A method and system for detecting average electric field of air atmosphere

Abstract

The present invention relates to the field of meteorological detection technology, specifically to a method and system for detecting an average electric field in the air atmosphere, comprising the following steps: based on real-time meteorological data, historical trend analysis is performed on the temperature, humidity, and wind speed data in the atmosphere, and by comparing with the real-time data, key meteorological factors that affect the measurement results of the electric field strength are identified, and a list of key meteorological factors is generated. In the present invention, by combining real-time meteorological data and historical trends to analyze key meteorological factors, the accuracy and timely response capability of atmospheric electric field strength measurement are enhanced, by identifying and calculating the sensitivity of meteorological factors and adjusting model parameters, the accuracy and adaptability of electric field strength prediction are optimized, by establishing an atmospheric electric field dependency model, the model's ability to predict weather changes is improved, the ability to predict electric field change trends is enhanced, and the estimation of the fluctuation range is optimized, providing data support for predicting extreme weather events and conducting climate research.

Description

Technical Field

[0001] The present invention relates to the field of meteorological detection technology, and in particular to a method and system for detecting an average electric field of the atmosphere in the air. Background Art

[0002] The field of meteorological detection technology aims to monitor and analyze physical and chemical phenomena in the atmosphere, including temperature, humidity, air pressure, wind speed, wind direction, and electric field, through a variety of methods, to provide a cornerstone for weather forecasting and climate research. It supports the prediction of extreme weather events and disaster prevention through real-time monitoring of the state of the Earth's atmosphere. It uses ground-based meteorological stations, satellites, radars, and sensors carried on aircraft and drones to collect and analyze meteorological data to help scientists and meteorological personnel analyze weather patterns, climate change, and the interaction between the atmosphere and the environment, conduct agricultural planning, ensure the safety of aerospace activities, and respond to the challenges brought by climate change.

[0003] Among them, the aerial atmospheric average electric field detection method is used to measure and analyze the changes in the electric field in the atmosphere. By monitoring the electric field intensity in the atmosphere, it predicts weather changes, including the prediction and research of thunderstorm extreme weather. Combined with highly sensitive electric field sensors on the ground, aircraft and unmanned aerial vehicles, it collects electric field data in real time to help meteorologists assess the risk of thunderstorms, make more accurate predictions of weather changes, improve the accuracy and timeliness of weather forecasts, and provide information on atmospheric electrodynamics for scientific research.

[0004] Traditional electric field detection technology has limitations in real-time data processing and trend analysis. It relies on static models and cannot dynamically adjust model parameters to match real-time meteorological changes, resulting in inaccurate measurement and prediction of electric field data under rapidly changing atmospheric conditions. When dealing with atmospheric disturbances, there is a lack of effective methods to simulate and predict fluctuations in electric field intensity, which limits its application in extreme weather prediction and scientific research. It cannot accurately predict and analyze electric field changes caused by atmospheric disturbances, affecting the efficiency of early warning of extreme weather events such as thunderstorms, and posing safety risks. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for detecting the average electric field of the atmosphere in the air.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, a method for detecting the average electric field of the atmosphere in the air, comprising the following steps:

[0007] S1: Based on real-time meteorological data, historical trend analysis is performed on the temperature, humidity and wind speed data in the atmosphere. By comparing with real-time data, key meteorological factors that affect the measurement results of electric field strength are identified and a list of key meteorological factors is generated;

[0008] S2: Based on the list of key meteorological factors and in combination with the atmospheric electric field measurement data, the influence of various meteorological factors on the electric field measurement is analyzed, the sensitivity values ​​of various meteorological factors are calculated, and the sensitivity score data is obtained;

[0009] S3: Based on the sensitivity scoring data, the scoring data of various meteorological factors are used as input parameters to establish a dependency model between the atmospheric electric field and environmental variables, thereby obtaining an atmospheric environment dependency model;

[0010] S4: using the atmospheric environment dependence model to simulate the dynamic behavior of atmospheric fluid, combining the real-time meteorological data to simulate atmospheric disturbances, and obtaining atmospheric disturbance simulation results;

[0011] S5: Based on the atmospheric disturbance simulation results and in combination with real-time meteorological data, analyze and predict the changing trend of the electric field intensity in the atmosphere, including the predicted electric field intensity value, change rate, and fluctuation range, and obtain the intensity trend analysis results;

[0012] S6: Based on the intensity trend analysis results, adjust the prediction model parameters according to the real-time meteorological parameters, match various atmospheric conditions, calculate the average atmospheric electric field strength value, and generate electric field strength detection data.

[0013] As a further solution of the present invention, the list of key meteorological factors includes temperature change trend analysis results, humidity fluctuation rate information, and wind speed anomaly data; the sensitivity score data includes temperature impact weight information, humidity impact weight information, and wind speed impact weight data; the atmospheric environment dependence model includes temperature adjustment factor data, humidity impact coefficient information, and wind speed correlation analysis information; the atmospheric disturbance simulation results include disturbance intensity level information, disturbance duration estimation results, and disturbance range prediction information; the intensity trend analysis results include electric field strength peak prediction results, long-term change trend analysis results, and key change node information; the electric field strength detection data includes parameter adjustment algorithm, model update frequency information, and adaptive feedback mechanism.

[0014] As a further solution of the present invention, based on real-time meteorological data, historical trend analysis is performed on the temperature, humidity and wind speed data in the atmosphere, and by comparing with the real-time data, key meteorological factors affecting the electric field strength measurement results are identified. The steps of generating a list of key meteorological factors are specifically as follows:

[0015] S101: Based on real-time meteorological data, analyze the temperature, humidity, and wind speed data in the current atmosphere, compare them with historical data of the same period, analyze the change trends of various meteorological data, and generate meteorological change comparison results;

[0016] S102: Based on the meteorological change comparison results, analyzing the correlation between various meteorological factors and changes in electric field strength, identifying key meteorological factors affecting the electric field strength, and generating a record of factors affecting the electric field;

[0017] S103: Based on the electric field influencing factor records, calculate the influence levels of various meteorological factors on the electric field strength, classify the influence levels according to their priorities, and generate a list of key meteorological factors.

[0018] As a further solution of the present invention, based on the list of key meteorological factors and in combination with the atmospheric electric field measurement data, the influence of various meteorological factors on the electric field measurement is analyzed, and the sensitivity values ​​of various meteorological factors are calculated. The steps of obtaining the sensitivity score data are specifically as follows:

[0019] S201: Based on the list of key meteorological factors and in combination with atmospheric electric field measurement data, analyzing the influence of various meteorological factors on electric field measurement results, evaluating the sensitivity of various meteorological factors, and generating factor sensitivity records;

[0020] S202: Based on the sensitivity records of the factors, the interactions among various meteorological factors and their impacts on the electric field strength are calculated, including temperature, humidity and wind speed, the interdependence and joint effects of various meteorological factors are evaluated, and a meteorological interaction evaluation result is generated;

[0021] S203: Based on the meteorological interaction evaluation results, analyze the impact degree and frequency of the combination of multiple meteorological factors, identify the impact degree on the electric field strength, calculate the impact weight score of the combination of multiple factors, and generate sensitivity score data.

[0022] As a further solution of the present invention, based on the sensitivity scoring data, the scoring data of various meteorological factors are used as input parameters to establish a dependency model between the atmospheric electric field and environmental variables. The steps of obtaining the atmospheric environment dependency model are specifically as follows:

[0023] S301: Analyze the sensitivity scores of various meteorological factors based on the sensitivity scoring data, calculate the weight values ​​of various meteorological factors on the electric field strength, and generate meteorological factor weight calculation results;

[0024] S302: Based on the meteorological factor weight calculation results, setting model parameters of the influence of various meteorological factors on the electric field, including setting influence coefficients and adjustment thresholds for various meteorological factors, optimizing the responsiveness and accuracy of the model, and generating model parameter setting results;

[0025] S303: According to the model parameter setting result, a dependency model between the atmospheric electric field and environmental variables is constructed, the electric field variation process under various atmospheric conditions is simulated, and an atmospheric environment dependency model is generated.

[0026] As a further solution of the present invention, the atmospheric environment dependence model is used to simulate the dynamic behavior of atmospheric fluid, and the atmospheric disturbance is simulated in combination with real-time meteorological data to obtain the atmospheric disturbance simulation result. Specifically, the steps are as follows:

[0027] S401: Based on the atmospheric environment dependence model and in combination with real-time meteorological information, adjusting model parameters to match changes in actual meteorological conditions, simulating the real-time impact of changes in multiple meteorological factors on the electric field, and generating real-time model adjustment data;

[0028] S402: Based on the real-time model adjustment data, simulate the dynamic behavior of the atmospheric fluid, including airflow movement, temperature and humidity changes, analyze and record the impact of various meteorological factors on atmospheric disturbances, and generate an atmospheric fluid analysis record;

[0029] S403: Analyze and identify key disturbance data, including disturbance frequency and intensity, based on the atmospheric fluid analysis record, evaluate the impact of atmospheric disturbance on the change of electric field intensity, and obtain atmospheric disturbance simulation results.

[0030] As a further solution of the present invention, based on the atmospheric disturbance simulation results, combined with real-time meteorological data, the changing trend of the electric field intensity in the atmosphere is analyzed and predicted, including the predicted electric field intensity value, change rate, and fluctuation range. The steps of obtaining the intensity trend analysis results are specifically as follows:

[0031] S501: Based on the atmospheric disturbance simulation results, combined with real-time temperature, humidity, and wind speed data, the electric field measurement data is updated, the trend and volatility of the atmospheric electric field strength data are analyzed, and abnormal data is identified to generate instant electric field analysis results;

[0032] S502: using the instant electric field analysis result, by comparing with historical data of the same period, predicting the fluctuation range of the electric field strength, including the peak and valley values ​​of the electric field strength, and generating an electric field fluctuation prediction result;

[0033] S503: Based on the electric field fluctuation prediction results, an exponential smoothing algorithm is used to predict the atmospheric electric field strength value, including the peak and valley values ​​and fluctuation range, the speed and frequency of the electric field change, analyze the performance and risk level of the atmospheric average electric field under various meteorological conditions, and generate intensity trend analysis results.

[0034] As a further solution of the present invention, the exponential smoothing algorithm is according to the formula:

[0035]

[0036] Calculate the electric field strength value, where is the predicted electric field strength at the next time point, is the smoothing parameter, is the actual electric field strength at the current time point, is the predicted electric field strength at the current time point, is the weight coefficient of humidity, is the humidity value at the current time point, is the weight coefficient of temperature, is the temperature value at the current time point, is the weight coefficient of wind speed, is the wind speed value at the current time point, Is the index representing the current time point.

[0037] As a further solution of the present invention, based on the intensity trend analysis results, the prediction model parameters are adjusted according to the real-time meteorological parameters, a variety of atmospheric conditions are matched, the atmospheric average electric field strength value is calculated, and the steps of generating electric field strength detection data are specifically as follows:

[0038] S601: Based on the intensity trend analysis result, evaluate the influence of various meteorological parameters on the electric field prediction model, evaluate the relationship between the changes of various parameters and the electric field intensity fluctuation, identify the influence coefficients and sensitivity parameters that need to be adjusted, and generate a prediction model adjustment parameter list;

[0039] S602: adjusting the electric field prediction model parameters according to the prediction model adjustment parameter list and in combination with real-time meteorological conditions, including adjusting the influence weights of temperature, humidity, and wind speed, optimizing the responsiveness of the model, and generating a model parameter update result;

[0040] S603: Based on the model parameter update result, combined with real-time meteorological information and electric field measurement data, the average electric field strength value of the atmosphere is calculated, including the instantaneous value and change trend of the electric field strength, and the electric field strength detection data is generated.

[0041] An air atmosphere average electric field detection system, the air atmosphere average electric field detection system is used to perform the above-mentioned air atmosphere average electric field detection method, the system comprises:

[0042] The real-time meteorological analysis module compares and analyzes the real-time temperature, humidity, and wind speed data with historical data based on real-time meteorological data, identifies key meteorological factors that affect the electric field strength, and generates meteorological information analysis results;

[0043] The meteorological factor evaluation module analyzes the influence of various meteorological factors on the electric field measurement based on the meteorological information analysis result, calculates the sensitivity of the influence of various meteorological factors on the electric field, and generates an environmental factor sensitivity score;

[0044] The dependency analysis module uses the environmental factor sensitivity score to construct a dependency model of electric field intensity and meteorological factors and generates electric field response evaluation information;

[0045] The atmospheric impact simulation module simulates the dynamic behavior of atmospheric fluid based on the electric field response evaluation information, analyzes the impact of atmospheric disturbances on the atmospheric electric field intensity under various meteorological conditions, and generates atmospheric fluctuation analysis data;

[0046] The average intensity analysis module uses the atmospheric fluctuation analysis data to analyze the changing trend of the electric field intensity, and combines the electric field measurement data to calculate the atmospheric average electric field intensity value, including the instantaneous value and changing trend of the electric field intensity, and generates electric field intensity detection data.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by combining real-time meteorological data and historical trends to analyze key meteorological factors, the accuracy and timely response capability of atmospheric electric field strength measurement are improved, the sensitivity of meteorological factors is identified and calculated and model parameters are adjusted to optimize the accuracy and adaptability of electric field strength prediction, ensure that data matches actual atmospheric conditions more accurately, establish a dependency model between atmospheric electric field and environmental variables and simulate atmospheric disturbances, improve the model's ability to predict weather changes, enhance the ability to predict electric field change trends through real-time updating and simulation, optimize the estimation of fluctuation range, improve the accuracy and efficiency of atmospheric electric field measurement, and provide data support for predicting extreme weather events and conducting climate research. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] Embodiment 1

[0060] See also Figure 1 The present invention provides a technical solution, a method for detecting the average electric field of the atmosphere in the air, comprising the following steps:

[0061] S1: Based on real-time meteorological data, historical trend analysis is performed on the temperature, humidity and wind speed data in the atmosphere. By comparing with real-time data, key meteorological factors that affect the measurement results of electric field strength are identified and a list of key meteorological factors is generated;

[0062] S2: Based on the list of key meteorological factors and combined with the atmospheric electric field measurement data, the influence of various meteorological factors on the electric field measurement is analyzed, the sensitivity values ​​of various meteorological factors are calculated, and the sensitivity score data is obtained;

[0063] S3: Based on the sensitivity score data, the score data of various meteorological factors are used as input parameters to establish a dependency model between the atmospheric electric field and environmental variables, and obtain an atmospheric environment dependency model;

[0064] S4: Use the atmospheric environment dependence model to simulate the dynamic behavior of atmospheric fluids, combine real-time meteorological data to simulate atmospheric disturbances, and obtain atmospheric disturbance simulation results;

[0065] S5: Based on the atmospheric disturbance simulation results and combined with real-time meteorological data, analyze and predict the changing trend of the electric field intensity in the atmosphere, including the predicted electric field intensity value, change rate, and fluctuation range, and obtain the intensity trend analysis results;

[0066] S6: Based on the intensity trend analysis results, adjust the prediction model parameters according to the real-time meteorological parameters, match various atmospheric conditions, calculate the average atmospheric electric field strength value, and generate electric field strength detection data.

[0067] The list of key meteorological factors includes temperature change trend analysis results, humidity fluctuation information, and wind speed anomaly data. The sensitivity score data includes temperature impact weight information, humidity impact weight information, and wind speed impact weight data. The atmospheric environment dependence model includes temperature adjustment factor data, humidity impact coefficient information, and wind speed correlation analysis information. The atmospheric disturbance simulation results include disturbance intensity level information, disturbance duration estimation results, and disturbance range prediction information. The intensity trend analysis results include electric field strength peak prediction results, long-term change trend analysis results, and key change node information. The electric field strength detection data includes parameter adjustment algorithm, model update frequency information, and adaptive feedback mechanism.

[0068] See also Figure 2 Based on real-time meteorological data, historical trend analysis is performed on the temperature, humidity, and wind speed data in the atmosphere. By comparing with real-time data, key meteorological factors that affect the electric field strength measurement results are identified. The steps for generating a list of key meteorological factors are as follows:

[0069] S101: Based on real-time meteorological data, analyze the temperature, humidity, and wind speed data in the current atmosphere, compare them with historical data of the same period, analyze the change trends of various meteorological data, and generate the specific process of meteorological change comparison results as follows;

[0070] In sub-step S101, based on real-time meteorological data, the temperature, humidity, and wind speed data in the current atmosphere are analyzed and compared with historical data of the same period. The multivariate regression analysis method is used to quantitatively analyze the changing trends of various meteorological factors. The temperature, humidity, and wind speed meteorological data are integrated and time series standardized. A multivariate regression model is used to construct a regression equation with time as the independent variable and meteorological data as the dependent variable. The changing trends of various meteorological factors over time are estimated. Using the time series regression model, the formula is: ,in, is the model intercept, indicating the starting reference value, Respectively represent the linear influence coefficients of temperature, humidity and wind speed on the results, Represents the corresponding meteorological variable data, It is the error term, representing the random fluctuation in the data. The error is minimized through statistical methods, and meteorological changes are predicted to generate meteorological change comparison results.

[0071] S102: Based on the meteorological change comparison results, the correlation between various meteorological factors and the change of electric field strength is analyzed, key meteorological factors affecting the electric field strength are identified, and the specific process of generating a record of factors affecting the electric field is as follows;

[0072] In sub-step S102, based on the meteorological change comparison results, the partial least squares regression model is used to analyze the correlation between various meteorological factors and changes in electric field intensity, and the meteorological change comparison results are sorted out. The meteorological factors potentially associated with the electric field intensity are selected as independent variables, and the electric field intensity is used as the dependent variable to construct a data model. The PLSR model can be used to deal with multicollinearity problems and improve the prediction accuracy of the model. The formula is: ,in, is the intercept of the equation, For temperature Electric field strength The influence coefficient of Humidity Electric field strength The influence coefficient of Wind speed Electric field strength The influence coefficient of Indicate model errors, identify key meteorological factors affecting electric field strength, and generate records of factors affecting the electric field.

[0073] S103: Based on the records of factors affecting the electric field, the influence levels of various meteorological factors on the electric field strength are calculated, and priority classification is performed according to the influence levels. The specific process of generating a list of key meteorological factors is as follows;

[0074] In sub-step S103, based on the record of electric field influencing factors, the influence levels of various meteorological factors on the electric field strength are calculated, and priority classification is performed according to the influence levels. Based on the record of electric field influencing factors, the weighted scoring method is used to quantify and prioritize the influencing factors. The scoring model is weighted according to the previous regression coefficient, and the formula is: ,in, is the weight coefficient corresponding to the importance evaluation of temperature factor, is the weight coefficient corresponding to the importance of humidity factor, is the weight coefficient corresponding to the importance evaluation of wind speed factor, is a quantitative index or rating of temperature, A quantitative index or rating of humidity. is a quantitative index or rating of wind speed, It represents the comprehensive impact score of the target meteorological factors and generates a list of key meteorological factors.

[0075] See also Figure 3 Based on the list of key meteorological factors and combined with the atmospheric electric field measurement data, the influence of various meteorological factors on the electric field measurement is analyzed, and the sensitivity values ​​of various meteorological factors are calculated. The specific steps to obtain the sensitivity score data are as follows:

[0076] S201: Based on the list of key meteorological factors and combined with the atmospheric electric field measurement data, analyze the impact of various meteorological factors on the electric field measurement results, evaluate the sensitivity of various meteorological factors, and generate the specific process of factor sensitivity records;

[0077] In sub-step S201, based on the list of key meteorological factors and combined with the atmospheric electric field measurement data, the influence of various meteorological factors on the electric field measurement results is analyzed. The sensitivity analysis method is used to construct a partial derivative matrix to quantitatively evaluate the influence of temperature, humidity and wind speed on the electric field measurement. The partial derivatives of the influence of multiple meteorological factors on the electric field measurement results are calculated to evaluate the sensitivity of the influence. The formula is: ,in, Representative factors sensitivity, is the partial derivative, which is used to quantify the electric field strength Sensitivity to individual meteorological factors, is the measured value of the electric field strength, Indicates the electric field strength The rate of change of the target variable is used to describe the electric field strength when the target variable changes slightly. The degree of response, Indicates meteorological factors It can analyze the independent impact of multiple factors, predict the changing trends under differentiated environmental conditions, and generate factor sensitivity records.

[0078] S202: Based on the factor sensitivity records, the interaction between multiple meteorological factors and their impact on the electric field strength, including temperature, humidity and wind speed, is calculated, the interdependence and joint effects of multiple meteorological factors are evaluated, and the specific process of generating meteorological interaction evaluation results is as follows;

[0079] In sub-step S202, based on the factor sensitivity records, the interaction effect analysis model is used to evaluate the interaction between multiple meteorological factors and their impact on the electric field strength. By constructing a multivariate interaction model, the combined effect of temperature, humidity and wind speed is analyzed. The formula is: ,in, is the model intercept, For temperature Individual influence coefficients, Humidity The individual influence coefficient of Wind speed The individual influence coefficient of represents the interaction coefficient between temperature and humidity factors, represents the interaction coefficient between temperature factor and wind speed factor, represents the interaction coefficient between humidity factor and wind speed factor, The error term is used to evaluate the impact of individual factors and the interdependence and joint effects between factors, and generate meteorological interaction evaluation results.

[0080] S203: Based on the meteorological interaction assessment results, the influence degree and frequency of the combination of multiple meteorological factors are analyzed, and the influence degree on the electric field strength is identified, and the influence weight scores of the combination of multiple factors are calculated to generate the sensitivity score data. The specific process is as follows;

[0081] In sub-step S203, based on the meteorological interaction evaluation results, the influence degree and frequency of multiple meteorological factor combinations are analyzed, and the weighted scoring method is used to evaluate the influence weight of each combination on the electric field strength. A weighted scoring model is constructed to calculate the weighted score of the influence of multiple factor combinations on the electric field. The formula is: ,in, Represents the total weight score, Is a factor The weight of Is a factor The influence coefficient of the meteorological factors is used to identify the combination of meteorological factors that has the greatest impact on the electric field strength and generate sensitivity score data.

[0082] See also Figure 4 Based on the sensitivity score data, the score data of various meteorological factors are used as input parameters to establish a dependency model between the atmospheric electric field and environmental variables. The specific steps to obtain the atmospheric environment dependency model are as follows:

[0083] S301: Based on the sensitivity score data, the sensitivity scores of various meteorological factors are analyzed, and the weight values ​​of various meteorological factors on the electric field strength are calculated. The specific process of generating the meteorological factor weight calculation result is as follows;

[0084] In sub-step S301, based on the sensitivity score data, the influence weights of various meteorological factors on the electric field strength are analyzed, and the sensitivity scores of various meteorological factors are converted into relative weights using a standardized processing method to ensure that the weight values ​​reasonably reflect the actual influence of multiple factors. The formula is: ,in, Indicates meteorological factors The weight value of Is a factor The sensitivity score, The meteorological factor weight calculation results are generated by standardizing the scores and ensuring that the factor weights sum to 1 for the total number of meteorological factors considered, maintaining the fairness of weight distribution.

[0085] S302: Based on the calculation results of meteorological factor weights, model parameters of the influence of various meteorological factors on the electric field are set, including setting influence coefficients and adjustment thresholds for various meteorological factors, optimizing the responsiveness and accuracy of the model, and generating the specific process of the model parameter setting results is as follows;

[0086] In sub-step S302, based on the calculation results of meteorological factor weights, model parameters are set to adjust the response of multiple meteorological factors to the electric field, define the influence coefficients and adjustment thresholds of multiple meteorological factors, optimize the responsiveness and accuracy of the model, and use the stepwise regression method to set the coefficients and thresholds of multiple factors. The formula is: ,in, Indicates meteorological factors The model parameters, is the adjustment factor, is the weight value of the target factor, The threshold offset is used to dynamically adjust the model parameters according to real-time data, optimize the prediction accuracy, and generate model parameter setting results.

[0087] S303: According to the model parameter setting results, a dependency model between the atmospheric electric field and environmental variables is constructed to simulate the electric field variation process under various atmospheric conditions. The specific process of generating the atmospheric environment dependency model is as follows;

[0088] In sub-step S303, according to the model parameter setting results, a dependency model between the atmospheric electric field and environmental variables is constructed to simulate the change process of the electric field under various atmospheric conditions. The composite simulation method is used to integrate the influence of multiple parameters and simulate the dynamic relationship between the electric field and meteorological conditions. The formula is: ,in, It's in time The electric field strength, It is a meteorological factor The model parameters, Is a factor In time The value of is used to predict the behavior of the electric field under target meteorological conditions and generate an atmospheric environment dependent model.

[0089] See also Figure 5 , using the atmospheric environment dependence model to simulate the dynamic behavior of atmospheric fluid, combined with real-time meteorological data to simulate atmospheric disturbances, the steps to obtain the atmospheric disturbance simulation results are as follows:

[0090] S401: Based on the atmospheric environment dependence model, combined with real-time meteorological information, the model parameters are adjusted to match the changes in actual meteorological conditions, and the real-time impact of changes in various meteorological factors on the electric field is simulated. The specific process of generating real-time model adjustment data is as follows;

[0091] In sub-step S401, based on the atmospheric environment dependency model, the model parameters are adjusted using real-time meteorological information to match the changes in actual meteorological conditions. The model parameters of each meteorological factor are dynamically updated using an adaptive adjustment algorithm through real-time data stream input. The model parameters are adjusted by calculating the real-time change rate and prediction error of multiple meteorological factors to ensure that the output of the model is highly consistent with the actual observation data. The formula is: ,in, are the model parameters for the next time step, are the model parameters at the current time step, is the learning rate, is the prediction error, is the electric field strength parameter The partial derivative of Indicates the electric field strength The differential of is used to describe the electric field strength The increment during the change, Representation parameters The differential of Incremental changes during the process, improving the responsiveness of the model in a real-time environment and generating real-time model adjustment data.

[0092] S402: Based on the real-time model adjustment data, the dynamic behavior of the atmospheric fluid is simulated, including airflow movement, temperature and humidity changes, and the influence of various meteorological factors on atmospheric disturbances is analyzed and recorded. The specific process of generating the atmospheric fluid analysis record is as follows;

[0093] In sub-step S402, based on the real-time model adjustment data, the dynamic behavior of atmospheric fluid is simulated, and the fluid dynamics model is applied to consider the impact of airflow movement, temperature and humidity changes on atmospheric disturbances. Through numerical simulation methods and combined with meteorological data, the impact of airflow velocity, temperature gradient and humidity changes on atmospheric stability is analyzed. The formula is: ,in, Indicates at time The atmospheric disturbances It is a meteorological factor The influence coefficient of Is a factor In time The dynamic change value of the meteorological factors is analyzed to analyze the impact of the synergistic effect of various meteorological factors on atmospheric disturbances and generate atmospheric fluid analysis records.

[0094] S403: According to the atmospheric fluid analysis records, analyze and identify key disturbance data, including disturbance frequency and intensity, evaluate the impact of atmospheric disturbance on the change of electric field intensity, and obtain the specific process of atmospheric disturbance simulation results;

[0095] In sub-step S403, according to the atmospheric fluid analysis records, key disturbance data, including disturbance frequency and intensity, are analyzed, and statistical analysis methods are used to evaluate the impact of disturbance factors on the change of electric field intensity. Through regression analysis, the disturbance parameters with the highest correlation with the change of electric field intensity, the mode and intensity of the impact are identified. The formula is: ,in, represents the disturbance response of the electric field strength, and are the frequency and intensity of the disturbance, is the intercept coefficient of the regression model, representing the baseline value of the electric field intensity disturbance response without the influence of disturbance frequency and disturbance intensity. is the disturbance frequency Response to electric field intensity disturbance The coefficient of influence quantifies the average change in the electric field intensity perturbation response for each unit change in the perturbation frequency. is the disturbance intensity Response to electric field intensity disturbance The coefficient of influence quantifies the average change in the electric field intensity disturbance response for each unit change in disturbance intensity. is the error term, which analyzes the impact of disturbance on the electric field and generates the atmospheric disturbance simulation results.

[0096] See also Figure 6 Based on the atmospheric disturbance simulation results and combined with real-time meteorological data, the changing trend of the electric field intensity in the atmosphere is analyzed and predicted, including the predicted electric field intensity value, change rate, and fluctuation range. The specific steps for obtaining the intensity trend analysis results are as follows:

[0097] S501: Based on the atmospheric disturbance simulation results, combined with real-time temperature, humidity, and wind speed data, the electric field measurement data is updated, the trend and volatility of the atmospheric electric field intensity data are analyzed, and abnormal data is identified. The specific process of generating instant electric field analysis results is as follows;

[0098] In sub-step S501, based on the atmospheric disturbance simulation results, combined with real-time temperature, humidity, and wind speed data, the electric field measurement data is updated and analyzed. The trend and volatility of the electric field strength data are systematically analyzed through time series analysis technology, and the abnormal point detection algorithm is used to identify and mark abnormal fluctuations in the data. The formula is: ,in, It's in time The electric field strength variation trend, is real-time electric field data, It is a smoothing coefficient, which is used to adjust the impact of real-time data, identify and process abnormal points in the data, and generate instant analysis results of the electric field.

[0099] S502: using the instant analysis results of the electric field, by comparing the historical data of the same period, predicting the fluctuation range of the electric field strength, including the peak and valley values ​​of the electric field strength, the specific process of generating the electric field fluctuation prediction results is as follows;

[0100] In sub-step S502, the real-time analysis results of the electric field are used to compare the historical data of the same period and use a statistical model to predict the fluctuation range of the electric field intensity. The formula is: ,in, is the predicted electric field strength for the next period, is the electric field strength at the current time, is the historical average electric field strength, is the autoregressive coefficient, which represents the influence of historical data on future predictions. is the error term. Based on the changes in historical and current data, the peak and valley values ​​of the future electric field strength are predicted to generate the electric field fluctuation prediction results.

[0101] S503: Based on the electric field fluctuation prediction results, an exponential smoothing algorithm is used to predict the atmospheric electric field intensity value, including the peak and valley values ​​and fluctuation range, the speed and frequency of electric field changes, and the performance and risk level of the atmospheric average electric field under various meteorological conditions. The specific process of generating the intensity trend analysis results is as follows;

[0102] In sub-step S503, based on the electric field fluctuation prediction results, an exponential smoothing algorithm is used to predict the peak and valley values ​​and fluctuation range of the atmospheric electric field intensity, adjust the weight of historical data in the prediction, analyze the speed and frequency of electric field changes, evaluate the electric field performance and risk level under differentiated meteorological conditions, and generate intensity trend analysis results.

[0103] Exponential smoothing algorithm, according to the formula:

[0104]

[0105] Calculate the electric field strength value, where is the predicted electric field strength at the next time point, is the smoothing parameter, which represents the weight of the latest observation. is the actual electric field strength at the current time point, is the predicted electric field strength at the current time point, is the weight coefficient of humidity, is the humidity value at the current time point, is the weight coefficient of temperature, is the temperature value at the current time point, is the weight coefficient of wind speed, is the wind speed value at the current time point, It is the index representing the current time point, which is used to indicate the data point at the target time in time series analysis.

[0106] The specific execution process of the formula is as follows:

[0107] Collect historical meteorological data and electric field strength data, perform regression analysis on the relationship between temperature, humidity, wind speed and electric field strength, and use the least squares method to calculate the weight coefficient , , , substitute the target coefficient into the exponential smoothing formula for calculation, combine the original electric field data and real-time meteorological observation data, predict the electric field intensity at the next time point, and output the predicted electric field intensity value.

[0108] See also Figure 7 Based on the intensity trend analysis results, the prediction model parameters are adjusted according to the real-time meteorological parameters, a variety of atmospheric conditions are matched, the average atmospheric electric field strength value is calculated, and the steps for generating electric field strength detection data are as follows:

[0109] S601: Based on the intensity trend analysis results, the influence of various meteorological parameters on the electric field prediction model is evaluated, the relationship between the changes of various parameters and the fluctuation of electric field intensity is evaluated, the influence coefficients and sensitivity parameters that need to be adjusted are identified, and the specific process of generating a list of adjustment parameters of the prediction model is as follows;

[0110] In sub-step S601, based on the intensity trend analysis results, the impact of various meteorological parameters on the electric field prediction model is evaluated. Through correlation analysis and regression model, the relationship between the changes in various parameters and the fluctuation of electric field intensity is analyzed, and the key influence coefficients and sensitivity parameters that need to be adjusted are identified. The formula is: ,in, Representative parameters The influence coefficient of is the adjustment coefficient obtained based on the correlation analysis, is a parameter With electric field strength The standard deviation of , indicating the volatility sensitivity, Indicates specific meteorological parameters, including temperature, humidity, wind speed, represents the electric field strength, is an index identifier used to indicate the target meteorological parameter. A statistical calculation representing the standard deviation of a measured variable How does the change of affect the electric field strength Fluctuations in the forecast model are detected, model parameters that need to be adjusted are identified, and a list of adjustment parameters for the forecast model is generated.

[0111] S602: According to the prediction model adjustment parameter list, combined with the real-time meteorological conditions, the electric field prediction model parameters are adjusted, including adjusting the influence weights of temperature, humidity, and wind speed, optimizing the responsiveness of the model, and generating the model parameter update results. The specific process is as follows;

[0112] In sub-step S602, according to the prediction model adjustment parameter list, combined with the real-time meteorological conditions, the electric field prediction model parameters are adjusted, and the influence weights of temperature, humidity, and wind speed meteorological factors are adjusted through a dynamic update algorithm. The formula is: ,in, is the updated weight, is the original weight, is the learning rate, It is the difference adjusted based on real-time data and sensitivity analysis that optimizes the model's responsiveness and predicts electric field changes under a variety of meteorological conditions, generating model parameter update results.

[0113] S603: Based on the model parameter update result, combined with real-time meteorological information and electric field measurement data, the average electric field strength value of the atmosphere is calculated, including the instantaneous value and change trend of the electric field strength, and the specific process of generating electric field strength detection data is as follows;

[0114] In sub-step S603, based on the model parameter update results, combined with real-time meteorological information and electric field measurement data, the atmospheric electric field strength is calculated, and the updated prediction model is used to calculate the instantaneous value and change trend of the electric field strength. The formula is: ,in, It's in time The electric field strength value, It is a meteorological factor The weight of Is a factor In time It can capture and analyze the real-time changes and trends of electric field strength and generate electric field strength detection data.

[0115] See also Figure 8 , an air atmosphere average electric field detection system, the air atmosphere average electric field detection system is used to perform the above-mentioned air atmosphere average electric field detection method, the system comprises:

[0116] The real-time meteorological analysis module compares and analyzes the real-time temperature, humidity, and wind speed data with historical data based on real-time meteorological data, identifies key meteorological factors that affect the electric field strength, and generates meteorological information analysis results;

[0117] The meteorological factor evaluation module analyzes the impact of various meteorological factors on electric field measurement based on the results of meteorological information analysis, calculates the sensitivity of various meteorological factors to the electric field, and generates environmental factor sensitivity scores;

[0118] The dependency analysis module uses the environmental factor sensitivity scores to construct a dependency model of electric field intensity and meteorological factors and generate electric field response assessment information;

[0119] The atmospheric impact simulation module simulates the dynamic behavior of atmospheric fluid based on the electric field response evaluation information, analyzes the impact of atmospheric disturbances on the atmospheric electric field strength under various meteorological conditions, and generates atmospheric fluctuation analysis data;

[0120] The average intensity analysis module uses atmospheric fluctuation analysis data to analyze the changing trend of electric field intensity. Combined with the electric field measurement data, it calculates the average atmospheric electric field intensity value, including the instantaneous value and changing trend of the electric field intensity, and generates electric field intensity detection data.

[0121] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for detecting the average electric field of the atmosphere in the air, characterized in that: The following steps are involved: Based on real-time meteorological data, historical trend analysis is performed on the temperature, humidity, and wind speed data in the atmosphere. By comparing with real-time data, key meteorological factors that affect the measurement results of electric field strength are identified and a list of key meteorological factors is generated; Based on the list of key meteorological factors and in combination with atmospheric electric field measurement data, the influence of various meteorological factors on electric field measurement is analyzed, sensitivity values ​​of various meteorological factors are calculated, and sensitivity score data is obtained; Based on the sensitivity scoring data, the scoring data of various meteorological factors are used as input parameters to establish a dependency model between the atmospheric electric field and environmental variables, thereby obtaining an atmospheric environment dependency model; Using the atmospheric environment dependence model to simulate the dynamic behavior of atmospheric fluid, combining real-time meteorological data to simulate atmospheric disturbances, and obtaining atmospheric disturbance simulation results; Based on the atmospheric disturbance simulation results, combined with real-time meteorological data, the changing trend of the electric field intensity in the atmosphere is analyzed and predicted, including the predicted electric field intensity value, change rate, and fluctuation range, to obtain the intensity trend analysis results; Based on the intensity trend analysis results, the prediction model parameters are adjusted according to the real-time meteorological parameters, a variety of atmospheric conditions are matched, the average atmospheric electric field intensity value is calculated, and the electric field intensity detection data is generated; Based on the atmospheric disturbance simulation results, combined with real-time meteorological data, the changing trend of the electric field intensity in the atmosphere is analyzed and predicted, including the predicted electric field intensity value, change rate, and fluctuation range. The steps for obtaining the intensity trend analysis results are specifically as follows: Based on the atmospheric disturbance simulation results, combined with real-time temperature, humidity, and wind speed data, the electric field measurement data is updated, the trend and volatility of the atmospheric electric field strength data are analyzed, and abnormal data is identified to generate instant electric field analysis results; By using the instant electric field analysis results and comparing with historical data of the same period, the fluctuation range of the electric field strength, including the peak and valley values ​​of the electric field strength, is predicted, and an electric field fluctuation prediction result is generated; Using the instant analysis results of the electric field and comparing the historical data of the same period, a statistical model is used to predict the fluctuation range of the electric field intensity. The formula is: ,in, is the predicted electric field strength for the next period, is the electric field strength at the current time, is the historical average electric field strength, is the autoregressive coefficient, which represents the influence of historical data on future predictions. is the error term, which predicts the peak and valley values ​​of the future electric field intensity based on the changes in historical and current data, and generates the prediction results of the electric field fluctuation; Based on the electric field fluctuation prediction results, an exponential smoothing algorithm is used to predict the atmospheric electric field strength value, including the peak and valley values ​​and fluctuation range, the speed and frequency of electric field changes, analyze the performance and risk level of the atmospheric average electric field under various meteorological conditions, and generate intensity trend analysis results; The outlier detection algorithm is used to identify and mark abnormal fluctuations in the data. The formula is: ,in, It's in time The electric field strength variation trend, is real-time electric field data, is the smoothing coefficient, which is used to adjust the impact of real-time data, identify and process abnormal points in the data, and generate instant analysis results of the electric field; The exponential smoothing algorithm is based on the formula: Calculate the electric field strength value, where is the predicted electric field strength at the next time point, is the smoothing parameter, is the actual electric field strength at the current time point, is the predicted electric field strength at the current time point, is the weight coefficient of humidity, is the humidity value at the current time point, is the weight coefficient of temperature, is the temperature value at the current time point, is the weight coefficient of wind speed, is the wind speed value at the current time point, is the index representing the current time point; Based on the intensity trend analysis results, the prediction model parameters are adjusted according to the real-time meteorological parameters, a variety of atmospheric conditions are matched, the average atmospheric electric field strength value is calculated, and the steps of generating electric field strength detection data are specifically as follows: Based on the intensity trend analysis results, evaluate the impact of various meteorological parameters on the electric field prediction model, evaluate the relationship between the changes in various parameters and the fluctuation of electric field intensity, identify the influence coefficients and sensitivity parameters that need to be adjusted, and generate a list of adjustment parameters for the prediction model; According to the prediction model adjustment parameter list, combined with real-time meteorological conditions, the electric field prediction model parameters are adjusted, including adjusting the influence weights of temperature, humidity, and wind speed, optimizing the responsiveness of the model, and generating model parameter update results; Based on the model parameter update results, combined with real-time meteorological information and electric field measurement data, the average electric field strength value of the atmosphere is calculated, including the instantaneous value and change trend of the electric field strength, to generate electric field strength detection data.

2. The method for detecting the average electric field of the air atmosphere according to claim 1, characterized in that: The list of key meteorological factors includes temperature change trend analysis results, humidity fluctuation rate information, and wind speed anomaly data; the sensitivity score data includes temperature impact weight information, humidity impact weight information, and wind speed impact weight data; the atmospheric environment dependence model includes temperature adjustment factor data, humidity impact coefficient information, and wind speed correlation analysis information; the atmospheric disturbance simulation results include disturbance intensity level information, disturbance duration estimation results, and disturbance range prediction information; the intensity trend analysis results include electric field strength peak prediction results, long-term change trend analysis results, and key change node information; the electric field strength detection data includes parameter adjustment algorithm, model update frequency information, and adaptive feedback mechanism.

3. The method for detecting the average electric field of the air atmosphere according to claim 1, characterized in that: Based on real-time meteorological data, historical trend analysis is performed on the temperature, humidity, and wind speed data in the atmosphere. By comparing with real-time data, key meteorological factors that affect the electric field strength measurement results are identified. The steps for generating a list of key meteorological factors are as follows: Based on real-time meteorological data, analyze the temperature, humidity, and wind speed data in the current atmosphere, compare them with historical data of the same period, analyze the changing trends of various meteorological data, and generate meteorological change comparison results; Based on the meteorological change comparison results, the correlation between various meteorological factors and changes in electric field strength is analyzed, key meteorological factors affecting electric field strength are identified, and a record of factors affecting electric field strength is generated; Based on the electric field influencing factor records, the influence levels of various meteorological factors on the electric field strength are calculated, and priority classification is performed according to the influence levels to generate a list of key meteorological factors.

4. The method for detecting the average electric field of the air atmosphere according to claim 1, characterized in that: Based on the list of key meteorological factors, combined with the atmospheric electric field measurement data, the influence of various meteorological factors on the electric field measurement is analyzed, and the sensitivity values ​​of various meteorological factors are calculated. The steps of obtaining the sensitivity score data are specifically as follows: Based on the list of key meteorological factors and in combination with atmospheric electric field measurement data, the influence of various meteorological factors on electric field measurement results is analyzed, the sensitivity of various meteorological factors is evaluated, and factor sensitivity records are generated; Based on the sensitivity records of the factors, the interaction between various meteorological factors and their impact on the electric field strength, including temperature, humidity and wind speed, is calculated, the interdependence and joint effects of various meteorological factors are evaluated, and meteorological interaction evaluation results are generated; Based on the meteorological interaction assessment results, the impact degree and frequency of the combination of multiple meteorological factors are analyzed, and the impact degree on the electric field strength is identified, the impact weight scores of the combination of multiple factors are calculated, and sensitivity score data is generated.

5. The method for detecting the average electric field of the air atmosphere according to claim 1, characterized in that: Based on the sensitivity scoring data, the scoring data of various meteorological factors are used as input parameters to establish a dependency model between the atmospheric electric field and environmental variables. The steps of obtaining the atmospheric environment dependency model are specifically as follows: Based on the sensitivity scoring data, analyzing the sensitivity scores of various meteorological factors, calculating the weight values ​​of various meteorological factors on the electric field strength, and generating meteorological factor weight calculation results; Based on the meteorological factor weight calculation results, setting model parameters of the impact of various meteorological factors on the electric field, including setting impact coefficients and adjustment thresholds for various meteorological factors, optimizing the responsiveness and accuracy of the model, and generating model parameter setting results; According to the model parameter setting results, a dependency model between the atmospheric electric field and environmental variables is constructed, the electric field variation process under various atmospheric conditions is simulated, and an atmospheric environment dependency model is generated.

6. The method for detecting the average electric field of the air atmosphere according to claim 1, characterized in that: The steps of using the atmospheric environment dependence model to simulate the dynamic behavior of atmospheric fluid and combining the real-time meteorological data to simulate atmospheric disturbances to obtain the atmospheric disturbance simulation results are as follows: Based on the atmospheric environment dependence model, combined with real-time meteorological information, the model parameters are adjusted to match the changes in actual meteorological conditions, the real-time impact of changes in various meteorological factors on the electric field is simulated, and real-time model adjustment data is generated; Based on the real-time model adjustment data, the dynamic behavior of the atmospheric fluid is simulated, including airflow movement, temperature and humidity changes, and the impact of various meteorological factors on atmospheric disturbances is analyzed and recorded to generate an atmospheric fluid analysis record; According to the atmospheric fluid analysis records, key disturbance data, including disturbance frequency and intensity, are analyzed and identified, the impact of atmospheric disturbances on changes in electric field intensity is evaluated, and atmospheric disturbance simulation results are obtained.

7. An airborne atmospheric average electric field detection system, characterized in that: According to any one of claims 1 to 6, the method for detecting the average electric field of the air atmosphere comprises: The real-time meteorological analysis module compares and analyzes the real-time temperature, humidity, and wind speed data with historical data based on real-time meteorological data, identifies key meteorological factors that affect the electric field strength, and generates meteorological information analysis results; The meteorological factor evaluation module analyzes the influence of various meteorological factors on the electric field measurement based on the meteorological information analysis result, calculates the sensitivity of the influence of various meteorological factors on the electric field, and generates an environmental factor sensitivity score; The dependency analysis module uses the environmental factor sensitivity score to construct a dependency model of electric field intensity and meteorological factors and generates electric field response evaluation information; The atmospheric impact simulation module simulates the dynamic behavior of atmospheric fluid based on the electric field response evaluation information, analyzes the impact of atmospheric disturbances on the atmospheric electric field intensity under various meteorological conditions, and generates atmospheric fluctuation analysis data; The average intensity analysis module uses the atmospheric fluctuation analysis data to analyze the changing trend of the electric field intensity, and combines the electric field measurement data to calculate the atmospheric average electric field intensity value, including the instantaneous value and changing trend of the electric field intensity, and generates electric field intensity detection data.

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