an electric signal feature vector by using a time sequence convolutional network; s3, inputting the combined feature tensor into the lightweight assessment model, and outputting a health degree scoring signal; s4, responding to the meta-learning activation instruction, loading historical data of equipment to construct a parameter optimization set, performing online fine tuning on the early warning model based on a meta-learning framework, and generating a fault determination parameter; and S5, analyzing real-time signal characteristics according to the fine-tuned judgment parameters, and outputting graded early warning signals to a monitoring terminal. According to the method, the problems of early state prediction and accurate early warning of the relay under small sample fault data can be 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 relates to the field of environmental monitoring, and discloses a plateau region carbon emission monitoring management method and system, electronic equipment and a medium, and the method comprises the following steps: constructing an atmospheric boundary layer dynamic model for a plateau low-pressure and strong-turbulence environment, and achieving environmental adaptability modeling by correcting a turbulence diffusion coefficient and localized combustion efficiency; based on unmanned aerial vehicle group dynamic path planning and a ground station collaborative sensing network, flight parameters are adjusted in real time in combination with the concentration gradient, and time-space continuous multi-source fusion monitoring data are generated; a high-resolution carbon emission field is output through embedding a combustion efficiency correction factor and a space correlation graph structure by adopting a data assimilation algorithm with mixed ensemble Kalman filtering and a graph convolutional network; and constructing frozen soil degradation index parameters, and driving a transfer learning early warning model to realize methane flux dynamic prediction and multi-stage early warning triggering. According to the invention, the problems of insufficient carbon emission monitoring precision and lack of frozen soil degradation correlation evaluation in a plateau complex environment are solved.

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 discloses a water conservancy project building full life cycle management method based on BIM technical analysis, and relates to the technical field of building full life cycle management. And respectively constructing three structural indexes, namely a microcrack expansion rate index F1, a damp-heat permeation combined degradation index F2 and a dynamic stiffness phase deviation index F3. The multi-dimensional feature system not only covers key risk sources such as material microcosmic degradation, environmental coupling effect and mechanical response mutation, but also breaks through a traditional extensive method which depends on single sensing data to perform health judgment, and provides a digital expression model with physical interpretation force for structural risks. According to the method, a unified health data standard system is established, and the health data standard system can be used as a core data source of upper-layer applications such as dynamic structure evaluation, an early warning model and visual mapping.

The invention provides an artificial intelligence early warning and management method for a smart ocean, and is applied to the field of data processing application. Aiming at the problems that intelligent ocean data is large in scale and complex in multiple sources, threat identification is single in the prior art, an early warning model lacks self-adaptive adjustment and is prone to false alarm and missing alarm, and data security is difficult to guarantee, the method is based on intelligent ocean multi-source data and a preset data set containing data types, security levels and other labels; after preprocessing, a deep learning framework is used to train a threat identification and early warning model. The model detects four types of features such as sensitive information in real-time data, extracts parameters, matches threat features to determine risk levels, and establishes mapping relationships between data types and threat and abnormal modes. Through abnormal threshold evaluation, false alarms are removed to generate initial early warning parameters, a machine learning iterative optimization model is combined, risk data are finally sorted, a response strategy is constructed and the like, a safety management scheme is formulated to generate a result, and intelligent upgrading from passive response to active defense is realized.

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 provides an intraoperative hypotension early warning method and related equipment, and is applied to the technical field of data processing. The method comprises the following steps: processing a preset hypotension early warning model based on a training sample set with identification information to generate a target hypotension early warning model; processing the operation information of the target user in a preset time period, and generating an interval prediction sub-task and an intra-operation prediction sub-task; processing the interval prediction subtask and the intra-operative prediction subtask to generate a preset prediction feature vector; processing the physiological motion parameter information of the target user to generate a blood pressure limiting factor of the target user; processing the physiological state information of the target user to generate an ambulatory blood pressure influence factor of the target user; and processing the preset prediction feature vector, the blood pressure limiting factor of the target user and the ambulatory blood pressure influence factor of the target user based on the target hypotension early warning model to generate hypotension early warning information of the target user.

The invention relates to the technical field of coal mining equipment, and discloses a mining roadway stability real-time monitoring and early warning system which comprises the following steps: S1, multi-source sensor network deployment, S2, data transmission and preprocessing, S3, stability index dynamic calculation, S4, fusion early warning model construction and S5, graded early warning triggering. Roadway surrounding rock deformation, stress, vibration and environmental parameters are collected in real time through a multi-source sensing network, efficient data transmission and preprocessing are achieved in combination with an industrial looped network, surrounding rock strain energy density, displacement convergence rate, support failure coefficient and other key indexes are dynamically calculated, a fusion early warning model is constructed based on machine learning, and the early warning accuracy is improved. According to the method, accurate evaluation of the stability level is achieved, grading alarm and emergency control are automatically triggered when a threshold value is reached, a'perception-analysis-decision-response 'closed loop is formed through the design, the real-time performance of roadway stability monitoring and the early warning reliability are remarkably improved, and accidents such as roof fall and wall caving are effectively prevented.

The invention provides a solid waste storage yard safety risk early warning method, and belongs to the technical field of solid waste storage yard safety risk, and the method comprises the steps: firstly building a three-dimensional structure model through a geological radar, determining an initial key region, and laying a multi-layer monitoring network comprising a deep displacement meter, a pore water pressure meter and an optical fiber temperature sensor on the basis; the pollution risk is evaluated by applying a percolate migration and diffusion mechanism model, an MSLNN multi-level neural network prediction model is established to analyze the stability of a pile body, and a multi-level early warning threshold value is set. The key innovation lies in that parameters of a sensor node arrangement equation set are dynamically adjusted through a balance judgment function, a closed-loop iteration mechanism is formed, the monitoring network layout and the early warning model are continuously optimized until convergence conditions are met, and the safety risk early warning accuracy of the solid waste storage yard is remarkably improved.

The invention provides a landslide monitoring and early warning method based on air-sky-earthquake integration, and belongs to the technical field of landslide disaster monitoring. Air-sky-ground-earthquake monitoring data are integrated, multi-dimensional parameters such as surface deformation, topographic change, internal strain, crack propagation, rainfall intensity and micro-earthquake activity are covered, the limitation of a single monitoring means is solved, and a space-time complementary three-dimensional monitoring system is formed. According to a data analysis processing result, an early warning response rule is set, an early warning model is constructed through a multi-source data fusion technology in combination with an optimal weighted random forest method and a comprehensive scoring model, real-time evaluation and early warning grade judgment of landslide risks are realized, and visual display of monitoring data is realized. The model is iteratively trained by introducing reward and punishment factors, and weight distribution of different data sources is dynamically adjusted, so that the early warning model has adaptive ability, early warning requirements of long-term slow deformation and short-term accelerated deformation are considered, and the early warning accuracy of the model is improved.

The invention provides a mine water prevention and control risk early warning method and system based on earthquake and geology fusion, and belongs to the field of soil layer or rock drilling, the method comprises the steps that real-time earthquake data of an earthquake monitoring module and geological exploration data of a geological exploration module are received, and the geological exploration module comprises a drilling detection module and a hydrogeological analysis module; based on the real-time seismic data, the geological exploration data and a preset water prevention and control risk early warning model, obtaining a risk level including a risk level and a corresponding water prevention and control execution measure; triggering target execution equipment such as drilling equipment to perform prevention and treatment according to execution measures; drilling a monitoring hole and arranging an optical fiber osmometer to collect osmotic pressure data at a first risk grade; at the second risk level, grouting plugging is conducted on the crack drilling grouting holes; constructing a water guide hole in the aquifer at the third risk level, and guiding underground water to a safe area through a drainage pump group. According to the method, multi-source data are fused to realize graded accurate early warning and prevention.

The invention relates to the technical field of financial science and technology and risk management, and discloses an inter-bank bond market abnormal fluctuation risk monitoring system and method. The system comprises a multi-source data fusion module, a dynamic risk factor engine module, an intelligent early warning model module, a pressure conduction simulation module, a visual decision support module and a supervision co-processing module. Multi-source heterogeneous data are collected in real time through a distributed crawler framework, dynamic analysis and anomaly detection of risk factors are realized in combination with an adaptive weight algorithm and a space-time diagram neural network, and a risk conduction path under an extreme scene is simulated by adopting a multi-agent modeling technology. And risk early warning and co-processing are realized by means of three-dimensional visualization and intelligent contract technologies. According to the invention, the problems of difficulty in data integration, early warning lagging, unclear conduction path and the like in traditional bond market risk monitoring are solved, and full-dimension data fusion, real-time dynamic monitoring, accurate risk early warning and intelligent co-processing are realized.

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 field of hardware encryption, and discloses a geological disaster monitoring scene-based low-power-consumption hardware encryption system, which comprises a layered architecture module for constructing a terminal sensing layer and a cloud platform layer, generating a three-dimensional risk thermodynamic diagram, and carrying out short-term early warning model and emergency deduction; the encryption and decryption module is used for carrying out multi-dimensional identity verification based on protocol layering and national secret algorithm fusion, carrying out dynamic management and eavesdropping monitoring on a secret key, optimizing a data processing flow, and establishing a secure channel to dynamically negotiate whether encryption or decryption is needed; the key management module is used for implementing a triple binding mechanism, carrying out processing and risk assessment on the monitoring data, and carrying out key backup and recovery by adopting a dynamic key period according to a generated risk level; and the safety protection module is used for performing intelligent management on the power consumption of the system based on the active state of the system, selecting a compression strategy according to the data type by adopting a dynamic compression algorithm, performing safety monitoring on hardware and firmware, performing intrusion monitoring on the system and performing automatic repair.

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 relates to a thunderstorm and gale identification method based on multi-dimensional characteristic parameter fusion, and belongs to the field of radio, and the method comprises the steps: employing a neighborhood value method to remove the electromagnetic interference of dual-line polarization Doppler weather radar data, carrying out the rollback fuzzy processing, and then carrying out the meshing; dual-polarization radar data, wind profile radar data, sounding data and ground automatic station data are respectively obtained, and respective characteristic parameters are extracted from the data; carrying out normalization processing on the characteristic parameters, and obtaining corresponding parameters; constructing a multi-parameter integrated early warning model based on the plurality of fusion parameters, and training and verifying the model; and inputting parameters into the trained model for operation, and outputting the thunderstorm gale probability P. According to the thunderstorm gale identification method, through combination of multi-source data fusion and multiple physical mechanism fusion and integration of innovation of a machine learning model, the technology of thunderstorm gale identification from single index dependence to multi-dimensional collaboration and from static threshold value to dynamic self-adaption is realized.

The invention discloses a multi-dimensional fusion medical resource intelligent monitoring and early warning method. According to the method and the device, the integrity and the accuracy of the medical resource data are improved through a multi-dimensional data fusion and dynamic calibration mechanism. Multi-source data of a medical institution system, a regional health platform, wearable equipment and the like are integrated, comprehensive data coverage and timely updating are ensured, and resource monitoring blind areas caused by information islands are reduced. Through the processing flows of noise filtering, missing value filling, abnormal value identification and the like, the influence of data errors on resource evaluation is reduced, quantitative analysis of key indexes such as medical resource supply and patient demands is closer to the actual situation, and the timeliness of medical resource risk early warning and the effectiveness of decision support are enhanced. The hierarchical early warning model constructed based on the multi-modal feature matrix can identify potential risks from multiple dimensions of resource exhaustion, service saturation, regional propagation and the like, and ensures reasonable distribution and efficient utilization of medical resources in different scenes.

The invention discloses a real-time monitoring, regulating and controlling method for concrete pouring cracks of a high-rise building, and the method comprises the steps: arranging a distributed optical fiber sensor array and an ultrasonic detector in a concrete pouring region of the high-rise building, and collecting the data of a temperature field and a strain field in concrete in real time based on the distributed optical fiber sensor array; the collected data parameters are transmitted to a data processing terminal, and a crack early warning model is established in combination with concrete material characteristic parameters; analyzing the received data by using a crack early warning model, and judging whether the concrete has cracks and the development trend of the cracks; and when the crack is monitored or the crack development risk exists, the regulation and control system is started. The system has the beneficial effects that all-directional real-time monitoring of the internal state of concrete is realized through combination of distributed optical fibers and ultrasonic detection, the coverage range is wider, and data is more comprehensive. A crack early warning model constructed based on multiple parameters is combined with a deep learning architecture, so that the precision of crack identification and trend prediction is greatly improved.