High-temperature heat wave pre-identification method based on temperature and humidity composite index

Abstract

The invention discloses a high-temperature heat wave pre-identification method based on a temperature and humidity composite index, and the method comprises the following steps: (1), downloading and extracting daily maximum temperature and daily relative humidity data in summer every year through an open source website, calculating an average value of wet bulb temperature, and setting a threshold value according to different degrees of influences on a human body; (2) screening lattice points exceeding a threshold value, performing zoning analysis on areas exceeding the threshold value, and screening spatial regional character lattice points; (3) judging the continuity of the temperature and humidity composite high-temperature heat wave event; (4) calculating the comprehensive indexes of the events, and evaluating the early warning state of the temperature and humidity compound high-temperature heat wave events according to the sequence of the comprehensive indexes of the events over the years; according to the invention, the influence of high-temperature heat waves is evaluated efficiently, accurately and comprehensively.

Description

Technical Field

[0001] The present invention relates to the field of weather warning technology, and in particular to a high-temperature heat wave pre-identification method based on a temperature-humidity composite index. Background Art

[0002] Under high humidity conditions, the human body's heat dissipation mechanism is limited, making it difficult to cope with persistent heat waves with a combination of temperature and humidity, which has serious impacts on human health and the environment. Traditional meteorological high temperature warning systems typically rely on a single temperature threshold to identify heat wave events. For example, when the maximum temperature in a certain area exceeds a certain critical temperature (such as 35°C) for several consecutive days, a high temperature warning is triggered. However, this method does not consider the impact of humidity on human heat dissipation and thermal induction, and it is difficult to fully reflect the potential threat of actual heat wave events to human health. At the same time, traditional warnings often rely on single-site data or single-day data, which cannot accurately identify the spatial distribution characteristics and temporal continuity of heat waves. This leads to limited warning coverage and low accuracy, making it difficult to effectively respond to regional and continuous high temperature heat wave events. Summary of the Invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a high-temperature heat wave pre-identification method based on the temperature-humidity composite index. By identifying the spatial continuity of high-temperature heat wave events and the correlation with human perception, the problem that the existing technology is difficult to effectively deal with high-temperature and high-humidity composite heat waves.

[0004] Technical solution: The present invention provides a method for pre-identifying a high-temperature heat wave based on a temperature-humidity composite index, comprising the following steps: (1) Download and extract the daily maximum temperature and daily relative humidity data of each summer from an open source website, calculate the average wet-bulb temperature, and set thresholds based on the degree of impact on the human body; (2) Screening grid points that exceed the threshold, dividing the area exceeding the threshold into zones for analysis, and screening spatial regional character points; (3) Determine the continuity of temperature-humidity composite high-temperature heat wave events; (4) Calculate the comprehensive index of the event, rank the events according to the comprehensive index of occurrence over the years, and evaluate the warning status of the temperature and humidity combined high temperature heat wave event.

[0005] Furthermore, in step (1), the formula for calculating the average wet-bulb temperature is as follows: Where TW is the wet-bulb temperature in degrees Celsius; m is the daily maximum temperature in degrees Celsius; r is the daily relative humidity in percentage; atan is the inverse tangent function; and the threshold value set according to the degree of impact on the human body is 30°C-40°C.

[0006] Furthermore, step (2) is specifically as follows: the connectivity between the grid points is calculated based on the machine learning algorithm. If the edges or corners of the grid points touch each other, they are connected; then the area of ​​each region is determined.

[0007] Furthermore, in step (2), the area of ​​each region is determined as follows: the shortest distance between all connected regions on the same day is calculated. If the distance is less than 250 kilometers, the regions are merged. Then, the area of ​​each merged region is calculated. Regions with an area greater than 6,000 square kilometers are selected and renumbered.

[0008] Furthermore, step (3) includes the following steps: (31) Get the connected area number of the first day, set the area without the number to 0, and then multiply it with the two-dimensional matrix of the second day. The non-zero area obtained is the overlapping area, and the value is assigned to 1; (32) By multiplying the area assigned a value of 1 by the weighted grid area, the area of ​​the overlapping region that meets the area threshold condition can be calculated. The formula is as follows: ; Among them, c means saving the overlapping matrices of two consecutive days; n1 means keeping only the matrix of the first day number; n2 means the matrix of the adjacent day; (33) All non-zero values ​​in the overlapping matrices of two adjacent days are assigned to 1, and then multiplied by the matrix of the adjacent day to obtain the area number overlapping with the high temperature and high humidity day; according to the number index, the connected areas overlapping with the high temperature and high humidity day are extracted to obtain the overall spatial distribution, and the formula is as follows: ; Where l represents the matrix that stores the overlapping area numbers; c1 represents the overlapping binary matrix of two adjacent days; n2 represents the matrix of one adjacent day; (34) Compare the calculated overlapping area with 50% of the area of ​​the smaller area on both days. If the overlapping area is greater than or equal to 50% of the area of ​​the smaller area, the event is considered to be continuous over the two days. The formula is as follows: ; Among them, R overlay Indicates the overlapping area ratio, in percentage; S overlay Indicates the overlapping area in square meters (m) 2 ; S1 and S2 represent the affected area of ​​the current day and the adjacent day, in square meters 2 .

[0009] Furthermore, step (4) is as follows: first, record all the area numbers involved in each event, obtain the union of the areas, and calculate the boundary range of each event; second, calculate the difference between the end date and the start date of each event, that is, the duration of the event; then calculate the difference between each grid point and the threshold value every day during the event, and solve the intensity of the event; finally, calculate the comprehensive index of the event; according to the ranking of the comprehensive index of events over the years, evaluate the warning status of the temperature and humidity composite high temperature heat wave event; among them, the formula for calculating the boundary range is as follows: ; Among them, Y s The boundary range of a single event, in square meters 2 ;K (m,n) is the union of connected regions involved in a single event; Q (m,n) is the area corresponding to the grid point, in square meters m 2 ; The formula for calculating strength is as follows: ; Where X is the intensity of a single event, in degrees Celsius; e is the start date of the event; l is the end date of the event; TW (m,n) is the wet bulb temperature in degrees Celsius (℃); R is the threshold of the event; The formula for calculating the comprehensive impact index is as follows: ; Among them, W is the comprehensive impact index of a single event; U is the duration of a single event, in days; X is the intensity of a single event, in degrees Celsius; Y is the boundary range of a single event, in square meters. 2 ; The formula for calculating the total number of occurrence days is as follows: ; Among them, Q (m,n) is the total number of occurrence days, in days; U (m,n) The duration is in days.

[0010] The high temperature heat wave pre-identification system based on the temperature and humidity composite index described in the present invention includes: Acquisition module: used to download and extract the daily maximum temperature and daily relative humidity data of each summer from an open source website, calculate the average wet-bulb temperature, and set thresholds based on the degree of impact on the human body; Screening module: used to screen grid points that exceed the threshold, divide the area that exceeds the threshold into zones for analysis, and screen spatial regional character points; Judgment module: used to judge the continuity of events; Calculation module: used to calculate the comprehensive indicators of events, sort the events according to the comprehensive index of occurrence over the years, and evaluate the warning status of temperature and humidity combined high temperature heat wave events.

[0011] An electronic device described in the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, it implements any one of the methods for pre-identifying high-temperature heat waves based on a temperature-humidity composite index.

[0012] The present invention provides a storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any one of the methods for pre-identifying a high-temperature heat wave based on a temperature-humidity composite index.

[0013] Beneficial effects: Compared with the existing technology, the present invention has the following significant advantages: first, it considers the impact of humidity on human heat dissipation and thermal induction, is close to the human body's actual perception of the thermal environment, and more comprehensively reflects the potential threat of actual heat wave events to human health; second, it uses the overlapping area ratio method to judge the cross-day continuity of the event, and adds multi-dimensional quantitative indicators to dynamically visualize the early warning system, thereby efficiently and accurately evaluating the impact of high temperature heat waves. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flow chart of the present invention; Figure 2 is a connected region graph of the present invention; Figure 3 This is the event continuity judgment of the present invention. DETAILED DESCRIPTION

[0015] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0016] like Figure 1 As shown, an embodiment of the present invention provides a method for pre-identifying a high temperature heat wave based on a temperature-humidity composite index, comprising the following steps: (1) Download and extract the daily maximum temperature and daily relative humidity data of each summer from an open source website and calculate the average wet-bulb temperature. Then set the threshold according to the different degrees of impact on the human body. The formula for calculating the average wet-bulb temperature is as follows: ; Where TW is the wet-bulb temperature in degrees Celsius; m is the daily maximum temperature in degrees Celsius; r is the daily relative humidity in percentage; atan is the inverse tangent function; and the threshold value set according to the degree of impact on the human body is 30°C-40°C.

[0017] (2) Screening grid points that exceed the threshold, dividing the area exceeding the threshold into zones for analysis, and screening spatial regional character points; The details are as follows: Based on the machine learning algorithm, the connectivity between the grid points is calculated. If the edges or corners of the grid points touch each other, they are connected; then the area of ​​each region is determined; the area of ​​each region is determined as follows: the shortest distance between all connected regions on the same day is calculated. If the distance is less than 250 kilometers, they are merged, and then the area of ​​each merged region is calculated. Regions with an area greater than 6,000 square kilometers are selected and renumbered. Figure 2 As shown, 1 and 2 are the numbers of connected regions identified on the previous day, while the colored areas represent connected regions from adjacent days. Given the close proximity of two connected regions on the same day, they may be identified as the same connected region from adjacent days. Traditionally, region 1 would overlap with the purple region, and region 2 might also meet the overlap condition with the purple region, resulting in two events being misidentified and an overestimation of the number of events. To avoid errors caused by double counting, the shortest distance between all connected regions on the same day is calculated. If the distance is less than 250 kilometers, the two regions are merged to ensure counting accuracy.

[0018] (3) Determine the continuity of the temperature-humidity composite heat wave event; including the following steps: (31) Get the connected area number of the first day, set the area without the number to 0, and then multiply it with the two-dimensional matrix of the second day. The non-zero area obtained is the overlapping area, and the value is assigned to 1; (32) By multiplying the area assigned a value of 1 by the weighted grid area, the area of ​​the overlapping region that meets the area threshold condition can be calculated. The formula is as follows: ; Among them, c means saving the overlapping matrices of two consecutive days; n1 means keeping only the matrix of the first day number; n2 means the matrix of the adjacent day; (33) All non-zero values ​​in the overlapping matrices of two adjacent days are assigned to 1, and then multiplied by the matrix of the adjacent day to obtain the area number overlapping with the high temperature and high humidity day; according to the number index, the connected areas overlapping with the high temperature and high humidity day are extracted to obtain the overall spatial distribution, and the formula is as follows: ; Where l represents the matrix that stores the overlapping area numbers; c1 represents the overlapping binary matrix of two adjacent days; n2 represents the matrix of one adjacent day; (34) Compare the calculated overlapping area with 50% of the area of ​​the smaller area on both days. If the overlapping area is greater than or equal to 50% of the area of ​​the smaller area, the event is considered to be continuous over the two days. The formula is as follows: ; Among them, R overlay Indicates the overlapping area ratio, in percentage; S overlay Indicates the overlapping area in square meters (m) 2 ; S1 and S2 represent the affected area of ​​the current day and the adjacent day, in square meters 2 .

[0019] like Figure 3 As shown in the figure, if the boundary ranges of D1 and D2 overlap and the overlapping area exceeds 50%, then the two high temperature and high humidity days are the same event, and the connected area number of D1 is recorded; if R overlay If the value is less than the set threshold, the event is discontinuous and the inspection continues in other areas.

[0020] (4) Calculate the comprehensive index of the event, sort the comprehensive index of events over the years, and evaluate the early warning status of temperature-humidity composite high-temperature heat wave events. The details are as follows: First, record the numbers of all regions involved in each event, obtain the union of the regions, and calculate the boundary range of each event; secondly, calculate the difference between the end date and the start date of each event, that is, the duration of the event; then calculate the difference between each grid point and the threshold value every day during the event, and solve the intensity of the event; finally, calculate the comprehensive index of the event; sort the comprehensive index of events over the years, and evaluate the early warning status of temperature-humidity composite high-temperature heat wave events; among them, the formula for calculating the boundary range is as follows: ; Among them, Y s The boundary range of a single event, in square meters 2 ;K (m,n) is the union of connected regions involved in a single event; Q (m,n) is the area corresponding to the grid point, in square meters m 2 ; The formula for calculating strength is as follows: ; Where X is the intensity of a single event, in degrees Celsius; e is the start date of the event; l is the end date of the event; TW (m,n) is the wet bulb temperature in degrees Celsius (℃); R is the threshold of the event; The formula for calculating the comprehensive impact index is as follows: ; Among them, W is the comprehensive impact index of a single event; U is the duration of a single event, in days; X is the intensity of a single event, in degrees Celsius; Y is the boundary range of a single event, in square meters. 2 ; The formula for calculating the total number of occurrence days is as follows: ; The embodiment of the present invention further provides a high temperature heat wave pre-identification system based on a temperature and humidity composite index, comprising: Acquisition module: used to download and extract the daily maximum temperature and daily relative humidity data of each summer from an open source website, calculate the average wet-bulb temperature, and set thresholds based on the degree of impact on the human body; Screening module: used to screen grid points that exceed the threshold, divide the area that exceeds the threshold into zones for analysis, and screen spatial regional character points; Judgment module: used to judge the continuity of events; Calculation module: used to calculate the comprehensive indicators of events, sort the events according to the comprehensive index of occurrence over the years, and evaluate the warning status of temperature and humidity combined high temperature heat wave events.

[0021] An embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, it implements any one of the methods for pre-identifying high-temperature heat waves based on a temperature-humidity composite index.

[0022] An embodiment of the present invention further provides a storage medium storing a computer program. When the computer program is executed by a processor, it implements any one of the methods for pre-identifying a high-temperature heat wave based on a temperature-humidity composite index.

Claims

1. A high temperature heat wave pre-identification method based on temperature and humidity composite index, characterized in that: The following steps are involved: (1) Download and extract the daily maximum temperature and daily relative humidity data of each summer from an open source website, calculate the average wet-bulb temperature, and set thresholds based on the different degrees of impact on the human body; (2) Screening out grid points that exceed the threshold, dividing the area that exceeds the threshold into zones for analysis, and screening out spatial regional character points; (3) Determine the continuity of temperature-humidity composite heat wave events; (4) Calculate the comprehensive index of the event, rank the events according to the comprehensive index of occurrence over the years, and evaluate the warning status of the temperature and humidity combined high temperature heat wave event.

2. A high temperature heat wave pre-identification method based on temperature and humidity composite index according to claim 1, characterized in that: In step (1), the formula for calculating the average wet-bulb temperature is as follows: ； Among them, TW is the wet-bulb temperature, in degrees Celsius; m is the daily maximum temperature, in degrees Celsius; r is the daily relative humidity, in percentage; atan is the inverse tangent function; the threshold value set according to the different degrees of impact on the human body is 30℃-40℃.

3. The high temperature heat wave pre-identification method based on the temperature and humidity composite index according to claim 1 is characterized in that: Step (2) is as follows: the connectivity between grid points is calculated based on a machine learning algorithm. If the edges or corners of the grid points touch each other, they are connected; then the area of ​​each region is determined.

4. The high temperature heat wave pre-identification method based on the temperature and humidity composite index according to claim 2 is characterized in that: In step (2), the area of ​​each region is determined as follows: the shortest distance between all connected regions on the same day is calculated. If the distance is less than 250 kilometers, they are merged. Then the area of ​​each merged region is calculated. Regions with an area greater than 6,000 square kilometers are selected and renumbered.

5. The high temperature heat wave pre-identification method based on the temperature and humidity composite index according to claim 1 is characterized in that: Step (3) includes the following steps: (31) Get the connected area number of the first day, set the area without this number to 0, and then multiply it with the two-dimensional matrix of the second day. The non-zero area obtained is the overlapping area, and the value is assigned to 1; (32) Multiply the area assigned a value of 1 by the weighted grid area to calculate the area of ​​the overlapping area that meets the area threshold condition. The formula is as follows: ； Among them, c means saving the overlapping matrices of two consecutive days; n1 means keeping only the matrix with the first day number; n2 means the matrix of the adjacent day; (33) All non-zero values ​​in the overlapping matrix of two adjacent days are assigned to 1, and then multiplied with the matrix of the adjacent day to obtain the area number overlapping with the high temperature and high humidity day; according to the number index, the connected area overlapping with the high temperature and high humidity day is extracted to obtain the overall spatial distribution, and the formula is as follows: ； Among them, l represents the matrix that stores the overlapping area numbers; c1 represents the overlapping binary matrix of two consecutive days; n2 represents the matrix of one consecutive day; (34) The calculated overlapping area is compared with 50% of the smaller area on both days. If the overlapping area is greater than or equal to 50% of the smaller area, the event is considered to be continuous within two days. The formula is as follows: ； Among them, R overlay Indicates the overlap area ratio, in percentage; S overlay Indicates the overlapping area in square meters (m) 2 ; S1 and S2 represent the affected area of ​​the current day and the adjacent day, in square meters (m) 2 .

6. The high temperature heat wave pre-identification method based on the temperature and humidity composite index according to claim 1 is characterized in that: Step (4) is as follows: first, record all the area numbers involved in each event, obtain the union of the areas, and calculate the boundary range of each event; second, calculate the difference between the end date and the start date of each event, that is, the duration of the event; Then, the difference between each grid point and the threshold value is calculated every day during the event, and the intensity of the event is solved; finally, the comprehensive index of the event is calculated; according to the ranking of the comprehensive index of events over the years, the warning status of the temperature and humidity composite high temperature heat wave event is evaluated; among them, the calculation boundary range formula is as follows: ； Among them, Y s is the boundary range of a single event, in square meters (m) 2 ; K (m,n) is the union of connected regions involved in a single event; Q (m,n) is the area corresponding to the grid point, in square meters m 2 ; The formula for calculating strength is as follows: ； Where X is the intensity of a single event, in degrees Celsius; e is the start date of the event; l is the end date of the event; TW (m,n) is the wet bulb temperature in degrees Celsius (℃); R is the threshold of the event; The formula for calculating the comprehensive impact index is as follows: ； Among them, W is the comprehensive impact index of a single event; U is the duration of a single event, in days; X is the intensity of a single event, in degrees Celsius; Y is the boundary range of a single event, in square meters. 2 ; The formula for calculating the total number of occurrence days is as follows: ； Among them, Q (m,n) is the total number of occurrence days, in days; U (m,n) Duration, in days.

7. A high temperature heat wave pre-identification system based on temperature and humidity composite index, characterized in that: include: Acquisition module: used to download and extract the daily maximum temperature and daily relative humidity data of each summer from the open source website, calculate the average value of the wet-bulb temperature, and set thresholds according to the different degrees of impact on the human body; Screening module: used to screen grid points that exceed the threshold, divide the area that exceeds the threshold into zones for analysis, and screen the spatial regional character points; Judgment module: used to judge the continuity of events; Calculation module: used to calculate the comprehensive indicators of events, sort the events according to the comprehensive index of occurrence over the years, and evaluate the warning status of temperature and humidity combined high temperature heat wave events.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is loaded into a processor, a high temperature heat wave pre-identification method based on a temperature and humidity composite index according to any one of claims 1 to 6 is implemented.

9. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a high temperature heat wave pre-identification method based on a temperature and humidity composite index according to any one of claims 1 to 6 is implemented.

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