Freeze-thaw strength calculation method and device based on surface temperature

Abstract

The invention discloses a freeze-thaw strength calculation method and device based on surface temperature, and the method comprises the steps: obtaining a plurality of groups of surface temperature remote sensing data in a target time period in a target region, and arranging the surface temperature remote sensing data according to a time sequence; calculating positive accumulated temperature and negative accumulated temperature in the target time period based on the surface temperature remote sensing data; according to the positive accumulated temperature and the negative accumulated temperature, the freeze-thaw strength index in the target time period is calculated, and the freeze-thaw strength index is in direct proportion to the product of the positive accumulated temperature and the negative accumulated temperature and is in inverse proportion to the quadratic sum of the positive accumulated temperature and the negative accumulated temperature. Therefore, according to the method and the device provided by the invention, the influence of the temperature accumulation effect on the freeze-thaw cycle is accurately quantified by calculating the freeze-thaw strength, and more reliable data support is provided for freeze-thaw risk assessment in the environment and engineering.

Description

Technical Field

[0001] The invention relates to the technical field of remote sensing data processing, and in particular to a method and device for calculating freeze-thaw intensity based on ground surface temperature. Background Art

[0002] With the rapid development of remote sensing technology, large-scale surface observation using remote sensing data has become a core means of environmental monitoring. Especially in permafrost areas with harsh environments and scarce data, the application of remote sensing technology in permafrost mapping, disaster prediction and other fields has been widely and deeply carried out. With more and more large-scale engineering projects being built in periglacial areas, freeze-thaw erosion, as the third largest type of rock and soil erosion after water erosion and wind erosion, has increasingly become a key factor restricting engineering development, and has thus received widespread attention.

[0003] Freeze-thaw erosion is mainly caused by the frequent alternation of positive and negative atmospheric temperatures, which causes phase changes of water in the rock and soil and differential expansion and contraction of different mineral particles. Therefore, when studying freeze-thaw erosion, temperature (including air temperature and land surface temperature (LST)) is usually regarded as a key analysis variable. At present, researchers usually use reanalysis data (such as ERA5, etc.) or remote sensing data (such as MODIS and Landsat products) to generate relevant parameters such as average annual temperature (MAAT), positive and negative accumulated temperature (DDT; DDT), frozen number (Frozen Number), freeze-thaw cycle days (FTCD), etc.

[0004] These parameters provide key data support for analyzing the quantification of the degree of mechanical damage to rock and soil caused by surface temperature fluctuations on a monthly to annual scale, but they are insufficient in capturing features when reflecting the complexity of the freeze-thaw process and cannot accurately assess the freeze-thaw intensity in frozen soil areas. For example, the positive and negative accumulated temperature parameters mainly focus on measuring the accumulation of surface temperature, focusing on the continuous intensity and duration in a single period during freezing or thawing, while ignoring the interaction between the melting stage and the freezing stage, and cannot fully reflect the complexity of the freeze-thaw cycle in frozen soil areas. Especially in frozen soil areas, the freeze-thaw process involves not only temperature changes in the surface, but also multiple dynamic feedbacks of soil, groundwater and ecosystems. Therefore, a single positive and negative accumulated temperature parameter is difficult to capture these multi-dimensional changes, resulting in inaccurate assessment results of the environmental and engineering stability of frozen soil areas in practical applications. The freezing number model only considers the total amount of positive and negative accumulated temperatures, but ignores the impact of the relative amplitude of temperature changes on rock and soil, and cannot accurately reflect the freeze-thaw cycle process. For mountainous areas with large temperature changes or small differences in positive and negative accumulated temperatures, the freezing number model has serious deficiencies. At the same time, the freeze-thaw cycle days parameter, as an important freeze-thaw erosion assessment indicator, cannot accurately measure the impact of temperature amplitude and lacks reflection of cumulative effects, which limits its application in refined environmental monitoring.

[0005] In view of this, a method and device for calculating freeze-thaw intensity based on ground surface temperature are provided to solve the problem in the prior art that freeze-thaw erosion intensity cannot be accurately quantified. Summary of the invention

[0006] To this end, an embodiment of the present invention provides a freeze-thaw intensity calculation method and device based on surface temperature. By calculating the freeze-thaw intensity, the impact of the temperature accumulation effect on the freeze-thaw cycle is accurately quantified, providing more reliable data support for freeze-thaw risk assessment in the environment and engineering.

[0007] In order to achieve the above purpose, the embodiment of the present invention provides the following technical solutions:

[0008] The present invention provides a method for calculating freeze-thaw intensity based on ground surface temperature, the method comprising:

[0009] Acquire multiple groups of surface temperature remote sensing data in a target area during a target period, wherein each group of surface temperature remote sensing data is arranged in a time series;

[0010] Calculating the positive accumulated temperature and negative accumulated temperature within the target period based on the surface temperature remote sensing data;

[0011] A freeze-thaw intensity index within the target time period is calculated according to the positive accumulated temperature and the negative accumulated temperature, wherein the freeze-thaw intensity index is proportional to the product of the positive accumulated temperature and the negative accumulated temperature and inversely proportional to the sum of the squares of the positive accumulated temperature and the negative accumulated temperature.

[0012] In some embodiments, the surface temperature remote sensing data includes daytime surface temperature data, a quality control layer of daytime surface temperature, nighttime surface temperature data, a quality control layer of nighttime surface temperature, daytime observation time, nighttime observation time, daytime satellite observation angles, and nighttime satellite observation angles.

[0013] In some embodiments, obtaining multiple sets of surface temperature remote sensing data in a target area during a target period of time further includes:

[0014] The land surface temperature remote sensing data is preprocessed.

[0015] In some embodiments, the surface temperature remote sensing data is preprocessed, specifically including:

[0016] Converting the land surface temperature remote sensing data into a projection coordinate system and into GeoTIFF format data;

[0017] Eliminate low-quality pixels in the surface temperature remote sensing data and fill in abnormal data points;

[0018] The land surface temperature remote sensing data are converted into degrees Celsius.

[0019] In some embodiments, the positive accumulated temperature in the target period is calculated using a first expression, where the first expression is:

[0020]

[0021] Among them, DDT represents positive accumulated temperature, LST i represents the average surface temperature on the i-th day, M T Indicates the number of days during the target period when the average surface temperature is greater than 0°C.

[0022] In some embodiments, the negative accumulated temperature in the target period is calculated using a second expression, where the second expression is:

[0023]

[0024] Among them, DDF represents negative accumulated temperature, LST i represents the average surface temperature on the i-th day, M F Indicates the number of days during the target period when the average surface temperature is no greater than 0°C.

[0025] In some embodiments, the freeze-thaw intensity index within the target period is calculated using a third expression, and the third expression is:

[0026]

[0027] Among them, FTI represents freeze-thaw intensity index, DDF represents negative accumulated temperature, and DDT represents positive accumulated temperature.

[0028] The present invention also provides a freeze-thaw intensity calculation device based on ground surface temperature, the device comprising:

[0029] A data acquisition unit, used to acquire multiple groups of surface temperature remote sensing data in a target area during a target period, wherein each group of surface temperature remote sensing data is arranged in a time series;

[0030] A first calculation unit is used to calculate the positive accumulated temperature and negative accumulated temperature in the target time period based on the surface temperature remote sensing data;

[0031] The second calculation unit is used to calculate the freeze-thaw intensity index within the target time period according to the positive accumulated temperature and the negative accumulated temperature, wherein the freeze-thaw intensity index is proportional to the product of the positive accumulated temperature and the negative accumulated temperature and inversely proportional to the sum of the squares of the positive accumulated temperature and the negative accumulated temperature.

[0032] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0033] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and the computer program implements the steps of the above method when executed by a processor.

[0034] The freeze-thaw intensity calculation method and device based on surface temperature provided by the present invention obtain multiple groups of surface temperature remote sensing data in a target area during a target period, and each group of surface temperature remote sensing data is arranged in a time series; the positive accumulated temperature and the negative accumulated temperature in the target period are calculated based on the surface temperature remote sensing data; the freeze-thaw intensity index in the target period is calculated according to the positive accumulated temperature and the negative accumulated temperature, and the freeze-thaw intensity index is proportional to the product of the positive accumulated temperature and the negative accumulated temperature, and inversely proportional to the sum of the squares of the positive accumulated temperature and the negative accumulated temperature. In this way, the method and device provided by the present invention can accurately quantify the impact of the temperature accumulation effect on the freeze-thaw cycle by calculating the freeze-thaw intensity, and provide more reliable data support for freeze-thaw risk assessment in the environment and engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0036] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0037] Figure 1 A flow chart of a method for calculating freeze-thaw intensity based on ground surface temperature provided by the present invention;

[0038] Figure 2 This is a schematic diagram of the structure of the freeze-thaw intensity calculation device based on ground surface temperature provided by the present invention;

[0039] Figure 3 This is a structural block diagram of a computer device provided by the present invention. DETAILED DESCRIPTION

[0040] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] First, the following explanations are given for the specific terms used in the present invention:

[0042] Mean Annual Air Temperature (MAAT): The arithmetic mean of the daily average temperature throughout the year. It is calculated by adding up the daily average temperature throughout the year and dividing it by the number of days (usually 365 or 366 days). It is used to describe the long-term temperature characteristics of a region and provide basic data for research in the fields of environment and engineering.

[0043] Negative accumulated temperature (Degree days freezing index, DDF): refers to the cumulative sum of daily average temperatures below 0℃ in a period of time;

[0044] Positive accumulated temperature (Degree days thawing index, DDT): refers to the total accumulated temperature of daily average temperature above 0℃ in a period of time;

[0045] Frozen Number (F): A dimensionless parameter that uses positive and negative accumulated temperatures to determine the freeze-thaw state of the ground. It can effectively reflect the balance of surface heat. Usually, 0.5 is used as the threshold. F values ​​greater than or equal to 0.5 indicate permafrost areas, while values ​​less than 0.5 indicate seasonal frozen areas. The Frozen Number model is concise and has strong physical significance. It can not only be used to quickly determine the state of frozen soil, but also provide a reference for predicting the impact of climate change on frozen soil dynamics. It is of great value in analyzing and predicting frozen soil changes.

[0046] Freeze-thaw cycle days (FTCD): A freeze-thaw cycle is defined as a maximum temperature greater than 0℃ and a minimum temperature less than 0℃ in a day. The annual freeze-thaw cycle days refer to the number of days in a year when freeze-thaw cycles occur.

[0047] Although there are currently a variety of parameters to evaluate the changes in freeze-thaw cycles, the freeze-thaw cycle process is complex and involves different scales and indicators, and the existing monitoring methods and parameters have obvious shortcomings. Traditional indicators such as the number of freeze-thaw cycle days and the number of freezes are difficult to fully reflect the actual intensity of the freeze-thaw process. Especially in mountainous areas with complex terrain, the climatic conditions are complex and the temperature changes are drastic. The cumulative effect of positive and negative accumulated temperatures plays a key role in the freeze-thaw process. However, the existing parameters cannot accurately capture this cumulative effect, resulting in limitations in the prediction and evaluation of freeze-thaw processes in the fields of frozen soil engineering and road construction. It is urgent to provide a new calculation method to better quantify the freeze-thaw effect under complex terrain, so as to improve the accuracy of engineering safety and stability assessment and meet actual needs.

[0048] In view of this, this method proposes a freeze-thaw intensity calculation method based on surface temperature. This method quantitatively evaluates the calculation of freeze-thaw erosion intensity indicators based on surface temperature remote sensing data. It is suitable for areas with freeze-thaw cycles. This method can quickly evaluate the impact of freeze-thaw processes on rock and soil and the environment, especially in long-term and large-scale permafrost environments.

[0049] In a specific embodiment, Figure 1 As shown, the freeze-thaw intensity calculation method based on ground surface temperature provided by the present invention comprises the following steps:

[0050] S110: Acquire multiple groups of surface temperature remote sensing data in the target area during the target time period, and each group of the surface temperature remote sensing data is arranged in a time series; wherein the surface temperature remote sensing data includes daytime surface temperature data, daytime surface temperature quality control layer, nighttime surface temperature data, nighttime surface temperature quality control layer, daytime observation time, nighttime observation time, daytime satellite observation angle, and nighttime satellite observation angle.

[0051] S120: Calculate the positive accumulated temperature and the negative accumulated temperature in the target time period based on the surface temperature remote sensing data.

[0052] S130: Calculate the freeze-thaw intensity index in the target period according to the positive accumulated temperature and the negative accumulated temperature, wherein the freeze-thaw intensity index is proportional to the product of the positive accumulated temperature and the negative accumulated temperature, and inversely proportional to the sum of the squares of the positive accumulated temperature and the negative accumulated temperature. The freeze-thaw intensity index (FTI) is used to ensure that when the values ​​of DDF (negative accumulated temperature) and DDT (positive accumulated temperature) are both high, the FTI is large, indicating that the freeze-thaw intensity is high; and when one of the values ​​of DDF and DDT is high and the other is low, the FTI is close to 0, indicating that the freeze-thaw intensity is low, thereby accurately quantifying the impact of the temperature accumulation effect on the freeze-thaw cycle.

[0053] In step S110, in a specific usage scenario, the land surface temperature remote sensing data is obtained by downloading the MODIS time series land surface temperature data (LST, 1km). The data is in HDF format and includes the following key layers and their specific meanings:

[0054] LST_Day_1km: daytime land surface temperature data, with a spatial resolution of 1 km, unit: Kelvin (K);

[0055] QC_Day: Quality control layer for daytime surface temperature, indicating data quality, cloud cover, etc.

[0056] LST_Night_1km: nighttime land surface temperature data, with a spatial resolution of 1 km, unit: Kelvin (K);

[0057] QC_Night: Quality control layer for nighttime surface temperature, indicating data quality, cloud cover, etc.

[0058] Day_view_time: daytime observation time (local solar time), used to indicate the time when the satellite observes the surface temperature;

[0059] Night_view_time: Nighttime observation time (local solar time), used to indicate the time when the satellite observes the surface temperature;

[0060] Day_view_angle: satellite observation angle during the day, indicating the observation angle of the sensor relative to the ground surface;

[0061] Night_view_angle: The satellite observation angle at night, which indicates the observation angle of the sensor relative to the ground surface.

[0062] Since MODIS includes day and night data from Terra and Aqua satellites, the LST_Day_1km and LST_Night_1km layers in the data from these two satellites are named Terra and Aqua, respectively. day ,Terra night ,Aqua day ,Aqua night .

[0063] It should be understood that the Moderate-resolution Imaging Spectroradiometer (MODIS) is a large-scale space remote sensing instrument developed by NASA. Its main purpose is to understand the changes in the global climate and the impact of human activities on the climate. MODIS sensors are carried on Terra and Aqua satellites. MODIS sensors capture data in 36 mutually aligned spectral bands, covering from visible light to infrared bands, and provide observation data of the earth's surface every 1 to 2 days, which can repeatedly observe the entire earth's surface. MODIS data has a wide spectral range, with a total of 36 bands, ranging from 0.4 microns to 14.4 microns; MODIS data has a short revisit period. Terra and Aqua satellites are both sun-synchronous polar-orbiting satellites. Terra passes in the morning local time and Aqua passes in the afternoon local time. The combination of the two can obtain updated data at least twice a day and twice a night; MODIS data products are divided into different levels according to the processing level, from level 0 (raw data) to level 5 and above (products developed according to various application models). These data are widely used. Used in many fields such as land use, climate change research, and natural disaster monitoring; the basic parameters of MODIS include an orbit altitude of 705 kilometers, a scanning frequency of 20.3 revolutions per minute, a coverage area spanning 2,330 kilometers (cross-track) and 10 kilometers (along-track), a telescope diameter of 17.78 cm, dimensions of 1.0x1.6x1.0 meters, a weight of 228.7 kg, a power of 162.5 watts (single-track average), a data transmission rate of 10.6 Mbps (daytime peak) and 6.1 Mbps (track average), and a degree of digitization of 12 bits.

[0064] Furthermore, in order to improve the data quality involved in the calculation, multiple sets of surface temperature remote sensing data in the target area during the target period are obtained, and then it also includes:

[0065] The land surface temperature remote sensing data is preprocessed.

[0066] Preprocessing can specifically include:

[0067] Converting the land surface temperature remote sensing data into a projection coordinate system and into GeoTIFF format data;

[0068] Eliminate low-quality pixels in the surface temperature remote sensing data and fill in abnormal data points;

[0069] The land surface temperature remote sensing data are converted into degrees Celsius.

[0070] Still taking the above usage scenario as an example, the default coordinate system of MODIS data is Sinusoidal projection. During preprocessing, use tools such as ArcGIS or MODIS Reprojection Tool to stitch and crop, convert to a suitable projection coordinate system, and convert to the common GeoTIFF format; and use the quality control layer (QC) to remove low-quality pixels. Usually, only pixels with a quality mark of "0" or "1" are considered high quality, retaining high-quality surface temperature data. Fill in the data for outliers and vacant values, and convert the LST data to degrees Celsius (℃) using the following formula, thereby obtaining daily seamless high-quality surface temperature data:

[0071] LST×0.02-273.15

[0072] In step S120, the positive accumulated temperature in the target period is calculated using a first expression, where the first expression is:

[0073]

[0074] Among them, DDT represents positive accumulated temperature, LST i represents the average surface temperature on the i-th day, M T Indicates the number of days during the target period when the average surface temperature is greater than 0°C.

[0075] In step S120, the negative accumulated temperature in the target period is calculated using a second expression, where the second expression is:

[0076]

[0077] Among them, DDF represents negative accumulated temperature, LST i represents the average surface temperature on the i-th day, M F Indicates the number of days during the target period when the average surface temperature is no greater than 0°C.

[0078] Still using the above usage scenario, the average surface temperature is calculated using the following formula, and the negative accumulated temperature and positive accumulated temperature are calculated using the surface temperature data:

[0079] LST i =Terra day +Terra night +Aqua day +Aqua night

[0080]

[0081] Among them, LST iIt represents the average surface temperature on the i-th day, represented by the arithmetic mean of the MODIS surface temperatures of Terra and Aqua on the i-th day. DDF is negative accumulated temperature, which refers to the sum of daily accumulated temperatures with surface temperatures less than or equal to 0°C, which is related to soil freezing and ice and snow formation; DDT is positive accumulated temperature, which refers to the sum of daily accumulated temperatures with surface temperatures greater than 0°C, which is usually related to ice and snow melting and permafrost thawing. F and M T They correspond to the number of days in a year when the temperature is below (or equal to) and above 0℃ respectively.

[0082] In step S130, the freeze-thaw intensity index in the target period is calculated using a third expression, and the third expression is:

[0083]

[0084] Among them, FTI represents freeze-thaw intensity index, DDF represents negative accumulated temperature, and DDT represents positive accumulated temperature.

[0085] Specifically, the index is divided into two parts:

[0086] Part I It means that this term can only be larger when both DDF and DDT are larger, that is, the freeze-thaw intensity is larger. When one of DDF and DDT is larger and the other is smaller, this term is smaller because the denominator increases faster.

[0087] The second part, lg(DDF+DDT+1), ensures that the freeze-thaw intensity index will increase monotonically with the increase of DDF and DDT. Specifically, without this logarithmic term, when the values ​​of DDF and DDT are equal (for example, both are equal to 100 and 10000), the calculated FTI values ​​will be the same according to the first part of the formula. However, in fact, larger values ​​of DDF and DDT obviously correspond to stronger freeze-thaw intensity. This logarithmic term introduces the accumulated total amount and takes the logarithm, so that when the DDF and DDT values ​​increase, the FTI value also increases significantly, ensuring a more accurate reflection of the relationship between freeze-thaw intensity and the cumulative effect of temperature.

[0088] In the above specific implementation manner, the freeze-thaw intensity calculation method based on surface temperature provided by the present invention obtains multiple groups of surface temperature remote sensing data in the target area during the target period, and each group of surface temperature remote sensing data is arranged in a time series; the positive accumulated temperature and the negative accumulated temperature in the target period are calculated based on the surface temperature remote sensing data; the freeze-thaw intensity index in the target period is calculated according to the positive accumulated temperature and the negative accumulated temperature, and the freeze-thaw intensity index is proportional to the product of the positive accumulated temperature and the negative accumulated temperature, and inversely proportional to the sum of the squares of the positive accumulated temperature and the negative accumulated temperature. In this way, the method and device provided by the present invention can accurately quantify the impact of the temperature accumulation effect on the freeze-thaw cycle by calculating the freeze-thaw intensity, and provide more reliable data support for freeze-thaw risk assessment in the environment and engineering.

[0089] Furthermore, when the freeze-thaw intensity index is calculated based on the surface temperature, a more accurate freeze-thaw intensity index FTI is calculated by comprehensively considering the relative values ​​of positive and negative accumulated temperatures (DDF and DDT). FTI ensures that when both DDF and DDT are high at the same time, the FTI value is larger, indicating that the freeze-thaw effect is stronger; when one of the parameters is larger and the other is smaller, the FTI value is close to 0, reflecting a weaker freeze-thaw intensity; at the same time, the index can also effectively reflect the influence of the temperature accumulation effect on the freeze-thaw intensity, and effectively reflect the influence of positive and negative accumulated temperatures on freeze-thaw erosion.

[0090] Furthermore, in complex terrains, especially in mountainous areas, FTI has significant applicability and importance. By utilizing high-quality seamless time-series surface temperature data from MODIS of Terra and Aqua, it accurately reflects the freeze-thaw intensity of different terrain units. By analyzing the changes in FTI over a long time series, it helps to reveal the trend of annual freeze-thaw changes, providing important information support for a deep understanding of the periglacial environment and periglacial landforms, and providing reliable data support for environmental monitoring and engineering applications.

[0091] In addition to the above method, the present invention also provides a freeze-thaw intensity calculation device based on ground surface temperature, such as Figure 2 As shown, the device comprises:

[0092] The data acquisition unit 210 is used to acquire multiple groups of surface temperature remote sensing data in a target area during a target period, wherein each group of surface temperature remote sensing data is arranged in a time series;

[0093] A first calculation unit 220 is used to calculate the positive accumulated temperature and negative accumulated temperature in the target time period based on the surface temperature remote sensing data;

[0094] The second calculation unit 230 is used to calculate the freeze-thaw intensity index within the target time period according to the positive accumulated temperature and the negative accumulated temperature, wherein the freeze-thaw intensity index is proportional to the product of the positive accumulated temperature and the negative accumulated temperature, and inversely proportional to the sum of the squares of the positive accumulated temperature and the negative accumulated temperature.

[0095] In some embodiments, the surface temperature remote sensing data includes daytime surface temperature data, a quality control layer of daytime surface temperature, nighttime surface temperature data, a quality control layer of nighttime surface temperature, daytime observation time, nighttime observation time, daytime satellite observation angles, and nighttime satellite observation angles.

[0096] In some embodiments, obtaining multiple sets of surface temperature remote sensing data in a target area during a target period of time further includes:

[0097] The land surface temperature remote sensing data is preprocessed.

[0098] In some embodiments, the surface temperature remote sensing data is preprocessed, specifically including:

[0099] Converting the land surface temperature remote sensing data into a projection coordinate system and into GeoTIFF format data;

[0100] Eliminate low-quality pixels in the surface temperature remote sensing data and fill in abnormal data points;

[0101] The land surface temperature remote sensing data are converted into degrees Celsius.

[0102] In some embodiments, the positive accumulated temperature in the target period is calculated using a first expression, where the first expression is:

[0103]

[0104] Among them, DDT represents positive accumulated temperature, LST i represents the average surface temperature on the i-th day, M T Indicates the number of days during the target period when the average surface temperature is greater than 0°C.

[0105] In some embodiments, the negative accumulated temperature in the target period is calculated using a second expression, where the second expression is:

[0106]

[0107] Among them, DDF represents negative accumulated temperature, LST i represents the average surface temperature on the i-th day, M F Indicates the number of days during the target period when the average surface temperature is no greater than 0°C.

[0108] In some embodiments, the freeze-thaw intensity index within the target period is calculated using a third expression, and the third expression is:

[0109]

[0110] Among them, FTI represents freeze-thaw intensity index, DDF represents negative accumulated temperature, and DDT represents positive accumulated temperature.

[0111] In the above specific implementation manner, the freeze-thaw intensity calculation device based on surface temperature provided by the present invention obtains multiple groups of surface temperature remote sensing data in the target area during the target period, and each group of surface temperature remote sensing data is arranged in a time series; the positive accumulated temperature in the target period is calculated based on the surface temperature remote sensing data; the freeze-thaw intensity index in the target period is calculated according to the positive accumulated temperature and the negative accumulated temperature, and the freeze-thaw intensity index is proportional to the product of the positive accumulated temperature and the negative accumulated temperature, and inversely proportional to the sum of the squares of the positive accumulated temperature and the negative accumulated temperature. In this way, the device provided by the present invention can accurately quantify the impact of the temperature accumulation effect on the freeze-thaw cycle by calculating the freeze-thaw intensity, and provide more reliable data support for freeze-thaw risk assessment in the environment and engineering.

[0112] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a model prediction. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The model prediction of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0113] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0114] Corresponding to the above embodiment, the embodiment of the present invention further provides a computer storage medium, which contains one or more program instructions, wherein the one or more program instructions are used to execute the above method.

[0115] The present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the above method.

[0116] In the embodiment of the present invention, the processor may be an integrated circuit chip having the ability to process signals. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0117] The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor for execution, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The processor reads the information in the storage medium and completes the steps of the above method in combination with its hardware.

[0118] The storage medium may be a memory, which may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0119] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0120] The volatile memory may be a random access memory (RAM) which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DRRAM).

[0121] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0122] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the present invention can be implemented using a combination of hardware and software. When software is used, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any media that facilitates the transmission of computer programs from one place to another. Storage media can be any available media that can be accessed by general or special-purpose computers.

[0123] The above specific implementation methods further illustrate the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for calculating freeze-thaw intensity based on ground surface temperature, characterized in that: The method comprises: Acquire multiple groups of surface temperature remote sensing data in a target area during a target period, wherein each group of surface temperature remote sensing data is arranged in a time series; Calculating the positive accumulated temperature and negative accumulated temperature within the target period based on the surface temperature remote sensing data; A freeze-thaw intensity index within the target time period is calculated according to the positive accumulated temperature and the negative accumulated temperature, wherein the freeze-thaw intensity index is proportional to the product of the positive accumulated temperature and the negative accumulated temperature and inversely proportional to the sum of the squares of the positive accumulated temperature and the negative accumulated temperature.

2. The freeze-thaw intensity calculation method based on ground surface temperature according to claim 1, characterized in that: The surface temperature remote sensing data includes daytime surface temperature data, quality control layer of daytime surface temperature, nighttime surface temperature data, quality control layer of nighttime surface temperature, daytime observation time, nighttime observation time, daytime satellite observation angle and nighttime satellite observation angle.

3. The freeze-thaw intensity calculation method based on ground surface temperature according to claim 1, characterized in that: Acquire multiple sets of surface temperature remote sensing data in the target area during the target period, and then include: The land surface temperature remote sensing data is preprocessed.

4. The freeze-thaw intensity calculation method based on ground surface temperature according to claim 3, characterized in that: Preprocessing the surface temperature remote sensing data specifically includes: Converting the land surface temperature remote sensing data into a projection coordinate system and into GeoTIFF format data; Eliminate low-quality pixels in the surface temperature remote sensing data and fill in abnormal data points; The land surface temperature remote sensing data are converted into degrees Celsius.

5. The freeze-thaw intensity calculation method based on ground surface temperature according to claim 1, characterized in that: The positive accumulated temperature in the target period is calculated using a first expression, where the first expression is: Among them, DDT represents positive accumulated temperature, LST i represents the average surface temperature on the i-th day, M T Indicates the number of days during the target period when the average surface temperature is greater than 0°C.

6. The freeze-thaw intensity calculation method based on ground surface temperature according to claim 1, characterized in that: The negative accumulated temperature in the target period is calculated using a second expression, where the second expression is: Among them, DDF represents negative accumulated temperature, LST i represents the average surface temperature on the i-th day, M F Indicates the number of days during the target period when the average surface temperature is no greater than 0°C.

7. The freeze-thaw intensity calculation method based on ground surface temperature according to claim 1, characterized in that: The freeze-thaw intensity index within the target period is calculated using a third expression, and the third expression is: Among them, FTI represents freeze-thaw intensity index, DDF represents negative accumulated temperature, and DDT represents positive accumulated temperature.

8. A freeze-thaw intensity calculation device based on ground surface temperature, characterized in that: The device comprises: A data acquisition unit, used to acquire multiple groups of surface temperature remote sensing data in a target area during a target period, wherein each group of surface temperature remote sensing data is arranged in a time series; A first calculation unit is used to calculate the positive accumulated temperature and negative accumulated temperature in the target time period based on the surface temperature remote sensing data; The second calculation unit is used to calculate the freeze-thaw intensity index within the target time period according to the positive accumulated temperature and the negative accumulated temperature, wherein the freeze-thaw intensity index is proportional to the product of the positive accumulated temperature and the negative accumulated temperature and inversely proportional to the sum of the squares of the positive accumulated temperature and the negative accumulated temperature.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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