48 results about "Tornado" patented technology

The invention relates to the technical field of meteorological detection, in particular to a tornado collaborative observation method based on detection equipment. The method comprises the following steps: carrying out real-time monitoring on regional micro-pressure and meteorological echoes of a suspicious target region through a micromanometer and an S-band new-generation weather radar, and carrying out severe convection occurrence matching prediction to obtain a severe convection occurrence prediction target region; performing solar radio calibration processing on the corresponding radar detection equipment and triggering a tornado detection strategy cooperation instruction to perform severe convection target three-dimensional cooperation observation and target strong correlation area fusion division so as to generate a strong convection target and other strong correlation area radar field; performing equal-intensity convection characteristic analysis on the radar field of the equal-intensity correlation area of the severe convection target to generate a tornado equal-intensity convection characteristic data set; and obtaining a preset tornado feature library and carrying out tornado identification analysis to generate a tornado occurrence susceptible lattice point region. According to the invention, the monitoring precision and real-time performance of the tornado detection process can be improved.

The invention discloses a tornado identification and path prediction method based on X-band dual-polarization radar data, which mainly utilizes six observed quantities of two lowest elevation angles of a radar, combines a deep learning model Unet, introduces an attention mechanism and position coding information, optimizes a data transmission process, and constructs a weak supervision type lightweight identification model TD-Unet. The model carries out feature extraction on multi-elevation-angle radar data, and generates a tornado identification probability and a geographic azimuth map. In the recognition result, a geographic coordinate sequence where the tornado is located is further extracted, a particle filtering algorithm is optimized based on tornado motion features, a path prediction algorithm is constructed, and path prediction is achieved through the extracted real-time tornado coordinate sequence. According to the method, the accuracy and efficiency of tornado identification can be improved, a scientific and feasible method is provided for path prediction, and powerful technical support is provided for meteorological early warning and disaster prevention and control.

The invention discloses a physical constraint fused multi-scale extreme gale event AI identification method and device and a medium, and relates to the technical field of atmospheric science and artificial intelligence. According to the method, a novel multi-scale extreme gale event identification model is constructed by integrating multi-scale meteorological data. The model effectively captures extreme gale features under different scales through a multi-scale feature extraction module, a feature fusion sharing module and a multi-task output module. In addition, a physical constraint loss design is introduced into the model to ensure that outputs such as wind speed and air pressure conform to meteorological dynamics rules. Through deep learning analysis of meteorological data and combination of physical mechanism understanding, different types of extreme gale events such as hurricane, thunderstorm gale and tornado can be effectively distinguished, and reliable data support and scientific basis are provided for meteorological prediction and emergency response.

The invention discloses a multi-source data and physical information fused tornado space-time intelligent prediction method, which comprises the steps of collecting observation data of a Doppler weather radar and reanalysis data of a mid-term weather forecast center, performing projection conversion, space alignment, time interpolation and the like of space-time dimensions, and fusing key physical characteristics of the tornado; establishing a space-time convolution model network suitable for tornado multi-scale feature extraction, inputting fusion features and corresponding coordinate information, and outputting a prediction control quantity of tornado space-time evolution; a physical control equation of the atmospheric motion and thermodynamic process is converted into a penalty term, a space-time convolution model loss function is embedded, and physical information constraint is carried out on network parameters; and obtaining tornado prediction control quantities of different time windows in the future, and generating a probability graph of the potential development area of the tornado. Through fusion of multi-source data and a physical mechanism, future tornado event probability is quantified, and reliable data support and scientific basis are provided for meteorological prediction and emergency response.

The invention discloses a tornado micro-pressure change data monitoring system and method, and relates to the technical field of micro-pressure data processing, and the system comprises an area radar starting information judgment module, an optimal sampling frequency analysis module, an area tornado micro-pressure comprehensive information judgment module, an area secondary monitoring execution module and an area micro-pressure early warning module. According to the method, the radar starting requirement is scientifically judged by analyzing the data collected by the micromanometer, resource waste is avoided, the sampling frequency of the micromanometer is optimized according to the environment and micropressure observation conditions, and the data collection effectiveness is ensured. And comprehensive multi-source data is deeply analyzed, the micro-pressure state is accurately judged, and a numerical basis is provided for subsequent decision making. And if necessary, targeted secondary monitoring is carried out, and the meteorological condition is further verified. And early warning information can be issued in time, so that related personnel can take precaution and response measures in advance, and losses possibly caused by tornados are reduced.

The invention discloses a tornado identification method and device under a power failure, equipment and a medium, and belongs to the field of power weather forecast. According to the method, meteorological data of a three-dimensional space of a power grid fault area are obtained, high-altitude and low-altitude circulation situation analysis is carried out respectively to obtain a weather background trend and a small and medium-scale weather process, and a vertical structure model under an air temperature-air pressure logarithmic coordinate system is constructed to analyze thermal instability, dynamic conditions and convection intensity characteristics. And performing comprehensive judgment by combining a high-altitude feature judgment condition and a ground feature judgment condition. According to the method, the limitation of traditional radar monitoring in a complex terrain area is broken through, precise identification of the tornado is achieved through multi-dimensional meteorological element coupling analysis, an objective basis is provided for power grid fault attribution, and the disaster prevention emergency capacity of electric power facilities in extreme weather is remarkably improved.

The invention relates to the technical field of meteorology, and provides a meteorological early warning analysis system based on tornado model construction analysis, which comprises a server, a meteorological data acquisition module, a tornado model evaluation module and a meteorological early warning analysis module, and is characterized in that the meteorological data acquisition module is used for acquiring meteorological data of at least two sources of a monitoring area; the tornado model evaluation module evaluates the meteorological data of at least two sources based on the meteorological data to form a meteorological evaluation result, and the meteorological early warning analysis module provides early warning analysis according to the analysis result to prompt a manager to perform real-time early warning prompt. Through cooperation between the meteorological data acquisition module and the tornado model evaluation module, meteorological parameters of a monitoring area can be acquired, and it is guaranteed that the whole system has the advantages of being high in early warning accuracy, high in real-time analysis capacity, high in meteorological analysis fusion capacity, good in refined evaluation capacity and high in intelligent degree.

The application relates to the technical field of meteorological detection, in particular to a tornado cooperative observation method based on a detection device. The method comprises the following steps: performing real-time monitoring on regional micro-pressure and meteorological echoes of a suspicious target area through a micro-pressure gauge and an S-band new-generation weather radar, and performing strong convective occurrence matching prediction to obtain a strong convective occurrence prediction target area; performing solar radio calibration processing on corresponding radar detection equipment and triggering a tornado detection strategy cooperative instruction to perform strong convective target three-dimensional cooperative observation and target strong correlation area fusion division, so as to generate a strong convective target and strong correlation area radar field; performing strong convective feature analysis on the strong convective target and strong correlation area radar field, so as to generate a tornado and strong convective feature data set; acquiring a preset tornado feature library and performing tornado recognition analysis, so as to generate a tornado occurrence prone grid area. The application can improve the monitoring accuracy and real-time performance of the tornado detection process.

The present invention discloses a collaborative observation method, system, equipment and storage medium for the entire life history of a convective cell. The method includes: tracking the motion trajectory of the same convective cell; screening a suitable convective cell as a target for collaborative observation; and selecting a weather radar to conduct continuous collaborative observation of the target convective cell. This application ensures that the observation target at the previous and next moments is the same convective cell through the identification and tracking technology of convective cells. At the same time, a method for selecting a collaborative observation radar based on the change in the azimuth angle of the observed target is proposed to ensure that the observation results at the previous and next moments are comparable. The method provided by this application is of great significance for monitoring the intensity, vertical structure changes and physical parameter calculation of hail, tornadoes, downbursts and thunderstorms, and is of great value for improving the application of collaborative observation results in severe convection monitoring, early warning and mechanism research.

The invention relates to the technical field of weather radar monitoring and early warning, and particularly provides a weather radar tornado identification false alarm suppression method, which comprises the steps of acquiring an event to be predicted; substituting the event to be predicted into a pre-constructed tornado prediction model to obtain a tornado prediction result; and selectively executing tornado early warning based on the tornado prediction result. Wherein the step of constructing the tornado prediction model comprises the steps of obtaining a plurality of historical tornado events, and preprocessing the plurality of historical tornado events to obtain a historical tornado event data set; determining and optimizing an influence radius based on the historical tornado event data set to obtain an optimized influence radius; and constructing a tornado prediction model based on the historical tornado event data set and the optimized influence radius. According to the method, secondary judgment on the weather radar tornado recognition result is achieved, the situation of tornado false alarm is relieved, the accuracy of tornado early warning is improved, the trust degree of the public to tornado early warning is improved, and waste of emergency resources is avoided.

Computer-aided procedure that features: Detecting a first drilling platform at a first location by a processor (721) and using a first machine learning model at least partially based on a first data set containing spectral data describing a gas emission from the first location; Detect, by the processor (721), an environmental event within a minimum distance from the drilling platform; Determine, by the processor (721), a probability of damage to the first drilling platform as a result of the environmental event; and Predictions of a suitable location for a second drilling platform at a second site without a drilling platform, at least partially based on a similarity between the terrain of the second site and the terrain of the first site, and a probability of damage resulting from the environmental event at the second site, wherein the environmental event is a fire, landslide, tornado, hurricane, or flood.

The invention discloses a short-term wind power prediction method and system considering regional and meteorological characteristics, and the method comprises the following steps: carrying out the modal decomposition of wind power data and meteorological characteristic data through employing successive variational modal decomposition, and carrying out the optimization of a penalty factor of SVMD through employing a Red-beak-Blue-Tapie optimization algorithm, so as to obtain an optimal power modal component; constructing a long short-term memory prediction model, and constructing an optimized LSTM prediction model by taking the hidden unit number, the maximum training period and the initial learning rate of the prediction model as optimization objects of a tornado optimization algorithm; and inputting the optimal power mode component obtained by decomposition, the meteorological feature data and the regional feature data into the optimized LSTM model for training and prediction to obtain a predicted value of the power mode component, and performing superposition reconstruction to obtain a wind power prediction result. According to the method, the regional and meteorological characteristics are combined with a double-layer parameter optimization strategy, so that the prediction stability and accuracy can be improved.

The application discloses a tornado-affected power distribution conductor wind load calculation method and system and a storage medium. The method comprises the following steps: constructing a tornado wind field model, simplifying the wind field into a main leading tangential wind speed distribution; extracting a conductor segment center position under a span configuration, and measuring a radial spatial relationship of the conductor segment center position relative to a tornado center; establishing a wind load integral calculation model of each span segment of the conductor, and calculating an aerodynamic load distribution of adjacent span conductors; converting the conductor aerodynamic force into an external load of a power distribution pole structure through load direction decomposition and structural stress analysis; and superimposing conductor transmission loads and pole body wind pressure, and calculating a total wind load response of the power distribution pole. The method realizes accurate modeling and distribution of the conductor aerodynamic force in the space and structure layers. The method is suitable for wind load analysis of a power distribution line under the action of tornadoes and other local strong winds, and provides fine input parameters for safety evaluation of a power distribution system structure.

The invention discloses a tornado field unmanned aerial vehicle tracking method and system based on multi-mode perception and dynamic path planning and an unmanned aerial vehicle, and the method comprises the steps: integrating meteorological radar data, a satellite cloud picture and multivariate data collection equipment sensing information, combining reinforcement learning and a multi-target optimization algorithm, and planning an optimal tracking path; the unmanned aerial vehicle cluster works cooperatively with a self-organizing network through hierarchical formation, and realizes real-time measurement and minute-level data return of a three-dimensional wind field in a tornado core area by adopting local storage and satellite direct transmission dual channels. According to the tornado field unmanned aerial vehicle tracking method based on multi-modal sensing and dynamic path planning, the problems of low resolution, response lag and the like of a traditional detection means can be solved, and rapid tracking and accurate measurement of a tornado three-dimensional wind field are achieved.

The invention discloses a high-degree-of-freedom self-detection wind energy intelligent self-adjustment system and method. The system comprises a sensor module, a central control module and a signal transmission module. The method comprises the following steps of: acquiring data, preprocessing the data, constructing a'tornado 'model from two angles of wind speed and track through an obtained data model, calculating necessary variables according to a tornado attraction mechanism, attraction, air density, contact area and drag force, finally simulating a maximum power value, and outputting the obtained maximum power. The high-degree-of-freedom self-detection wind energy intelligent self-adjusting system is provided by analyzing the power generation efficiency of high-altitude wind power generation, factors influencing the high-degree-of-freedom self-detection wind energy intelligent self-adjusting system are analyzed through the central system, the maximum power generation efficiency can be achieved, the electric quantity is large, the efficiency is high, and the power generation efficiency is high. And the predicted annual energy output can reach more than 6500 hours.

The present invention discloses a tornado micro-pressure change data monitoring system and method, which relates to the technical field of micro-pressure data processing. The system includes: a regional radar activation information determination module, an optimal sampling frequency analysis module, a regional tornado micro-pressure comprehensive information determination module, a regional secondary monitoring execution module, and a regional micro-pressure early warning module. By analyzing the data collected by the micro-pressure gauge, the present invention scientifically judges the radar activation requirements, avoids resource waste, optimizes the sampling frequency of the micro-pressure gauge according to the environmental and micro-pressure observation conditions, and ensures the effectiveness of data collection. Through in-depth analysis of multi-source data, it accurately judges the micro-pressure state and provides a numerical basis for subsequent decision-making. Conducts targeted secondary monitoring when necessary to further verify the meteorological conditions. It can also issue early warning information in a timely manner, enabling relevant personnel to take preventive and response measures in advance and reducing the possible losses caused by tornadoes.

The invention discloses a storm radar characteristic quantity driven machine learning tornado identification method, and belongs to the field of meteorological disaster weather identification, and the method comprises the following steps: obtaining a storm radar characteristic quantity containing key characteristic information through characteristic extraction and optimization processing based on body sweeping radabundance data; performing feature quantity normalization processing and feature quantity missing value processing on the storm radar feature quantity to obtain an input sequence containing a plurality of elevation angle layer data; and inputting the input sequence into a recognition model fusing Transform and CNN, and outputting a tornado recognition result. According to the method, the technical problems of feature redundancy, insufficient local and global feature fusion, data missing sensitivity and the like in a traditional tornado identification method are effectively solved.

The present invention discloses a method for tornado identification and path prediction based on X-band dual-polarization radar data. The method mainly utilizes six observation quantities of the two lowest elevation angles of the radar, combines the deep learning model Unet, introduces the attention mechanism and position coding information, optimizes the data transmission process, and constructs a weakly supervised lightweight recognition model TD-Unet. The model extracts features from multi-elevation radar data to generate a tornado recognition probability and a geographic orientation map. In the recognition result, the geographic coordinate sequence of the tornado is further extracted, and the particle filter algorithm is optimized based on the tornado motion characteristics to construct a path prediction algorithm, and the path prediction is realized through the extracted real-time tornado coordinate sequence. The present invention can improve the accuracy and efficiency of tornado identification, provide a scientific and feasible method for path prediction, and provide strong technical support for meteorological warning and disaster prevention and control.

The invention discloses a collaborative observation method, system and device for the full life history of flow monomers, and a storage medium. The method comprises the following steps: tracking the motion trails of the same flow monomer; screening a proper convection monomer as a collaborative observation target; and selecting a weather radar to carry out continuous collaborative observation on the target convection monomer. According to the invention, the observation targets at the previous and later moments are ensured to be the same convection monomer through the identification and tracking technology of the convection monomer. Meanwhile, the method for selecting the collaborative observation radar based on the azimuth angle change of the observation target is provided, and it can be ensured that observation results of the previous and later moments have comparability. The method provided by the invention has important significance for monitoring the strength and vertical structure change of hail, tornado, downburst and thunderstorm strong wind and calculating physical parameters, and has important value for improving the application of a collaborative observation result in severe convection monitoring and early warning and mechanism research.

The application discloses a super-large tornado wind tunnel horizontal moving system, which comprises a track, a walking wheel set and a fan mounting platform, the fan mounting platform is provided with a fan section and an airflow ascending section mounted above a tornado flow guide device, the fan section is connected with the airflow ascending section, the walking wheel set is mounted at the bottom of both ends of the fan mounting platform, and the walking wheels of the walking wheel set are rollingly mounted on the track. The fan mounting platform arranged above the tornado flow guide device is mounted on the walking wheel set, so that the fan mounting platform can walk along the track, the movement of the tornado can be simulated, and the super-large tornado wind tunnel horizontal moving system is closer to the actual situation, and is more reliable and accurate compared with the static simulation system in the prior art.

The invention relates to the field of cleaning equipment, in particular to a tornado forming device and a tornado cleaning system.The tornado forming device comprises a channel assembly arranged on a mounting unit, an axial power assembly arranged at the dust collecting end of the channel assembly and a tangential power assembly arranged at the connecting end of the channel assembly and to-be-cleaned equipment; and the regulation and control module comprises an electrostatic sensor arranged on the mounting unit and an ion wind bar connected with the tornado forming unit, the effect of disturbing axial linear airflow into tornado is achieved, a strong air vortex formed by the miniature tornado can roll up foreign matters on a cleaning surface far away from the cleaning surface, and the cleaning effect is improved. Due to the pressure difference between the air holes of the tornado and the atmosphere, dust is rolled up along with vortex of the tornado and cannot escape, secondary pollution of the dust is eradicated, and the non-contact type deep cavity cleaning effect is achieved. Meanwhile, closed-loop static electricity accurate elimination is achieved through the regulation and control module, static electricity is effectively eliminated through the dynamic regulation and control technology of the regulation and control module, high-voltage breakdown of a to-be-cleaned product is avoided, and meanwhile energy is saved.

The present invention relates to the technical field of meteorological display, and discloses a weather radar intelligent integrated monitoring and display system and method, including a tornado monitoring and prediction system. The tornado monitoring and prediction system includes: an environmental factor monitoring module, which uses the environmental factor monitoring module to monitor and collect data on temperature, air pressure, dryness and humidity, cumulonimbus clouds and wind fields in the monitoring area; an environmental factor data processing module, which predicts the flow direction of air currents based on the monitored and collected temperature and air pressure data and in combination with the wind field data. This weather radar intelligent integrated monitoring and display system and method, through the tornado monitoring and prediction system, uses the environmental factor monitoring module to collect data on temperature, air pressure, dryness and humidity, cumulonimbus clouds and wind fields in the monitoring area, and predicts the flow direction of wind field air currents in the cumulonimbus cloud area and the dry-wet intersection area where tornadoes are likely to occur. According to the height, rotation diameter and wind speed of the predicted wind field air currents, tornadoes can be predicted.

The invention relates to the technical field of equidistant spiral motion, in particular to a planning method for equidistant spiral motion with width by taking a stool dragon as a prototype, which comprises the following steps of: 1, establishing a generalized equidistant spiral motion model, and determining a motion track and calculating the speed; 2, the model in the step 1 is optimized, the coordinate range of the bench where collision occurs is determined, and a strategy for avoiding collision is planned; 3, on the basis of the models in the step 1 and the step 2, calculating the minimum pitch of the U-turn space; step 4, optimizing a turn-round path; and 5, calculating the maximum traveling speed. According to the method, the real-time motion trail of the equidistant spiral motion with the width such as the bench dragon can be analyzed, and the research result can be used for damage prediction in nature such as tornado and typhoon.

The present invention discloses a multi-scale AI identification method, device and medium for extreme gale events that integrate physical constraints, and relates to the fields of atmospheric science and artificial intelligence technology. The method constructs a novel multi-scale extreme gale event identification model by integrating multi-scale meteorological data. The model effectively captures the characteristics of extreme gale at different scales through three modules: multi-scale feature extraction, feature fusion sharing, and multi-task output. In addition, the model introduces a physical constraint loss design to ensure that outputs such as wind speed and air pressure conform to the laws of meteorological dynamics. Through deep learning analysis of meteorological data, combined with the understanding of physical mechanisms, the present invention can effectively distinguish different types of extreme gale events such as hurricanes, thunderstorms and tornadoes, providing reliable data support and scientific basis for meteorological forecasting and emergency response.

The present invention discloses a tornado wind tunnel with an adjustable ascending channel height, comprising a movable platform and a deflector assembly, an airflow ascending section, and a fan section mounted on the movable platform. The rotatable and adjustable deflectors of the deflector assembly are arranged circumferentially along the airflow ascending section. The output port of the deflector assembly is connected to the input port of the airflow ascending section, and the input port of the fan section is connected to the output port of the airflow ascending section. An air inlet control assembly for blocking the radial air inlet area is installed at the input port or the output port of the deflector assembly. The present invention simulates the horizontal movement of a tornado on the ground by mounting the deflector assembly, the airflow ascending section, and the fan section on a movable platform. By adjusting the rotation angle of the deflector and the radial air inlet area of ​​the input port or the output port of the deflector assembly, tornado wind fields with different swirl ratios can be simulated.

Conventional replacement fastening components only secure the ridge end, so in the event of extremely strong winds such as typhoons or tornadoes, the top end of the replaced roofing material can easily be lifted up by the lever principle. [Solution] The replaceable fastening member of the present invention consists of a replaceable base member and a replaceable head-side upper fixing member. The replaceable base member has the same basic configuration as the fastening member, and the replaceable head-side upper fixing member has a U-shape with a lower insertion part, a vertical upright part, and a head-side upper fixing part. The inner height of the vertical upright part is higher than the outer height of the head-finding contact surface. When installing the replaceable fastening member, after fixing the replaceable base member to the base plate, the replaceable head-side upper fixing member is inserted so that it is positioned on the upper and lower outer sides of the head-finding contact surface of the replaceable base member, and is positioned so that the outer side of the head-finding contact surface of the replaceable base member and the inner side of the vertical upright part of the replaceable head-side upper fixing member come into contact.

A thunderstorm cloud real-time simulation method based on cloud chart comprises the following steps: step S1: obtaining a pixel point ray direction, constructing a large world cloud layer space, and calculating the effective rendering length of the pixel point ray; step S2: based on a two-dimensional cloud chart texture, a three-dimensional noise texture and a position-density function, calculating the bulk density of the volume cloud in the pixel point ray direction to obtain a thunderstorm cloud shape; step S3: constructing a vortex field, calculating the centripetal force received by the cloud body during movement, offsetting the cloud chart sampling UV to realize the movement control of the thunderstorm cloud; and step S4: according to the light position, the light color, the cloud body preset color and the sampling point density, the cloud body color in the pixel point ray direction is obtained by combining the light transmission model; the thunderstorm cloud real-time simulation method based on the cloud chart can quickly and real-timely generate a tornado type thunderstorm cloud in a large world range, and to a certain extent, solves the simulation problem of a complex volume cloud.

The present disclosure pertains to a roof anchoring system designed to enhance the structural resilience of buildings and prevent roof uplift during severe weather conditions such as storms, tornadoes, and hurricanes. The system addresses the need for a cost-effective, easily installable solution that can integrate with existing roof structures or new roof builds, thereby providing greater rigidity, among other benefits. Components include a base affixed to a bearing wall, a vertical reinforcement member extending from the base, radius arms connected to the vertical reinforcement member via angular adjustment members, and radius brackets securing the radius arms to the roof rafters and timber walls / sheeting.

The invention discloses a power transmission tower full-field stress distribution real-time online response method, and belongs to the technical field of power transmission tower structural mechanical state online response, and the method comprises the steps: carrying out the time-frequency analysis of the displacement response time history of the tower top of a power transmission tower in a tornado event, obtaining a structural response time-frequency energy spectrum, and positioning the lowest fundamental frequency moment; setting a time domain range based on the lowest fundamental frequency moment, acquiring acceleration response time histories of the power transmission tower at different heights in a tornado event in the time domain range, and estimating a structural instantaneous modal shape at the lowest fundamental frequency moment; constructing and correcting a high-fidelity finite element model of the power transmission tower; the corrected power transmission tower high-fidelity finite element model is loaded, wind load distribution at the lowest fundamental frequency moment is recognized, and instantaneous structure full-field stress distribution of the power transmission tower at the lowest fundamental frequency moment is obtained through static calculation. According to the method, the tower body load distribution mode can be recognized in real time in the non-stationary wind environment, and then the instantaneous structural mechanical state is effectively inverted.

The invention provides a harmonic detection method and device based on improved SVD and parameter optimization VMD. The method comprises the following steps: carrying out improved noise reduction preprocessing on a noisy harmonic signal, and determining an effective rank by adopting singular value shrinkage to realize noise separation; an improved tornado optimization algorithm is adopted to optimize a preset modal number and a penalty factor of variational modal decomposition, the improved tornado optimization algorithm introduces a thunderstorm number adaptive adjustment strategy, and the thunderstorm number is dynamically adjusted according to population diversity to balance global exploration and local development capabilities; constructing a fitness function, and according to the improved tornado optimization algorithm TOC algorithm, minimizing the fitness function to obtain an optimal preset modal number and a penalty factor; decomposing the denoised signal by using an optimal preset modal number and a penalty factor to obtain each harmonic modal component; and modulating each modal component based on a second-order central difference Teager energy operator, and extracting instantaneous amplitude and frequency information of harmonic waves.