A method and system for monitoring and early warning of severe convective weather on power lines based on satellite remote sensing

Abstract

This invention discloses a method and system for monitoring and early warning of severe convective weather on power lines based on satellite remote sensing. The technical solution employed in this invention includes: identifying and tracking the occurrence and evolution of mesoscale convective systems using brightness and temperature data obtained from satellite remote sensing; extracting the main evolutionary features of the mesoscale convective system based on its evolutionary process, and predicting the impact area of ​​the mesoscale convective system at the next moment using weighted averaging and Lagrange interpolation based on these features; issuing early warnings for transmission towers located within the impact area of ​​the mesoscale convective system based on the prediction results and the locations of each transmission tower on the power line, marking the transmission towers within the impact area, and outputting the early warning results. This invention can rapidly output early warning results, increasing the practicality of forecasts and improving the power grid's early warning capability for severe convective weather.

Description

Technical Field

[0001] This invention belongs to the field of weather monitoring and early warning technology, and relates to a method and system for monitoring and early warning of severe convective weather on power lines based on satellite remote sensing. Background Technology

[0002] It has been reported that 80% of weather-related power transmission line failures are caused by severe convective weather such as tornadoes and downbursts. In recent years, with the continuous increase in electricity demand and the frequent occurrence of severe convective weather in my country, the possibility of power transmission lines being damaged by severe convective weather has greatly increased.

[0003] During the warm season, when the atmospheric stratification is unstable, there is abundant water vapor in the air, and sufficient convective impact force, atmospheric convection develops vigorously, forming weather systems known as convective weather systems, such as thunderstorms, tornadoes, squall lines, and hail. Among these, mesoscale (spatial 10T) systems are particularly important. 0 ~10 3 On the order of km, in terms of time 10 3 ~10 5 Convective weather systems that last approximately 20 minutes to 1 day (3 seconds) are called mesoscale convective systems (MCS). Severe convective weather, in meteorology, refers to a type of hazardous weather that occurs and disappears suddenly, moves rapidly, undergoes drastic weather changes, and has extremely destructive power. It mainly includes thunderstorms, hail, tornadoes, and short-duration heavy rainfall. Mesoscale convective systems are the direct influencing systems that produce severe convective weather.

[0004] Brightness temperature is an assumed temperature. At a given wavelength, if an actual object has the same radiation intensity as a blackbody, then the brightness temperature of that blackbody at that wavelength is called the brightness temperature of the object. In meteorology, brightness temperature observed by meteorological satellites can reflect cloud top height, and thus the level of convection activity. Generally, the lower the brightness temperature, the more active the convection. Therefore, brightness temperature is widely used to identify and track cloud saturation systems (MCS).

[0005] Currently, severe convective weather warnings for power lines mainly rely on subjective judgment methods based on observations such as satellites and radar, lacking quantitative analysis. Summary of the Invention

[0006] To address the lack of objectivity in existing early warning methods, this invention provides a method and system for early warning of severe convective weather for power lines based on observational data. It utilizes brightness temperature obtained from satellite observations, sets a series of thresholds, extracts the occurrence and evolution process of the MCS (Multi-Signal System) from the brightness temperature field at continuous intervals, and predicts the affected area of ​​this MCS at the next moment based on the evolutionary characteristics. According to the affected area, transmission towers within the affected area are marked, and early warning results are quickly output.

[0007] Therefore, one technical solution adopted by the present invention is: a method for monitoring and early warning of severe convective weather on power lines based on satellite remote sensing, which includes:

[0008] Step 1: Use brightness and temperature data obtained from satellite remote sensing to identify and track the occurrence and evolution of mesoscale convective systems;

[0009] Step 2: Based on the evolution process of the mesoscale convective system, extract the main evolutionary features of the mesoscale convective system, and based on its main evolutionary features, use weighted average and Lagrange interpolation methods to predict the influence area of ​​this mesoscale convective system at the next moment (the next half hour or one hour, depending on the brightness temperature temporal resolution).

[0010] Step 3: Based on the prediction results and the location of each transmission tower of the power line, issue early warnings for the transmission towers located within the influence area of ​​the mesoscale convection system, mark the transmission towers located within the influence area, and output the early warning results.

[0011] This invention enables early warning of severe convective weather along power lines by predicting the movement of mesoscale convective systems.

[0012] Further, step 1 includes:

[0013] Step 11: Using brightness temperature, set cold cloud threshold, cold core threshold, and area threshold, extract several cold cloud systems, and obtain their location information; the selected thresholds comprehensively consider computational efficiency and recognition accuracy, increase the practicality of the forecast, and improve the power grid's early warning capability for severe convective weather;

[0014] Step 12: Use the overlapping area method to track the evolution of each cold cloud system.

[0015] Furthermore, in step 11, utilizing the low brightness temperature characteristic of mesoscale convective systems, based on the two-dimensional brightness temperature matrix at each moment, several continuous regions are first obtained using a 233K threshold and an 8-connectivity method. Then, regions with a minimum brightness temperature greater than 228K or an area less than 3000 km² are removed. 2 The region is selected to reduce the impact of stratiform clouds and small-to-medium-scale convective systems, and finally several regions that meet the conditions are obtained, which are called cold cloud systems (CCS).

[0016] In step 12, the overlapping area method is used to track the movement and evolution of each CCS. That is, based on the overlapping area ratio of two consecutive time points, it is determined whether they are associated as the same cold cloud system. The overlapping area ratio R i,j The calculation method is as follows:

[0017]

[0018] Among them, R i,j for and The ratio of overlapping areas; A i A j They are respectively and The area; This indicates the calculation of the overlapping area of ​​two regions;

[0019] Using a 15% overlap ratio threshold, that is, when R i,j When ≥15%, and If multiple CCSs at time t simultaneously satisfy R with a single CCS at time t+1, they are considered to be the same CCS. i,j When ≥15%, it is considered that multiple CCSs at time t merge into a new CCS at time t+1; if a CCS at time t and multiple CCSs at time t+1 simultaneously satisfy R i,j When the percentage is ≥15%, the CCS at time t is considered to have split into several new CCSs at time t+1; a CCS that has been successfully tracked for more than 3 hours is defined as a new mesoscale convection system.

[0020] After the above steps, the evolution process of the mesoscale convective system at each time point is obtained.

[0021] Further, step 2 includes:

[0022] Step 21: Extract the main evolutionary features of the mesoscale convective system based on its evolutionary process;

[0023] Step 22: Based on the main evolutionary characteristics of the mesoscale convective system, predict the moving velocity of the mesoscale convective system at the next moment, and then calculate the geometric center position of the influence region of the mesoscale convective system at the next moment.

[0024] Step 23: Based on the Lagrange interpolation method, the influence region of the mesoscale convective system at time t+1 is predicted using the influence region of the mesoscale convective system at five times (t-4) to t.

[0025] Furthermore, in step 21,

[0026] The rectangular area generated by the four points of maximum and minimum latitude and longitude of the cold cloud system region at each moment of the mesoscale convective system is taken as the influence range of the mesoscale convective system at that moment.

[0027] The velocity vector of the mesoscale convective system at each moment is calculated using the following method:

[0028]

[0029] in:

[0030] Let be the velocity vector of the nth mesoscale convective system at time t;

[0031] dS is the displacement vector of the nth mesoscale convective system at time t, defined as the line connecting the geometric centers of the influence regions of the mesoscale convective system at time t and time t-1.

[0032] dt represents the temporal resolution of the brightness temperature data;

[0033] Through the above steps, the main evolutionary features of the mesoscale convective system are extracted;

[0034] In step 22, the velocity of the mesoscale convective system at time t is given the main weight, and the velocities before time t are weighted by the area of ​​the affected region. The velocity of the mesoscale convective system at the next time step is calculated using the following method:

[0035]

[0036] in:

[0037] Let be the velocity vector of the nth mesoscale convective system at time i, where t = 2, 3, 4, ..., t-1;

[0038] Based on the moving speed at time t+1 and the position of this mesoscale convection system at time t, the geometric center of the affected area at time t+1 is obtained.

[0039] In step 23, the size of the influence region of the mesoscale convective system at time t+1 is calculated using the following method:

[0040]

[0041]

[0042] in, These are the t-th ... i The latitude and longitude lengths at any given time.

[0043] Another technical solution adopted in this invention is: a power line severe convective weather monitoring and early warning system based on satellite remote sensing, which includes:

[0044] The identification and tracking unit uses brightness and temperature data obtained from satellite remote sensing to identify and track the occurrence and evolution of mesoscale convective systems;

[0045] The influence area prediction unit extracts the main evolution characteristics of the mesoscale convective system based on its evolution process, and uses weighted average and Lagrange interpolation methods to predict the influence area of ​​the mesoscale convective system at the next moment based on its main evolution characteristics.

[0046] The early warning unit, based on the prediction results and the location of each transmission tower of the power line, issues early warnings for transmission towers located within the influence area of ​​the mesoscale convection system, marks the transmission towers located within the influence area, and outputs the early warning results.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] 1. Based on the characteristics of MCS in China and surrounding areas, this invention selects different thresholds, which differ significantly from the thresholds used in other methods. While minimizing the amount of computation, it aims to avoid missing strong MCS events. The current threshold selection can identify more than 95% of heavy precipitation weather (precipitation intensity greater than 20 mm / hr).

[0049] 2. This invention uses weighted average and Lagrange interpolation methods to comprehensively consider the position information of the MCS at the current time and previous times, predict the influence area of ​​the MCS at the next time. The calculation involves fewer variables and can quickly obtain the prediction results. Attached Figure Description

[0050] To more clearly illustrate the technical solutions or embodiments of the present invention, the accompanying drawings required for the technical solutions or embodiments will be briefly introduced below.

[0051] Figure 1 This is a flowchart illustrating the monitoring and early warning method of the present invention;

[0052] Figure 2 This is a schematic diagram of the monitoring and early warning system of the present invention. Detailed Implementation

[0053] The present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the described embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0054] Example 1

[0055] This embodiment presents a method for monitoring and early warning of severe convective weather on power lines based on satellite remote sensing. The flowchart of this method is as follows: Figure 1 As shown, it includes the following steps:

[0056] Step 1 involves using brightness and temperature data obtained from satellite remote sensing to identify and track the occurrence and evolution of mesoscale convective systems. Step 1 consists of the following steps:

[0057] Step 11: Utilizing the low brightness temperature characteristic of MCS, a two-dimensional brightness temperature matrix is ​​generated for each time point (determined by the brightness temperature time resolution, typically half an hour or one hour). First, a 233K threshold and an 8-connectivity method are used to obtain several continuous regions. Then, regions with a minimum brightness temperature greater than 228K or an area less than 3000 km² are removed. 2 The goal is to reduce the impact of stratiform clouds and small cloud systems (MCS) in specific regions, ultimately identifying several regions that meet the criteria, termed Cold Cloud Systems. Two locations, 228K and 3000km, were selected. 2 The threshold can identify more than 95% of heavy precipitation weather (precipitation intensity greater than 20 mm / hr) while significantly reducing the amount of computation.

[0058] Step 12: Track the movement and evolution of each CCS using the overlap area method. Specifically, based on the overlap area ratio between two consecutive time points, determine whether they are associated as the same cold cloud system. The overlap area ratio R... i,j The calculation method is as follows:

[0059]

[0060] in:

[0061] R i,j for and The ratio of overlapping areas;

[0062] A i A j They are respectively and The area;

[0063] This indicates the calculation of the overlapping area of ​​two regions.

[0064] Using a 15% overlap ratio threshold, that is, when R i,j When ≥15%, and Marked as the same CCS, that is, considering time t as It developed into at time t+1 Specifically, if at time t there are multiple CCSs and at time t+1 a CCS simultaneously satisfy R i,j When ≥15%, it is considered that multiple CCSs at time t are merged into a new CCS at time t+1 (e.g., when...). All with Satisfy R i,j When it is ≥15%, it is considered At time t+1, they merge into If a CCS at time t and multiple CCSs at time t+1 simultaneously satisfy R...i,j When ≥15%, it is considered that the CCS at time t splits into several new CCSs at time t+1 (e.g., when...). and All satisfy R i,j When it is ≥15%, it is considered It splits into at time t+1. (Three CCSs). Finally, a CCS that has been successfully tracked continuously for more than 3 hours is defined as a new mesoscale convection system.

[0065] After the above steps, the evolution process of MCS at each time point can be obtained.

[0066] Step 2: Based on the evolution of the mesoscale convective system, extract its main evolutionary features. Then, based on these features, use weighted averaging and Lagrange interpolation to predict the influence region of the mesoscale convective system at the next time step. Step 2 consists of the following steps:

[0067] Step 21: Based on the aforementioned MCS evolution process, extract the main features of MCS evolution:

[0068] The rectangular area formed by the four points of maximum and minimum latitude and longitude of the CCS region at each time step of the MCS is taken as the influence range of the MCS at that time step.

[0069] The velocity vector of the MCS at each time step is calculated using the following method:

[0070]

[0071] in:

[0072] Let n be the velocity vector of the nth MCS at time t;

[0073] dS is the displacement vector of the nth MCS at time t, defined as the line connecting the geometric centers of the MCS influence regions at time t and time t-1;

[0074] dt represents the time resolution of the brightness temperature data.

[0075] After the above steps, the main evolutionary features of MCS can be extracted.

[0076] Step 22: Based on the main evolutionary characteristics of the MCS, predict the movement speed of the MCS at the next moment, and then calculate the geometric center position of the MCS's influence area at the next moment:

[0077] The moving velocity of the MCS at time t is given the main weight, and the moving velocities before time t are weighted by the area of ​​the affected region to reduce the influence of observation noise. The moving velocity of the MCS at the next time step is calculated using the following method:

[0078]

[0079] in:

[0080] Let be the velocity vector of the nth MCS at time i, where t = 2, 3, 4, ..., t-1.

[0081] Based on the moving speed at time t+1 and the position of this MCS at time t, the geometric center of the affected area at time t+1 can be obtained.

[0082] Step 23: Based on the Lagrange interpolation method, predict the size of the influence region of the MCS at time t+1 using the influence regions of the MCS at five times (t-4) to t.

[0083] The size of the influence region of the MCS at time t+1 is calculated using the following method:

[0084]

[0085]

[0086] in:

[0087] They are respectively the nth MCS and the tth i The latitude and longitude lengths at any given time.

[0088] Step 3: Based on the prediction results and the location of each transmission tower of the power line, issue early warnings for the transmission towers located within the influence area of ​​the mesoscale convection system, mark the transmission towers located within the influence area, and output the early warning results.

[0089] Example 2

[0090] This embodiment provides a power line severe convective weather monitoring and early warning system based on satellite remote sensing, which consists of a component identification and tracking unit, an impact area prediction unit, and an early warning unit, such as... Figure 2 As shown.

[0091] The identification and tracking unit uses brightness and temperature data obtained from satellite remote sensing to identify and track the occurrence and evolution of mesoscale convective systems.

[0092] The identification and tracking unit consists of a cold cloud system extraction subunit and a cold cloud system tracking subunit.

[0093] The cold cloud system extraction subunit: using brightness temperature, setting cold cloud threshold, cold core threshold and area threshold, extracting several cold cloud systems, and obtaining their location information.

[0094] In the cold cloud system extraction subunit, taking advantage of the low brightness temperature characteristic of mesoscale convective systems, based on the two-dimensional brightness-temperature matrix at each moment, several continuous regions are first obtained using a 233K threshold and an 8-connectivity method. Then, regions with a minimum brightness temperature greater than 228K or an area less than 3000 km² are removed. 2 The goal is to reduce the impact of stratiform clouds and small-to-medium-scale convective systems in certain regions, ultimately obtaining several regions that meet the criteria, which are called cold cloud systems (CCS).

[0095] The aforementioned cold cloud system tracking subunit uses the overlapping area method to track the evolution of each cold cloud system.

[0096] In the aforementioned cold cloud system tracking subunit, the overlapping area method is used to track the movement and evolution of each CCS. Specifically, based on the overlap area ratio between two consecutive time points, it is determined whether they are associated as the same cold cloud system. The overlap area ratio R... i,j The calculation method is as follows:

[0097]

[0098] Among them, R i,j for and The ratio of overlapping areas; A i A j They are respectively and The area; This indicates the calculation of the overlapping area of ​​two regions;

[0099] Using a 15% overlap ratio threshold, that is, when R i,j When ≥15%, and Marked as the same CCS, that is, considering time t as It developed into at time t+1 Specifically, if at time t there are multiple CCSs and at time t+1 a CCS simultaneously satisfy R i,j When ≥15%, it is considered that multiple CCSs at time t are merged into a new CCS at time t+1 (e.g., when...). All with Satisfy R i,j When it is ≥15%, it is considered At time t+1, they merge into If a CCS at time t and multiple CCSs at time t+1 simultaneously satisfy R... i,jWhen ≥15%, it is considered that the CCS at time t splits into several new CCSs at time t+1 (e.g., when...). and All satisfy R i,j When it is ≥15%, it is considered It splits into at time t+1. (Three CCSs). Finally, a CCS that has been successfully tracked continuously for more than 3 hours is defined as a new mesoscale convection system.

[0100] After the above steps, the evolution process of the mesoscale convective system at each time point is obtained.

[0101] The influence area prediction unit extracts the main evolutionary features of the mesoscale convective system based on its evolutionary process, and then uses weighted average and Lagrange interpolation methods to predict the influence area of ​​the mesoscale convective system at the next moment based on these main evolutionary features.

[0102] The influence area prediction unit consists of an evolution feature extraction subunit, an influence area geometric center location calculation subunit, and an influence area size prediction subunit.

[0103] Evolutionary Feature Extraction Subunit: Based on the evolutionary process of the mesoscale convective system, extract the main evolutionary features of the mesoscale convective system.

[0104] The sub-unit for calculating the geometric center of the affected region: Based on the main evolutionary characteristics of the mesoscale convection system, the moving velocity of the mesoscale convection system at the next moment is predicted, and then the geometric center of the affected region of the mesoscale convection system at the next moment is calculated.

[0105] Influence region size prediction sub-unit: Based on the Lagrange interpolation method, the influence region size of the mesoscale convective system at time t+1 is predicted using the influence region of the mesoscale convective system at five times (t-4) to t.

[0106] In the aforementioned evolutionary feature extraction subunit,

[0107] The rectangular area generated by the four points of maximum and minimum latitude and longitude of the cold cloud system region at each moment of the mesoscale convective system is taken as the influence range of the mesoscale convective system at that moment.

[0108] The velocity vector of the mesoscale convective system at each moment is calculated using the following method:

[0109]

[0110] in:

[0111] Let be the velocity vector of the nth mesoscale convective system at time t;

[0112] dS is the displacement vector of the nth mesoscale convective system at time t, defined as the line connecting the geometric centers of the influence regions of the mesoscale convective system at time t and time t-1.

[0113] dt represents the temporal resolution of the brightness temperature data;

[0114] Through the above steps, the main evolutionary features of the mesoscale convective system are extracted;

[0115] In the sub-unit for calculating the geometric center location of the influence region, the moving velocity of the mesoscale convective system at time t is given the main weight, and the moving velocities before time t are weighted by the area of ​​the influence region. The moving velocity of the mesoscale convective system at the next moment is calculated using the following method:

[0116]

[0117] in:

[0118] Let be the velocity vector of the nth mesoscale convective system at time i, where t = 2, 3, 4, ..., t-1;

[0119] Based on the moving speed at time t+1 and the position of this mesoscale convection system at time t, the geometric center of the affected area at time t+1 is obtained.

[0120] In the aforementioned sub-unit for predicting the size of the influence region, the size of the influence region of the mesoscale convective system at time t+1 is calculated using the following method:

[0121]

[0122]

[0123] in, These are the t-th ... i The latitude and longitude lengths at any given time.

[0124] The early warning unit, based on the prediction results and the location of each transmission tower of the power line, issues early warnings for transmission towers located within the influence area of ​​the mesoscale convection system, marks the transmission towers located within the influence area, and outputs the early warning results.

[0125] The above embodiments are merely illustrative of the technical concept and features of the present invention, intended to enable those skilled in the art to understand and implement the invention, but do not imply any limitation on the scope of protection of the present invention. Any equivalent changes or modifications based on the technical spirit of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring and early warning of severe convective weather on power lines based on satellite remote sensing, characterized in that, include: Step 1: Use brightness and temperature data obtained from satellite remote sensing to identify and track the occurrence and evolution of mesoscale convective systems; Step 2: Based on the evolution process of the mesoscale convective system, extract the main evolutionary features of the mesoscale convective system, and based on its main evolutionary features, use weighted average and Lagrange interpolation methods to predict the influence area of ​​this mesoscale convective system at the next moment. Step 3: Based on the prediction results and the location of each transmission tower of the power line, issue early warnings for the transmission towers located within the influence area of ​​the mesoscale convection system, mark the transmission towers located within the influence area, and output the early warning results; Step 2 includes: Step 21: Extract the main evolutionary features of the mesoscale convective system based on its evolutionary process; Step 22: Based on the main evolutionary characteristics of the mesoscale convective system, predict the moving velocity of the mesoscale convective system at the next moment, and then calculate the geometric center position of the influence region of the mesoscale convective system at the next moment. Step 23: Based on the Lagrange interpolation method, using ( t -4)~ t Prediction of the influence area of ​​mesoscale convective systems at five time points t Size of the influence region of the mesoscale convective system at time +1; In step 21, The rectangular area generated by the four points of maximum and minimum latitude and longitude of the cold cloud system region at each moment of the mesoscale convective system is taken as the influence range of the mesoscale convective system at that moment. The velocity vector of the mesoscale convective system at each moment is calculated using the following method: ， in: For the first n The mesoscale convection system at the _ ... t The velocity vector at any given moment; For the first n The No. 1 mesoscale convection system in t The displacement vector at time t is defined as t Time and t The line connecting the geometric centers of the region of influence of the mesoscale convection system at time -1; The temporal resolution of the brightness temperature data; Through the above steps, the main evolutionary features of the mesoscale convective system are extracted; In step 22, the mesoscale convection system is used in t The speed of movement at any given moment has the most significant weight. t The velocity of the mesoscale convective system at the next moment is calculated by weighting the velocity of the affected region according to the area of ​​the area before the current moment: ， in: For the first n The mesoscale convection system at the _ ... i The velocity vector at time t, t =2,3,4,…, t -1; According to the above t The velocity at time +1, and the mesoscale convection system at... t The position at time, to obtain t The geometric center of the region affected at time +1; In step 23, the following method is used for calculation. t Size of the region of influence of the mesoscale convective system at time +1: ， ， in, , The first n Mesoscale convection system No. The latitude and longitude lengths at any given time.

2. The method for monitoring and early warning of severe convective weather on power lines based on satellite remote sensing according to claim 1, characterized in that, Step 1 includes: Step 11: Using brightness temperature, set cold cloud threshold, cold core threshold and area threshold, extract several cold cloud systems and obtain their location information; Step 12: Use the overlapping area method to track the evolution of each cold cloud system.

3. The method for monitoring and early warning of severe convective weather on power lines based on satellite remote sensing according to claim 2, characterized in that, In step 11, taking advantage of the low brightness temperature characteristic of mesoscale convective systems, based on the two-dimensional brightness temperature matrix at each moment, several continuous regions are first obtained using a 233K threshold and an 8-connectivity method. Then, regions with a minimum brightness temperature greater than 228K or an area less than 3000 km² are removed. 2 The region is selected to reduce the impact of stratiform clouds and small-to-medium-scale convective systems, and finally several regions that meet the conditions are obtained, which are called cold cloud systems (CCS). In step 12, the overlapping area method is used to track the movement and evolution of each CCS. That is, based on the overlapping area ratio of two consecutive time points, it is determined whether they are associated as the same cold cloud system. The calculation method is as follows: in, for and The ratio of overlapping areas; , They are respectively and The area; This indicates the calculation of the overlapping area of ​​two regions; Using a 15% overlap ratio threshold, that is, when At that time, and CCSs that are continuously and successfully tracked for more than 3 hours are defined as new mesoscale convective systems. After the above steps, the evolution process of the mesoscale convective system at each time point is obtained.

4. A power line severe convective weather monitoring and early warning system based on satellite remote sensing, characterized in that, include: The identification and tracking unit uses brightness and temperature data obtained from satellite remote sensing to identify and track the occurrence and evolution of mesoscale convective systems; The influence area prediction unit extracts the main evolution characteristics of the mesoscale convective system based on its evolution process, and uses weighted average and Lagrange interpolation methods to predict the influence area of ​​the mesoscale convective system at the next moment based on its main evolution characteristics. The early warning unit, based on the prediction results and the location of each transmission tower of the power line, issues early warnings for transmission towers located within the influence area of ​​the mesoscale convection system, marks the transmission towers located within the influence area, and outputs the early warning results; The influence area prediction unit includes: Evolutionary Feature Extraction Subunit: Based on the evolutionary process of the mesoscale convective system, extract the main evolutionary features of the mesoscale convective system; Sub-unit for calculating the geometric center of the affected region: Based on the main evolutionary characteristics of the mesoscale convective system, predict the moving velocity of the mesoscale convective system at the next moment, and then calculate the geometric center of the affected region of the mesoscale convective system at the next moment; Influence area size prediction sub-unit: Based on Lagrange interpolation method, using ( t -4)~ t Prediction of the influence area of ​​mesoscale convective systems at five time points t Size of the influence region of the mesoscale convective system at time +1; In the aforementioned evolutionary feature extraction subunit, The rectangular area generated by the four points of maximum and minimum latitude and longitude of the cold cloud system region at each moment of the mesoscale convective system is taken as the influence range of the mesoscale convective system at that moment. The velocity vector of the mesoscale convective system at each moment is calculated using the following method: ， in: For the first n The mesoscale convection system at the _ ... t The velocity vector at any given moment; For the first n The No. 1 mesoscale convection system in t The displacement vector at time t is defined as t Time and t The line connecting the geometric centers of the region of influence of the mesoscale convection system at time -1; The temporal resolution of the brightness temperature data; Through the above steps, the main evolutionary features of the mesoscale convective system are extracted; In the sub-unit for calculating the geometric center location of the influence region, the mesoscale convective system is used. t The speed of movement at any given moment has the most significant weight. t The velocity of the mesoscale convective system at the next moment is calculated by weighting the velocity of the affected region according to the area of ​​the area before the current moment: ， in: For the first n The mesoscale convection system at the _ ... i The velocity vector at time t, t =2,3,4,…, t -1; According to the above t The velocity at time +1, and the mesoscale convection system at... t The position at time, to obtain t The geometric center of the region affected at time +1; In the aforementioned sub-unit for predicting the size of the affected area, the following method is used to calculate... t Size of the region of influence of the mesoscale convective system at time +1: ， ， in, , The first n Mesoscale convection system No. The latitude and longitude lengths at any given time.

5. The power line severe convective weather monitoring and early warning system based on satellite remote sensing according to claim 4, characterized in that, The identification and tracking unit includes: Cold cloud system extraction sub-unit: Using brightness temperature, set cold cloud threshold, cold core threshold and area threshold, extract several cold cloud systems and obtain their location information; Cold cloud system tracking sub-unit: The overlapping area method is used to track the evolution of each cold cloud system.

6. The power line severe convective weather monitoring and early warning system based on satellite remote sensing according to claim 4, characterized in that, In the cold cloud system extraction subunit, taking advantage of the low brightness temperature characteristic of mesoscale convective systems, based on the two-dimensional brightness-temperature matrix at each moment, several continuous regions are first obtained using a 233K threshold and an 8-connectivity method. Then, regions with a minimum brightness temperature greater than 228K or an area less than 3000 km² are removed. 2 The region is selected to reduce the impact of stratiform clouds and small-to-medium-scale convective systems, and finally several regions that meet the conditions are obtained, which are called cold cloud systems (CCS). In the aforementioned cold cloud system tracking subunit, the overlapping area method is used to track the movement and evolution of each CCS. Specifically, based on the overlap area ratio between two consecutive time points, it is determined whether they are associated as the same cold cloud system. The calculation method is as follows: in, for and The ratio of overlapping areas; , They are respectively and The area; This indicates the calculation of the overlapping area of ​​two regions; Using a 15% overlap ratio threshold, that is, when At that time, and CCSs that are continuously and successfully tracked for more than 3 hours are defined as new mesoscale convective systems. After the above steps, the evolution process of the mesoscale convective system at each time point is obtained.

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