Methods, devices and storage media for extreme temperature diagnosis

Abstract

This application relates to the field of temperature detection technology, specifically to a method, apparatus, and storage medium for extreme temperature diagnosis. The method includes: acquiring historical temperature data for a preset geographical area within a preset historical time period; determining the historical seasonal average temperature for each season within each first historical time period based on the historical temperature data from each temperature collection point within the preset geographical area; determining the daily temperature deviation value based on the actual daily temperature and the historical seasonal average temperature of the current season; determining a preset time window corresponding to the current day; sorting the daily temperature deviation values ​​within the preset time window in ascending order; and determining the daily temperature threshold based on the sorted temperature deviation values ​​and the historical seasonal average temperature of the current season; and determining a target extreme temperature threshold for the temperature collection point based on the daily temperature thresholds of the temperature collection point within the preset historical time period to determine whether an extreme temperature event has occurred.

Description

Technical Field

[0001] This application relates to the field of temperature detection technology, and specifically to a method, apparatus and storage medium for diagnosing extreme temperatures. Background Technology

[0002] In recent years, driven by global warming, extreme temperature events have become more frequent and intense. Summer heat waves and droughts, and winter cold waves and disasters, have severely impacted the safety of power grid equipment and power supply in various countries. Therefore, it is necessary to accurately identify extreme temperature events to analyze their frequency, intensity, and duration under the background of global warming. Currently, extreme temperature events are typically identified based on percentile threshold methods, defined as rare events exceeding / below the percentile threshold for daily maximum / lowest temperatures. However, if this method uses a long time range (e.g., 15 days, 31 days), known as a "seasonal window," when calculating the percentile threshold for extreme temperature events, this will mix the average seasonal cycle into the extreme threshold, resulting in biases that vary with season, region, time period, and dataset, thus undermining the generally accepted properties of the percentile-based definition of extremes. Conversely, shorter operating windows (e.g., 5 days) limit the sample size on which the percentile threshold is based and reflect daily variability in the threshold, which also contributes to bias. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, and storage medium for diagnosing extreme temperatures that remove seasonal cyclical variations.

[0004] To achieve the above objectives, the first aspect of this application provides a method for diagnosing extreme temperatures, 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 a preset geographical area, the historical seasonal average temperature for each season within each first historical time range is determined based on the historical temperature data of the temperature collection point. The first historical time range includes multiple seasons.

[0007] For each day within the first historical time frame, the temperature deviation value for that day is determined based on the actual temperature of that day and the historical seasonal average temperature of that season.

[0008] For each day within the first historical time range, a preset time window corresponding to that day is determined, wherein the preset time window includes multiple consecutive days in which the current day falls;

[0009] For each day's corresponding preset time window, the temperature deviation values ​​of each day within the preset time window are sorted in ascending order, and the temperature threshold for that day is determined based on the sorted temperature deviation values ​​and the historical seasonal average temperature of the current day.

[0010] For each temperature collection point within a preset geographical area, the target extreme temperature threshold for the temperature collection point is determined based on the daily temperature threshold of the temperature collection point within a preset historical time period.

[0011] For each temperature collection point within a preset geographical area, the temperature of the day is assessed based on the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event has occurred.

[0012] In this embodiment of the application, the temperature data includes daily maximum temperature and daily minimum temperature. 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 historical daily minimum temperature for each day within 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 for each day within each season; and determining the historical seasonal minimum average temperature of each season based on the historical daily minimum temperature for each day within each season.

[0013] In this embodiment of the 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 average temperature of the season includes: determining the difference between the actual daily maximum temperature of the day and the historical average maximum temperature of the season as the high temperature deviation value of the day; and determining the difference between the actual daily minimum temperature of the day and the historical average minimum temperature of the season as the low temperature deviation value of the day.

[0014] In this embodiment, the temperature deviation value includes a high temperature deviation value and a low temperature deviation value. Determining the temperature threshold for the day based on the sorted temperature deviation values ​​and the historical average temperature of the current season includes: for each day's corresponding preset time window, 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 for the day; determining the low temperature threshold for the day by summing the target low temperature deviation value for the day with the historical lowest average temperature of the current season; for each day's corresponding preset time window, 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 for the day; and determining the high temperature threshold for the day by summing the target high temperature deviation value with the historical highest average temperature of the current season.

[0015] In this embodiment of the 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 based on the daily temperature thresholds of the temperature collection point within a preset historical time period includes: determining the average low temperature threshold within the preset historical time period based on the daily low temperature thresholds of 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 within the preset historical time period based on the daily high temperature thresholds of 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 this embodiment of the 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 based on the daily temperature thresholds of the temperature collection point within a preset historical time period includes: determining the lowest low temperature threshold within the preset historical time period based on the daily low temperature thresholds of 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 within the preset historical time period based on the daily high temperature thresholds of the temperature collection point; and determining the highest high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0017] In this embodiment of the application, the target extreme temperature threshold includes a target extreme high temperature threshold and a target extreme low temperature threshold. Judging whether an extreme temperature event has occurred based on the target extreme temperature threshold of the temperature collection point includes: determining that an extreme high temperature event has occurred if the temperature of the day is higher than the target extreme high temperature threshold; and determining that an extreme low temperature event has occurred if the temperature of the day is lower than the target extreme low temperature threshold.

[0018] In this embodiment of the application, the method further 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.

[0019] A second aspect of this application provides an apparatus for diagnosing extreme temperatures, comprising:

[0020] The data acquisition module is used to acquire historical temperature data for a preset geographical area within a preset historical time period.

[0021] The seasonal average temperature determination module is used to determine the historical seasonal average temperature of each season within each first historical time range for each temperature collection point within a preset geographical area, based on the historical temperature data of the temperature collection point. The first historical time range includes multiple seasons.

[0022] The temperature deviation value determination module is used to determine the temperature deviation value for each day within a first historical time range based on the actual temperature of the day and the historical average temperature of the season. The window setting module is used to determine the preset time window corresponding to each day within a first historical time range, wherein the preset time window includes multiple consecutive days of the day.

[0023] The temperature threshold determination module is used to sort the daily temperature deviation values ​​within a preset time window in ascending order for each day, and determine the temperature threshold for that day based on the sorted temperature deviation values ​​and the historical seasonal average temperature of the current day. It is also used to determine the target extreme temperature threshold for each temperature collection point within a preset geographical area, based on the daily temperature thresholds of the temperature collection point within a preset historical time period. The extreme temperature event judgment module is used to judge the temperature of each day within a preset geographical area and a preset historical time period based on the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event has occurred.

[0024] In this embodiment of the application, the temperature data includes daily maximum temperature and 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 historical daily minimum temperature of each day within 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 within each season; and determining the historical seasonal minimum average temperature of each season based on the historical daily minimum temperature of each day within each season.

[0025] In this embodiment of the 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 average temperature of the season in which the day is located. This includes: determining the difference between the actual daily maximum temperature of the day and the historical average maximum temperature of the season in which the day is located as the high temperature deviation value of the day; and determining the difference between the actual daily minimum temperature of the day and the historical average minimum temperature of the season in which the day is located as the low temperature deviation value of the day.

[0026] In this embodiment, 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 for the day based on the sorted temperature deviation values ​​and the historical average temperature of the season in which the day is located. This includes: for a 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 for the day; determining the sum of the target low temperature deviation value for the day and the historical lowest average temperature of the season in which the day is located as the low temperature threshold for the day; for a 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 for the day; and determining the sum of the target high temperature deviation value and the historical highest average temperature of the season in which the day is located as the high temperature threshold for the day.

[0027] In this 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 is used to determine the target extreme temperature threshold of the temperature collection point based on the daily temperature thresholds of the temperature collection point within a preset historical time period. This includes: determining the average low temperature threshold within the preset historical time period based on the daily low temperature thresholds of 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 within the preset historical time period based on the daily high temperature thresholds of 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 this 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 is used to determine the target extreme temperature threshold of the temperature collection point based on the daily temperature thresholds of the temperature collection point within a preset historical time period. This includes: determining the lowest low temperature threshold within the preset historical time period based on the daily low temperature thresholds of 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 within the preset historical time period based on the daily high temperature thresholds of 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 this embodiment of the 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 based on the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event has occurred. This includes: determining that an extreme high temperature event has occurred on the day if the temperature of the day is higher than the target extreme high temperature threshold; and determining that an extreme low temperature event has occurred on the day if the temperature of the day is lower than the target extreme low temperature threshold.

[0030] In this embodiment, the apparatus further includes a frequency determination module, configured to determine the number of extreme high-temperature events or extreme low-temperature events occurring within a preset time range, and to 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 number of events. A third aspect of this application provides a machine-readable storage medium storing instructions that cause a machine to perform a method for extreme temperature diagnosis according to any of the above embodiments.

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

[0032] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0033] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0034] Figure 1 The illustration shows a flowchart of a method for diagnosing extreme temperatures according to an embodiment of this application;

[0035] Figure 2 This schematic diagram illustrates a structural block diagram of an extreme temperature diagnostic device according to an embodiment of this application;

[0036] Figure 3 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0038] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0039] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0040] Figure 1 The illustration schematically shows a flowchart of a method for diagnosing extreme temperatures according to an embodiment of this application. Figure 1 As shown in the embodiment of this application, a method for diagnosing extreme temperatures is provided, which may include the following steps.

[0041] Step 101: Obtain historical temperature data for a preset geographical area within a preset historical time period;

[0042] Step 102: For each temperature collection point within the preset geographical area, based on the historical temperature data of the temperature collection point, determine the historical seasonal average temperature of each season within each first historical time range in the historical time period, 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 for that day based on the actual temperature of that day and the historical seasonal average temperature of the season in which it is located;

[0044] Step 104: For each day within the first historical time range, determine the preset time window corresponding to that day, wherein the preset time window includes multiple consecutive days in which the current day falls;

[0045] Step 105: For each day's corresponding preset time window, sort the temperature deviation values ​​of each day in the preset time window in ascending order, and determine the temperature threshold for the day based on the sorted temperature deviation values ​​and the historical seasonal average temperature of the day.

[0046] Step 106: For each temperature collection point within the preset geographical area, determine the target extreme temperature threshold of the temperature collection point based on the daily temperature threshold of the temperature collection point within the preset historical time period.

[0047] Step 107: For each temperature collection point within a preset geographical area, determine the temperature of the day based on the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event has occurred.

[0048] The processor can acquire historical temperature data for a preset geographical area within a preset historical time period. The preset historical time period can include multiple first historical time ranges, and each first historical time range can include multiple seasons. For example, assuming 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 can include 30 first historical time ranges, and each first historical time range includes 4 seasons. The preset geographical area can include multiple temperature collection points. For each temperature collection point within the preset geographical area, the processor can determine the historical average temperature of each season within each first historical time range based on the historical temperature data of the collection point. For each day within each first historical time range, the processor can acquire the actual temperature of that day and determine the temperature deviation value of that day based on the actual temperature and the historical average temperature of the season in which that day falls. For example, assuming the processor is targeting day A within the first historical time range, and the season in which that day falls is winter, the processor can determine the temperature deviation value of day A based on the actual temperature of day A and the historical average winter temperature within that first historical time range. For each day within a first historical time range, the processor can determine a preset time window corresponding to that day. This preset time window includes multiple consecutive days, such as the current day plus ±d days. For each preset time window, the processor can sort the daily temperature deviation values ​​within that window in ascending order. For example, assuming d is 10, the processor can use the current day, the 10 days before it, and the 10 days after it as the preset time window for that day. For the 21 days included within the preset time window, the processor can determine the daily temperature deviation values, sort them in ascending order, and determine the daily temperature threshold based on the sorted temperature deviation values ​​and the historical seasonal average temperature of the current day. For each temperature collection point within a preset geographical area, the processor can determine the target extreme temperature threshold corresponding to the temperature collection point based on the daily temperature threshold of the temperature collection point within a preset historical time period. For each temperature collection point within a preset geographical area within a preset historical time period, the processor can detect the daily temperature based on the target extreme temperature threshold of that temperature collection point to determine whether an extreme temperature event has occurred that day.

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

[0050] Temperature data includes daily maximum and minimum temperatures. The processor can obtain the historical daily maximum and minimum temperatures for each day within a preset historical time period. For each season included in the first historical time range, the processor can determine the historical seasonal average temperature for each season. The processor can determine the historical daily maximum and minimum temperatures for each day within each season within the first historical time range. Based on the historical daily maximum temperatures for each day within each season, the processor determines the historical seasonal maximum average temperature for that season. Based on the historical daily minimum temperatures for each day within each season, the processor determines the historical seasonal minimum average temperature for each season.

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

[0052] Temperature data can include daily maximum and minimum temperatures. The processor can obtain the historical daily maximum and minimum temperatures for each day within a preset historical time period. For each day within a first historical time range, the processor can obtain the historical daily maximum temperature and determine the season for that day. The processor can also obtain the historical daily maximum temperatures for the days included in that season and determine the historical average maximum temperature for that season based on the historical daily maximum temperatures for those days. After determining the historical average maximum temperature for each day within the preset historical time period, the difference between the actual daily maximum temperature and the historical average maximum temperature for that season can be determined as the high-temperature deviation value for that day. Similarly, the processor can determine the low-temperature deviation value for that day by comparing the actual daily minimum temperature with the historical average minimum temperature for that season.

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

[0054] The temperature deviation values ​​include high-temperature deviation values ​​and low-temperature deviation values. For each preset time window, the processor can determine the daily temperature deviation value within that window. Both the daily high-temperature and low-temperature deviation values ​​can be sorted in ascending order. For each preset time window, and given the sorted sequence of low-temperature deviation values, the processor can determine the target low-temperature deviation value for that day as the first preset percentage of the low-temperature deviation value in the sequence. For example, if the processor sets the first preset percentage to 5%, then the value at 5% of the low-temperature deviation value sequence will be determined as the target low-temperature deviation value for that day. After determining the target low-temperature deviation value for that day, the processor can determine the low-temperature threshold for that day by summing the target low-temperature deviation value for that day with the historical average lowest temperature of the current season. For each preset time window, and given the sorted sequence of high-temperature deviation values, the processor can determine the target high-temperature deviation value for that day as the second preset percentage of the high-temperature deviation value in the sequence. For example, if the processor sets the second preset percentage to 95%, then the value at 95% of the high-temperature deviation value sequence will be determined as the target high-temperature deviation value for that day. After determining the target high temperature deviation value for the day, the processor can use the sum of the target high temperature deviation value for the day and the historical average highest temperature of the season in which the day is located to determine 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 a temperature collection point based on the daily temperature thresholds of the temperature collection point within a preset historical time period includes: determining the average low temperature threshold within the preset historical time period based on the daily low temperature thresholds of 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 within the preset historical time period based on the daily high temperature thresholds of 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 area, the processor can determine the target extreme temperature threshold for that collection point based on the daily temperature thresholds within a preset historical time period. The processor can also determine the average low temperature threshold for that collection point within the preset historical time period based on the daily low temperature thresholds, and set this average low temperature threshold as the target extreme low temperature threshold for that collection point. Similarly, the processor can determine the average high temperature threshold for that collection point within the preset historical time period based on the daily high temperature thresholds, and set this average high temperature threshold as the target extreme high temperature threshold for that 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 a temperature collection point based on the daily temperature thresholds of the temperature collection point within a preset historical time period includes: determining the lowest low temperature threshold within the preset historical time period based on the daily low temperature thresholds of 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 within the preset historical time period based on the daily high temperature thresholds of 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 area, the processor can determine the target extreme temperature threshold for that collection point based on the daily temperature thresholds within a preset historical time period. The processor can also determine the lowest low temperature threshold for that collection point within the preset historical time period based on the daily low temperature thresholds, and set this lowest low temperature threshold as the target extreme low temperature threshold for that collection point. Similarly, the processor can determine the highest high temperature threshold for that collection point within the preset historical time period based on the daily high temperature thresholds, and set this highest high temperature threshold as the target extreme high temperature threshold for that 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. Determining whether an extreme temperature event has occurred based on the target extreme temperature threshold of the temperature collection point includes: determining that an extreme high temperature event has occurred if the temperature of the day is higher than the target extreme high temperature threshold; and determining that an extreme low temperature event has occurred if the temperature of the day is lower than the target extreme low temperature threshold.

[0060] The target extreme temperature threshold includes a target extreme high temperature threshold and a target extreme low temperature threshold. The processor can use these thresholds to determine whether a daily temperature event has occurred within a historical time period. If the temperature on a given day is higher than the target extreme high temperature threshold, the processor can determine that an extreme high temperature event has occurred that day. If the temperature on a given day is lower than the target extreme low temperature threshold, the processor can determine that an extreme low temperature event has occurred that day.

[0061] In one embodiment, the method further 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 for the temperature collection points, the processor can set a preset time range according to user needs. It then uses the target extreme temperature threshold to determine whether extreme high-temperature or extreme-low-temperature events will occur within the preset time range. For example, the preset time range could be a future period. The processor can determine the number of extreme events occurring within the preset time range and, based on the number of events, determine the frequency of extreme high-temperature events and / or the frequency of extreme low-temperature events within the preset time range.

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

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

[0065] The seasonal average temperature determination module 202 is used to determine the historical seasonal average temperature of each season within each first historical time range for each temperature collection point within a preset geographical area, based on the historical temperature data of the temperature collection point. The first historical time range includes multiple seasons.

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

[0067] The window setting module 204 is used to determine the preset time window corresponding to each day within each first historical time range, wherein the preset time window includes multiple 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 day, and determine the temperature threshold of the day based on the sorted temperature deviation values ​​and the historical seasonal average temperature of the day; it is also used to determine the target extreme temperature threshold of each temperature collection point within a preset geographical area based on the temperature threshold of the temperature collection point in the preset historical time period.

[0069] The extreme temperature event judgment module 206 is used to judge the temperature of each day within a preset historical time period for each temperature collection point within a preset geographical area, based on the target extreme temperature threshold of the temperature collection point, to determine whether an extreme temperature event has occurred.

[0070] In one embodiment, the temperature data includes daily maximum temperature and daily minimum temperature. The seasonal average temperature determination module 202 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 historical daily minimum temperature for each day within 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 for each day within each season; and determining the historical seasonal minimum average temperature of each season based on the historical daily minimum temperature for each day within 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 based on the actual temperature of the day and the historical average temperature of the season in which the day is located, including: determining the difference between the actual daily maximum temperature of the day and the historical average maximum temperature of the season in which the day is located as the high temperature deviation value of the day; and determining the difference between the actual daily minimum temperature of the day and the historical average minimum 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 for the day based on the sorted temperature deviation values ​​and the historical average temperature of the season in which the day is located. This includes: for a preset time window corresponding to each day, determining the low temperature deviation value at a first preset percentage in the sorted low temperature deviation value sequence as the target low temperature deviation value for the day; determining the sum of the target low temperature deviation value for the day and the historical lowest average temperature of the season in which the day is located as the low temperature threshold for the day; for a preset time window corresponding to each day, determining the high temperature deviation value at a second preset percentage in the sorted high temperature deviation value sequence as the target high temperature deviation value for the day; and determining the sum of the target high temperature deviation value and the historical highest average temperature of the season in which the day is located as the high temperature threshold for 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 based on the daily temperature thresholds of the temperature collection point within a preset historical time period, including: determining the average low temperature threshold within the preset historical time period based on the daily low temperature thresholds of 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 within the preset historical time period based on the daily high temperature thresholds of 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 based on the daily temperature thresholds of the temperature collection point within a preset historical time period, including: determining the lowest low temperature threshold within the preset historical time period based on the daily low temperature thresholds of 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 within the preset historical time period based on the daily high temperature thresholds of 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 based on the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event has occurred, including: if the temperature of the day is higher than the target extreme high temperature threshold, determining that an extreme high temperature event has occurred on the day; if the temperature of the day is lower than the target extreme low temperature threshold, determining that an extreme low temperature event has occurred on the day.

[0076] In one embodiment, such as Figure 2 As shown, the extreme temperature diagnosis device 200 further includes: a frequency determination module 207, used to determine the number of extreme high temperature events or extreme low temperature events occurring within a preset time range, and to 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 acquire historical temperature data for a preset geographical area within a preset historical time period. The preset historical time period can include multiple first historical time ranges, and each first historical time range can include multiple seasons. For example, assuming 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 can include 30 first historical time ranges, and each first historical time range includes 4 seasons. The preset geographical area can include multiple temperature collection points. For each temperature collection point within the preset geographical area, the seasonal average temperature determination module 202 can determine the historical seasonal average temperature for each season within each first historical time range in the historical time period based on the historical temperature data of the temperature collection point. The temperature data includes daily maximum and minimum temperatures. The seasonal average temperature determination module 202 can acquire the historical daily maximum and minimum temperatures for each day within the preset historical time period. The seasonal average temperature determination module 202 can determine the historical seasonal average temperature for each season included in the first historical time range. The seasonal average temperature determination module 202 can determine the historical daily maximum temperature and historical daily minimum temperature for each day within each season included in the first historical time range. Based on the historical daily maximum temperature for each day within each season, the historical seasonal maximum average temperature for that season is determined. The seasonal average temperature determination module 202 can determine the historical seasonal minimum average temperature for each season based on the historical daily minimum temperature for each day within each season.

[0078] For each day within a first historical time frame, the temperature deviation determination module 203 can obtain the historical daily maximum temperature for that day and determine the season. The module can also obtain the historical daily maximum temperatures for all days included in that season and determine the historical seasonal maximum average temperature for that season. After determining the historical seasonal maximum average temperature for each day within the preset historical time frame, the difference between the actual daily maximum temperature and the historical seasonal maximum average temperature for that day can be determined as the high-temperature deviation value for that day. Similarly, the temperature deviation determination module 203 can determine the low-temperature deviation value for that day by comparing the actual daily minimum temperature with the historical seasonal minimum average temperature for that day. For example, assuming the temperature deviation determination module 203 is targeting day A within the first historical time frame, and the season for that day is winter, the module can determine the temperature deviation value for day A based on the actual temperature of day A and the historical winter average temperature within the first historical time frame.

[0079] For each day within a first historical time range, the window setting module 204 can determine a preset time window corresponding to that day. The preset time window includes multiple consecutive days including the current day. For example, the window setting module 204 can use the current day and ±d days as the preset time window corresponding to that day. For each preset time window, the temperature threshold determination module 205 can sort the temperature deviation values ​​of each day within the preset time window in ascending order. For example, assuming d is 10, the temperature threshold determination module 205 can use the current day, the 10 days before it, and the 10 days after it as the preset time window corresponding to that day. For the 21 days included within the preset time window, the temperature threshold determination module 205 can determine the temperature deviation values ​​of each day, sort them in ascending order, and determine the temperature threshold for that day based on the sorted temperature deviation values ​​and the historical seasonal average temperature of the current season. The temperature deviation values ​​include high-temperature deviation values ​​and low-temperature deviation values. For a preset time window corresponding to each day, and for a sorted sequence of low-temperature deviation values, the temperature threshold determination module 205 can determine the low-temperature deviation value at the first preset percentage in the sequence as the target low-temperature deviation value for that day corresponding to the preset time window. For example, if the temperature threshold determination module 205 sets the first preset percentage to 5%, then 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 for that day. After determining the target low-temperature deviation value for that day, the temperature threshold determination module 205 can determine the low-temperature threshold for that day by summing the target low-temperature deviation value for that day with the historical seasonal minimum average temperature of that season. For a preset time window corresponding to each day, and for a sorted sequence of high-temperature deviation values, the temperature threshold determination module 205 can determine the high-temperature deviation value at the second preset percentage in the sequence as the target high-temperature deviation value for that day corresponding to the preset time window. For example, if the temperature threshold determination module 205 sets the second preset percentage to 95%, then 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 for that day. After determining the target high temperature deviation value for the day, the temperature threshold determination module 205 can determine the high temperature threshold for the day by summing the target high temperature deviation value for the day with the historical average highest temperature of the season in which the day is located.

[0080] For each temperature collection point within a preset geographical area, the temperature threshold determination module 205 can determine the target extreme temperature threshold corresponding to the temperature collection point based on the daily temperature thresholds of the temperature collection point within a preset historical time period. The temperature threshold determination module 205 can also determine the average low temperature threshold of the temperature collection point within the preset historical time period based on the daily low temperature thresholds of the temperature collection point, and determine the obtained average low temperature threshold as the target extreme low temperature threshold for the temperature collection point. Similarly, the temperature threshold determination module 205 can determine the average high temperature threshold of the temperature collection point within the preset historical time period based on the daily high temperature thresholds of the temperature collection point, and determine the obtained average high temperature threshold as the target extreme high temperature threshold for the temperature collection point. Alternatively, the temperature threshold determination module 205 can determine the lowest low temperature threshold of the temperature collection point within the preset historical time period based on the daily low temperature thresholds of the temperature collection point, and determine the obtained lowest low temperature threshold as the target extreme low temperature threshold for the temperature collection point. Similarly, the temperature threshold determination module 205 can determine the highest 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 highest high temperature threshold as the target extreme high temperature threshold of the temperature collection point.

[0081] For each temperature collection point within a preset geographical area, the extreme temperature event judgment module 206 can detect the daily temperature based on the target extreme temperature threshold for that collection point to determine whether an extreme temperature event has occurred that day. If the daily temperature is higher than the target extreme high temperature threshold, the extreme temperature event judgment module 206 can determine that an extreme high temperature event has occurred that day. If the daily temperature is lower than the target extreme low temperature threshold, it can determine that an extreme low temperature event has occurred that day. Furthermore, a frequency determination module 207 is included. The frequency determination module 207 can set a preset time range according to user needs and determine whether an extreme high temperature event or an extreme low temperature event has occurred within the preset time range based on 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 extreme events occurring 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] By using the above technical solutions, the deviation caused by the length of the operating window can be reduced or eliminated by removing the average value of the seasonal cycle, thereby improving the accuracy of the frequency estimation of extreme temperature events.

[0083] The memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like 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 that stores instructions that, when executed by a processor, cause the processor to perform a method for diagnosing extreme temperatures according to any of the above embodiments.

[0085] In one embodiment, a computer device is provided, which 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, memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes 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). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The database stores temperature-related data. The network interface A02 communicates with external terminals via a network connection. When executed by the processor A01, the computer program B02 implements a method for diagnosing extreme temperatures.

[0086] This application provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring historical temperature data for a preset geographical area within a preset historical time period; for each temperature collection point within the preset geographical area, determining the historical seasonal average temperature for each season within each first historical time period based on the historical temperature data of the collection point, wherein the first historical time period includes multiple seasons; for each day within each first historical time period, determining the temperature deviation value for that day based on the actual temperature of that day and the historical seasonal average temperature of the season in which it is located; for each day within each first historical time period, determining the temperature deviation value compared to the actual temperature of that day and the historical seasonal average temperature of the season in which it is located. The system defines a preset time window corresponding to each day, which includes multiple consecutive days. For each day's preset time window, the daily temperature deviation values ​​are sorted in ascending order, and the daily temperature threshold is determined based on the sorted temperature deviation values ​​and the historical seasonal average temperature of the day. For each temperature collection point within a preset geographical area, the target extreme temperature threshold for that point is determined based on the daily temperature thresholds within a preset historical time period. Finally, for each day within a preset historical time period, the temperature at each temperature collection point within the preset geographical area is assessed based on the target extreme temperature threshold to determine whether an extreme temperature event has occurred.

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

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

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

[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 a temperature collection point based on the daily temperature thresholds of the temperature collection point within a preset historical time period includes: determining the average low temperature threshold within the preset historical time period based on the daily low temperature thresholds of 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 within the preset historical time period based on the daily high temperature thresholds of 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 a temperature collection point based on the daily temperature thresholds of the temperature collection point within a preset historical time period includes: determining the lowest low temperature threshold within the preset historical time period based on the daily low temperature thresholds of 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 within the preset historical time period based on the daily high temperature thresholds of 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. Determining whether an extreme temperature event has occurred based on the target extreme temperature threshold of the temperature collection point includes: determining that an extreme high temperature event has occurred if the temperature of the day is higher than the target extreme high temperature threshold; and determining that an extreme low temperature event has occurred if the temperature of the day is lower than the target extreme low temperature threshold.

[0093] In one embodiment, the method further 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 understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function 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] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0100] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0101] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0102] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for diagnosing extreme temperatures, characterized in that, The method includes: Obtain historical temperature data for a preset geographical area within a preset historical time period, wherein the historical temperature data includes daily maximum temperature and daily minimum temperature; For each temperature collection point within the preset geographical area, the historical seasonal average temperature of each season within each first historical time range is determined based on the historical temperature data of the temperature collection point, wherein the first historical time range includes multiple seasons; For each day within a first historical time frame, the temperature deviation value for that day is determined based on the actual temperature of that day and the historical seasonal average temperature of the season in which it occurs. The temperature deviation value includes a high temperature deviation value and a low temperature deviation value. For each day within a first historical time range, a preset time window corresponding to that day is determined, wherein the preset time window includes multiple consecutive days in which the current day falls; For each day's corresponding preset time window, the temperature deviation values ​​of each day within the preset time window are sorted in ascending order, and the temperature threshold for that day is determined based on the sorted temperature deviation values ​​and the historical seasonal average temperature of the current day. For each temperature collection point within the preset geographical area, a target extreme temperature threshold for the temperature collection point is determined based on 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, the temperature of the day is determined based on the target extreme temperature threshold of the temperature collection point to determine whether an extreme temperature event has occurred. The step of determining the temperature deviation value of a day based on the actual temperature of the day and the historical average temperature of the season includes: determining the difference between the actual maximum temperature of the day and the historical average maximum temperature of the season as the high temperature deviation value of the day; and determining the difference between the actual minimum temperature of the day and the historical average minimum temperature of the season as the low temperature deviation value of the day. The step of determining the temperature threshold for the day based on the sorted temperature deviation values ​​and the historical seasonal average temperature of the current day includes: For each day's corresponding preset time window, 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 target low temperature deviation value for the day is determined by the sum of the historical seasonal minimum average temperature for that day. For each day's corresponding preset time window, the high temperature deviation value at the second preset percentage in 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 is summed with the historical seasonal maximum average temperature value of the current day to determine the high temperature threshold for that day.

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

3. The method for diagnosing extreme temperatures 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. Determining the target extreme temperature threshold of the temperature collection point based on the daily temperature thresholds of the temperature collection point within a preset historical time period includes: The average low temperature threshold within the preset historical time period is determined based on the daily low temperature threshold of the temperature collection point within the preset historical time period. The average low temperature threshold is determined as the target extreme low temperature threshold of the temperature collection point; The average high temperature threshold within the preset historical time period is determined based on the daily high temperature threshold of 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.

4. The method for diagnosing extreme temperatures 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. Determining the target extreme temperature threshold of the temperature collection point based on the daily temperature thresholds of the temperature collection point within a preset historical time period includes: The lowest low temperature threshold within the preset historical time period is determined based on the daily low temperature threshold of the temperature collection point within the preset historical time period. The lowest low temperature threshold is determined as the target extreme low temperature threshold of the temperature collection point; The highest high temperature threshold within the preset historical time period is determined based on the daily high temperature threshold of 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.

5. The method for diagnosing extreme temperatures 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 step of determining whether an extreme temperature event has occurred based on the target extreme temperature threshold of the temperature collection point includes: An extreme high-temperature event is determined to have occurred on a given day if the temperature exceeds the target extreme high-temperature threshold. An extreme low temperature event is determined to have occurred on a given day if the temperature is below the target extreme low temperature threshold.

6. The method for diagnosing extreme temperatures according to claim 5, characterized in that, The method further includes: Determine the number of extreme high temperature events or extreme low temperature events that occur within a preset time range; The frequency of extreme high-temperature events and / or the frequency of extreme low-temperature events within the preset time range are determined based on the number of events.

7. A device for diagnosing extreme temperatures, characterized in that, include: The memory is configured to store instructions; as well as A processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the method for diagnosing extreme temperatures according to any one of claims 1 to 6.

8. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the method for diagnosing extreme temperatures according to any one of claims 1 to 6.

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