Extreme air temperature diagnosis method and device and storage medium

Abstract

The invention relates to the technical field of air temperature detection, in particular to an extreme air temperature diagnosis method and device and a storage medium. The method comprises the following steps: acquiring historical air temperature data of a preset territorial range in a preset historical time period; according to the historical air temperature data of each air temperature collection point in the preset territorial range, determining the historical season average air temperature of each season in each first historical time range; according to the actual air temperature of each day and the historical season average air temperature of the current season, the air temperature deviation value of each day is determined, a preset time window corresponding to the current day is determined, and the air temperature deviation values of each day in the preset time window are ranked from small to large; determining an air temperature threshold value of the current day according to the sorted air temperature deviation value and the historical season average air temperature of the season of the current day; and determining a target extreme air temperature threshold of the air temperature acquisition point according to the daily air temperature threshold of the air temperature acquisition point in the preset historical time period so as to determine whether an extreme air temperature event occurs or not.

Description

Technical Field

[0001] The present application relates to the technical field of temperature detection, and specifically to a method, device and storage medium for extreme temperature diagnosis. Background Art

[0002] In recent years, driven by global warming, extreme temperature events have become more frequent and stronger. High temperature drought in summer and low temperature cold wave disasters in winter have had serious impacts on the safety of power grid equipment and power supply in various countries. Therefore, it is necessary to accurately identify temperature extreme events to analyze the frequency, intensity and duration of extreme high and low temperature events under the background of global warming. At present, extreme high and low temperature events are usually diagnosed and identified based on the percentile threshold method, that is, defined as rare events that exceed / fall below the daily maximum / low temperature percentile threshold. However, if this method uses a longer time range (such as 15 days, 31 days) when calculating the percentile threshold of temperature extreme events, it is called a "seasonal window", which will mix the average seasonal cycle into the extreme threshold, resulting in deviations that vary with seasons, regions, time periods and data sets, resulting in the destruction of the generally accepted properties of the percentile-based extreme definition. Shorter operating windows (such as 5 days) in turn limit the number of samples on which the percentile threshold is based and reflect daily variability in the threshold, which can also cause deviations. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a method, device and storage medium for diagnosing extreme temperatures that remove seasonal periodic changes.

[0004] In order to achieve the above-mentioned object, the first aspect of the present application provides a method for diagnosing extreme temperature, the method comprising:

[0005] Obtain historical temperature data for a preset geographical area within a preset historical time period;

[0006] For each temperature collection point within the preset geographical range, determine the historical seasonal average temperature of each season within each first historical time range in the historical time period according to the historical temperature data of the temperature collection point, wherein the first historical time range includes multiple seasons;

[0007] For each day within the first historical time range, determine the temperature deviation value of the day according to the actual temperature of the day and the historical seasonal average temperature of the season;

[0008] For each day within each first historical time range, determine a preset time window corresponding to the day, wherein the preset time window includes a plurality of consecutive days in which the day is located;

[0009] For the preset time window corresponding to each day, the temperature deviation values ​​of each day in the preset time window are sorted in ascending order, and the temperature threshold of the day is determined according to the sorted temperature deviation values ​​and the historical seasonal average temperature of the season in which the day is located;

[0010] For each temperature collection point within a preset geographical area, a target extreme temperature threshold of the temperature collection point is determined according to the daily temperature threshold of the temperature collection point within a preset historical time period;

[0011] For each temperature collection point within the preset geographical area, every day within the preset historical time period, the temperature of the day is judged according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event occurs.

[0012] In an embodiment of the present application, the temperature data includes the daily maximum temperature and the daily minimum temperature, and determining the historical seasonal average temperature of each season within each first historical time range in the historical time period includes: determining the historical daily maximum temperature and the historical daily minimum temperature of each day in each season within each first historical time range; determining the historical seasonal maximum average temperature of each season based on the historical daily maximum temperature of each day in each season; determining the historical seasonal minimum average temperature of each season based on the historical daily minimum temperature of each day in each season.

[0013] In an embodiment of the present application, the temperature data includes the daily maximum temperature and the daily minimum temperature. Determining the temperature deviation value of the day based on the actual temperature of the day and the historical seasonal average temperature of the season includes: determining the difference between the daily actual maximum temperature of the day and the historical seasonal maximum average temperature of the season in which the day is located as the high temperature deviation value of the day; determining the difference between the daily actual minimum temperature of the day and the historical seasonal minimum average temperature of the season in which the day is located as the low temperature deviation value of the day.

[0014] In an embodiment of the present application, the temperature deviation value includes a high temperature deviation value and a low temperature deviation value. According to the sorted temperature deviation value and the historical seasonal average temperature of the season in which the day is located, determining the temperature threshold of the day includes: for the preset time window corresponding to each day, determining the low temperature deviation value at the first preset percentage of the sorted low temperature deviation value sequence as the target low temperature deviation value of the day; determining the sum of the target low temperature deviation value of the day and the historical seasonal lowest average temperature of the season in which the day is located as the low temperature threshold of the day; for the preset time window corresponding to each day, determining the high temperature deviation value at the second preset percentage of the sorted high temperature deviation value sequence as the target high temperature deviation value of the day; and determining the sum of the target high temperature deviation value and the historical seasonal highest average temperature of the season in which the day is located as the high temperature threshold of the day.

[0015] In an embodiment of the present application, the target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. Determining the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in the preset historical time period includes: determining the average low temperature threshold in the preset historical time period according to the low temperature threshold of each day in the preset historical time period at the temperature collection point; determining the average low temperature threshold as the target extreme low temperature threshold of the temperature collection point; determining the average high temperature threshold in the preset historical time period according to the high temperature threshold of each day in the preset historical time period at the temperature collection point; and determining the average high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0016] In an embodiment of the present application, the target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. Determining the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in the preset historical time period includes: determining the lowest low temperature threshold in the preset historical time period according to the low temperature threshold of the temperature collection point every day in the preset historical time period; determining the lowest low temperature threshold as the target extreme low temperature threshold of the temperature collection point; determining the highest high temperature threshold in the preset historical time period according to the high temperature threshold of the temperature collection point every day in the preset historical time period; and determining the highest high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0017] In an embodiment of the present application, the target extreme temperature threshold includes a target extreme high temperature threshold and a target extreme low temperature threshold. The temperature of the day is judged according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event has occurred, including: when the temperature of the day is higher than the target extreme high temperature threshold, it is determined that an extreme high temperature event has occurred on the day; when the temperature of the day is lower than the target extreme low temperature threshold, it is determined that an extreme low temperature event has occurred on the day.

[0018] In an embodiment of the present application, the method also includes: determining the number of extreme high temperature events or extreme low temperature events occurring within a preset time range; determining the frequency of extreme high temperature events and / or the frequency of extreme low temperature events occurring within the preset time range based on the number of events.

[0019] A second aspect of the present application provides a device for diagnosing extreme temperature, comprising:

[0020] A data acquisition module is used to obtain historical temperature data of a preset geographical area within a preset historical time period;

[0021] A seasonal average temperature determination module is used to determine the historical seasonal average temperature of each season in each first historical time range in the historical time period according to the historical temperature data of each temperature collection point within the preset geographical range, wherein the first historical time range includes multiple seasons;

[0022] A temperature deviation value determination module is used to determine the temperature deviation value of the day according to the actual temperature of the day and the historical seasonal average temperature of the season for each day within the first historical time range; a window setting module is used to determine a preset time window corresponding to the day for each day within the first historical time range, wherein the preset time window includes multiple consecutive days in which the day is located;

[0023] The temperature threshold determination module is used to sort the temperature deviation values ​​of each day in the preset time window in ascending order for the preset time window corresponding to each day, and determine the temperature threshold of the day according to the sorted temperature deviation values ​​and the historical seasonal average temperature of the season in which the day is located; it is also used to determine the target extreme temperature threshold of the temperature collection point for each temperature collection point within the preset geographical range according to the temperature threshold of each day of the temperature collection point within the preset historical time period; the extreme temperature event judgment module is used to judge the temperature of the day according to the target extreme temperature threshold of the temperature collection point for each temperature collection point within the preset geographical range every day within the preset historical time period to determine whether an extreme temperature event has occurred.

[0024] In an embodiment of the present application, the temperature data includes the daily maximum temperature and the daily minimum temperature. The seasonal average temperature determination module is used to determine the historical seasonal average temperature of each season within each first historical time range in the historical time period, including: determining the historical daily maximum temperature and the historical daily minimum temperature of each day in each season within each first historical time range; determining the historical seasonal maximum average temperature of each season based on the historical daily maximum temperature of each day in each season; determining the historical seasonal minimum average temperature of each season based on the historical daily minimum temperature of each day in each season.

[0025] In an embodiment of the present application, the temperature data includes the daily maximum temperature and the daily minimum temperature. The temperature deviation value determination module is used to determine the temperature deviation value of the day based on the actual temperature of the day and the historical seasonal average temperature of the season, including: determining the difference between the daily actual maximum temperature of the day and the historical seasonal maximum average temperature of the season in which the day is located as the high temperature deviation value of the day; determining the difference between the daily actual minimum temperature of the day and the historical seasonal minimum average temperature of the season in which the day is located as the low temperature deviation value of the day.

[0026] In an embodiment of the present application, the temperature deviation value includes a high temperature deviation value and a low temperature deviation value. The temperature threshold determination module is used to determine the temperature threshold of the day based on the sorted temperature deviation value and the historical seasonal average temperature of the season in which the day is located. The module includes: for each preset time window corresponding to each day, the low temperature deviation value at the first preset percentage of the sorted low temperature deviation value sequence is determined as the target low temperature deviation value of the day; the sum of the target low temperature deviation value of the day and the historical seasonal lowest average temperature of the season in which the day is located is determined as the low temperature threshold of the day; for each preset time window corresponding to each day, the high temperature deviation value at the second preset percentage of the sorted high temperature deviation value sequence is determined as the target high temperature deviation value of the day; the sum of the target high temperature deviation value and the historical seasonal highest average temperature of the season in which the day is located is determined as the high temperature threshold of the day.

[0027] In an embodiment of the present application, the target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. The temperature threshold determination module is used to determine the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in the preset historical time period. The module includes: determining the average low temperature threshold in the preset historical time period according to the low temperature threshold of each day in the preset historical time period at the temperature collection point; determining the average low temperature threshold as the target extreme low temperature threshold of the temperature collection point; determining the average high temperature threshold in the preset historical time period according to the high temperature threshold of each day in the preset historical time period at the temperature collection point; and determining the average high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0028] In an embodiment of the present application, the target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. The temperature threshold determination module is used to determine the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in the preset historical time period. The module includes: determining the lowest low temperature threshold in the preset historical time period according to the low temperature threshold of each day in the preset historical time period at the temperature collection point; determining the lowest low temperature threshold as the target extreme low temperature threshold of the temperature collection point; determining the highest high temperature threshold in the preset historical time period according to the high temperature threshold of each day in the preset historical time period at the temperature collection point; and determining the highest high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0029] In an embodiment of the present application, the target extreme temperature threshold includes a target extreme high temperature threshold and a target extreme low temperature threshold. The extreme temperature event judgment module is used to judge the temperature of the day according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event has occurred, including: when the temperature of the day is higher than the target extreme high temperature threshold, it is determined that an extreme high temperature event has occurred on the day; when the temperature of the day is lower than the target extreme low temperature threshold, it is determined that an extreme low temperature event has occurred on the day.

[0030] In an embodiment of the present application, the device further includes: a frequency determination module, which is used to determine the number of extreme high temperature events or extreme low temperature events that occur within a preset time range, and determine the frequency of extreme high temperature events and / or the frequency of extreme low temperature events within the preset time range according to the number of events. A third aspect of the present application provides a machine-readable storage medium, which stores instructions for causing a machine to execute the method for extreme temperature diagnosis according to any one of the above embodiments.

[0031] The above technical solution, by removing the average value of the seasonal cycle, can reduce or eliminate the deviation caused by the length of the operating window, thereby improving the accuracy of the frequency estimation of extreme temperature events.

[0032] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0034] Figure 1 A schematic diagram of a process flow of a method for diagnosing extreme temperature according to an embodiment of the present application is shown;

[0035] Figure 2 The structure block diagram of an extreme temperature diagnosis device according to an embodiment of the present application is schematically shown;

[0036] Figure 3 The internal structure diagram of a computer device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0038] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0039] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0040] Figure 1 The flowchart of a method for diagnosing extreme temperature according to an embodiment of the present application is schematically shown. Figure 1 As shown, an embodiment of the present application provides a method for diagnosing extreme temperatures, which may include the following steps.

[0041] Step 101, obtaining historical temperature data of a preset geographical area within a preset historical time period;

[0042] Step 102: for each temperature collection point within the preset geographical range, determine the historical seasonal average temperature of each season within each first historical time range in the historical time period according to the historical temperature data of the temperature collection point, wherein the first historical time range includes multiple seasons;

[0043] Step 103: for each day within the first historical time range, determine the temperature deviation value of the day according to the actual temperature of the day and the historical seasonal average temperature of the season;

[0044] Step 104: for each day within the first historical time range, determine a preset time window corresponding to the day, wherein the preset time window includes a plurality of consecutive days in which the day is located;

[0045] Step 105: for the preset time window corresponding to each day, sort the temperature deviation values ​​of each day in the preset time window in ascending order, and determine the temperature threshold of the day according to the sorted temperature deviation values ​​and the historical seasonal average temperature of the season in which the day is located;

[0046] Step 106: for each temperature collection point within the preset geographical range, determine the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day of the temperature collection point within the preset historical time period;

[0047] Step 107: for each temperature collection point within the preset geographical area and every day within the preset historical time period, the temperature of the day is judged according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event occurs.

[0048] The processor can obtain historical temperature data within a preset historical time period in a preset geographical range. The preset historical time period may include multiple first historical time ranges, and the first historical time range may include multiple seasons. For example, assuming that the processor sets the preset historical time period to 30 years and the first historical time range to 1 year, the preset historical time period may include 30 first historical time ranges, and each first historical time range includes 4 seasons. The preset geographical range may include multiple temperature collection points. For each temperature collection point within the preset geographical range, the processor can determine the historical seasonal average temperature of each season within each first historical time range in the historical time period based on the historical temperature data of the temperature collection point. For each day within each first historical time range, the processor can obtain the actual temperature of each day, and determine the temperature deviation value of the day based on the actual temperature of each day and the historical seasonal average temperature of the season in which the day is located. For example, assuming that the processor targets the Ath day within the first historical time range, and the season of the day is winter, the processor can determine the temperature deviation value of the Ath day based on the actual temperature of the Ath day and the historical winter average temperature within the first historical time range. For each day within each first historical time range, the processor can determine a preset time window corresponding to each day, wherein the preset time window is a plurality of consecutive days including the day, for example, the processor can use the day and ±d days of the day as the preset time window corresponding to the day. For the preset time window corresponding to each day, the processor can sort the temperature deviation values ​​of each day within the preset time window in order from small to large, for example, assuming that d is 10, the processor can use the day, and the 10 days before and after the day as the preset time windows corresponding to the day, for the 21 days included in the preset time window, the processor can determine the temperature deviation value of each day, and sort them in order from small to large, and determine the temperature threshold of the day according to the sorted temperature deviation value and the historical seasonal average temperature of the season in which the day is located. For each temperature collection point within the preset geographical range, the processor can determine the target extreme temperature threshold corresponding to the temperature collection point based on the temperature threshold of each day at the temperature collection point within the preset historical time period. For each temperature collection point within the preset geographical range every day within the preset historical time period, the processor can detect the temperature of each day based on the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event occurs on that day.

[0049] In one embodiment, the temperature data includes the daily maximum temperature and the daily minimum temperature, and determining the historical seasonal average temperature of each season within each first historical time range in the historical time period includes: determining the historical daily maximum temperature and the historical daily minimum temperature of each day in each season within each first historical time range; determining the historical seasonal maximum average temperature of each season based on the historical daily maximum temperature of each day in each season; determining the historical seasonal minimum average temperature of each season based on the historical daily minimum temperature of each day in each season.

[0050] The temperature data includes the daily maximum temperature and the daily minimum temperature. The processor can obtain the historical daily maximum temperature and the historical daily minimum temperature of each day in the preset historical time period. The processor can determine the historical seasonal average temperature of each season for each season included in the first historical time range. The processor can determine the historical daily maximum temperature and the historical daily minimum temperature of each day in each season included in each first historical time range, and determine the historical seasonal maximum average temperature of the season according to the historical daily maximum temperature of each day in each season. The processor can determine the historical seasonal minimum average temperature of each season according to the historical daily minimum temperature of each day in each season.

[0051] In one embodiment, the temperature data includes the daily maximum temperature and the daily minimum temperature. Determining the temperature deviation value of the day based on the actual temperature of the day and the historical seasonal average temperature of the season includes: determining the difference between the actual daily maximum temperature of the day and the historical seasonal maximum average temperature of the season in which the day is located as the high temperature deviation value of the day; determining the difference between the actual daily minimum temperature of the day and the historical seasonal minimum average temperature of the season in which the day is located as the low temperature deviation value of the day.

[0052] The temperature data may include the daily maximum temperature and the daily minimum temperature. The processor may obtain the historical daily maximum temperature and the historical daily minimum temperature of each day within a preset historical time period. For each day within each first historical time range, the processor may obtain the historical daily maximum temperature of each day and determine the season of the day. The processor may obtain the historical daily maximum temperature of each day included in the season of the day, and determine the historical seasonal maximum average temperature of the season based on the historical daily maximum temperature of each day included in the season. After determining the historical seasonal maximum average temperature of the season of each day within the preset historical time period, the difference between the daily actual maximum temperature of each day and the historical seasonal maximum average temperature of the season of the day may be determined as the high temperature deviation value of the day. Similarly, the processor may determine the difference between the daily actual minimum temperature of the day and the historical seasonal minimum average temperature of the season of the day as the low temperature deviation value of the day.

[0053] In one embodiment, the temperature deviation value includes a high temperature deviation value and a low temperature deviation value. According to the sorted temperature deviation value and the historical seasonal average temperature of the season in which the day is located, determining the temperature threshold of the day includes: for the preset time window corresponding to each day, determining the low temperature deviation value at the first preset percentage of the sorted low temperature deviation value sequence as the target low temperature deviation value of the day; determining the sum of the target low temperature deviation value of the day and the historical seasonal lowest average temperature of the season in which the day is located as the low temperature threshold of the day; for the preset time window corresponding to each day, determining the high temperature deviation value at the second preset percentage of the sorted high temperature deviation value sequence as the target high temperature deviation value of the day; and determining the sum of the target high temperature deviation value and the historical seasonal highest average temperature of the season in which the day is located as the high temperature threshold of the day.

[0054] The temperature deviation value includes a high temperature deviation value and a low temperature deviation value. The processor can determine the temperature deviation value of each day in the preset time window for the preset time window corresponding to each day. For the high temperature deviation value and the low temperature deviation value of each day, the processor can sort them in order from small to large. For the preset time window corresponding to each day, for the sorted low temperature deviation value sequence, the processor can determine the low temperature deviation value of the first preset percentage in the sequence as the target low temperature deviation value of the day corresponding to the preset time window. For example, the processor can set the first preset percentage to 5%, and the processor can determine the value corresponding to 5% of the low temperature deviation value sequence as the target low temperature deviation value of the day. After determining the target low temperature deviation value of the day, the processor can determine the sum of the target low temperature deviation value of the day and the historical seasonal minimum average temperature of the season in which the day is located as the low temperature threshold of the day. For the preset time window corresponding to each day, for the sorted high temperature deviation value sequence, the processor can determine the high temperature deviation value of the second preset percentage in the sequence as the target high temperature deviation value of the day corresponding to the preset time window. For example, the processor can set the second preset percentage to 95%, and the processor can determine the value corresponding to 95% of the high temperature deviation value sequence as the target high temperature deviation value of the day. After determining the target high temperature deviation value for the day, the processor may determine the sum of the target high temperature deviation value for the day and the highest average temperature of the historical season for the season in which the day occurs as the high temperature threshold for the day.

[0055] In one embodiment, the target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. Determining the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in the preset historical time period includes: determining the average low temperature threshold in the preset historical time period according to the low temperature threshold of each day in the preset historical time period at the temperature collection point; determining the average low temperature threshold as the target extreme low temperature threshold of the temperature collection point; determining the average high temperature threshold in the preset historical time period according to the high temperature threshold of each day in the preset historical time period at the temperature collection point; and determining the average high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0056] The target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. For each temperature collection point within a preset geographical range, the processor can determine the target extreme temperature threshold of the temperature collection point based on the daily temperature threshold of the temperature collection point within a preset historical time period. The processor can determine the average low temperature threshold of the temperature collection point within the preset historical time period based on the daily low temperature threshold of the temperature collection point within the preset historical time period, and determine the obtained average low temperature threshold as the target extreme low temperature threshold of the temperature collection point. Similarly, the processor can determine the average high temperature threshold of the temperature collection point within the preset historical time period based on the daily high temperature threshold of the temperature collection point within the preset historical time period, and determine the obtained average high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0057] In one embodiment, the target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. Determining the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in the preset historical time period includes: determining the lowest low temperature threshold in the preset historical time period according to the low temperature threshold of each day in the preset historical time period at the temperature collection point; determining the lowest low temperature threshold as the target extreme low temperature threshold of the temperature collection point; determining the highest high temperature threshold in the preset historical time period according to the high temperature threshold of each day in the preset historical time period at the temperature collection point; and determining the highest high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0058] The target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. For each temperature collection point within a preset geographical range, the processor can determine the target extreme temperature threshold of the temperature collection point based on the temperature threshold of each day within a preset historical time period. The processor can determine the lowest low temperature threshold of the temperature collection point within the preset historical time period based on the low temperature threshold of each day within the preset historical time period, and determine the obtained lowest low temperature threshold as the target extreme low temperature threshold of the temperature collection point. Similarly, the processor can determine the highest high temperature threshold of the temperature collection point within the preset historical time period based on the high temperature threshold of each day within the preset historical time period, and determine the obtained highest high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0059] In one embodiment, the target extreme temperature threshold includes a target extreme high temperature threshold and a target extreme low temperature threshold. The temperature of the day is judged according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event has occurred, including: when the temperature of the day is higher than the target extreme high temperature threshold, it is determined that an extreme high temperature event has occurred on the day; when the temperature of the day is lower than the target extreme low temperature threshold, it is determined that an extreme low temperature event has occurred on the day.

[0060] The target extreme temperature threshold includes a target extreme high temperature threshold and a target extreme low temperature threshold. The processor can judge the temperature of each day in the historical time period according to the target extreme high temperature threshold and the target extreme low temperature threshold to determine whether an extreme temperature event has occurred. If the temperature of the day is higher than the target extreme high temperature threshold, the processor can determine that an extreme high temperature event has occurred on that day. If the temperature of the day is lower than the target extreme low temperature threshold, it can be determined that an extreme low temperature event has occurred on that day.

[0061] In one embodiment, the method also includes: determining the number of extreme high temperature events or extreme low temperature events occurring within a preset time range; and determining the frequency of extreme high temperature events and / or the frequency of extreme low temperature events occurring within the preset time range based on the number of events.

[0062] After determining the target extreme temperature threshold of the temperature collection point, the processor can set a preset time range according to the user's needs, and judge whether an extreme high temperature event or an extreme low temperature event occurs within the preset time range by using the target extreme temperature threshold of the temperature collection point. For example, the preset time range can be a future time period. The processor can determine the number of extreme events that occur within the preset time range, and determine the frequency of extreme high temperature events and / or extreme low temperature events within the preset time range based on the obtained number of events.

[0063] like Figure 2As shown, in one embodiment, a device 200 for diagnosing extreme temperature is provided, comprising:

[0064] The data acquisition module 201 is used to acquire historical temperature data of a preset geographical area within a preset historical time period;

[0065] The seasonal average temperature determination module 202 is used to determine the historical seasonal average temperature of each season in each first historical time range in the historical time period according to the historical temperature data of the temperature collection point for each temperature collection point within the preset geographical range, wherein the first historical time range includes multiple seasons;

[0066] The temperature deviation value determination module 203 is used to determine the temperature deviation value of the day according to the actual temperature of the day and the historical seasonal average temperature of the season for each day within the first historical time range;

[0067] The window setting module 204 is used to determine a preset time window corresponding to each day within each first historical time range, wherein the preset time window includes a plurality of consecutive days in which the current day is located;

[0068] The temperature threshold determination module 205 is used to sort the temperature deviation values ​​of each day in the preset time window in ascending order for each preset time window corresponding to each day, and determine the temperature threshold of the day according to the sorted temperature deviation values ​​and the historical seasonal average temperature of the season in which the day is located; and is also used to determine the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day of the temperature collection point in the preset historical time period for each temperature collection point within the preset geographical range;

[0069] The extreme temperature event judgment module 206 is used to judge the temperature of each temperature collection point within a preset geographical range every day within a preset historical time period according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event occurs.

[0070] In one embodiment, the temperature data includes the daily maximum temperature and the daily minimum temperature. The seasonal average temperature determination module 202 is used to determine the historical seasonal average temperature of each season in each first historical time range in the historical time period, including: determining the historical daily maximum temperature and the historical daily minimum temperature of each day in each season in each first historical time range; determining the historical seasonal maximum average temperature of each season based on the historical daily maximum temperature of each day in each season; determining the historical seasonal minimum average temperature of each season based on the historical daily minimum temperature of each day in each season.

[0071] In one embodiment, the temperature data includes the daily maximum temperature and the daily minimum temperature. The temperature deviation value determination module 203 is used to determine the temperature deviation value of the day according to the actual temperature of the day and the historical seasonal average temperature of the season, including: determining the difference between the actual daily maximum temperature of the day and the historical seasonal maximum average temperature of the season in which the day is located as the high temperature deviation value of the day; determining the difference between the actual daily minimum temperature of the day and the historical seasonal minimum average temperature of the season in which the day is located as the low temperature deviation value of the day.

[0072] In one embodiment, the temperature deviation value includes a high temperature deviation value and a low temperature deviation value. The temperature threshold determination module 205 is used to determine the temperature threshold of the day according to the sorted temperature deviation value and the historical seasonal average temperature of the season in which the day is located. The module includes: for each preset time window corresponding to each day, the low temperature deviation value at the first preset percentage of the sorted low temperature deviation value sequence is determined as the target low temperature deviation value of the day; the sum of the target low temperature deviation value of the day and the historical seasonal lowest average temperature of the season in which the day is located is determined as the low temperature threshold of the day; for each preset time window corresponding to each day, the high temperature deviation value at the second preset percentage of the sorted high temperature deviation value sequence is determined as the target high temperature deviation value of the day; the sum of the target high temperature deviation value and the historical seasonal highest average temperature of the season in which the day is located is determined as the high temperature threshold of the day.

[0073] In one embodiment, the target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. The temperature threshold determination module 205 is used to determine the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in the preset historical time period, including: determining the average low temperature threshold in the preset historical time period according to the low temperature threshold of each day in the preset historical time period at the temperature collection point; determining the average low temperature threshold as the target extreme low temperature threshold of the temperature collection point; determining the average high temperature threshold in the preset historical time period according to the high temperature threshold of each day in the preset historical time period at the temperature collection point; and determining the average high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0074] In one embodiment, the target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. The temperature threshold determination module 205 is used to determine the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in the preset historical time period, including: determining the lowest low temperature threshold in the preset historical time period according to the low temperature threshold of each day in the preset historical time period at the temperature collection point; determining the lowest low temperature threshold as the target extreme low temperature threshold of the temperature collection point; determining the highest high temperature threshold in the preset historical time period according to the high temperature threshold of each day in the preset historical time period at the temperature collection point; and determining the highest high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0075] In one embodiment, the target extreme temperature threshold includes a target extreme high temperature threshold and a target extreme low temperature threshold. The extreme temperature event judgment module 206 is used to judge the temperature of the day according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event has occurred, including: when the temperature of the day is higher than the target extreme high temperature threshold, it is determined that an extreme high temperature event has occurred on the day; when the temperature of the day is lower than the target extreme low temperature threshold, it is determined that an extreme low temperature event has occurred on the day.

[0076] In one embodiment, Figure 2 As shown, the device 200 for diagnosing extreme temperature also includes: a frequency determination module 207, which is used to determine the number of extreme high temperature events or extreme low temperature events occurring within a preset time range, and determine the frequency of extreme high temperature events and / or the frequency of extreme low temperature events occurring within the preset time range based on the number of events.

[0077] The data acquisition module 201 can obtain historical temperature data of a preset geographical range within a preset historical time period. The preset historical time period may include multiple first historical time ranges, and the first historical time range may include multiple seasons. For example, assuming that the data acquisition module 201 sets the preset historical time period to 30 years and the first historical time range to 1 year, the preset historical time period may include 30 first historical time ranges, and each first historical time range includes 4 seasons. The preset geographical range may include multiple temperature collection points. For each temperature collection point within the preset geographical range, the seasonal average temperature determination module 202 can determine the historical seasonal average temperature of each season within each first historical time range in the historical time period according to the historical temperature data of the temperature collection point. The temperature data includes the daily maximum temperature and the daily minimum temperature. The seasonal average temperature determination module 202 can obtain the historical daily maximum temperature and the historical daily minimum temperature of each day within the preset historical time period. The seasonal average temperature determination module 202 can determine the historical seasonal average temperature of each season included in the first historical time range. The seasonal average temperature determination module 202 can determine the historical daily maximum temperature and the historical daily minimum temperature of each season included in each first historical time range, and determine the historical seasonal maximum average temperature of the season based on the historical daily maximum temperature of each day in each season. The seasonal average temperature determination module 202 can determine the historical seasonal minimum average temperature of each season based on the historical daily minimum temperature of each day in each season.

[0078] For each day within each first historical time range, the temperature deviation value determination module 203 can obtain the historical daily maximum temperature of each day, and determine the season of the day. The temperature deviation value determination module 203 can obtain the historical daily maximum temperature of each day included in the season of the day, and determine the historical seasonal maximum average temperature of the season according to the historical daily maximum temperature of each day included in the season. After determining the historical seasonal maximum average temperature of the season of each day in the preset historical time period, the difference between the daily actual maximum temperature of each day and the historical seasonal maximum average temperature of the season of the day can be determined as the high temperature deviation value of the day. Similarly, the temperature deviation value determination module 203 can determine the difference between the daily actual minimum temperature of the day and the historical seasonal minimum average temperature of the season of the day as the low temperature deviation value of the day. For example, assuming that the temperature deviation value determination module 203 is for the Ath day within the first historical time range, and the season of the day is winter, the temperature deviation value determination module 203 can determine the temperature deviation value of the Ath day according to the actual temperature of the Ath day and the historical winter average temperature within the first historical time range.

[0079] For each day within each first historical time range, the window setting module 204 can determine a preset time window corresponding to each day, wherein the preset time window is a plurality of consecutive days including the day, for example, the window setting module 204 can use the day and the day ±d days as the preset time window corresponding to the day. For the preset time window corresponding to each day, the temperature threshold determination module 205 can sort the temperature deviation values ​​of each day within the preset time window in order from small to large, for example, assuming that d is 10, the temperature threshold determination module 205 can use the day, and the 10 days before and after the day as the preset time windows corresponding to the day, for the 21 days included in the preset time window, the temperature threshold determination module 205 can determine the temperature deviation values ​​of each day, and sort them in order from small to large, and determine the temperature threshold of the day according to the sorted temperature deviation values ​​and the historical seasonal average temperature of the season in which the day is located. The temperature deviation value includes a high temperature deviation value and a low temperature deviation value. For the preset time window corresponding to each day and the sorted low temperature deviation value sequence, the temperature threshold determination module 205 can determine the low temperature deviation value of the first preset percentage in the sequence as the target low temperature deviation value of the day corresponding to the preset time window. For example, the temperature threshold determination module 205 can set the first preset percentage to 5%, and the temperature threshold determination module 205 can determine the value corresponding to 5% of the low temperature deviation value sequence as the target low temperature deviation value of the day. After determining the target low temperature deviation value of the day, the temperature threshold determination module 205 can determine the sum of the target low temperature deviation value of the day and the historical seasonal minimum average temperature of the season in which the day is located as the low temperature threshold of the day. For the preset time window corresponding to each day and the sorted high temperature deviation value sequence, the temperature threshold determination module 205 can determine the high temperature deviation value of the second preset percentage in the sequence as the target high temperature deviation value of the day corresponding to the preset time window. For example, the temperature threshold determination module 205 can set the second preset percentage to 95%, and the temperature threshold determination module 205 can determine the value corresponding to 95% of the high temperature deviation value sequence as the target high temperature deviation value of the day. After determining the target high temperature deviation value for the day, the temperature threshold determination module 205 may determine the sum of the target high temperature deviation value for the day and the highest average temperature of the historical season for the season in which the day is located as the high temperature threshold for the day.

[0080] For each temperature collection point within the preset geographical range, the temperature threshold determination module 205 can determine the target extreme temperature threshold corresponding to the temperature collection point according to the temperature threshold of each day in the preset historical time period, and the temperature threshold determination module 205 can determine the average low temperature threshold of the temperature collection point in the preset historical time period according to the low temperature threshold of each day in the preset historical time period, and determine the obtained average low temperature threshold as the target extreme low temperature threshold of the temperature collection point. Similarly, the temperature threshold determination module 205 can determine the average high temperature threshold of the temperature collection point in the preset historical time period according to the high temperature threshold of each day in the preset historical time period, and determine the obtained average high temperature threshold as the target extreme high temperature threshold of the temperature collection point. Or the temperature threshold determination module 205 can determine the lowest low temperature threshold of the temperature collection point in the preset historical time period according to the low temperature threshold of each day in the preset historical time period, and determine the obtained lowest low temperature threshold as the target extreme low temperature threshold of the temperature collection point. Similarly, the temperature threshold determination module 205 can determine the highest high temperature threshold of the temperature collection point within a preset historical time period based on the daily high temperature threshold of the temperature collection point within the preset historical time period, and determine the obtained highest high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0081] For each temperature collection point within the preset geographical range and every day within the preset historical time period, the extreme temperature event judgment module 206 can detect the temperature of each day according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event occurs on that day. When the temperature of the day is higher than the target extreme high temperature threshold, the extreme temperature event judgment module 206 can determine that an extreme high temperature event occurs on that day. When the temperature of the day is lower than the target extreme low temperature threshold, it can be determined that an extreme low temperature event occurs on that day. Further, it also includes a frequency determination module 207, which can set a preset time range according to the needs of the user, and judge whether an extreme high temperature event or an extreme low temperature event occurs within the preset time range through the target extreme temperature threshold of the temperature collection point. For example, the preset time range can be a future time period. The frequency determination module 207 can determine the number of events in which extreme events occur within the preset time range, and determine the frequency of extreme high temperature events and / or the frequency of extreme low temperature events within the preset time range based on the obtained number of events.

[0082] Through the above technical solution, by removing the average value of the seasonal cycle, the deviation caused by the length of the operating window can be reduced or eliminated, thereby improving the accuracy of the frequency estimation of extreme temperature events.

[0083] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip. Those skilled in the art will understand that

[0084] In one embodiment, a machine-readable storage medium is provided, on which instructions are stored. When the instructions are executed by a processor, the processor is configured to perform the method for extreme temperature diagnosis according to any one of the above embodiments.

[0085] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure) and a database (not shown in the figure) connected via a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02 and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store data related to the temperature. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, a method for diagnosing extreme temperatures is implemented.

[0086] The embodiment of the present application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: obtaining historical temperature data of a preset geographical range within a preset historical time period; for each temperature collection point within the preset geographical range, determining the historical seasonal average temperature of each season within each first historical time range in the historical time period based on the historical temperature data of the temperature collection point, wherein the first historical time range includes multiple seasons; for each day within each first historical time range, determining the temperature deviation value of the day based on the actual temperature of the day and the historical seasonal average temperature of the season; for each day within each first historical time range, determining the temperature deviation value of the day based on the actual temperature of the day and the historical seasonal average temperature of the season; for each day within each first historical time range, determining the temperature deviation value of the day based on the actual temperature of the day and the historical seasonal average temperature of the season; a preset time window corresponding to a day, wherein the preset time window includes multiple consecutive days in which the current day is located; for the preset time window corresponding to each day, the temperature deviation values ​​of each day in the preset time window are sorted in ascending order, and the temperature threshold of the current day is determined according to the sorted temperature deviation values ​​and the historical seasonal average temperature of the season in which the current day is located; for each temperature collection point within the preset geographical area, the target extreme temperature threshold of the temperature collection point is determined according to the temperature threshold of each day of the temperature collection point within the preset historical time period; for each temperature collection point within the preset geographical area, every day within the preset historical time period, the temperature of the current day is judged according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event occurs.

[0087] In one embodiment, the temperature data includes the daily maximum temperature and the daily minimum temperature, and determining the historical seasonal average temperature of each season within each first historical time range in the historical time period includes: determining the historical daily maximum temperature and the historical daily minimum temperature of each day in each season within each first historical time range; determining the historical seasonal maximum average temperature of each season based on the historical daily maximum temperature of each day in each season; determining the historical seasonal minimum average temperature of each season based on the historical daily minimum temperature of each day in each season.

[0088] In one embodiment, the temperature data includes the daily maximum temperature and the daily minimum temperature. Determining the temperature deviation value of the day based on the actual temperature of the day and the historical seasonal average temperature of the season includes: determining the difference between the actual daily maximum temperature of the day and the historical seasonal maximum average temperature of the season in which the day is located as the high temperature deviation value of the day; determining the difference between the actual daily minimum temperature of the day and the historical seasonal minimum average temperature of the season in which the day is located as the low temperature deviation value of the day.

[0089] In one embodiment, the temperature deviation value includes a high temperature deviation value and a low temperature deviation value. According to the sorted temperature deviation value and the historical seasonal average temperature of the season in which the day is located, determining the temperature threshold of the day includes: for the preset time window corresponding to each day, determining the low temperature deviation value at the first preset percentage of the sorted low temperature deviation value sequence as the target low temperature deviation value of the day; determining the sum of the target low temperature deviation value of the day and the historical seasonal lowest average temperature of the season in which the day is located as the low temperature threshold of the day; for the preset time window corresponding to each day, determining the high temperature deviation value at the second preset percentage of the sorted high temperature deviation value sequence as the target high temperature deviation value of the day; and determining the sum of the target high temperature deviation value and the historical seasonal highest average temperature of the season in which the day is located as the high temperature threshold of the day.

[0090] In one embodiment, the target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. Determining the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in the preset historical time period includes: determining the average low temperature threshold in the preset historical time period according to the low temperature threshold of each day in the preset historical time period at the temperature collection point; determining the average low temperature threshold as the target extreme low temperature threshold of the temperature collection point; determining the average high temperature threshold in the preset historical time period according to the high temperature threshold of each day in the preset historical time period at the temperature collection point; and determining the average high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0091] In one embodiment, the target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. Determining the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in the preset historical time period includes: determining the lowest low temperature threshold in the preset historical time period according to the low temperature threshold of each day in the preset historical time period at the temperature collection point; determining the lowest low temperature threshold as the target extreme low temperature threshold of the temperature collection point; determining the highest high temperature threshold in the preset historical time period according to the high temperature threshold of each day in the preset historical time period at the temperature collection point; and determining the highest high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0092] In one embodiment, the target extreme temperature threshold includes a target extreme high temperature threshold and a target extreme low temperature threshold. The temperature of the day is judged according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event has occurred, including: when the temperature of the day is higher than the target extreme high temperature threshold, it is determined that an extreme high temperature event has occurred on the day; when the temperature of the day is lower than the target extreme low temperature threshold, it is determined that an extreme low temperature event has occurred on the day.

[0093] In one embodiment, the method also includes: determining the number of extreme high temperature events or extreme low temperature events occurring within a preset time range; and determining the frequency of extreme high temperature events and / or the frequency of extreme low temperature events occurring within the preset time range based on the number of events.

[0094] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt 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.) that contain computer-usable program code.

[0095] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0096] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0097] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0098] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0099] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0100] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0101] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0102] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for diagnosing extreme temperature, characterized in that: The method comprises: Obtain historical temperature data for a preset geographical area within a preset historical time period; For each temperature collection point within the preset geographical range, determine the historical seasonal average temperature of each season within each first historical time range in the historical time period according to the historical temperature data of the temperature collection point, wherein the first historical time range includes multiple seasons; For each day within the first historical time range, determine the temperature deviation value of the day according to the actual temperature of the day and the historical seasonal average temperature of the season; For each day within each first historical time range, determine a preset time window corresponding to the day, wherein the preset time window includes a plurality of consecutive days in which the day is located; For the preset time window corresponding to each day, the temperature deviation values ​​of each day in the preset time window are sorted in ascending order, and the temperature threshold of the day is determined according to the sorted temperature deviation values ​​and the historical seasonal average temperature of the season in which the day is located; For each temperature collection point within the preset geographical range, determine the target extreme temperature threshold of the temperature collection point according to the daily temperature threshold of the temperature collection point within a preset historical time period; For each temperature collection point within the preset geographical area, every day within the preset historical time period, the temperature of the day is judged according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event occurs.

2. The method for diagnosing extreme temperature according to claim 1, characterized in that: The temperature data includes the daily maximum temperature and the daily minimum temperature, and the determining of the historical seasonal average temperature of each season within each first historical time range in the historical time period includes: Determine the historical daily maximum temperature and the historical daily minimum temperature for each day in each season within each first historical time range; Determine the historical seasonal maximum average temperature for each season based on the historical daily maximum temperature for each day in each season; The historical seasonal minimum average temperature of each season is determined based on the historical daily minimum temperature of each day in each season.

3. The method for diagnosing extreme temperature according to claim 1, characterized in that: The temperature data includes the daily maximum temperature and the daily minimum temperature. The temperature deviation value of the day is determined based on the actual temperature of the day and the historical seasonal average temperature of the season, including: The difference between the actual maximum temperature of the day and the historical maximum average temperature of the season in which the day falls is determined as the high temperature deviation value of the day; The difference between the actual daily minimum temperature of the day and the historical seasonal minimum average temperature of the season in which the day occurs is determined as the low temperature deviation value of the day.

4. The method for diagnosing extreme temperature according to claim 3, characterized in that: The temperature deviation value includes a high temperature deviation value and a low temperature deviation value. The temperature threshold value of the day is determined according to the sorted temperature deviation value and the historical seasonal average temperature of the season in which the day is located, including: For the preset time window corresponding to each day, the low temperature deviation value at the first preset percentage of the sorted low temperature deviation value sequence is determined as the target low temperature deviation value for that day; The sum of the target low temperature deviation value of the day and the historical seasonal minimum average temperature of the season in which the day falls is determined as the low temperature threshold of the day; For the preset time window corresponding to each day, the high temperature deviation value at the second preset percentage of the sorted high temperature deviation value sequence is determined as the target high temperature deviation value for that day; The target high temperature deviation value and the historical seasonal maximum average temperature value of the season in which the day occurs are summed to determine the high temperature threshold for the day.

5. The method for diagnosing extreme temperature according to claim 1, characterized in that: The target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. The determination of the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in a preset historical time period includes: Determine the average low temperature threshold value within the preset historical time period according to the low temperature threshold value of each day at the temperature collection point within the preset historical time period; Determining the average low temperature threshold as the target extreme low temperature threshold of the temperature collection point; Determine the average high temperature threshold value within the preset historical time period according to the high temperature threshold value of each day at the temperature collection point within the preset historical time period; The average high temperature threshold is determined as the target extreme high temperature threshold of the temperature collection point.

6. The method for diagnosing extreme temperature according to claim 1, characterized in that: The target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. The determination of the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in a preset historical time period includes: Determine the lowest low temperature threshold value within the preset historical time period according to the low temperature threshold value of each day at the temperature collection point within the preset historical time period; Determining the lowest low temperature threshold as the target extreme low temperature threshold of the temperature collection point; Determine the highest high temperature threshold value within the preset historical time period according to the high temperature threshold value of each day at the temperature collection point within the preset historical time period; The highest high temperature threshold is determined as the target extreme high temperature threshold of the temperature collection point.

7. The method for diagnosing extreme temperature according to claim 1, characterized in that: The target extreme temperature threshold includes a target extreme high temperature threshold and a target extreme low temperature threshold. The judging of the temperature of the day according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event occurs includes: When the temperature on that day is higher than the target extreme high temperature threshold, it is determined that an extreme high temperature event occurs on that day; When the temperature on that day is lower than the target extreme low temperature threshold, it is determined that an extreme low temperature event occurs on that day.

8. The method for diagnosing extreme temperature according to claim 7, characterized in that: The method further comprises: Determine the number of extreme heat events or extreme cold events that occur within a preset time frame; The frequency of occurrence of extreme high temperature events and / or the frequency of occurrence of extreme low temperature events within the preset time range is determined based on the number of events.

9. A device for diagnosing extreme temperatures, characterized in that: include: A data acquisition module is used to obtain historical temperature data of a preset geographical area within a preset historical time period; A seasonal average temperature determination module, for determining, for each temperature collection point within the preset geographical area, the historical seasonal average temperature of each season within each first historical time range in the historical time period according to the historical temperature data of the temperature collection point, wherein the first historical time range includes multiple seasons; A temperature deviation value determination module is used to determine the temperature deviation value of the day according to the actual temperature of the day and the historical seasonal average temperature of the season for each day within the first historical time range; A window setting module, for determining a preset time window corresponding to each day within each first historical time range, wherein the preset time window includes a plurality of consecutive days in which the current day is located; The temperature threshold determination module is used to sort the temperature deviation values ​​of each day in the preset time window corresponding to each day in ascending order, and determine the temperature threshold of the day according to the sorted temperature deviation values ​​and the historical seasonal average temperature of the season in which the day is located; and is also used to determine the target extreme temperature threshold of each temperature collection point within the preset geographical range according to the temperature threshold of each day of the temperature collection point within the preset historical time period; The extreme temperature event judgment module is used to judge the temperature of each temperature collection point within the preset geographical range every day within the preset historical time period according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event occurs.

10. The device for diagnosing extreme temperature according to claim 9, characterized in that: The temperature data includes the daily maximum temperature and the daily minimum temperature. The seasonal average temperature determination module is used to determine the historical seasonal average temperature of each season within each first historical time range in the historical time period, including: Determine the historical daily maximum temperature and the historical daily minimum temperature for each day in each season within each first historical time range; Determine the historical seasonal maximum average temperature for each season based on the historical daily maximum temperature for each day in each season; The historical seasonal minimum average temperature of each season is determined based on the historical daily minimum temperature of each day in each season.

11. The device for diagnosing extreme temperature according to claim 9, characterized in that: The temperature data includes the daily maximum temperature and the daily minimum temperature. The temperature deviation value determination module is used to determine the temperature deviation value of the day according to the actual temperature of the day and the historical seasonal average temperature of the season, including: The difference between the actual maximum temperature of the day and the historical maximum average temperature of the season in which the day falls is determined as the high temperature deviation value of the day; The difference between the actual daily minimum temperature of the day and the historical seasonal minimum average temperature of the season in which the day occurs is determined as the low temperature deviation value of the day.

12. The device for diagnosing extreme temperature according to claim 11, characterized in that: The temperature deviation value includes a high temperature deviation value and a low temperature deviation value. The temperature threshold determination module is used to determine the temperature threshold of the day according to the sorted temperature deviation values ​​and the historical seasonal average temperature of the season in which the day is located, including: For the preset time window corresponding to each day, the low temperature deviation value at the first preset percentage of the sorted low temperature deviation value sequence is determined as the target low temperature deviation value for that day; The sum of the target low temperature deviation value of the day and the historical seasonal minimum average temperature of the season in which the day falls is determined as the low temperature threshold of the day; For the preset time window corresponding to each day, the high temperature deviation value at the second preset percentage of the sorted high temperature deviation value sequence is determined as the target high temperature deviation value for that day; The target high temperature deviation value and the historical seasonal maximum average temperature value of the season in which the day occurs are summed to determine the high temperature threshold for the day.

13. The device for diagnosing extreme temperature according to claim 9, characterized in that: The target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. The temperature threshold determination module is used to determine the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in a preset historical time period at the temperature collection point, including: Determine the average low temperature threshold value within the preset historical time period according to the low temperature threshold value of each day at the temperature collection point within the preset historical time period; Determining the average low temperature threshold as the target extreme low temperature threshold of the temperature collection point; Determine the average high temperature threshold value within the preset historical time period according to the high temperature threshold value of each day at the temperature collection point within the preset historical time period; The average high temperature threshold is determined as the target extreme high temperature threshold of the temperature collection point.

14. The device for diagnosing extreme temperature according to claim 9, characterized in that: The target extreme temperature threshold includes a target extreme low temperature threshold and a target extreme high temperature threshold. The temperature threshold determination module is used to determine the target extreme temperature threshold of the temperature collection point according to the temperature threshold of each day in a preset historical time period at the temperature collection point, including: Determine the lowest low temperature threshold value within the preset historical time period according to the low temperature threshold value of each day at the temperature collection point within the preset historical time period; Determining the lowest low temperature threshold as the target extreme low temperature threshold of the temperature collection point; Determine the highest high temperature threshold value within the preset historical time period according to the high temperature threshold value of each day at the temperature collection point within the preset historical time period; The highest high temperature threshold is determined as the target extreme high temperature threshold of the temperature collection point.

15. The device for diagnosing extreme temperature according to claim 9, characterized in that: The target extreme temperature threshold includes a target extreme high temperature threshold and a target extreme low temperature threshold. The extreme temperature event judgment module is used to judge the temperature of the day according to the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event occurs, including: When the temperature on that day is higher than the target extreme high temperature threshold, it is determined that an extreme high temperature event occurs on that day; When the temperature on that day is lower than the target extreme low temperature threshold, it is determined that an extreme low temperature event occurs on that day.

16. The device for diagnosing extreme temperature according to claim 9, characterized in that: The device also includes: The frequency determination module is used to determine the number of extreme high temperature events or extreme low temperature events occurring within a preset time range, and determine the frequency of extreme high temperature events and / or the frequency of extreme low temperature events occurring within the preset time range based on the number of events.

17. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions for causing a machine to execute the method for diagnosing extreme air temperature according to any one of claims 1 to 8.

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