CN118212320A - Method, device, equipment and storage medium for drawing strong convection strong wind distribution diagram of power grid - Google Patents

Abstract

The present invention relates to the technical field of power transmission line disaster distribution map drawing, and specifically provides a method, device, equipment and storage medium for drawing a power grid strong convection and strong wind distribution map, including: drawing a maximum wind zone map of the area to be analyzed based on historical wind speed observation record data of the area to be analyzed; drawing a strong convection and strong wind distribution map of the area to be analyzed based on historical strong convection and strong wind information of the area to be analyzed; superimposing the maximum wind zone map of the area to be analyzed and the strong convection and strong wind distribution map to obtain an initial strong convection and strong wind distribution map of the area to be analyzed; adding a preset legend corresponding to the analysis object to the initial strong convection and strong wind distribution map of the area to be analyzed according to the coordinate information of the analysis object, to obtain a strong convection and strong wind distribution map of the area to be analyzed. The strong convection and strong wind distribution map of the present invention can describe the gust situation, and is the basis for guiding power transmission lines to prevent strong convection wind-induced disasters, especially wind deviation disasters.

Description

Method, device, equipment and storage medium for drawing distribution map of severe convection and strong wind in power grid

Technical Field

The present invention relates to the technical field of power transmission line disaster distribution map drawing, and in particular to a method, device, equipment and storage medium for drawing a severe convection gale distribution map of a power grid.

Background technique

In recent years, severe convective weather has shown an increasing trend. Among them, strong convective winds such as tornadoes and thunderstorms often cause transmission lines to trip due to wind deflection, and even tower collapse, line breakage and other malicious faults, which seriously threaten the safety of the power grid. Disaster distribution maps are an important basis for guiding differentiated design and operation and maintenance of lines.

At present, there is still no method for drawing relevant distribution maps for severe convective wind disasters.

Summary of the invention

In order to overcome the above-mentioned defects, the present invention proposes a method, device, equipment and storage medium for drawing a distribution map of severe convection and strong winds in a power grid.

In a first aspect, a method for drawing a distribution map of severe convection and strong winds in a power grid is provided, the method comprising:

Draw the maximum wind area map of the area to be analyzed based on the historical wind speed observation record data of the area to be analyzed;

Draw a distribution map of strong convective winds in the area to be analyzed based on the strong convective wind information of the area to be analyzed in previous years;

Superimposing the maximum wind zone map and the strong convective wind distribution map of the area to be analyzed to obtain an initial strong convective wind distribution map of the area to be analyzed;

According to the coordinate information of the analysis object, a preset legend corresponding to the analysis object is added to the initial strong convection and strong wind distribution map of the area to be analyzed to obtain the strong convection and strong wind distribution map of the area to be analyzed.

Preferably, the drawing of the maximum wind zone map of the area to be analyzed based on the historical wind speed observation record data of the area to be analyzed includes:

The maximum wind speed within a preset time period in the historical wind speed observation record data of the area to be analyzed is taken as a sample to obtain a historical maximum wind speed sample sequence of the area to be analyzed;

The maximum wind speed sample sequence of the area to be analyzed over the years is equivalent to the instantaneous maximum wind speed value 10m above the ground;

The Gumbel distribution is used to fit the maximum wind speed sample sequence of the area to be analyzed, and the maximum wind speed with a return period of 30 years, 50 years, and 100 years in each area of the measurement area is obtained;

Obtaining the grid-format DEM data of the area to be analyzed;

Selecting DEM elevation data of the raster-format DEM data of the area to be analyzed;

Taking the DEM elevation data as the collaborative interpolation object, the collaborative Kriging interpolation method is used to correct the maximum wind speeds once in 30 years, 50 years, and 100 years to obtain a raster file;

The wind zones of the grid file are graded, and the graded grid files are converted into contour lines to obtain a maximum wind zone map of the area to be analyzed.

Furthermore, the classifying of wind zones in the grid file includes:

The wind speed is divided into the following levels: <23.5m/s, 23.5m/s, 25m/s, 27m/s, 29m/s, 31m/s, 33m/s, 35m/s, 37m/s, 39m/s, 41m/s, 43m/s, 45m/s, 50m/s. Wind speed >50m/s is divided into levels of 2m/s, and wind speed <23.5m/s is divided into one level.

Preferably, the step of drawing a distribution map of strong convective winds in the area to be analyzed based on the strong convective wind information of the area to be analyzed over the years includes:

Based on the historical severe convection wind information of the area to be analyzed, determine the annualized frequency of severe convection within the grid in the grid image corresponding to the area to be analyzed;

The Gaussian kernel smoothing method is used to smooth the annualized frequency of severe convection in the grid, and the frequency is divided into intervals of 1 time per year to obtain the distribution map of severe convective winds in the area to be analyzed.

Furthermore, the severe convective winds include at least one of tornadoes, thunderstorms, and downbursts.

Furthermore, the severe convective gale information includes: the time and location information of the occurrence point of the severe convective gale.

Furthermore, the annualized frequency of severe convection within a grid in the grid image corresponding to the area to be analyzed is the number of historical severe convection points within the grid divided by the recording time, where the unit of the recording time is year.

Preferably, the analysis objects include: transmission line towers, substations, power plants and administrative division boundaries.

In a second aspect, a device for drawing a distribution map of strong convection and strong winds in a power grid is provided, and the device for drawing a distribution map of strong convection and strong winds in a power grid comprises:

The first drawing module is used to draw a maximum wind zone map of the area to be analyzed based on the historical wind speed observation record data of the area to be analyzed;

The second drawing module is used to draw a distribution map of strong convective winds in the area to be analyzed based on the strong convective wind information of the area to be analyzed in previous years;

The third drawing module is used to superimpose the maximum wind area map and the strong convective wind distribution map of the area to be analyzed to obtain an initial strong convective wind distribution map of the area to be analyzed;

The fourth drawing module is used to add a preset legend corresponding to the analysis object to the initial strong convection and strong wind distribution map of the area to be analyzed according to the coordinate information of the analysis object, so as to obtain the strong convection and strong wind distribution map of the area to be analyzed.

Preferably, the first drawing module is specifically used for:

The maximum wind speed within a preset time period in the historical wind speed observation record data of the area to be analyzed is taken as a sample to obtain a historical maximum wind speed sample sequence of the area to be analyzed;

The maximum wind speed sample sequence of the area to be analyzed over the years is equivalent to the instantaneous maximum wind speed value 10m above the ground;

The Gumbel distribution is used to fit the maximum wind speed sample sequence of the area to be analyzed, and the maximum wind speed with a return period of 30 years, 50 years, and 100 years in each area of the measurement area is obtained;

Obtaining the grid-format DEM data of the area to be analyzed;

Selecting DEM elevation data of the raster-format DEM data of the area to be analyzed;

Taking the DEM elevation data as the collaborative interpolation object, the collaborative Kriging interpolation method is used to correct the maximum wind speeds once in 30 years, 50 years, and 100 years to obtain a raster file;

The wind zones of the grid file are graded, and the graded grid files are converted into contour lines to obtain a maximum wind zone map of the area to be analyzed.

Furthermore, the classifying of wind zones in the grid file includes:

The wind speed is divided into the following levels: <23.5m/s, 23.5m/s, 25m/s, 27m/s, 29m/s, 31m/s, 33m/s, 35m/s, 37m/s, 39m/s, 41m/s, 43m/s, 45m/s, 50m/s. Wind speed >50m/s is divided into levels of 2m/s, and wind speed <23.5m/s is divided into one level.

Preferably, the second drawing module is specifically used for:

Based on the historical severe convection wind information of the area to be analyzed, determine the annualized frequency of severe convection within the grid in the grid image corresponding to the area to be analyzed;

The Gaussian kernel smoothing device is used to smooth the annualized frequency of severe convection in the grid and divide it into intervals of 1 time/year to obtain the distribution map of severe convective winds in the area to be analyzed.

Furthermore, the severe convective winds include at least one of tornadoes, thunderstorms, and downbursts.

Furthermore, the severe convective gale information includes: the time and location information of the occurrence point of the severe convective gale.

Furthermore, the annualized frequency of severe convection within a grid in the grid image corresponding to the area to be analyzed is the number of historical severe convection points within the grid divided by the recording time, where the unit of the recording time is year.

Preferably, the analysis objects include: transmission line towers, substations, power plants and administrative division boundaries.

In a third aspect, a computer device is provided, comprising: one or more processors;

The processor is configured to execute one or more programs;

When the one or more programs are executed by the one or more processors, the method for drawing a distribution map of severe convection and strong winds in a power grid is implemented.

In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed, the method for drawing a distribution map of severe convection and strong winds in a power grid is implemented.

The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

The present invention relates to the technical field of power transmission line disaster distribution map drawing, and specifically provides a method, device, equipment and storage medium for drawing a power grid strong convective wind distribution map, including: drawing a maximum wind zone map of the area to be analyzed based on the historical wind speed observation record data of the area to be analyzed; drawing a strong convective wind distribution map of the area to be analyzed based on the historical strong convective wind information of the area to be analyzed; superimposing the maximum wind zone map of the area to be analyzed and the strong convective wind distribution map to obtain an initial strong convective wind distribution map of the area to be analyzed; adding a preset legend corresponding to the analysis object to the initial strong convective wind distribution map of the area to be analyzed according to the coordinate information of the analysis object, to obtain a strong convective wind distribution map of the area to be analyzed. The strong convective wind distribution map of the present invention can describe the gust situation, and is the basis for guiding power transmission lines to prevent strong convective wind-induced disasters, especially wind yaw disasters; at the same time, it describes the historical frequency distribution of historical strong convective disasters in the region, which can provide support for studying the distribution characteristics of strong convective disasters and predicting strong convective disasters.

BRIEF DESCRIPTION OF THE DRAWINGS

1 is a schematic flow chart of the main steps of a method for drawing a distribution map of severe convection and strong winds in a power grid according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of the main structure of a device for drawing a distribution map of severe convection and strong winds in a power grid according to an embodiment of the present invention.

Detailed ways

The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

As disclosed in the background technology, severe convective weather has increased in recent years. Tornadoes, thunderstorms and other strong convective winds often cause transmission lines to trip due to wind deflection, and even tower collapse, line breakage and other malignant faults, which seriously threaten the safety of the power grid. Disaster distribution maps are an important basis for guiding differentiated design and operation and maintenance of power lines.

At present, there is still no method for drawing relevant distribution maps for severe convective wind disasters.

In order to improve the above-mentioned problems, the present invention relates to the technical field of drawing distribution maps of power transmission line disasters, and specifically provides a method, device, equipment and storage medium for drawing distribution maps of strong convective strong winds in power grids, including: drawing a maximum wind zone map of the area to be analyzed based on the wind speed observation record data of the area to be analyzed over the years; drawing a strong convective strong wind distribution map of the area to be analyzed based on the strong convective strong wind information of the area to be analyzed over the years; superimposing the maximum wind zone map of the area to be analyzed and the strong convective strong wind distribution map to obtain an initial strong convective strong wind distribution map of the area to be analyzed; adding a preset legend corresponding to the analysis object to the initial strong convective strong wind distribution map of the area to be analyzed according to the coordinate information of the analysis object, to obtain a strong convective strong wind distribution map of the area to be analyzed. The strong convective strong wind distribution map of the present invention can describe the gust situation, and is the basis for guiding power transmission lines to prevent strong convective wind-induced disasters, especially wind deviation disasters; at the same time, it describes the historical frequency distribution of historical strong convective disasters in the region, which can provide support for studying the distribution characteristics of strong convective disasters and predicting strong convective disasters.

The above scheme is described in detail below.

Example 1

Refer to Figure 1, which is a schematic flow chart of the main steps of a method for drawing a distribution map of strong convection and strong winds in a power grid according to an embodiment of the present invention. As shown in Figure 1, the method for drawing a distribution map of strong convection and strong winds in a power grid according to an embodiment of the present invention mainly includes the following steps:

Step S101: drawing a maximum wind zone map of the area to be analyzed based on historical wind speed observation record data of the area to be analyzed;

Step S102: drawing a distribution map of strong convective winds in the area to be analyzed based on the strong convective wind information of the area to be analyzed in previous years;

Step S103: superimposing the maximum wind zone map and the strong convective wind distribution map of the area to be analyzed to obtain an initial strong convective wind distribution map of the area to be analyzed;

Step S104: adding a preset legend corresponding to the analysis object to the initial strong convective wind distribution map of the area to be analyzed according to the coordinate information of the analysis object, so as to obtain the strong convective wind distribution map of the area to be analyzed.

In this embodiment, the drawing of the maximum wind zone map of the area to be analyzed based on the historical wind speed observation record data of the area to be analyzed includes:

The maximum wind speed within a preset time period in the historical wind speed observation record data of the area to be analyzed is taken as a sample to obtain a historical maximum wind speed sample sequence of the area to be analyzed; in this embodiment, the preset time period may be 3s;

The maximum wind speed sample sequence of the area to be analyzed over the years is equivalent to the instantaneous maximum wind speed value 10m above the ground;

The Gumbel distribution is used to fit the maximum wind speed sample sequence of the area to be analyzed, and the maximum wind speed with a return period of 30 years, 50 years, and 100 years in each area of the measurement area is obtained;

Obtaining the grid-format DEM data of the area to be analyzed;

Selecting DEM elevation data of the raster-format DEM data of the area to be analyzed;

Taking the DEM elevation data as the collaborative interpolation object, the collaborative Kriging interpolation method is used to correct the maximum wind speeds once in 30 years, 50 years, and 100 years to obtain a raster file;

The wind zones of the grid file are graded, and the graded grid files are converted into contour lines to obtain a maximum wind zone map of the area to be analyzed.

In one embodiment, in order to improve the accuracy and precision of the wind speed frequency value results of each meteorological station, the original data of the wind speed observation record data of the meteorological station should be reviewed before the wind speed correction is performed. The processing of the meteorological station wind observation data mainly includes original data review, wind speed height correction, and time conversion. First, the reliability, representativeness, and consistency of the data should be reviewed, and the standard "Statistical Method of Ground Standard Climate Values" GB/T34412-2017 should be referred to; on this basis, the height correction is performed, and the actual height of the anemometer at the meteorological station is converted to 10m high; if the year of the gust sample sequence of the meteorological station is insufficient, it is necessary to perform time conversion, and convert the maximum wind speed to the maximum wind speed, that is, use the maximum wind speed divided by the maximum wind speed of the day to obtain the daily gust coefficient, and analyze the stability of the coefficient. The original maximum wind speed observation data of each station is merged by multiplying the daily maximum wind speed by the average gust coefficient of the interval level.

In a specific implementation, co-kriging interpolation is used for correction, and the method includes:

1) Obtain the complete grid-based DEM of the survey area:

Import the existing 30m resolution .GIFf format standardized DEM file, which should cover the entire survey area, and use the clipping tool to obtain the DEM data within the survey area;

2) Select DEM elevation data as the collaborative interpolation object and use collaborative kriging interpolation for correction:

Since wind speed is closely related to altitude, DEM elevation data is used as the collaborative interpolation object to interpolate wind speed information;

3) Convert the correction result into a grid file and replace the previously drawn grid file under the same influence range. After completion, re-divide the wind zone.

4) Use ArcGIS to convert the drawn raster file into contour lines and mark the wind speed value of each contour line.

In one embodiment, the classifying the wind zones of the grid file comprises:

The wind speed is divided into the following levels: <23.5m/s, 23.5m/s, 25m/s, 27m/s, 29m/s, 31m/s, 33m/s, 35m/s, 37m/s, 39m/s, 41m/s, 43m/s, 45m/s, 50m/s. Wind speed >50m/s is divided into levels of 2m/s, and wind speed <23.5m/s is divided into one level.

In this embodiment, the method of drawing a distribution map of strong convective winds in the area to be analyzed based on the strong convective wind information of the area to be analyzed in previous years includes:

Based on the historical severe convection wind information of the area to be analyzed, determine the annualized frequency of severe convection within the grid in the grid image corresponding to the area to be analyzed;

The Gaussian kernel smoothing method is used to smooth the annualized frequency of severe convection in the grid, and the frequency is divided into intervals of 1 time per year to obtain the distribution map of severe convective winds in the area to be analyzed.

The severe convective winds include at least one of tornadoes, thunderstorms and downbursts.

The severe convective gale information includes: the time and location information of the occurrence point of the severe convective gale.

In one embodiment, the annualized frequency of severe convection within a grid in the grid image corresponding to the area to be analyzed is the number of historical severe convection points within the grid divided by the recording time, where the unit of the recording time is year.

In this embodiment, the analysis objects include: transmission line towers, substations, power plants and administrative division boundaries.

In a specific implementation, vectorization is performed on the data of transmission line towers, substations, power plants and administrative divisions: first, the rotation coordinates of the transmission line towers, substations and power plants in the Excel table data are vectorized one by one according to the WGS84 coordinate system by adding X and Y. And the point set is converted to line according to the tower number field of the tower data to obtain the transmission line vectorization layer, in which the transmission lines, substations and power plants are classified according to voltage levels, attributes, etc., and are represented by point layers; transmission lines, administrative division lines, etc. are classified according to voltage levels and administrative levels, and are represented by line layers; water systems are represented by surface files. After all data are processed, projection changes are processed according to the WGS84 coordinate system. And the final coordinate system is defined as WGS84 and stored in a standard Geodatabase.

Finally, the coordinate systems of all data are unified, and the map is drawn according to the legend settings to complete the drawing of the severe convection and strong wind distribution map:

When the coordinate information related to the basic data is inconsistent, it is necessary to unify the coordinate information in the data, and dynamically project the input data based on the WGS84 coordinate system and continuously correct the small area. Display and analyze the same interface in different coordinate systems; set the layers and legends according to the specifications, and use 1:100,000 scale and WGS84 coordinate system for drawing.

Example 2

Based on the same inventive concept, the present invention also provides a device for drawing a distribution map of strong convection and strong winds in a power grid, as shown in FIG2 , the device for drawing a distribution map of strong convection and strong winds in a power grid comprises:

The first drawing module is used to draw a maximum wind zone map of the area to be analyzed based on the historical wind speed observation record data of the area to be analyzed;

The second drawing module is used to draw a distribution map of strong convective winds in the area to be analyzed based on the strong convective wind information of the area to be analyzed in previous years;

The third drawing module is used to superimpose the maximum wind area map and the strong convective wind distribution map of the area to be analyzed to obtain an initial strong convective wind distribution map of the area to be analyzed;

The fourth drawing module is used to add a preset legend corresponding to the analysis object to the initial strong convection and strong wind distribution map of the area to be analyzed according to the coordinate information of the analysis object, so as to obtain the strong convection and strong wind distribution map of the area to be analyzed.

Preferably, the first drawing module is specifically used for:

The maximum wind speed within a preset time period in the historical wind speed observation record data of the area to be analyzed is taken as a sample to obtain a historical maximum wind speed sample sequence of the area to be analyzed;

The maximum wind speed sample sequence of the area to be analyzed over the years is equivalent to the instantaneous maximum wind speed value 10m above the ground;

The Gumbel distribution is used to fit the maximum wind speed sample sequence of the area to be analyzed, and the maximum wind speed with a return period of 30 years, 50 years, and 100 years in each area of the measurement area is obtained;

Obtaining the grid-format DEM data of the area to be analyzed;

Selecting DEM elevation data of the raster-format DEM data of the area to be analyzed;

Taking the DEM elevation data as the collaborative interpolation object, the collaborative Kriging interpolation method is used to correct the maximum wind speeds once in 30 years, 50 years, and 100 years to obtain a raster file;

The wind zones of the grid file are graded, and the graded grid files are converted into contour lines to obtain a maximum wind zone map of the area to be analyzed.

Furthermore, the step of classifying the wind zones of the grid file includes:

The wind speed is divided into the following levels: <23.5m/s, 23.5m/s, 25m/s, 27m/s, 29m/s, 31m/s, 33m/s, 35m/s, 37m/s, 39m/s, 41m/s, 43m/s, 45m/s, 50m/s. Wind speed >50m/s is divided into levels of 2m/s, and wind speed <23.5m/s is divided into one level.

Preferably, the second drawing module is specifically used for:

Based on the historical severe convection wind information of the area to be analyzed, determine the annualized frequency of severe convection within the grid in the grid image corresponding to the area to be analyzed;

The Gaussian kernel smoothing device is used to smooth the annualized frequency of severe convection in the grid and divide it into intervals of 1 time/year to obtain the distribution map of severe convective winds in the area to be analyzed.

Furthermore, the severe convective winds include at least one of tornadoes, thunderstorms, and downbursts.

Furthermore, the severe convective gale information includes: the time and location information of the occurrence point of the severe convective gale.

Furthermore, the annualized frequency of severe convection within a grid in the grid image corresponding to the area to be analyzed is the number of historical severe convection points within the grid divided by the recording time, where the unit of the recording time is year.

Preferably, the analysis objects include: transmission line towers, substations, power plants and administrative division boundaries.

Example 3

Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, wherein the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a method for drawing a distribution map of severe convection and strong winds in a power grid in the above embodiment.

Example 4

Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a method for drawing a distribution map of severe convection and strong winds in a power grid in the above embodiment.

Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

The present invention is described with reference to the flowchart and/or block diagram of the method, device (system), and computer program product according to the embodiment of the present invention. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the process and/or box in the flowchart and/or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims (18)

1. A method for drawing a distribution map of severe convection and strong winds in a power grid, characterized in that the method comprises: Draw the maximum wind area map of the area to be analyzed based on the historical wind speed observation record data of the area to be analyzed; Draw a distribution map of strong convective winds in the area to be analyzed based on the strong convective wind information of the area to be analyzed in previous years; Superimposing the maximum wind zone map and the strong convective wind distribution map of the area to be analyzed to obtain an initial strong convective wind distribution map of the area to be analyzed; According to the coordinate information of the analysis object, a preset legend corresponding to the analysis object is added to the initial strong convection and strong wind distribution map of the area to be analyzed to obtain the strong convection and strong wind distribution map of the area to be analyzed. 2. The method according to claim 1, characterized in that the step of drawing a maximum wind zone map of the area to be analyzed based on the historical wind speed observation record data of the area to be analyzed comprises: The maximum wind speed within a preset time period in the historical wind speed observation record data of the area to be analyzed is taken as a sample to obtain a historical maximum wind speed sample sequence of the area to be analyzed; The maximum wind speed sample sequence of the area to be analyzed over the years is equivalent to the instantaneous maximum wind speed value 10m above the ground; The Gumbel distribution is used to fit the maximum wind speed sample sequence of the area to be analyzed, and the maximum wind speed with a return period of 30 years, 50 years, and 100 years in each area of the measurement area is obtained; Obtaining the grid-format DEM data of the area to be analyzed; Selecting DEM elevation data of the raster-format DEM data of the area to be analyzed; Taking the DEM elevation data as the collaborative interpolation object, the collaborative Kriging interpolation method is used to correct the maximum wind speeds once in 30 years, 50 years, and 100 years to obtain a raster file; The wind zones of the grid file are graded, and the graded grid files are converted into contour lines to obtain a maximum wind zone map of the area to be analyzed. 3. The method according to claim 2, wherein the step of classifying the wind zones in the grid file comprises: The wind speed is divided into the following levels: <23.5m/s, 23.5m/s, 25m/s, 27m/s, 29m/s, 31m/s, 33m/s, 35m/s, 37m/s, 39m/s, 41m/s, 43m/s, 45m/s, 50m/s. Wind speed >50m/s is divided into levels of 2m/s, and wind speed <23.5m/s is divided into one level. 4. The method according to claim 1, characterized in that the step of drawing a distribution map of strong convective winds in the area to be analyzed based on the strong convective wind information of the area to be analyzed over the years comprises: Based on the historical severe convection wind information of the area to be analyzed, determine the annualized frequency of severe convection within the grid in the grid image corresponding to the area to be analyzed; The Gaussian kernel smoothing method is used to smooth the annualized frequency of severe convection in the grid, and the frequency is divided into intervals of 1 time per year to obtain the distribution map of severe convective winds in the area to be analyzed. 5. The method according to claim 4, characterized in that the severe convective wind comprises at least one of a tornado, a thunderstorm, and a downburst. 6. The method according to claim 5, characterized in that the severe convective wind information includes: the time and location information of the occurrence point of the severe convective wind. 7. The method according to claim 6 is characterized in that the annualized frequency of severe convection within the grid in the grid image corresponding to the area to be analyzed is the number of historical severe convection points within the grid divided by the recording time, and the unit of the recording time is year. 8. The method according to claim 1, wherein the analysis objects include: transmission line towers, substations, power plants and administrative division boundaries. 9. A device based on a method for drawing a distribution map of severe convection and strong winds in a power grid, characterized in that the device comprises: The first drawing module is used to draw a maximum wind zone map of the area to be analyzed based on the historical wind speed observation record data of the area to be analyzed; The second drawing module is used to draw a distribution map of strong convective winds in the area to be analyzed based on the strong convective wind information of the area to be analyzed in previous years; The third drawing module is used to superimpose the maximum wind area map and the strong convective wind distribution map of the area to be analyzed to obtain an initial strong convective wind distribution map of the area to be analyzed; The fourth drawing module is used to add a preset legend corresponding to the analysis object to the initial strong convection and strong wind distribution map of the area to be analyzed according to the coordinate information of the analysis object, so as to obtain the strong convection and strong wind distribution map of the area to be analyzed. 10. The device according to claim 9, wherein the first drawing module is specifically used for: The maximum wind speed within a preset time period in the historical wind speed observation record data of the area to be analyzed is taken as a sample to obtain a historical maximum wind speed sample sequence of the area to be analyzed; The maximum wind speed sample sequence of the area to be analyzed over the years is equivalent to the instantaneous maximum wind speed value 10m above the ground; The Gumbel distribution is used to fit the maximum wind speed sample sequence of the area to be analyzed, and the maximum wind speed with a return period of 30 years, 50 years, and 100 years in each area of the measurement area is obtained; Obtaining the grid-format DEM data of the area to be analyzed; Selecting DEM elevation data of the raster-format DEM data of the area to be analyzed; Taking the DEM elevation data as the collaborative interpolation object, the collaborative Kriging interpolation method is used to correct the maximum wind speeds once in 30 years, 50 years, and 100 years to obtain a raster file; The wind zones of the grid file are graded, and the graded grid files are converted into contour lines to obtain a maximum wind zone map of the area to be analyzed. 11. The device according to claim 10, wherein said classifying the wind zones of said grid file comprises: The wind speed is divided into the following levels: <23.5m/s, 23.5m/s, 25m/s, 27m/s, 29m/s, 31m/s, 33m/s, 35m/s, 37m/s, 39m/s, 41m/s, 43m/s, 45m/s, 50m/s. Wind speed >50m/s is divided into levels of 2m/s, and wind speed <23.5m/s is divided into one level. 12. The device according to claim 9, wherein the second drawing module is specifically used for: Based on the historical severe convection wind information of the area to be analyzed, determine the annualized frequency of severe convection within the grid in the grid image corresponding to the area to be analyzed; The Gaussian kernel smoothing device is used to smooth the annualized frequency of severe convection in the grid and divide it into intervals of 1 time/year to obtain the distribution map of severe convective winds in the area to be analyzed. 13. The device according to claim 12, characterized in that the severe convective wind comprises at least one of a tornado, a thunderstorm, and a downburst. 14. The device according to claim 13, characterized in that the severe convective wind information includes: the time and location information of the occurrence point of the severe convective wind. 15. The device as claimed in claim 14 is characterized in that the annualized frequency of severe convection in the grid in the grid image corresponding to the area to be analyzed is the number of historical severe convection points in the grid divided by the recording time, and the unit of the recording time is year. 16. The device according to claim 9, characterized in that the analysis objects include: transmission line towers, substations, power plants and administrative division boundaries. 17. A computer device, comprising: one or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, the method for drawing a severe convection and strong wind distribution map of a power grid as described in any one of claims 1 to 8 is implemented. 18. A computer-readable storage medium, characterized in that a computer program is stored thereon, and when the computer program is executed, the method for drawing a distribution map of severe convection and strong winds in a power grid according to any one of claims 1 to 8 is implemented.