Day and night time based high temperature heat wave detection method, device and equipment

Abstract

The application relates to the technical field of high-temperature heat wave detection, in particular to a high-temperature heat wave detection method based on day and night time, which comprises the following steps: acquiring an hourly day and night distribution map of a target region in a target time period; constructing an hourly day and night judgment matrix of the target region in the target time period according to day and night information corresponding to a plurality of grids in the hourly day and night distribution map; acquiring hourly apparent temperature data of the target region in the target time period; acquiring hourly daytime apparent temperature data and hourly nighttime apparent temperature data of the target region in the target time period according to the hourly apparent temperature data of the target region in the target time period and the hourly day and night judgment matrix; acquiring temperature threshold data; and obtaining day and night high-temperature heat wave detection data of the target region in the target time period according to the hourly daytime apparent temperature data, the hourly nighttime apparent temperature data and the temperature threshold data.

Description

Technical Field

[0001] This invention relates to the field of high-temperature heat wave detection technology, and in particular to a method, apparatus, equipment, and storage medium for high-temperature heat wave detection based on day and night time. Background Technology

[0002] The continued growth of global heat waves has caused enormous socio-economic and natural ecological impacts. Monitoring changes in heat waves, attribution analysis, and impact assessment have received widespread attention, with diurnal heat waves being one of the most closely watched topics.

[0003] Currently, most studies analyze daytime / nighttime heat waves on a diurnal scale, defining daytime / nighttime heat waves as consecutive days where the daily maximum / lowest temperature exceeds a certain high-temperature threshold. However, the following problems exist: (1) Using the daily maximum / lowest temperature to represent daytime / nighttime temperature is only an approximation, and there may still be exceptions, i.e., the daily maximum / lowest temperature may occur simultaneously during the day or night; (2) Using diurnal temperature data to detect heat waves may lead to systematic bias, as the daily maximum temperature is an instantaneous temperature and cannot fully represent the temperature changes throughout the day. Since the human body's tolerance to high temperatures is usually only a few hours or even less, diurnal heat wave detection methods that do not consider the temperature changes throughout the day may identify "high-temperature fragments" as heat waves, making it difficult to accurately detect heat waves. Summary of the Invention

[0004] Based on this, the purpose of this invention is to provide a method, apparatus, device, and storage medium for detecting high-temperature heat waves based on day and night time. Based on the hourly day and night distribution map, a corresponding hourly day and night judgment matrix is ​​constructed. By acquiring the hourly perceived temperature data and the hourly day and night judgment matrix, the daytime perceived temperature data and the nighttime perceived temperature data are divided. And by using a preset temperature threshold, high-temperature heat waves are detected, thus achieving more accurate and efficient detection of high-temperature heat waves.

[0005] In a first aspect, embodiments of this application provide a method for detecting high-temperature heat waves based on day and night time, comprising the following steps:

[0006] Obtain an hourly day-night distribution map of the target area within a target time period, wherein the hourly day-night distribution map includes several grids and the day-night information corresponding to the grids;

[0007] Based on the day and night information corresponding to several grids in the hourly day and night distribution map, an hourly day and night judgment matrix for the target area within the target time period is constructed, wherein the hourly day and night judgment matrix includes day and night correlation vectors corresponding to several grids;

[0008] Obtain hourly perceived temperature data of the target area within the target time period. Based on the hourly perceived temperature data of the target area within the target time period and the hourly day-night judgment matrix, obtain hourly daytime perceived temperature data and hourly nighttime perceived temperature data of the target area within the target time period.

[0009] Temperature threshold data is obtained, and based on the hourly daytime perceived temperature data, hourly nighttime perceived temperature data, and temperature threshold data, daytime and nighttime high temperature heat wave detection data of the target area within the target time period is obtained.

[0010] Secondly, embodiments of this application provide a high-temperature heat wave detection device based on day and night time, comprising:

[0011] The hourly day-night distribution map acquisition module is used to acquire the hourly day-night distribution map of the target area within the target time period. The hourly day-night distribution map includes several grids and the day-night information corresponding to the grids.

[0012] The hourly day-night judgment matrix construction module is used to construct the hourly day-night judgment matrix of the target area within the target time period based on the day-night information corresponding to several grids in the hourly day-night distribution map.

[0013] The day and night perceived temperature data acquisition module is used to acquire hourly perceived temperature data of the target area within a target time period. Based on the hourly perceived temperature data of the target area within the target time period and the hourly day and night judgment matrix, it acquires hourly daytime perceived temperature data and hourly nighttime perceived temperature data of the target area within the target time period.

[0014] The day and night high temperature heat wave detection data acquisition module is used to acquire temperature threshold data. Based on the hourly daytime perceived temperature data, hourly nighttime perceived temperature data, and temperature threshold data, it obtains the day and night high temperature heat wave detection data of the target area within the target time period.

[0015] Thirdly, embodiments of this application provide a computer device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, it implements the steps of the high-temperature heat wave detection method based on day and night time as described in the first aspect.

[0016] Fourthly, embodiments of this application provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the high-temperature heat wave detection method based on day and night time as described in the first aspect.

[0017] In this application embodiment, a method, apparatus, device, and storage medium for detecting high temperature heat waves based on day and night time are provided. Based on the hourly day and night distribution map, a corresponding hourly day and night judgment matrix is ​​constructed. By acquiring the hourly perceived temperature data and the hourly day and night judgment matrix, the daytime perceived temperature data and the nighttime perceived temperature data are divided. And by using a preset temperature threshold, high temperature heat waves are detected, thus achieving more accurate and efficient detection of high temperature heat waves.

[0018] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0019] Figure 1 A flowchart illustrating a high-temperature heat wave detection method based on day and night time, provided in one embodiment of this application;

[0020] Figure 2 This is a flowchart illustrating step S3 of a day-night-based high-temperature heat wave detection method provided in one embodiment of this application.

[0021] Figure 3 This is a flowchart illustrating step S4 of a day-night-based high-temperature heat wave detection method provided in one embodiment of this application.

[0022] Figure 4 This is a flowchart illustrating step S4 of a day-night-based high-temperature heat wave detection method provided in one embodiment of this application.

[0023] Figure 5 A schematic diagram of a high-temperature heat wave detection device based on day and night time provided in one embodiment of this application;

[0024] Figure 6 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0025] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0026] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0027] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0028] Please see Figure 1 , Figure 1 The following is a flowchart illustrating a high-temperature heat wave detection method based on day and night time, according to an embodiment of this application. The method includes the following steps:

[0029] S1: Obtain the hourly day-night distribution map of the target area within the target time period.

[0030] The hourly day-night distribution map includes several grids and the day-night information corresponding to the grids. The day-night information is used to indicate whether the current hour's corresponding grid is in daytime or nighttime. The day-night information can be confirmed based on the grid's corresponding latitude and longitude information and date information.

[0031] The execution entity of the day-night-based high temperature heat wave detection method is the detection equipment (hereinafter referred to as the detection equipment). In an optional embodiment, the detection equipment may be a computer device, a server, or a server cluster composed of multiple computer devices.

[0032] In this embodiment, the detection device acquires an hourly day-night distribution map of the target area within a target time period. Specifically, the detection device acquires daily climate data of the target area input by the user within a target time period. Alternatively, it can establish a data connection with a preset network database through a preset interface to acquire daily climate data of the target area within a target time period from the network database.

[0033] S2: Based on the day and night information corresponding to several grids in the hourly day and night distribution map, construct the hourly day and night judgment matrix of the target area within the target time period.

[0034] The hourly day-night distribution map also includes geographic information corresponding to several grids, wherein the geographic information reflects the distribution of water and land in the grid and the corresponding outline of the land.

[0035] In this embodiment, to better highlight the day and night information corresponding to several grids in the hourly day and night distribution map, the detection device performs an initial binarization process on the hourly day and night distribution map to remove the geographical information corresponding to several grids in the hourly day and night distribution map, obtaining the hourly day and night distribution map after the initial binarization process, while retaining the corresponding day and night information. Then, the hourly day and night distribution map after the initial binarization process is binarized again. Based on the day and night information corresponding to several grids in the hourly day and night distribution map, the hourly day and night distribution map after the initial binarization process is transformed into an hourly day and night judgment matrix. The hourly day and night judgment matrix includes day and night correlation vectors corresponding to several grids. Specifically, when the value of the day and night correlation vector corresponding to a grid in the hourly day and night distribution map is 1, the grid represents daytime; when the value of the day and night correlation vector corresponding to a grid in the hourly day and night distribution map is 0, the grid represents nighttime.

[0036] S3: Obtain hourly perceived temperature data of the target area within the target time period. Based on the hourly perceived temperature data of the target area within the target time period and the hourly day-night judgment matrix, obtain the hourly daytime perceived temperature data and hourly nighttime perceived temperature data of the target area within the target time period.

[0037] The perceived temperature is the human body's equivalent temperature, which takes into account multiple meteorological factors such as air temperature, humidity, wind speed, and solar radiation.

[0038] In this embodiment, the detection device acquires hourly perceived temperature data of the target area within a target time period. Based on the hourly perceived temperature data of the target area within the target time period and the hourly day-night judgment matrix, it acquires hourly daytime perceived temperature data and hourly nighttime perceived temperature data of the target area within the target time period.

[0039] In an optional embodiment, the hourly perceived temperature data includes perceived temperature data corresponding to several grids; please refer to [link to relevant documentation]. Figure 2 , Figure 2 The flowchart of step S3 in the high-temperature heat wave detection method based on day and night time provided in one embodiment of this application includes steps S31 to S32, as follows:

[0040] S31: Based on the day-night correlation vectors corresponding to several grids in the hourly day-night judgment matrix, label the perceived temperature data corresponding to several grids in the hourly perceived temperature data, and obtain the daytime perceived temperature data and nighttime perceived temperature data corresponding to several grids.

[0041] In this embodiment, the detection device labels the perceived temperature data corresponding to several grids in the hourly perceived temperature data according to the day-night correlation vectors corresponding to several grids in the hourly day-night judgment matrix, and obtains the daytime perceived temperature data and nighttime perceived temperature data corresponding to several grids.

[0042] S32: Combine the daytime perceived temperature data to construct the hourly daytime perceived temperature data, and combine the nighttime perceived temperature data to construct the hourly nighttime perceived temperature data.

[0043] In this embodiment, the detection device combines the daytime perceived temperature data to construct the hourly daytime perceived temperature data, and combines the nighttime perceived temperature data to construct the hourly nighttime perceived temperature data.

[0044] S4: Obtain temperature threshold data. Based on the hourly daytime perceived temperature data, hourly nighttime perceived temperature data, and temperature threshold data, obtain the daytime and nighttime high temperature heat wave detection data of the target area within the target time period.

[0045] In this embodiment, the detection device acquires temperature threshold data, and based on the hourly daytime perceived temperature data, hourly nighttime perceived temperature data, and temperature threshold data, obtains daytime and nighttime high temperature heat wave detection data for the target area within the target time period.

[0046] In an optional embodiment, the temperature threshold data includes relative high temperature threshold data and absolute high temperature threshold data. The relative high temperature threshold data is determined by a certain percentile of the daily maximum perceived temperature data over many years within a certain time window. The absolute high temperature threshold data is the high temperature threshold data preset by the user. The relative high temperature threshold data includes daytime relative high temperature threshold data and nighttime high temperature threshold data. The absolute high temperature threshold data includes daytime absolute high temperature threshold data and nighttime absolute high temperature threshold data.

[0047] Please see Figure 3 , Figure 3 The flowchart of step S4 in the high-temperature heat wave detection method based on day and night time provided in one embodiment of this application includes steps S41 to S43, as follows:

[0048] S41: Obtain the daytime maximum perceived temperature dataset and the nighttime maximum perceived temperature dataset for several years in the target area.

[0049] The dataset of highest perceived temperature includes daily highest perceived temperature data for several corresponding years.

[0050] The daytime maximum perceived temperature dataset includes hourly daytime maximum perceived temperature data for several dates corresponding to several years, and the nighttime maximum perceived temperature dataset includes hourly nighttime maximum perceived temperature data for several dates corresponding to several years.

[0051] In this embodiment, the detection device acquires data sets of the highest daytime and nighttime perceived temperatures for several years corresponding to the target area. Specifically, the detection device acquires data sets of the highest daytime and nighttime perceived temperatures for several years corresponding to the target area input by the user, or it can establish a data connection with a preset network database through a preset interface to obtain data sets of the highest daytime and nighttime perceived temperatures for several years corresponding to the target area from the network database.

[0052] S42: Based on the daytime maximum perceived temperature dataset and nighttime maximum perceived temperature dataset corresponding to the several years, and a preset number of samples, combine and sort the hourly maximum perceived temperature data around the date corresponding to the hourly daytime maximum perceived temperature data in the daytime maximum perceived temperature dataset corresponding to the several years, which satisfy the number of samples, to obtain an hourly maximum perceived temperature data sequence. Combine and sort the hourly maximum perceived temperature data around the date corresponding to the hourly nighttime maximum perceived temperature data in the nighttime maximum perceived temperature dataset corresponding to the several years, which satisfy the number of samples, to obtain an hourly maximum perceived temperature data sequence.

[0053] In this embodiment, the detection device, based on the daytime maximum perceived temperature dataset and nighttime maximum perceived temperature dataset corresponding to the plurality of years, and a preset number of samples, combines and sorts the hourly maximum perceived temperature data around the date corresponding to the hourly daytime perceived temperature data in the plurality of years' daytime maximum perceived temperature dataset, which satisfies the specified number of samples, to obtain an hourly maximum perceived temperature data sequence. Similarly, it combines and sorts the hourly maximum perceived temperature data around the date corresponding to the hourly nighttime perceived temperature data in the plurality of years' nighttime maximum perceived temperature dataset, which satisfies the specified number of samples, to obtain an hourly maximum perceived temperature data sequence.

[0054] Specifically, the detection device acquires the dataset of the highest daytime perceived temperature from 1981 to 2020, sets the sampling number to 7, and combines and sorts the hourly highest daytime perceived temperature data for the seven days before and after the hour corresponding to the hourly daytime perceived temperature data from 1981 to 2020 to construct the hourly highest daytime perceived temperature data sequence, as follows:

[0055]

[0056] In the formula, U d d represents the hourly daytime highest perceived temperature data sequence, d represents the date corresponding to the hourly daytime highest perceived temperature data, and T1 represents the hourly daytime perceived temperature data.

[0057] The detection device acquires a dataset of the highest nighttime perceived temperature from 1981 to 2020. The sampling number is set to 7. The hourly highest nighttime perceived temperature data for the seven days preceding and following the hour corresponding to the hourly nighttime perceived temperature data from 1981 to 2020 are combined and sorted to construct the hourly highest nighttime perceived temperature data sequence, as described below:

[0058]

[0059] In the formula, U e T1 represents the hourly nighttime highest perceived temperature data sequence, e represents the date corresponding to the hourly nighttime highest perceived temperature data, and T2 represents the hourly nighttime perceived temperature data.

[0060] S43: Perform percentage processing on the hourly daytime maximum perceived temperature data sequence and the hourly nighttime maximum perceived temperature data sequence respectively. Based on a preset quantile threshold, extract the hourly daytime maximum perceived temperature data corresponding to the quantile threshold from the hourly daytime maximum perceived temperature data sequence as the daytime relative high temperature threshold data. Also, extract the hourly nighttime maximum perceived temperature data corresponding to the quantile threshold from the hourly nighttime maximum perceived temperature data sequence as the nighttime relative high temperature threshold data.

[0061] In this embodiment, the detection device performs percentage processing on the hourly daytime maximum perceived temperature data sequence and the hourly nighttime maximum perceived temperature data sequence, respectively. Based on a preset quantile threshold, the device extracts the hourly daytime maximum perceived temperature data corresponding to the quantile threshold from the hourly daytime maximum perceived temperature data sequence as the daytime relative high temperature threshold data, and extracts the hourly nighttime maximum perceived temperature data corresponding to the quantile threshold from the hourly nighttime maximum perceived temperature data sequence as the nighttime relative high temperature threshold data.

[0062] Specifically, the detection device performs percentage processing on the hourly daytime maximum perceived temperature data sequence and the hourly nighttime maximum perceived temperature data sequence, respectively. By setting a quantile threshold (in a preferred embodiment, the quantile threshold is set to 90%), the detection device extracts the hourly daytime maximum perceived temperature data corresponding to the quantile threshold from the hourly daytime maximum perceived temperature data sequence, i.e., the hourly daytime maximum perceived temperature data at the position index corresponding to the 90th percentile of the hourly daytime maximum perceived temperature data sequence, as the daytime relative high temperature threshold data. Similarly, it extracts the hourly nighttime maximum perceived temperature data corresponding to the quantile threshold from the hourly nighttime maximum perceived temperature data sequence, i.e., the hourly nighttime maximum perceived temperature data at the position index corresponding to the 90th percentile of the hourly nighttime maximum perceived temperature data sequence, as the nighttime relative high temperature threshold data.

[0063] In an optional embodiment, the diurnal heat wave detection data includes diurnal heat wave frequency data, diurnal heat wave intensity data, diurnal heat wave duration data, and diurnal heat wave start and end time data. The diurnal heat wave frequency data is defined as the number of diurnal heat wave events within a year; the diurnal heat wave duration data is defined as the total number of days with diurnal heat wave events within a year; the diurnal heat wave intensity data is the perceived temperature during the diurnal heat wave; and the diurnal heat wave start and end time data are the start time of the first diurnal heat wave and the end time of the last diurnal heat wave, respectively.

[0064] Please see Figure 4 , Figure 4 The flowchart of step S4 in the high-temperature heat wave detection method based on day and night time provided in one embodiment of this application is as follows: It also includes steps S44 to S47, as follows:

[0065] S44: If the hourly daytime perceived temperature data is simultaneously greater than both the daytime relative high temperature threshold data and the daytime absolute high temperature threshold data, the hourly daytime perceived temperature data is marked as daytime high temperature heat wave data, and several daytime high temperature heat wave data are obtained. If the hourly nighttime perceived temperature data is simultaneously greater than both the nighttime relative high temperature threshold data and the nighttime absolute high temperature threshold data, the hourly nighttime perceived temperature data is marked as nighttime high temperature heat wave data, and several nighttime high temperature heat wave data are obtained.

[0066] In this embodiment, the detection device compares the hourly daytime perceived temperature data with the daytime relative high temperature threshold data and the daytime absolute high temperature threshold data. If the hourly daytime perceived temperature data is greater than both the daytime relative high temperature threshold data and the daytime absolute high temperature threshold data, the hourly daytime perceived temperature data is marked as daytime high temperature heat wave data; otherwise, it is marked as non-daytime high temperature heat wave data, thus obtaining several daytime high temperature heat wave data.

[0067] The detection device compares the hourly nighttime perceived temperature data with the nighttime relative high temperature threshold data and the nighttime absolute high temperature threshold data. If the hourly nighttime perceived temperature data is greater than both the nighttime relative high temperature threshold data and the nighttime absolute high temperature threshold data, the hourly nighttime perceived temperature data is marked as nighttime high temperature heat wave data; otherwise, it is marked as non-nighttime high temperature heat wave data, thus obtaining several nighttime high temperature heat wave data.

[0068] S44: Obtain the number of daytime high temperature heat wave data and nighttime high temperature heat wave data respectively, and use them as the daytime high temperature heat wave frequency data and nighttime high temperature heat wave frequency data of the target area within the target time period to construct the daytime and nighttime high temperature heat wave frequency data.

[0069] In this embodiment, the detection device obtains the number of daytime high temperature heat wave data and nighttime high temperature heat wave data respectively, which are used as the daytime high temperature heat wave frequency data and nighttime high temperature heat wave frequency data of the target area within the target time period, and constructs the daytime and nighttime high temperature heat wave frequency data.

[0070] S45: Obtain the perceived temperature data corresponding to the several daytime high temperature heat wave data and nighttime high temperature heat wave data respectively, and use them as the daytime high temperature heat wave intensity data and nighttime high temperature heat wave intensity data of the target area within the target time period to construct the daytime and nighttime high temperature heat wave intensity data.

[0071] In this embodiment, the detection device obtains the perceived temperature data corresponding to the several daytime high temperature heat wave data and nighttime high temperature heat wave data, respectively, as the daytime high temperature heat wave intensity data and nighttime high temperature heat wave intensity data of the target area within the target time period, and constructs the daytime and nighttime high temperature heat wave intensity data.

[0072] S46: Obtain the hours corresponding to the plurality of daytime high temperature heat wave data and nighttime high temperature heat wave data respectively. Based on the hours corresponding to the plurality of daytime high temperature heat wave data, obtain the daytime high temperature heat wave duration data and daytime high temperature heat wave start and end time data of the target area within the target time period. Based on the hours corresponding to the plurality of nighttime high temperature heat wave data, obtain the nighttime high temperature heat wave duration data and nighttime high temperature heat wave start and end time data of the target area within the target time period. Construct the daytime and nighttime high temperature heat wave duration data and daytime and nighttime high temperature heat wave start and end time data.

[0073] In this embodiment, the detection device obtains the hours corresponding to the plurality of daytime and nighttime high-temperature heat wave data. Based on the hours corresponding to the plurality of daytime high-temperature heat wave data, it obtains the duration data and start-end time data of the daytime high-temperature heat wave in the target area within the target time period. Based on the hours corresponding to the plurality of nighttime high-temperature heat wave data, it obtains the duration data and start-end time data of the nighttime high-temperature heat wave in the target area within the target time period. The daytime and nighttime high-temperature heat wave duration data and daytime and nighttime high-temperature heat wave start-end time data are then constructed. The daytime high-temperature heat wave start-end time data includes the start time and end time data of the daytime high-temperature heat wave, and the nighttime high-temperature heat wave start-end time data includes the start time and end time data of the nighttime high-temperature heat wave.

[0074] Specifically, the detection equipment combines daytime high-temperature heat wave data with adjacent dates based on the dates corresponding to the several daytime high-temperature heat wave data, and sorts them according to date and time to construct a daytime high-temperature heat wave duration data group. The number of daytime high-temperature heat wave data in the daytime high-temperature heat wave duration data group is taken as the number of days of the daytime high-temperature heat wave duration, thereby obtaining the daytime high-temperature heat wave duration data of the target area within the target time period. The date corresponding to the starting daytime high-temperature heat wave data of the daytime high-temperature heat wave duration data group is taken as the start time data of the daytime high-temperature heat wave, and the date corresponding to the ending daytime high-temperature heat wave data of the daytime high-temperature heat wave duration data group is taken as the end time data of the daytime high-temperature heat wave.

[0075] The detection equipment combines nighttime heatwave data with adjacent dates based on the dates corresponding to the aforementioned nighttime heatwave data, and sorts them according to date and time to construct a nighttime heatwave duration data group. The number of nighttime heatwave data in the nighttime heatwave duration data group is taken as the number of days of the nighttime heatwave duration, thereby obtaining the nighttime heatwave duration data of the target area within the target time period. The date corresponding to the starting nighttime heatwave data of the nighttime heatwave duration data group is taken as the nighttime heatwave start time data, and the date corresponding to the ending nighttime heatwave data of the nighttime heatwave duration data group is taken as the nighttime heatwave end time data.

[0076] Please refer to Figure 5 , Figure 5 This is a schematic diagram of a day-night-based high-temperature heat wave detection device according to an embodiment of this application. The device can be implemented entirely or partially through software, hardware, or a combination of both. The device 5 includes:

[0077] The hourly day-night distribution map acquisition module 51 is used to acquire the hourly day-night distribution map of the target area within the target time period, wherein the hourly day-night distribution map includes several grids and the day-night information corresponding to the grids;

[0078] The hourly day-night judgment matrix construction module 52 is used to construct the hourly day-night judgment matrix of the target area within the target time period based on the day-night information corresponding to several grids in the hourly day-night distribution map.

[0079] The day and night perceived temperature data acquisition module 53 is used to acquire hourly perceived temperature data of the target area within a target time period. Based on the hourly perceived temperature data of the target area within the target time period and the hourly day and night judgment matrix, it acquires hourly daytime perceived temperature data and hourly nighttime perceived temperature data of the target area within the target time period.

[0080] The day and night high temperature heat wave detection data acquisition module 54 is used to acquire temperature threshold data. Based on the hourly daytime perceived temperature data, hourly nighttime perceived temperature data and temperature threshold data, it obtains the day and night high temperature heat wave detection data of the target area within the target time period.

[0081] In this embodiment, an hourly day-night distribution map acquisition module acquires an hourly day-night distribution map of the target area within a target time period, wherein the hourly day-night distribution map includes several grids and day-night information corresponding to the grids; an hourly day-night judgment matrix construction module constructs an hourly day-night judgment matrix of the target area within the target time period based on the day-night information corresponding to the several grids in the hourly day-night distribution map; a day-night perceived temperature data acquisition module acquires hourly perceived temperature data of the target area within the target time period, and acquires hourly daytime perceived temperature data and hourly nighttime perceived temperature data of the target area within the target time period based on the hourly perceived temperature data of the target area within the target time period and the hourly day-night judgment matrix; a day-night high-temperature heat wave detection data acquisition module acquires temperature threshold data, and obtains day-night high-temperature heat wave detection data of the target area within the target time period based on the hourly daytime perceived temperature data, hourly nighttime perceived temperature data, and temperature threshold data. Based on the hourly day-night distribution map, a corresponding hourly day-night judgment matrix is ​​constructed. By using the acquired hourly perceived temperature data and the hourly day-night judgment matrix, the daytime perceived temperature data and the nighttime perceived temperature data are divided. And by using the preset temperature threshold, high temperature heat waves are detected, thus achieving more accurate and efficient detection of high temperature heat waves.

[0082] Please refer to Figure 6 , Figure 6 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. The computer device 6 includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61; the computer device can store multiple instructions, which are adapted to be loaded and executed by the processor 61. Figures 1 to 4 The method steps of the illustrated embodiment can be found in the following documentation for detailed execution. Figures 1 to 4 The specific details of the illustrated embodiments will not be elaborated here.

[0083] The processor 61 may include one or more processing cores. The processor 61 connects to various parts of the server using various interfaces and lines, and executes various functions and processes data of the day-night-based high-temperature heat wave detection device 5 by running or executing instructions, programs, code sets, or instruction sets stored in memory 62, and by calling data from memory 62. Optionally, the processor 61 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 61 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required to be displayed on the touch screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 61 and may be implemented as a separate chip.

[0084] The memory 62 may include random access memory (RAM) or read-only memory. Optionally, the memory 62 may include a non-transitory computer-readable storage medium. The memory 62 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 62 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 62 may also be at least one storage device located remotely from the aforementioned processor 61.

[0085] This application embodiment also provides a storage medium that can store multiple instructions, which are adapted to be loaded and executed by a processor as described above. Figures 1 to 4 The method steps of the illustrated embodiment can be found in the following documentation for detailed execution. Figures 1 to 4 The specific details of the illustrated embodiments will not be elaborated here.

[0086] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0087] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0088] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the algorithm. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0089] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0090] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0091] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0092] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms.

[0093] This invention is not limited to the above-described embodiments. If any modifications or variations to this invention do not depart from the spirit and scope of this invention, and if such modifications and variations fall within the scope of the claims and equivalent technologies of this invention, then this invention also intends to include such modifications and variations.

Claims

1. A method for detecting high-temperature heat waves based on day and night time, characterized in that, Includes the following steps: Obtain an hourly day-night distribution map of the target area within a target time period, wherein the hourly day-night distribution map includes several grids and the day-night information corresponding to the grids; Based on the day and night information corresponding to several grids in the hourly day and night distribution map, an hourly day and night judgment matrix for the target area within the target time period is constructed, wherein the hourly day and night judgment matrix includes day and night correlation vectors corresponding to several grids; Obtain hourly perceived temperature data of the target area within the target time period. Based on the hourly perceived temperature data of the target area within the target time period and the hourly day-night judgment matrix, obtain hourly daytime perceived temperature data and hourly nighttime perceived temperature data of the target area within the target time period. Acquire temperature threshold data, wherein the temperature threshold data includes relative high temperature threshold data and absolute high temperature threshold data; the relative high temperature threshold data includes daytime relative high temperature threshold data and nighttime high temperature threshold data, and the absolute high temperature threshold data includes daytime absolute high temperature threshold data and nighttime absolute high temperature threshold data; Based on the hourly daytime perceived temperature data, hourly nighttime perceived temperature data, and temperature threshold data, if the hourly daytime perceived temperature data is simultaneously greater than both the daytime relative high temperature threshold data and the daytime absolute high temperature threshold data, the hourly daytime perceived temperature data is marked as daytime high temperature heat wave data, thus obtaining several daytime high temperature heat wave data; if the hourly nighttime perceived temperature data is simultaneously greater than both the nighttime relative high temperature threshold data and the nighttime absolute high temperature threshold data, the hourly nighttime perceived temperature data is marked as nighttime high temperature heat wave data, thus obtaining several nighttime high temperature heat wave data. Based on several daytime and nighttime high-temperature heat wave data, daytime and nighttime high-temperature heat wave detection data for the target area within the target time period are obtained.

2. The high-temperature heat wave detection method based on day and night time according to claim 1, characterized in that: The hourly perceived temperature data includes perceived temperature data corresponding to several grids.

3. The high-temperature heat wave detection method based on day and night time according to claim 2, characterized in that, The step of obtaining hourly daytime and nighttime perceived temperature data of the target area within the target time period based on hourly perceived temperature data and an hourly day-night judgment matrix includes the following steps: Based on the day-night correlation vectors corresponding to several grids in the hourly day-night judgment matrix, the perceived temperature data corresponding to several grids in the hourly perceived temperature data are labeled, and the daytime perceived temperature data and nighttime perceived temperature data corresponding to several grids are obtained. The daytime perceived temperature data are combined to construct the hourly daytime perceived temperature data, and the nighttime perceived temperature data are combined to construct the hourly nighttime perceived temperature data.

4. The high-temperature heat wave detection method based on day and night time according to claim 1, characterized in that, The process of obtaining the temperature threshold data includes the following steps: Obtain the daytime maximum perceived temperature dataset and the nighttime maximum perceived temperature dataset for several years in the target area. The daytime maximum perceived temperature dataset includes hourly daytime maximum perceived temperature data for several dates corresponding to several years, and the nighttime maximum perceived temperature dataset includes hourly nighttime maximum perceived temperature data for several dates corresponding to several years. Based on the daytime maximum perceived temperature dataset and nighttime maximum perceived temperature dataset corresponding to the aforementioned number of years, and a preset number of samples, the hourly maximum perceived temperature data around the date corresponding to the hourly daytime maximum perceived temperature data in the daytime maximum perceived temperature dataset corresponding to the aforementioned number of years, which satisfy the specified number of samples, are combined and sorted to obtain an hourly maximum perceived temperature data sequence. Similarly, the hourly maximum perceived temperature data around the date corresponding to the hourly nighttime maximum perceived temperature data in the nighttime maximum perceived temperature dataset corresponding to the aforementioned number of years, which satisfy the specified number of samples, are combined and sorted to obtain an hourly maximum nighttime maximum perceived temperature data sequence. The hourly maximum daytime perceived temperature data sequence and the hourly maximum nighttime perceived temperature data sequence are processed by percentage calculation. Based on a preset quantile threshold, the hourly maximum daytime perceived temperature data corresponding to the quantile threshold is extracted from the hourly maximum daytime perceived temperature data sequence as the daytime relative high temperature threshold data. Similarly, the hourly maximum nighttime perceived temperature data corresponding to the quantile threshold is extracted from the hourly maximum nighttime perceived temperature data sequence as the nighttime relative high temperature threshold data.

5. The high-temperature heat wave detection method based on day and night time according to claim 4, characterized in that: The daytime and nighttime high temperature heat wave detection data includes daytime and nighttime high temperature heat wave frequency data, daytime and nighttime high temperature heat wave intensity data, daytime and nighttime high temperature heat wave duration data, and daytime and nighttime high temperature heat wave start and end time data.

6. The high-temperature heat wave detection method based on day and night time according to claim 5, characterized in that, The step of obtaining daytime and nighttime high-temperature heat wave detection data for the target area within a target time period based on several daytime and nighttime high-temperature heat wave data includes the following steps: The number of daytime high temperature heat wave data and nighttime high temperature heat wave data are obtained respectively, and used as the daytime high temperature heat wave frequency data and nighttime high temperature heat wave frequency data of the target area within the target time period to construct the daytime and nighttime high temperature heat wave frequency data. The perceived temperature data corresponding to the several daytime high temperature heat wave data and nighttime high temperature heat wave data are obtained respectively, and used as the daytime high temperature heat wave intensity data and nighttime high temperature heat wave intensity data of the target area within the target time period, and the daytime and nighttime high temperature heat wave intensity data are constructed. The corresponding hours for the several daytime and nighttime high temperature heat wave data are obtained respectively. Based on the corresponding hours for the several daytime high temperature heat wave data, the duration data and start and end time data of the daytime high temperature heat wave in the target area within the target time period are obtained. Based on the corresponding hours for the several nighttime high temperature heat wave data, the duration data and start and end time data of the nighttime high temperature heat wave in the target area within the target time period are obtained. The daytime and nighttime high temperature heat wave duration data and daytime and nighttime high temperature heat wave start and end time data are constructed.

7. A high-temperature heat wave detection device based on day and night time, characterized in that, include: The hourly day-night distribution map acquisition module is used to acquire the hourly day-night distribution map of the target area within the target time period. The hourly day-night distribution map includes several grids and the day-night information corresponding to the grids. The hourly day-night judgment matrix construction module is used to construct the hourly day-night judgment matrix of the target area within the target time period based on the day-night information corresponding to several grids in the hourly day-night distribution map. The day and night perceived temperature data acquisition module is used to acquire hourly perceived temperature data of the target area within a target time period. Based on the hourly perceived temperature data of the target area within the target time period and the hourly day and night judgment matrix, it acquires hourly daytime perceived temperature data and hourly nighttime perceived temperature data of the target area within the target time period. The day and night high temperature heat wave detection data acquisition module is used to acquire temperature threshold data, wherein the temperature threshold data includes relative high temperature threshold data and absolute high temperature threshold data; the relative high temperature threshold data includes daytime relative high temperature threshold data and nighttime high temperature threshold data, and the absolute high temperature threshold data includes daytime absolute high temperature threshold data and nighttime absolute high temperature threshold data. Based on the hourly daytime perceived temperature data, hourly nighttime perceived temperature data, and temperature threshold data, if the hourly daytime perceived temperature data is simultaneously greater than both the daytime relative high temperature threshold data and the daytime absolute high temperature threshold data, the hourly daytime perceived temperature data is marked as daytime high temperature heat wave data, thus obtaining several daytime high temperature heat wave data; if the hourly nighttime perceived temperature data is simultaneously greater than both the nighttime relative high temperature threshold data and the nighttime absolute high temperature threshold data, the hourly nighttime perceived temperature data is marked as nighttime high temperature heat wave data, thus obtaining several nighttime high temperature heat wave data. Based on several daytime and nighttime high-temperature heat wave data, daytime and nighttime high-temperature heat wave detection data for the target area within the target time period are obtained.

8. A computer device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor; the computer program, when executed by the processor, implements the steps of the high-temperature heat wave detection method based on day and night time as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the high-temperature heat wave detection method based on day and night time as described in any one of claims 1 to 6.

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