Tornado micro-pressure change data monitoring system and method

Abstract

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.

Description

Technical Field

[0001] The invention relates to the technical field of micro-pressure data processing, and in particular to a tornado micro-pressure change data monitoring system and method. Background Art

[0002] Tornadoes are extremely destructive weather phenomena. The huge energy they release instantly can cause devastating damage to buildings and infrastructure in a short period of time, and have an immeasurable impact on people's production and life. When it comes to tornadoes, which are localized and extremely sudden weather phenomena, traditional meteorological observation methods can measure the radial velocity of precipitation particles to obtain airflow movement information. However, in terms of micro-pressure monitoring, the detection ability of non-precipitation-related micro-pressure changes is limited, and the measurement accuracy is difficult to guarantee. Therefore, it is necessary to build a tornado micro-pressure change data monitoring system that uses a micro-manometer and radar to help improve tornado early warning capabilities and reduce disaster losses.

[0003] Existing technologies such as the invention patent with announcement number: CN104778517B are micro-meteorological disaster early warning methods and systems based on micro-meteorological and satellite remote sensing data. Satellite remote sensing technology is applied to the monitoring of micro-meteorological disasters in power grids, realizing the combined application of macro-meteorology and micro-meteorology. The micro-meteorological data in the ground observation database and the micro-meteorological data in the satellite remote sensing observation database are transmitted to the early warning platform server through the GSM network. The measured data is deeply mined through the analysis program, and statistical information such as the maximum value, minimum value and average value of any historical time period is sorted out, and the specific information of each monitoring point is intuitively described to the user through graphics and curves. The scope of influence of the disaster weather is judged according to the disaster classification standard, and the power grid facilities affected by the disaster weather are accurately located.

[0004] The prior art, such as the invention patent with announcement number: CN109241161B, is a meteorological data management method, including: formulating the spatiotemporal resolution standard of multi-source meteorological data, and establishing a standard unified multi-dimensional grid weather data set; establishing data processing and data conversion standards based on the spatiotemporal resolution standard of the multi-dimensional grid weather data set, and gridding the multi-source meteorological data; using Cassandra technology to establish a distributed decentralized database, aggregating the gridded multi-source meteorological data and dividing it into multiple nodes, and at the same time unifying all nodes into a cluster to uniformly manage the meteorological data.

[0005] It can be seen from the above scheme that there are certain limitations in the efficient coordinated monitoring and rational use of resources in the monitoring of meteorological disaster data such as tornadoes. At present, most people tend to use monitoring equipment independently or perform simple data association, and often only perform simple comparisons between micro-pressure data and a small number of environmental parameters. However, in the actual complex meteorological monitoring scenarios, tornado micro-pressure monitoring requires comprehensive and multi-level consideration of the comprehensive impact and dynamic changes of multiple factors. The relatively independent and extensive monitoring mode is difficult to achieve the ideal level in terms of resource conservation, monitoring depth and warning accuracy, which may lead to waste of monitoring resources, inaccurate judgment of tornado micro-pressure characteristics, and the inability of the system to flexibly respond to meteorological changes. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a tornado micro-pressure change data monitoring system and method, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides a tornado micro-pressure change data monitoring system, comprising: The regional radar activation information determination module is used to collect regional tornado micropressure observation data through a micromanometer, analyze the tornado micropressure observation abnormality index, and thereby determine the regional radar activation information, wherein the regional radar activation information includes radar activation required and radar activation not required.

[0008] The optimal sampling frequency analysis module is used to synchronously collect regional environmental parameters when the regional radar activation information determines that the radar does not need to be activated, and to obtain the optimal sampling frequency of the micromanometer in combination with the tornado micropressure observation anomaly indicators, and to monitor the regional micropressure at the optimal sampling frequency of the micromanometer.

[0009] The regional tornado micro-pressure comprehensive information judgment module is used to synchronously turn on regional radar monitoring when the regional radar opening information is judged to be required radar opening, thereby obtaining regional tornado micro-pressure comprehensive data, analyzing to obtain regional tornado micro-pressure anomaly correction indicators, and judging regional tornado micro-pressure comprehensive information, wherein the regional tornado micro-pressure comprehensive information includes normal micro-pressure, abnormal micro-pressure and micro-pressure warning.

[0010] The regional secondary monitoring execution module is used to adjust the collection parameters of the micromanometer and the radar respectively when the regional tornado micropressure comprehensive information is judged to be micropressure abnormal, and analyze the secondary verification monitoring execution cycle in combination with the regional tornado micropressure abnormality correction index, thereby executing regional secondary verification monitoring.

[0011] The regional micro-pressure warning module is used to generate warning information and issue warnings to the preset management terminal when the regional tornado micro-pressure comprehensive information is a micro-pressure warning.

[0012] A second aspect of the present invention provides a method for monitoring tornado micro-pressure change data, comprising the following steps: S1, obtaining regional micro-pressure observation data through a micro-pressure gauge, analyzing the abnormal micro-pressure observation index, and judging regional radar opening information, wherein the regional radar opening information includes required radar opening and no radar opening. When the regional radar opening information is judged as no radar opening, S2 is executed; when the regional radar opening information is judged as required radar opening, S3 is executed; S2, synchronously collect regional environmental parameters, combine with micro-pressure observation abnormal indicators, obtain the optimal sampling frequency of the micro-manometer, and perform regional micro-pressure monitoring at the optimal sampling frequency of the micro-manometer; S3, synchronously start regional radar monitoring, thereby obtaining regional micro-pressure comprehensive data, analyzing to obtain regional micro-pressure comprehensive abnormal characteristic indicators, and judging regional micro-pressure comprehensive information, wherein the regional micro-pressure comprehensive information includes normal micro-pressure, abnormal micro-pressure and micro-pressure warning. When the regional micro-pressure comprehensive information is micro-pressure abnormal, execute S4, and when the regional micro-pressure comprehensive information is micro-pressure warning, execute S5; S4, respectively adjusting the acquisition parameters of the micromanometer and the radar, and analyzing the secondary verification monitoring execution cycle in combination with the regional micropressure comprehensive abnormal characteristic index, thereby executing the regional secondary verification monitoring; S5, generate warning information and publish the warning to the preset management terminal.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects: (1) The present invention provides a tornado micro-pressure change data monitoring system and method, which can realize efficient and accurate monitoring of tornado micro-pressure, effectively improve the meteorological disaster early warning capability, and scientifically judge the radar opening demand by analyzing the micro-pressure gauge collection data to avoid resource waste. The optimal sampling frequency analysis module can optimize the micro-pressure gauge sampling frequency according to the environment and micro-pressure observation conditions to ensure the effectiveness of data collection. Comprehensive multi-source data is deeply analyzed to accurately judge the micro-pressure state and provide a numerical basis for subsequent decision-making. Targeted secondary monitoring is carried out when necessary to further verify the meteorological conditions. It can also issue early warning information in a timely manner so that relevant personnel can take preventive and response measures in advance to reduce the losses that may be caused by tornadoes.

[0014] (2) The present invention accurately determines whether the regional radar is turned on or not by judging the regional radar opening information, thereby avoiding the blind start-up of the radar, effectively reducing energy consumption and equipment loss, and realizing the reasonable allocation of monitoring resources. When the abnormal index of the tornado micropressure observation is less than the verification index, it is determined that the radar does not need to be turned on, and only the micromanometer is relied on for continuous monitoring, maintaining the conventional monitoring efficiency and saving resources. When the abnormal index is greater than or equal to the verification index, the radar is turned on in time to give full play to its detection capability of a wide range of meteorological targets such as precipitation particles and airflow movement, thereby improving the accuracy of the judgment of the characteristics, intensity and development trend of tornado activities, laying a solid data foundation for subsequent more accurate early warning decisions, and enhancing the effectiveness and reliability of the monitoring system.

[0015] (3) The present invention avoids unnecessary resource investment and misjudgment interference by judging the comprehensive information of regional micro-pressure. If the micro-pressure is normal, it helps to further accurately grasp the details of micro-pressure changes and meteorological target characteristics, eliminate potential interference factors, and improve the accuracy of judging the potential threat of tornadoes. Once it is judged as a micro-pressure warning, the system immediately generates and releases warning information, allowing relevant personnel to know in time that a tornado is approaching or occurring, so that they can quickly take countermeasures to minimize the losses caused by the tornado, reflecting the efficiency and reliability of the monitoring system in responding to tornado disasters.

[0016] Of course, any product implementing the present invention does not necessarily need to achieve all of the above advantages at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the system module of the present invention.

[0018] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1 As shown, an embodiment of the present invention provides a tornado micro-pressure change data monitoring system, comprising: The regional radar activation information determination module is used to collect regional tornado micropressure observation data through a micromanometer, analyze the tornado micropressure observation abnormality index, and thereby determine the regional radar activation information, wherein the regional radar activation information includes radar activation required and radar activation not required.

[0021] In this embodiment, the tornado micro-pressure observation abnormal index is analyzed, and the specific process is as follows: During a preset monitoring period, regional tornado micropressure observation data are collected, and the regional tornado micropressure observation data include average micropressure value, micropressure change extreme value difference, micropressure change rate, gravity wave amplitude change extreme value difference and gravity wave period length change extreme value difference.

[0022] It should be noted that if the average micropressure value deviates significantly, such as being significantly higher or lower than the normal range, it may indicate abnormalities in the atmospheric environment, such as large-scale changes in weather systems or strong local air flow disturbances.

[0023] It should be noted that the extreme difference of micro-pressure change refers to the difference between the maximum micro-pressure value and the minimum micro-pressure value within a preset monitoring period.

[0024] The extreme difference of gravity wave amplitude variation refers to the difference between the maximum and minimum values ​​of the gravity wave amplitude recorded within a preset monitoring period.

[0025] The extreme value difference of the gravity wave period length change refers to the difference between the maximum and minimum values ​​of the gravity wave period length obtained within a preset monitoring period.

[0026] It should also be noted that the regional tornado micropressure observation data is collected in real time by a micromanometer at the second level. It should be noted that the extreme value difference of gravity wave amplitude change and the extreme value difference of gravity wave period length change are obtained by performing a five-minute sliding Fourier analysis on the data collected by the micromanometer.

[0027] It should be noted that the five-minute sliding Fourier analysis refers to the selection of continuous five-minute micro-pressure data as an analysis window each time during the monitoring process, and Fourier transform calculation is performed on it. Through Fourier transform, the micro-pressure data sequence in the time domain is decomposed into the superposition of sine waves and cosine waves of different frequency components, so as to obtain the spectrum information of the micro-pressure data within five minutes. The spectrum contains the amplitude and phase information of various frequency components in the micro-pressure signal, and then the relevant characteristics of the wave components such as gravity waves can be analyzed. After completing the analysis of this five-minute data, the time window slides forward for a certain time interval (for example, one minute), and new continuous five-minute micro-pressure data is selected again for the same Fourier transform calculation. This is repeated continuously. As time goes by, a series of time-varying spectra can be obtained, and these spectra together construct the three-dimensional dynamic spectrum of gravity waves "period-amplitude-time". This analysis method can dynamically capture the changes in the period, amplitude and other characteristics of gravity waves over time, which helps to more comprehensively and deeply understand the dynamic characteristics of gravity waves in the atmosphere and their relationship with tornado activity, and provides important data basis for accurately monitoring and studying tornado micropressure changes.

[0028] According to the analysis and processing of the regional tornado micropressure observation data, a tornado micropressure observation anomaly index is obtained, and the regional tornado micropressure observation index is used to characterize the degree of anomaly of the tornado micropressure observation data collected by the micromanometer.

[0029] In a specific embodiment, the tornado micro-pressure observation abnormality index is obtained in the following specific process: The reference micro-pressure observation data stored in the database are extracted, and the reference micro-pressure observation data include the ideal average micro-pressure value, the reference micro-pressure change extreme value difference, the reference micro-pressure change rate, the reference gravity wave amplitude change extreme value difference and the reference gravity wave period length change extreme value difference.

[0030] Based on the regional tornado micro-pressure observation data and the reference micro-pressure observation data, the tornado micro-pressure observation anomaly index is obtained through comprehensive analysis and processing.

[0031] Among them, the specific method of obtaining the abnormal index of tornado micro-pressure observation is as follows: ,in, It is the abnormal index of tornado micro-pressure observation. is the average micropressure value, is the extreme difference of micro-pressure change, is the rate of change of micropressure, is the extreme value difference of gravity wave amplitude change, is the extreme value difference of the gravity wave period length change, is the ideal average micro-pressure value, is the extreme difference of the reference micro-pressure change, is the reference micro-pressure change rate, is the extreme difference of the reference gravity wave amplitude change, is the extreme value difference of the reference gravity wave period length change, is the average micro-pressure value weight, is the weight of the extreme difference of micro-pressure change, is the weight of the micro-pressure change rate, is the weight of the extreme difference of gravity wave amplitude change, is the weight of the extreme difference of the gravity wave period length change, is a natural constant.

[0032] It should be noted that the average micro-pressure value weight, micro-pressure change extreme value difference weight, micro-pressure change rate weight, gravity wave amplitude change extreme value difference weight, gravity wave period length change extreme value difference weight are pre-set values ​​extracted directly from the database. The specific extraction method is to build a special data table in the database and correspond these weights to specific identifiers or indexes. For example, different fields can be set according to the weight type, and each field stores the corresponding weight. When a certain weight needs to be extracted, an accurate query is performed in the database according to its corresponding identifier (such as weight name or number) to obtain the required weight.

[0033] It should also be noted that the tornado micropressure observation anomaly index is obtained based on the comprehensive analysis and processing of regional tornado micropressure observation data, taking into account the mutual influence between these parameters. For example, if the average micropressure value changes rapidly in a short period of time, the rate of change of micropressure must be large. Changes in the average micropressure value will affect the density structure and stability of the atmosphere, and thus affect the generation and propagation of gravity waves. When the average micropressure value fluctuates abnormally, it may stimulate or inhibit gravity wave activity, resulting in changes in the extreme value difference of gravity wave amplitude changes. Large fluctuations in the average micropressure value may cause the gravity wave period to become unstable, increasing the extreme value difference of the gravity wave period length change. A large extreme value difference of micropressure change means that the air pressure changes dramatically, which is usually accompanied by a higher rate of micropressure change. When the extreme value difference of micropressure change is large, it indicates that the air pressure disturbance in the atmosphere is strong. This strong pressure disturbance can serve as an excitation source for gravity waves, enhancing the activity of gravity waves and causing an increase in the extreme value difference of gravity wave amplitude changes. When the extreme value difference of micro-pressure changes is large, the propagation speed and propagation characteristics of gravity waves will change, causing the gravity wave period length to be unstable, thereby increasing the extreme value difference of gravity wave period length changes. The rapid rate of micro-pressure change means that the intensity and frequency of air pressure changes are high. This strong pressure change will have a strong excitation effect on gravity waves in the atmosphere, causing the gravity wave amplitude to change rapidly, thereby increasing the extreme value difference of gravity wave amplitude changes. When the extreme value difference of gravity wave amplitude changes increases, it means that the gravity wave energy changes dramatically, which may affect the propagation characteristics of gravity waves, thereby causing the extreme value difference of gravity wave period length changes to change.

[0034] In a specific embodiment, by analyzing the abnormal index of tornado micropressure observation, the abnormal degree of tornado micropressure observation data collected by the micromanometer can be reflected, and on the one hand, efficient resource saving is achieved. When the index shows that the overall fluctuation amplitude of the micropressure observation data is small and is in a relatively stable range, it means that the current atmospheric environment state is stable and the abnormal degree of the micropressure data is low. At this time, it is determined that there is no need to turn on the radar, which avoids the unwarranted start of the radar equipment, thereby saving a lot of energy consumption and unwarranted loss of equipment, so that monitoring resources can be reasonably allocated, and resources are ensured to be concentrated on the key monitoring moments that are really needed. On the other hand, accurate judgment greatly improves monitoring efficiency and accuracy. When the index shows that the micropressure changes abnormally, indicating that the abnormal degree of micropressure data increases, the radar is turned on in time to give full play to its detection advantages of a wide range of meteorological targets such as precipitation particles and airflow movement, and obtain more key information combined with micromanometer data, so that the activity characteristics, intensity and development trend of the tornado can be analyzed more comprehensively and deeply, providing sufficient and accurate data support for subsequent early warning decisions, and effectively enhancing the ability of the entire monitoring system to deal with tornado disasters.

[0035] In this embodiment, the regional radar activation information is determined, and the specific analysis process is as follows: Extract the tornado micro-pressure observation anomaly verification indicators preset in the database.

[0036] If the tornado micro-pressure observation anomaly index is less than the tornado micro-pressure observation anomaly verification index, the regional radar activation information will be recorded as no radar activation is required.

[0037] If the tornado micropressure observation anomaly index is less than the tornado micropressure observation anomaly verification index, it means that during the current monitoring period, the overall fluctuation range of the regional tornado micropressure observation data collected by the micromanometer is small and in a relatively stable range. The atmospheric environment in the area is relatively stable at this time, and there is no trend that may indicate abnormal micropressure data. Based on this situation, relying solely on the micromanometer to continuously monitor in the current state can meet basic needs, without turning on the radar for additional monitoring, thereby avoiding energy consumption and equipment loss caused by the unwarranted startup of radar equipment, and achieving reasonable allocation and conservation of monitoring resources.

[0038] If the tornado micro-pressure observation anomaly index is greater than or equal to the tornado micro-pressure observation anomaly verification index, the regional radar activation information is recorded as the required radar activation.

[0039] If the tornado micropressure observation anomaly index is greater than or equal to the tornado micropressure observation anomaly verification index, it means that the atmospheric environment in the area is very likely to be in an unstable state, and the observed micropressure data shows an abnormal trend. At this time, it is impossible to fully and accurately grasp the meteorological conditions by relying solely on the micromanometer. It is urgent to start radar monitoring and use the radar's detection capabilities for precipitation particles, air flow movement and other broader meteorological targets to obtain more key information and combine it with the micromanometer data to more deeply and comprehensively analyze and judge the activity characteristics, intensity and development trends of the tornado, and provide more sufficient numerical basis for subsequent early warning decisions.

[0040] By judging the regional radar activation information, it is possible to accurately determine whether the regional radar is activated or not, thus avoiding the blind activation of the radar, effectively reducing energy consumption and equipment loss, and realizing the reasonable allocation of monitoring resources. When the abnormal index of the tornado micropressure observation is less than the verification index, it is determined that the radar does not need to be activated, and only the micromanometer is used for continuous monitoring, which maintains the efficiency of conventional monitoring and saves resources. When the abnormal index is greater than or equal to the verification index, the radar is activated in time to give full play to its detection capabilities for a wide range of meteorological targets such as precipitation particles and airflow movement, thereby improving the accuracy of the judgment of the characteristics, intensity and development trend of tornado activities, laying a solid data foundation for subsequent more accurate early warning decisions, and enhancing the effectiveness and reliability of the monitoring system.

[0041] The optimal sampling frequency analysis module is used to synchronously collect regional environmental parameters when the regional radar activation information determines that the radar does not need to be activated, and to obtain the optimal sampling frequency of the micromanometer in combination with the tornado micropressure observation anomaly indicators, and to monitor the regional micropressure at the optimal sampling frequency of the micromanometer.

[0042] In this embodiment, the optimal sampling frequency of the micromanometer is obtained, and the specific process is as follows: During the preset monitoring period, regional environmental parameters are synchronously collected, and the regional environmental parameters include average temperature, average humidity, maximum wind speed, accumulated precipitation and average atmospheric pressure.

[0043] It should be noted that the regional environmental parameters are collected and acquired through an environmental collection device, which includes a temperature sensor, a humidity sensor, a wind speed sensor, a rain gauge and a barometer.

[0044] According to the regional environmental parameters and the tornado micropressure observation anomaly index, the micromanometer sampling indicator factor is obtained by analysis and processing. The micromanometer sampling indicator factor is used to characterize the influence of the current regional environment on the working state of the micromanometer.

[0045] In a specific embodiment, the specific process of obtaining the micromanometer sampling indicator factor is as follows: Extract ideal regional environmental parameters, including ideal average temperature, ideal average humidity, reference maximum wind speed, ideal cumulative precipitation and ideal average atmospheric pressure.

[0046] According to the regional environmental parameters, ideal regional environmental parameters and tornado micropressure observation anomaly indicators, the micromanometer sampling indicator factors are analyzed and processed.

[0047] The specific method for obtaining the micromanometer sampling indicator factor is as follows: ,in, is the micromanometer sampling index factor, It is the abnormal index of tornado micro-pressure observation. is the average temperature, is the average humidity, is the maximum wind speed, is the cumulative precipitation, is the average atmospheric pressure, is the ideal average temperature, For the ideal average humidity, is the maximum reference wind speed, is the ideal cumulative precipitation, is the ideal average atmospheric pressure, and e is a natural constant.

[0048] It should be noted that the micromanometer sampling indicator factors are obtained by analyzing and processing the regional environmental parameters and the abnormal indicators of tornado micropressure observation, taking into account the mutual influence between them. For example, when the temperature rises, the air can accommodate more water vapor. When other conditions remain unchanged, the average humidity may increase, the air in the area with higher temperature rises, and the surrounding cold air supplements to form airflow, affecting the maximum wind speed. When the temperature rises, the movement of gas molecules intensifies and the pressure tends to increase. When the water vapor content (humidity) in the air reaches saturation and there are enough condensation nuclei, the water vapor will condense into water droplets or ice crystals to form precipitation, affecting the cumulative precipitation. When the humidity increases, the air mass per unit volume decreases. According to the ideal gas state equation, when the temperature remains unchanged, the air pressure will decrease accordingly. Stronger horizontal wind speeds can transport water vapor and precipitation particles to different areas and change the spatial distribution of precipitation. At the same time, wind speed affects the falling speed and trajectory of precipitation particles. When the wind speed is high, precipitation particles will be subjected to greater horizontal forces during the falling process, which may cause precipitation particles to break or evaporate, affecting the cumulative precipitation. When the air pressure decreases, the saturated water vapor pressure decreases, and the relative humidity may increase. The change in air pressure will cause the air to expand or compress adiabatically, thus affecting the temperature. According to the adiabatic process equation, when the air pressure decreases, the air will expand adiabatically and do work externally, and the temperature will decrease.

[0049] In a specific embodiment, by analyzing the micromanometer sampling indicator factor, the working state of the micromanometer corresponding to the current environment of the region can be fully characterized. The sampling frequency of the micromanometer can be dynamically adjusted according to different environmental conditions and the change of tornado micropressure. When the environment is stable and the micropressure change is small, avoid excessive sampling and waste of resources, and when the environment is unstable and the micropressure data is abnormal, ensure a high enough sampling frequency to capture key micropressure change details. This not only optimizes the resource utilization efficiency of the micromanometer, but also improves the accuracy and effectiveness of micropressure data collection, and provides an accurate numerical basis for the subsequent in-depth analysis of the micropressure change characteristics of the tornado and the accurate judgment of the tornado activity trend, which effectively enhances the monitoring and early warning capabilities of the entire monitoring system for tornado disasters.

[0050] Extract the micromanometer sampling indication factor threshold preset in the database.

[0051] The micromanometer sampling deviation factor is obtained by performing difference processing on the micromanometer sampling indication factor and the micromanometer sampling indication factor threshold.

[0052] It should be noted that the difference processing refers to the micromanometer sampling indication factor minus the micromanometer sampling indication factor threshold, and the result of the difference processing can be greater than zero, less than zero or equal to zero.

[0053] The sampling frequency adjustment value corresponding to each sampling deviation factor interval stored in the database is extracted, and the sampling frequency adjustment value corresponding to the interval where the micromanometer sampling deviation factor is located is mapped and extracted, and recorded as the micromanometer sampling frequency adjustment value.

[0054] The larger the micromanometer sampling deviation factor, the greater the impact of the environment on the micromanometer. A larger micromanometer sampling deviation factor indicates strong environmental interference, and the sampling frequency adjustment value extracted by its mapping is also larger. Increasing the sampling frequency can capture subtle micro-pressure changes and improve the accuracy of monitoring. Conversely, a smaller micromanometer sampling deviation factor means a smaller sampling frequency adjustment value extracted by its mapping. Extracting the micromanometer sampling frequency adjustment value by mapping the micromanometer sampling deviation factor can avoid resource waste and data redundancy, optimize equipment performance and data processing efficiency, and ensure accurate and efficient monitoring, rational use of resources, and stable system operation.

[0055] The current acquisition frequency of the micromanometer is obtained, and the optimal sampling frequency of the micromanometer is calculated according to the micromanometer sampling frequency adjustment value.

[0056] It should be noted that if the optimal sampling frequency of the micromanometer calculated according to the micromanometer sampling frequency adjustment value exceeds the preset rated maximum sampling frequency of the micromanometer, the preset rated maximum sampling frequency of the micromanometer is recorded as the optimal sampling frequency of the micromanometer.

[0057] The regional tornado micro-pressure comprehensive information judgment module is used to synchronously turn on regional radar monitoring when the regional radar opening information is judged to be required radar opening, thereby obtaining regional tornado micro-pressure comprehensive data, analyzing to obtain regional tornado micro-pressure anomaly correction indicators, and judging regional tornado micro-pressure comprehensive information, wherein the regional tornado micro-pressure comprehensive information includes normal micro-pressure, abnormal micro-pressure and micro-pressure warning.

[0058] In this embodiment, the regional tornado micro-pressure comprehensive data is obtained, and the specific process is as follows: During the monitoring period, micropressure data is collected by a micromanometer, and after a five-minute sliding Fourier analysis, the three-dimensional dynamic spectrum of the gravity wave "period-amplitude-time" is obtained, thereby obtaining the micropressure change data, which includes the change in the long-period amplitude of the gravity wave, the change in the short-period amplitude of the gravity wave and the maximum period length of the gravity wave. The maximum micropressure monitored by the micromanometer is obtained simultaneously, and the micropressure change data and the maximum micropressure are combined as the micromanometer monitoring parameters.

[0059] Radar monitoring parameters are obtained through radar monitoring, including the maximum echo intensity, average echo intensity, average radial velocity and maximum reflectivity factor value of the area.

[0060] It is important to understand that the maximum echo intensity is obtained by emitting electromagnetic waves through radar. When the electromagnetic waves encounter precipitation particles (raindrops, hail, etc.), dust, water vapor and other substances inside the tornado, they will be reflected. The radar receives the reflected signal and measures its intensity, and records the echo intensity in decibels.

[0061] The average radial velocity is obtained as follows: the radar calculates the radial velocity of the target object (such as tornado-related material) relative to the radar by measuring the frequency change between the electromagnetic wave emission and reflection signals (Doppler effect), records the radial velocity values ​​within the monitoring range, and performs averaging processing to obtain the average radial velocity.

[0062] The specific method of obtaining the maximum reflectivity factor value is as follows: the meteorological radar transmits electromagnetic wave pulses of specific frequency and wavelength (such as C band or S band) to the monitoring area. When these electromagnetic waves encounter precipitation particles, dust, water vapor and other targets, they are reflected and scattered. The reflected signal is received by the radar antenna and amplified, filtered and other processes. The radar system measures its intensity and then calculates the reflectivity factor based on parameters such as transmission power, antenna gain, wavelength, and the distance between the target and the radar. It is expressed in decibels. During the multi-elevation and azimuth scanning of the monitoring area, the reflectivity factor values ​​are continuously calculated and recorded to extract the maximum reflectivity factor value.

[0063] It should be understood that the reflectivity factor can be directly calculated by the built-in analysis module of the radar system.

[0064] The micromanometer monitoring parameters and radar monitoring parameters are combined as the regional tornado micropressure comprehensive data.

[0065] In this embodiment, the regional tornado micro-pressure anomaly correction index is obtained by analysis, and the specific analysis process is as follows: The reference micro-pressure comprehensive data stored in the database are extracted. The reference micro-pressure data include the reference gravity wave wavelength period amplitude change, the reference gravity wave short period amplitude change, the reference gravity wave maximum period length, the reference maximum micro-pressure, the reference maximum echo intensity of the region, the reference average echo intensity of the region, the reference average radial velocity of the region and the reference maximum reflectivity factor value of the region.

[0066] According to the regional tornado micro-pressure comprehensive data and reference micro-pressure comprehensive data, the regional tornado micro-pressure comprehensive anomaly characteristic value is obtained through analysis and processing.

[0067] The regional tornado micropressure comprehensive anomaly characteristic value is used to characterize the degree of anomaly of tornado micropressure data during comprehensive monitoring by micromanometer and radar.

[0068] It should be noted that the increase in the amplitude variation of the gravity wave period indicates that the characteristics of atmospheric fluctuations have changed, the propagation of gravity waves has been disturbed, and the temporal and spatial distribution of air pressure has been unbalanced, making the fluctuation law of micro-pressure data abnormal and deviating from the stable state. The increase in the amplitude variation of the short period means that small-scale meteorological fluctuations have intensified, the high-frequency changes of micro-pressure data have increased, and the maximum micro-pressure value has increased, indicating that the local extreme pressure is abnormal, forming a strong pressure gradient with the surrounding area, destroying the uniformity of air pressure, and directly causing significant abnormalities in micro-pressure data.

[0069] The increase in the maximum echo intensity indicates that there are more scatterers such as precipitation particles and dust in the monitoring area, and the radar wave reflection is enhanced. The aggregation of such substances affects the electromagnetic characteristics and energy distribution of the atmosphere, changes the movement of airflow, causes sudden changes in air pressure, and causes the fluctuation of micro-pressure data to intensify, and the degree of anomaly increases. The increase in the average echo intensity means that the precipitation or scatterers are widely distributed and dense, which gradually changes the optical and electromagnetic characteristics of the atmosphere, interferes with the uniformity of the air pressure field, causes the micro-pressure to deviate from the normal state, and the degree of anomaly increases. The large fluctuation of the average radial velocity reflects the disordered movement of airflow, strong shear and vortex interfere with the stability of the air pressure field, and the micro-pressure changes rapidly with the abnormal movement of airflow, causing abnormal data fluctuations and an increase in the degree of anomaly. When the maximum reflectivity factor value increases, it means that the number of precipitation particles in the area increases dramatically, the size becomes larger, or the phase transition is complex, resulting in an increase in the ability to scatter and reflect radar waves. The accumulation of a large number of precipitation particles changes the distribution of atmospheric water vapor and the heat exchange pattern, causing violent airflow movement and sudden changes in air pressure. For example, during the rapid condensation and growth of raindrops or the formation of hail, the release of latent heat intensifies convection, causing the air pressure field to become unbalanced. The micro-pressure data will fluctuate greatly and deviate from the mean in an instant, and the degree of anomaly will increase.

[0070] In a specific embodiment, the specific method for obtaining the regional tornado micro-pressure comprehensive abnormal characteristic value is as follows: , , , in, is the regional tornado micro-pressure comprehensive anomaly characteristic value, is the characterization factor of the micromanometer monitoring parameter, is the radar monitoring parameter characterization factor, is the amplitude variation of the gravity wavelength period, is the short-period amplitude variation of gravity waves, is the maximum period length of gravity waves, is the maximum micro pressure, is the maximum echo intensity, is the average echo intensity, is the average radial velocity, is the maximum reflectivity factor value, is the amplitude variation of the reference gravity wavelength period, is the short-period amplitude variation of reference gravity waves, is the maximum period length of the reference gravity wave, For reference, the maximum micro pressure, is the reference maximum echo intensity, is the reference average echo intensity, is the reference mean radial velocity, is the reference maximum reflectivity factor value, and e is a natural constant.

[0071] It is important to understand that the softplus function is a built-in function in Python. .

[0072] It should be noted that the regional tornado micropressure comprehensive anomaly characteristic values ​​obtained by analyzing and processing the regional tornado micropressure comprehensive data take into account the mutual influence between these parameters. For example, when the maximum echo intensity increases abnormally, it often means that there is strong convective activity or a large number of heavy precipitation particles gathering locally, which may increase the average echo intensity. A larger maximum echo intensity is often accompanied by a strong updraft or horizontal airflow shear, and these airflow movements will affect the average radial velocity. The higher the maximum echo intensity, the stronger the scattering of radar waves by the target, which usually corresponds to larger size and higher concentration of precipitation particles or other strong scatterers, and these situations will also lead to an increase in the maximum reflectivity factor value. When the average echo intensity increases, it may mean that the precipitation area is expanded or the concentration of precipitation particles increases, which will affect the airflow movement and thus change the average radial velocity. When the average echo intensity is high, it means that there are more precipitation particles or scatterers in the area, which increases the possibility of a high reflectivity factor value. Changes in the long-period amplitude variation may affect the overall stability of the atmosphere, and then affect the excitation and propagation characteristics of short-period gravity waves, resulting in changes in the short-period amplitude variation. The intensified changes in the amplitude of gravity waves may cause the vertical movement of the atmosphere to increase, resulting in the redistribution of air pressure and affecting the maximum micropressure.

[0073] In a specific embodiment, by analyzing the comprehensive abnormal characteristic value of regional tornado micropressure, the comprehensive change characteristics of micropressure and related meteorological elements caused by tornado activities can be accurately grasped, and the errors and interferences that may be caused by single data can be effectively filtered out, thereby improving the accuracy of the judgment of tornado micropressure status. It helps to more accurately identify the intensity and development stage of tornado activities, and can provide a solid and reliable data basis for subsequent decision-making (such as whether to start secondary monitoring, issue warnings, etc.), which greatly improves the scientificity, effectiveness and timeliness of the entire monitoring system in responding to tornado disasters, and effectively protects the safety of people's lives and property and the stable operation of society.

[0074] The historical usage parameters of the micromanometer and the radar are obtained synchronously to analyze the correction coefficients collected by regional equipment.

[0075] The historical usage parameters of the micromanometer include the cumulative usage time of the micromanometer and the average daily sampling times.

[0076] Radar usage parameters include the radar's cumulative working time and the total number of pulse transmissions.

[0077] In a specific embodiment, the specific process of obtaining the regional equipment acquisition correction coefficient is as follows: According to the historical use parameters of the micromanometer and the radar, the equipment comprehensive use status characteristic value is obtained through analysis and processing, and the equipment comprehensive use status characteristic value is used to characterize the historical use status of the micromanometer and the radar.

[0078] The comprehensive usage status characteristic value of the device is obtained as follows: , in, Comprehensive usage status characteristic value for the device, is the cumulative usage time of the micromanometer, is the average daily sampling times of the micromanometer, is the cumulative working time of the radar, is the total number of pulse transmissions of the radar, is the deviation factor corresponding to the cumulative usage time of the micromanometer unit, is the deviation factor corresponding to a single sampling of the micromanometer, is the deviation factor corresponding to the unit cumulative working time of the radar, is the deviation factor corresponding to the single pulse transmission of the radar, is a natural constant.

[0079] It is important to understand that the switch function is a built-in function in Python. .

[0080] It should be noted that the deviation factor corresponding to the unit cumulative use time of the micromanometer, the deviation factor corresponding to the single sampling of the micromanometer, the deviation factor corresponding to the unit cumulative working time of the radar, and the deviation factor corresponding to the single pulse emission of the radar are all pre-set in the database and can be directly extracted from the database when used. The specific extraction method is to construct a mapping set, that is, different micromanometer cumulative use time ranges, single sampling conditions, radar cumulative working time ranges, and single pulse emission conditions are used as the keys of the mapping set, and the corresponding deviation factors are used as the values ​​of the mapping set. For example, for the cumulative use time of the micromanometer, different time intervals can be set, and each interval corresponds to a specific unit cumulative use time deviation factor. For the single sampling of the micromanometer, the deviation factors corresponding to different types of sampling are set according to its sampling mode, accuracy and other factors. The intervals are divided according to the different characteristics of the cumulative working time and the single pulse emission, and the corresponding deviation factors are set. During the actual extraction, the corresponding key is searched in the mapping set according to the current cumulative usage time of the micromanometer, the single sampling situation, the cumulative working time and the single pulse emission status of the radar, so as to extract the corresponding deviation factor for the subsequent calculation of relevant parameters such as the regional equipment acquisition correction coefficient, ensuring that the entire monitoring system can accurately consider the impact of the equipment usage history on data monitoring and analysis, and improve the reliability and accuracy of the monitoring results.

[0081] It should also be noted that the comprehensive use status characteristic value of the equipment is obtained by analyzing and processing the historical use parameters of the micromanometer and the radar use parameters, taking into account the small mutual influence between them. For example, the longer the cumulative use time of the micromanometer, the more likely its internal sensors and other components will age and degrade, which may affect the accuracy of its measurement, and thus affect the reliability of the data obtained by the average daily sampling times. For example, after long-term use, the zero drift of the micromanometer may increase, resulting in systematic errors in the average daily sampling data. At the same time, if the average daily sampling times of the micromanometer are too high, it will accelerate the loss of its key components, shorten the cumulative use time, and the increase in the amount of data generated by frequent sampling may put a certain pressure on the radar data processing that works with it. In terms of radar, the increase in the cumulative working time will cause the performance of radar transmitters, receivers and other components to decay, affecting the stability of pulse transmission and the sensitivity of the received signal. The more the total number of pulse transmissions, the faster the radar equipment ages, and the changes in its key indicators such as transmission power and frequency stability affect the detection capability of meteorological targets, and indirectly affect the data association accuracy of the micromanometer in comprehensive monitoring.

[0082] In a specific embodiment, by analyzing the characteristic value of the comprehensive use status of the equipment, a comprehensive evaluation of the operating status of the micromanometer and radar equipment can be achieved, and the aging degree, performance degradation and potential problems of the equipment can be timely understood. When determining the regional equipment acquisition correction coefficient, reasonable adjustments can be made based on the characteristic value of the comprehensive use status of the equipment, so that the monitoring data can more accurately reflect the actual meteorological conditions, improve the accuracy of the entire monitoring system for tornado micropressure monitoring, and provide strong data support for more effective early warning of tornado disasters.

[0083] The acquisition correction coefficient corresponding to each usage status characteristic value interval stored in the database is extracted, and the acquisition correction coefficient corresponding to the interval where the comprehensive usage status characteristic value of the extracted equipment is located is mapped and recorded as the regional tornado micro-pressure anomaly correction index.

[0084] It should be noted that the cumulative use time and average daily sampling times of the micromanometer, as well as the cumulative working time and total number of pulse transmissions of the radar, will affect the performance of the equipment and thus the accuracy of the monitoring data. By extracting the regional tornado micropressure anomaly correction index based on the comprehensive use status characteristic value of the equipment, the impact of the equipment status on the monitoring data can be fully considered, and the tornado micropressure anomaly index can be reasonably corrected to ensure that the final monitoring results are closer to the actual situation and improve the reliability and accuracy of the entire monitoring system.

[0085] Based on the regional tornado micropressure comprehensive anomaly characteristic value and the regional equipment acquisition correction coefficient, the regional tornado micropressure anomaly correction index is analyzed and processed to obtain the regional tornado micropressure anomaly correction index, which is used to characterize the degree of tornado micropressure anomaly judgment after correction by regional micromanometer and radar comprehensive monitoring.

[0086] In a specific embodiment, the regional tornado micro-pressure anomaly correction index is obtained in the following manner: ,in, It is the correction index of regional tornado micro-pressure anomaly. is the regional tornado micro-pressure comprehensive anomaly characteristic value, Collect correction factors for regional equipment.

[0087] It is important to understand that the softsign function is a built-in function in Python. .

[0088] In a specific embodiment, by analyzing the regional tornado micro-pressure anomaly correction index, the abnormal degree of tornado micro-pressure can be judged more accurately, effectively eliminating misjudgments caused by factors such as equipment aging and environmental interference. When the index shows a low degree of abnormality, the system can reasonably maintain routine monitoring to avoid excessive response and waste of resources; and when the index shows a serious abnormality, it can trigger an early warning in time to reduce the losses that may be caused by the tornado, while also providing more accurate data support for meteorological research.

[0089] In this embodiment, the comprehensive information of regional tornado micro-pressure is determined, and the specific process is as follows: Extract the first micro-pressure verification index and the second micro-pressure verification index preset in the database.

[0090] If the regional tornado micro-pressure anomaly correction index is less than or equal to the first micro-pressure verification index, the regional tornado micro-pressure comprehensive information is recorded as normal micro-pressure.

[0091] If the regional tornado micro-pressure anomaly correction index is less than or equal to the first micro-pressure verification index, it means that the micro-pressure condition in the current area is within the normal fluctuation range and is not significantly affected by tornado activities or other abnormal meteorological factors. The atmospheric environment is relatively stable, and the relationship between various meteorological parameters conforms to the characteristics under normal weather conditions. The possibility of drastic weather changes such as tornadoes in the short term is low. The system can continue to maintain regular monitoring status without taking further special response measures.

[0092] If the regional tornado micro-pressure anomaly correction index is greater than the first micro-pressure verification index and less than or equal to the second micro-pressure verification index, the regional tornado micro-pressure comprehensive information is recorded as a micro-pressure anomaly.

[0093] If the regional tornado micro-pressure anomaly correction index is greater than the first micro-pressure verification index and less than or equal to the second micro-pressure verification index, it means that although the micro-pressure conditions in the current area have shown a certain degree of abnormality, this degree of abnormality is not enough to conclusively determine that a tornado is about to occur or has occurred. There may be some potential meteorological trends or interference factors that need further verification. At this time, it is necessary to conduct secondary monitoring of the area in order to more accurately capture the details of micro-pressure changes and meteorological target characteristics, further clarify the true state of the atmospheric environment, eliminate the possibility of misjudgment, and provide more reliable data support for subsequent accurate judgment of whether there is a tornado threat.

[0094] If the regional tornado micro-pressure anomaly correction index is greater than the second micro-pressure verification index, the regional tornado micro-pressure comprehensive information will be recorded as a micro-pressure warning.

[0095] If the regional tornado micro-pressure anomaly correction index is greater than the second micro-pressure verification index, it means that the atmospheric environment in the area is in a highly unstable state and the monitored micro-pressure data is highly abnormal. At this time, the abnormal relationship between various meteorological parameters and the significant change in micro-pressure have clearly shown the severity of the abnormal meteorological conditions. The system must immediately generate warning information and issue warnings to the preset management terminal so that relevant personnel can take effective prevention and response measures in a timely manner.

[0096] By judging the comprehensive information of regional micro-pressure, when it is judged that the micro-pressure is normal, unnecessary resource investment and misjudgment interference are avoided. If the micro-pressure is abnormal, it will help to further accurately grasp the details of micro-pressure changes and meteorological target characteristics, eliminate potential interference factors, and improve the accuracy of judging the potential threat of tornadoes. Once it is judged as a micro-pressure warning, the system immediately generates and issues warning information, allowing relevant personnel to know in time that a tornado is approaching or occurring, so that they can quickly take countermeasures to minimize the losses caused by the tornado, which reflects the efficiency and reliability of the monitoring system in responding to tornado disasters.

[0097] The regional secondary monitoring execution module is used to adjust the collection parameters of the micromanometer and the radar respectively when the regional tornado micropressure comprehensive information is judged to be micropressure abnormal, and analyze the secondary verification monitoring execution cycle in combination with the regional tornado micropressure abnormality correction index, thereby executing regional secondary verification monitoring.

[0098] In this embodiment, the secondary verification monitoring of the area is performed, and the specific process is as follows: The regional tornado micro-pressure anomaly correction index and the first micro-pressure verification index are processed by difference to obtain the regional tornado micro-pressure comprehensive anomaly deviation factor.

[0099] The secondary verification monitoring period corresponding to each comprehensive abnormal deviation factor interval stored in the database is extracted, and the secondary verification monitoring period corresponding to the interval of the comprehensive abnormal deviation factor of tornado micropressure in the extraction area is mapped and recorded as the secondary verification monitoring execution period.

[0100] The preset maximum rated sampling frequency of the micromanometer and the preset maximum rated resolution of the radar are extracted, thereby adjusting the micromanometer at the preset maximum rated sampling frequency and adjusting the radar at the preset maximum rated resolution, and performing regional secondary verification monitoring according to the secondary verification monitoring execution cycle.

[0101] In a specific embodiment, after the secondary monitoring is completed, the regional tornado micropressure anomaly correction index is re-analyzed and recorded as the regional tornado micropressure secondary monitoring characteristic value, and the regional tornado micropressure secondary monitoring characteristic value and the regional tornado micropressure anomaly correction index are differenced to obtain the regional tornado micropressure secondary monitoring deviation factor.

[0102] It should be noted that the difference processing refers to the subtraction of the regional tornado micropressure anomaly correction index from the regional tornado micropressure secondary monitoring characteristic value. The result of the difference processing can be greater than zero, less than zero or equal to zero.

[0103] When the regional tornado micro-pressure secondary monitoring deviation factor is greater than zero, an early warning prompt is directly generated.

[0104] When the regional tornado micropressure secondary monitoring deviation factor is less than zero, the adjustment parameters corresponding to each secondary monitoring deviation factor interval stored in the database are extracted, and the adjustment parameters corresponding to the interval in which the regional tornado micropressure secondary monitoring deviation factor is located are mapped and extracted, and recorded as the regional tornado micropressure monitoring equipment adjustment parameters. The regional tornado micropressure monitoring equipment is adjusted with the regional tornado micropressure monitoring equipment adjustment parameters.

[0105] It should be noted that the adjustment parameters of the regional tornado micro-pressure monitoring equipment include the micro-manometer sampling frequency adjustment value and the radar resolution adjustment value.

[0106] It should also be noted that if the extracted adjustment parameters of the regional tornado micro-pressure monitoring equipment exceed the preset rated minimum adjustment capacity of the equipment, the monitoring operation will be carried out with the preset rated minimum parameters of the equipment.

[0107] If the adjustment parameter of the regional tornado micro-pressure monitoring equipment is equal to zero, the micro-manometer will continue to be controlled to perform monitoring operations at the preset rated maximum sampling frequency and the radar at the preset rated maximum resolution.

[0108] The regional micro-pressure warning module is used to generate warning information and issue warnings to the preset management terminal when the regional tornado micro-pressure comprehensive information is a micro-pressure warning.

[0109] In a specific embodiment, the warning information may be “Attention! Monitoring data shows that within the range of [specific area name], the current atmospheric environment shows signs of strong instability, and a tornado is very likely to be forming or about to strike.” See also Figure 2 As shown, an embodiment of the present invention provides a method for monitoring tornado micro-pressure change data, comprising the following steps: S1, obtain regional micropressure observation data through a micropressure gauge, analyze the abnormal micropressure observation index, and judge the regional radar activation information, the regional radar activation information includes required radar activation and no radar activation required. When the regional radar activation information is judged as no radar activation required, execute S2, and when the regional radar activation information is judged as required radar activation, execute S3.

[0110] S2, synchronously collects regional environmental parameters, combines them with abnormal micropressure observation indicators, obtains the optimal sampling frequency of the micromanometer, and conducts regional micropressure monitoring at the optimal sampling frequency of the micromanometer.

[0111] It should be noted that, in a specific embodiment, after the regional radar activation information is judged as not requiring radar activation, the system continuously performs regional micro-pressure monitoring at the analyzed optimal sampling frequency of the micro-pressure gauge, obtains regional micro-pressure observation data, and analyzes micro-pressure observation abnormality indicators. In the continuous monitoring process, if within a specific monitoring period, the micro-pressure observation abnormality indicator exceeds the preset tornado micro-pressure observation abnormality verification indicator threshold, this situation indicates that the complexity of the meteorological environment has increased suddenly and potential risks are looming. The system immediately activates the radar equipment, fully opens the deep observation mode, and accurately captures the subtle changes in meteorological elements. On the contrary, if the micro-pressure observation abnormality indicator is always stable within the preset threshold range, it fully proves that the current meteorological environment is stable and there is no significant abnormal fluctuation. The system will continue to steadily carry out regional micro-pressure monitoring work at the optimal sampling frequency of the micro-pressure gauge and continue to accumulate data.

[0112] S3, synchronously start regional radar monitoring, thereby obtaining regional micro-pressure comprehensive data, analyzing to obtain regional micro-pressure comprehensive abnormal characteristic indicators, and judging regional micro-pressure comprehensive information, the regional micro-pressure comprehensive information includes normal micro-pressure, abnormal micro-pressure and micro-pressure warning. When the regional micro-pressure comprehensive information is micro-pressure abnormal, execute S4, and when the regional micro-pressure comprehensive information is micro-pressure warning, execute S5.

[0113] S4, respectively adjusts the acquisition parameters of the micropressure gauge and the radar, and analyzes the secondary verification monitoring execution cycle in combination with the regional micropressure comprehensive abnormal characteristic index, thereby executing the regional secondary verification monitoring.

[0114] S5, generate warning information and publish the warning to the preset management terminal.

[0115] The present invention provides a tornado micro-pressure change data monitoring system and method, which can realize efficient and accurate monitoring of tornado micro-pressure, effectively improve the meteorological disaster early warning capability, and scientifically judge the radar opening demand by analyzing the micro-pressure meter acquisition data to avoid resource waste. The optimal sampling frequency analysis module can optimize the micro-pressure meter sampling frequency according to the environment and micro-pressure observation conditions to ensure the effectiveness of data acquisition. Comprehensive multi-source data is deeply analyzed to accurately judge the micro-pressure state and provide a numerical basis for subsequent decision-making. Targeted secondary monitoring is carried out when necessary to further verify the meteorological conditions. It can also issue early warning information in a timely manner so that relevant personnel can take preventive and countermeasure measures in advance to reduce the losses that may be caused by tornadoes.

[0116] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0117] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. A tornado micro-pressure change data monitoring system, characterized in that: include: A regional radar activation information determination module is used to collect regional tornado micropressure observation data through a micromanometer, analyze the abnormal index of tornado micropressure observation, and thereby determine regional radar activation information, wherein the regional radar activation information includes radar activation required and radar activation not required; The optimal sampling frequency analysis module is used to synchronously collect regional environmental parameters when the regional radar opening information determines that the radar does not need to be opened, and obtain the optimal sampling frequency of the micromanometer in combination with the abnormal index of tornado micropressure observation, and monitor the regional micropressure at the optimal sampling frequency of the micromanometer; A regional tornado micro-pressure comprehensive information judgment module is used to synchronously start regional radar monitoring when the regional radar opening information is judged to be required radar opening, thereby obtaining regional tornado micro-pressure comprehensive data, analyzing to obtain regional tornado micro-pressure anomaly correction indicators, and judging regional tornado micro-pressure comprehensive information, wherein the regional tornado micro-pressure comprehensive information includes normal micro-pressure, abnormal micro-pressure and micro-pressure warning; The regional secondary monitoring execution module is used to adjust the acquisition parameters of the micromanometer and the radar respectively when the regional tornado micropressure comprehensive information is judged to be abnormal micropressure, and analyze the secondary verification monitoring execution cycle in combination with the regional tornado micropressure abnormality correction index, thereby executing the regional secondary verification monitoring; The regional micro-pressure warning module is used to generate warning information and issue warnings to the preset management terminal when the regional tornado micro-pressure comprehensive information is a micro-pressure warning.

2. A tornado micro-pressure change data monitoring system according to claim 1, characterized in that: The specific process of analyzing the abnormal index of tornado micro-pressure observation is as follows: Collect regional tornado micro-pressure observation data during a preset monitoring period, wherein the regional tornado micro-pressure observation data includes average micro-pressure value, micro-pressure change extreme value difference, micro-pressure change rate, gravity wave amplitude change extreme value difference and gravity wave period length change extreme value difference; According to the analysis and processing of the regional tornado micropressure observation data, a tornado micropressure observation anomaly index is obtained, and the regional tornado micropressure observation index is used to characterize the degree of anomaly of the tornado micropressure observation data collected by the micromanometer.

3. A tornado micro-pressure change data monitoring system according to claim 2, characterized in that: The specific analysis process of determining the radar activation information in the area is as follows: Extract the tornado micro-pressure observation anomaly verification index preset in the database; If the tornado micro-pressure observation anomaly index is less than the tornado micro-pressure observation anomaly verification index, the regional radar opening information is recorded as no radar opening is required; If the tornado micro-pressure observation anomaly index is greater than or equal to the tornado micro-pressure observation anomaly verification index, the regional radar activation information is recorded as the required radar activation.

4. A tornado micro-pressure change data monitoring system according to claim 3, characterized in that: The specific process of obtaining the optimal sampling frequency of the micromanometer is as follows: During the preset monitoring period, the regional environmental parameters are synchronously collected, and the regional environmental parameters include average temperature, average humidity, maximum wind speed, accumulated precipitation and average atmospheric pressure; According to the regional environmental parameters and the tornado micropressure observation abnormality index, the micromanometer sampling indicator factor is obtained by analysis and processing, and the micromanometer sampling indicator factor is used to characterize the influence of the current regional environment on the working state of the micromanometer; Extract the micromanometer sampling indication factor threshold preset in the database; The micromanometer sampling deviation factor is obtained by performing difference processing on the micromanometer sampling indication factor and the micromanometer sampling indication factor threshold; Extract the sampling frequency adjustment value corresponding to each sampling deviation factor interval stored in the database, and map and extract the sampling frequency adjustment value corresponding to the interval where the micromanometer sampling deviation factor is located, and record it as the micromanometer sampling frequency adjustment value; The current acquisition frequency of the micromanometer is obtained, and the optimal sampling frequency of the micromanometer is calculated according to the micromanometer sampling frequency adjustment value.

5. A tornado micro-pressure change data monitoring system according to claim 1, characterized in that: The specific process of obtaining regional tornado micro-pressure comprehensive data is as follows: During the monitoring period, the micropressure data is collected by the micromanometer, and after a five-minute sliding Fourier analysis, the three-dimensional dynamic spectrum of the gravity wave "period-amplitude-time" is obtained, thereby obtaining the micropressure change data, which includes the change in the amplitude of the gravity wave period, the change in the amplitude of the gravity wave short period and the maximum period length of the gravity wave. The maximum micropressure monitored by the micromanometer is obtained simultaneously, and the micropressure change data and the maximum micropressure are combined as the monitoring parameters of the micromanometer; Radar monitoring parameters are obtained through radar monitoring, including the maximum echo intensity, average echo intensity, average radial velocity and maximum reflectivity factor value of the area; The micromanometer monitoring parameters and radar monitoring parameters are combined as the regional tornado micropressure comprehensive data.

6. A tornado micro-pressure change data monitoring system according to claim 5, characterized in that: The analysis results in the correction index of regional tornado micro-pressure anomaly. The specific analysis process is as follows: According to the regional tornado micro-pressure comprehensive data analysis and processing, the regional tornado micro-pressure comprehensive anomaly characteristic value is obtained; The regional tornado micro-pressure comprehensive anomaly characteristic value is used to characterize the degree of anomaly of the tornado micro-pressure data during the comprehensive monitoring of the micro-manometer and the radar; Synchronously obtain the historical usage parameters of the micromanometer and the radar, and analyze the regional equipment acquisition correction coefficients; Based on the regional tornado micropressure comprehensive anomaly characteristic value and the regional equipment acquisition correction coefficient, the regional tornado micropressure anomaly correction index is analyzed and processed to obtain the regional tornado micropressure anomaly correction index, which is used to characterize the degree of tornado micropressure anomaly judgment after correction by regional micromanometer and radar comprehensive monitoring.

7. A tornado micro-pressure change data monitoring system according to claim 6, characterized in that: The specific process of determining the regional tornado micro-pressure comprehensive information is as follows: Extracting a first micro-pressure verification index and a second micro-pressure verification index preset in a database; If the regional tornado micro-pressure anomaly correction index is less than or equal to the first micro-pressure verification index, the regional tornado micro-pressure comprehensive information is recorded as normal micro-pressure; If the regional tornado micro-pressure anomaly correction index is greater than the first micro-pressure verification index and less than or equal to the second micro-pressure verification index, the regional tornado micro-pressure comprehensive information is recorded as micro-pressure anomaly; If the regional tornado micro-pressure anomaly correction index is greater than the second micro-pressure verification index, the regional tornado micro-pressure comprehensive information will be recorded as a micro-pressure warning.

8. A tornado micro-pressure change data monitoring system according to claim 7, characterized in that: The specific process of performing secondary verification monitoring of the area is as follows: The regional tornado micro-pressure anomaly correction index and the first micro-pressure verification index are processed by difference to obtain the regional tornado micro-pressure comprehensive anomaly deviation factor; Extract the secondary verification monitoring period corresponding to each comprehensive abnormal deviation factor interval stored in the database, and map the secondary verification monitoring period corresponding to the interval where the comprehensive abnormal deviation factor of tornado micropressure in the extracted area is located, which is recorded as the secondary verification monitoring execution period; The preset maximum rated sampling frequency of the micromanometer and the preset maximum rated resolution of the radar are extracted, thereby adjusting the micromanometer at the preset maximum rated sampling frequency and adjusting the radar at the preset maximum rated resolution, and performing regional secondary verification monitoring according to the secondary verification monitoring execution cycle.

9. A tornado micro-pressure change data monitoring system according to claim 6, characterized in that: The specific method of obtaining the correction index of the regional tornado micro-pressure anomaly is as follows: ， in, It is the correction index of regional tornado micro-pressure anomaly. is the regional tornado micro-pressure comprehensive anomaly characteristic value, Collect correction factors for regional equipment.

10. A method for a tornado micro-pressure change data monitoring system as claimed in any one of claims 1 to 8, characterized in that: The following steps are involved: S1, obtaining regional micro-pressure observation data through a micro-pressure gauge, analyzing the abnormal micro-pressure observation index, and judging regional radar opening information, wherein the regional radar opening information includes required radar opening and no radar opening. When the regional radar opening information is judged as no radar opening, S2 is executed; when the regional radar opening information is judged as required radar opening, S3 is executed; S2, synchronously collect regional environmental parameters, combine with micro-pressure observation abnormal indicators, obtain the optimal sampling frequency of the micro-manometer, and conduct regional micro-pressure monitoring at the optimal sampling frequency of the micro-manometer; S3, synchronously start regional radar monitoring, thereby obtaining regional micro-pressure comprehensive data, analyzing to obtain regional micro-pressure comprehensive abnormal characteristic indicators, and judging regional micro-pressure comprehensive information, wherein the regional micro-pressure comprehensive information includes normal micro-pressure, abnormal micro-pressure and micro-pressure warning. When the regional micro-pressure comprehensive information is micro-pressure abnormal, execute S4, and when the regional micro-pressure comprehensive information is micro-pressure warning, execute S5; S4, respectively adjusting the acquisition parameters of the micromanometer and the radar, and analyzing the secondary verification monitoring execution cycle in combination with the regional micropressure comprehensive abnormal characteristic index, thereby executing the regional secondary verification monitoring; S5, generate warning information and publish the warning to the preset management terminal.

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