Composite dry heat event identification method and system considering evapotranspiration influence

Abstract

The invention relates to the technical field of composite dry-heat event identification, and discloses a composite dry-heat event identification method and system considering evapotranspiration influence, and the method comprises the steps: obtaining a multi-time-scale standardized rainfall evapotranspiration index and a standardized air temperature index of a to-be-detected vegetation region, and calculating a correlation coefficient between each pair of index combinations, determining an index combination which has the highest absolute value of the correlation coefficient and passes significance verification as an optimal index combination; based on the combination of the optimal Copula function and the optimal index, and in combination with a conditional probability formula, calculating a composite dry heat event joint probability, inputting the composite dry heat event joint probability into a preset formula, and generating a standardized composite dry heat index; and according to the standardized composite dry heat index threshold, identifying a composite dry heat event grade corresponding to the standardized composite dry heat index. The method can solve the problem of inaccurate monitoring of the composite dry-heat event in the vegetation area due to neglect of evapotranspiration in an existing mode, improves the monitoring precision of the composite dry-heat event, and has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of compound dry heat event identification, and in particular to a compound dry heat event identification method and system considering the influence of evapotranspiration. Background Art

[0002] A compound dry-heat event refers to a compound extreme climate event in which drought and heat waves occur simultaneously. The impact of a compound dry-heat event often exceeds the individual impacts of heat waves and droughts, and its direct impacts include water shortages, vegetation death, and crop failures. In the long run, the continued effect of dry-heat conditions will further trigger a series of serious consequences, including a significant increase in the risk of wildfires, a decrease in the carbon absorption capacity of vegetation, and an increase in heat-related diseases, which will have a significant impact on human society and ecosystems. With the intensification of global warming, compound dry-heat events have shown a trend of continuous expansion in multiple dimensions such as frequency, duration, and scope of impact. Therefore, it is of great significance to accurately identify and monitor compound dry-heat events.

[0003] At present, existing studies mainly use the joint distribution of temperature and precipitation to identify and monitor changes in compound dry and hot events. However, existing monitoring methods fail to fully consider the impact of the synergistic effect of temperature, precipitation and evapotranspiration on compound dry and hot events. In fact, for the monitoring of compound dry and hot events, temperature conditions have an important impact on drought. During the occurrence of compound dry and hot events, high temperatures will have a significant impact on evaporation and vegetation transpiration processes. It is worth noting that only when the fluctuation amplitude of temperature is smaller than the fluctuation amplitude of precipitation, drought will be mainly controlled by precipitation. The actual situation of compound dry and hot events is obviously inconsistent with this assumption. The above limitations may make it difficult for existing methods to accurately identify and monitor the scope and severity of the impact of compound dry and hot events, and further cause deviations in the assessment of the impact on ecosystem carbon sequestration function and agricultural production.

[0004] Therefore, it is urgent to provide a technical solution to solve the above problems. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a composite dry heat event identification method and system considering the influence of evapotranspiration.

[0006] In a first aspect, the present invention provides a method for identifying a composite dry heat event taking into account the influence of evapotranspiration. The technical solution of the method is as follows:

[0007] Obtain standardized precipitation evapotranspiration index and standardized temperature index at multiple time scales for the vegetation area to be tested, calculate the Pearson correlation coefficient between each pair of index combinations, and determine the index combination with the highest absolute value of the Pearson correlation coefficient and passing the significance test as the optimal index combination; wherein each pair of index combinations includes the standardized precipitation evapotranspiration index of one time scale and the standardized temperature index of one time scale;

[0008] Based on the optimal Copula function and the optimal index combination, and in combination with the conditional probability formula, the joint probability of the composite dry heat event in the vegetation area to be detected is calculated, and the joint probability of the composite dry heat event is input into a preset formula to generate a standardized composite dry heat index for the vegetation area to be detected;

[0009] According to the standardized composite dry heat index threshold, a composite dry heat event level corresponding to the standardized composite dry heat index is identified.

[0010] The beneficial effects of the composite dry heat event identification method considering the influence of evapotranspiration of the present invention are as follows:

[0011] The method of the present invention can solve the problem of inaccurate monitoring of composite dry-heat events in vegetation areas due to neglect of evapotranspiration in existing methods, improve the monitoring accuracy of the impact range and severity of composite dry-heat events, and has broad application prospects.

[0012] Based on the above solution, the composite dry heat event identification method considering the influence of evapotranspiration of the present invention can be further improved as follows.

[0013] In an optional manner, the method further includes:

[0014] Using multiple preset Copula functions to construct joint distribution models corresponding to the optimal index combination, and evaluating the model fit goodness of each joint distribution model through multiple indicator evaluation methods;

[0015] The preset Copula function corresponding to the joint distribution model with the best model fit goodness of fit is determined as the optimal Copula function.

[0016] In an optional manner, the multiple preset Copula functions include: Frank Copula function, TCopula function and Gaussian Copula function; the multiple indicator evaluation methods include: Akaike Information Criterion, Bayesian Criterion and Root Mean Square Error.

[0017] In an optional manner, the conditional probability formula is: P=F(SPEI)-C(F(SPEI), F(STI));

[0018] Wherein, P represents the joint probability of the composite dry-heat event, C(·) represents the optimal copula function, F(·) represents the marginal distribution function, SPEI represents the optimal standardized precipitation evapotranspiration index in the optimal index combination, and STI represents the optimal standardized temperature index in the optimal index combination.

[0019] In an optional manner, the preset formula is: SCDHI=φ -1 [G(P)];

[0020] Wherein, the SCDHI represents the standardized composite dry heat index, φ -1 (·) represents the inverse function of the standard normal distribution cumulative distribution function, and G(·) represents the Gringorten empirical frequency distribution.

[0021] In an optional manner, the standardized composite dry heat index threshold includes: a first threshold, a second threshold, a third threshold, and a fourth threshold, which have decreasing values. The step of identifying the composite dry heat event level corresponding to the standardized composite dry heat index according to the standardized composite dry heat index threshold includes:

[0022] When the normalized composite dry heat index is not less than the first threshold, determining the composite dry heat event level as no composite dry heat event;

[0023] When the normalized composite dry heat index is less than the first threshold value and not less than the second threshold value, the composite dry heat event level is determined as a mild composite dry heat event;

[0024] When the normalized composite dry heat index is less than the second threshold value and not less than the third threshold value, the composite dry heat event level is determined as a moderate composite dry heat event;

[0025] When the normalized composite dry heat index is less than the third threshold value and not less than the fourth threshold value, determining the composite dry heat event level as a severe composite dry heat event;

[0026] When the normalized composite dry heat index is less than the fourth threshold, the composite dry heat event level is determined to be an extreme composite dry heat event.

[0027] In a second aspect, the present invention provides a composite dry heat event identification system that considers the influence of evapotranspiration. The technical solution of the system is as follows:

[0028] Includes: determination module, generation module and recognition module;

[0029] The determination module is used to obtain standardized precipitation evapotranspiration index and standardized temperature index of the vegetation area to be detected at multiple time scales, calculate the Pearson correlation coefficient between each pair of index combinations, and determine the index combination with the highest absolute value of the Pearson correlation coefficient and passing the significance check as the optimal index combination; wherein each pair of index combinations includes the standardized precipitation evapotranspiration index of one time scale and the standardized temperature index of one time scale;

[0030] The generation module is used to calculate the joint probability of the composite dry heat event in the vegetation area to be detected based on the optimal Copula function and the optimal index combination, in combination with the conditional probability formula, and input the joint probability of the composite dry heat event into a preset formula to generate a standardized composite dry heat index for the vegetation area to be detected;

[0031] The identification module is used to identify the composite dry heat event level corresponding to the standardized composite dry heat index according to the standardized composite dry heat index threshold.

[0032] The beneficial effects of the composite dry heat event identification system considering the influence of evapotranspiration of the present invention are as follows:

[0033] The system of the present invention can solve the problem of inaccurate monitoring of composite dry-heat events in vegetation areas due to neglect of evapotranspiration in existing methods, improve the monitoring accuracy of the impact range and severity of composite dry-heat events, and has broad application prospects.

[0034] Based on the above solution, the composite dry heat event identification system considering the influence of evapotranspiration of the present invention can be further improved as follows.

[0035] In an optional manner, the method further includes: a screening module; the screening module is configured to:

[0036] Using multiple preset Copula functions to construct joint distribution models corresponding to the optimal index combination, and evaluating the model fit goodness of each joint distribution model through multiple indicator evaluation methods;

[0037] The preset Copula function corresponding to the joint distribution model with the best model fit goodness of fit is determined as the optimal Copula function.

[0038] In a third aspect, the technical solution of an electronic device of the present invention is as follows:

[0039] The invention comprises a memory, a processor and a program stored in the memory and running on the processor. When the processor executes the program, the steps of the composite dry heat event identification method considering the influence of evapotranspiration of the present invention are realized.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium having the following technical solution:

[0041] The computer-readable storage medium stores instructions. When the computer-readable storage medium reads the instructions, the computer-readable storage medium executes the steps of the composite dry heat event identification method considering the influence of evapotranspiration according to the present invention.

[0042] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the drawings. In the drawings:

[0044] Figure 1 1 is a flow chart of an embodiment of a composite dry heat event identification method considering the influence of evapotranspiration according to the present invention;

[0045] Figure 2 Schematic diagram of the correlation distribution between SPEI and STI at different time scales;

[0046] Figure 3 This is a schematic diagram of the correlation comparison of the spatial range of monitoring of complex dry and hot events;

[0047] Figure 4 This is a schematic diagram of the differences in monitoring results regarding the frequency, severity, intensity and duration of complex dry heat events;

[0048] Figure 5 This is a schematic diagram of the differences in monitoring results of the composite dry-heat event in vegetation areas;

[0049] Figure 6 Schematic diagram of the structure of an embodiment of a composite dry heat event identification system considering the influence of evapotranspiration according to the present invention;

[0050] Figure 7 The figure is a schematic structural diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0051] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0052] Figure 1A flow chart of an embodiment of a method for identifying a compound dry heat event taking into account the influence of evapotranspiration provided by the present invention is shown. The method for identifying a compound dry heat event taking into account the influence of evapotranspiration can be executed by an electronic device such as a terminal device or a server. The terminal device can be any fixed or mobile terminal such as a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the method for identifying a compound dry heat event taking into account the influence of evapotranspiration by calling computer-readable instructions stored in a memory through a processor. As Figure 1 As shown, the following steps are included:

[0053] S1. Obtain the standardized precipitation evapotranspiration index and standardized temperature index of the vegetation area to be detected at multiple time scales, calculate the Pearson correlation coefficient between each pair of index combinations, and determine the index combination with the highest absolute value of the Pearson correlation coefficient and passing the significance test as the optimal index combination.

[0054] Among them, the vegetation area to be detected is the vegetation area that needs to be identified for the composite dry heat event in this embodiment. Each pair of index combinations includes a standardized precipitation evapotranspiration index of a time scale and a standardized temperature index of a time scale. The time scale can be one month, three months, six months, one year, etc., and there is no restriction on the words. In this embodiment, the standardized precipitation evapotranspiration index SPEI of multiple time scales includes: SPEI-1 (standardized precipitation evapotranspiration index of one month) and SPEI-3 (standardized precipitation evapotranspiration index of three months). The standardized temperature index STI is STI-1 (standardized temperature index of one month). The Pearson correlation coefficient refers to a statistic that measures the degree of linear correlation between two fixed-interval variables.

[0055] It should be noted that the Standardized Precipitation Evapotranspiration Index (SPEI-1) and Standardized Temperature Evapotranspiration Index (SPEI-3) for the vegetation areas under investigation were obtained from public databases at multiple time scales. Based on the assumption that temperatures over the same period of previous years follow a normal distribution, temperatures were normalized to construct the Standardized Temperature Index (STI-1) to characterize heatwave events. The SPEI and STI in each index pair were spatially and temporally aligned to ensure consistency in spatial resolution and time step.

[0056] It should be noted that, in this embodiment, the Pearson correlation coefficients of SPEI-1, SPEI-3 and STI-1 were calculated respectively to generate the following: Figure 2The correlation coefficient graph is shown, and the combination of SPEI-1 and STI-1 with the highest absolute value of correlation is selected for monitoring complex dry heat events.

[0057] S2. Based on the optimal Copula function and the optimal index combination, and in combination with the conditional probability formula, the joint probability of the composite dry heat event in the vegetation area to be detected is calculated, and the joint probability of the composite dry heat event is input into a preset formula to generate a standardized composite dry heat index for the vegetation area to be detected.

[0058] The default optimal copula function is the Gaussian copula function. The optimal index combination includes the optimal standardized precipitation evapotranspiration index and the optimal standardized temperature index. The standardized composite dry heat index ranges from -3 to 3.

[0059] The conditional probability formula is: P = F(SPEI) - C(F(SPEI), F(STI)); where P represents the joint probability of the composite dry-hot event, C(·) represents the optimal copula function, F(·) represents the marginal distribution function, SPEI represents the optimal standardized precipitation evapotranspiration index in the optimal index combination, and STI represents the optimal standardized temperature index in the optimal index combination.

[0060] It should be noted that P = P(SPEI≤spei,STI>sti) = P(SPEI≤spei) - P(SPEI≤spei,STI≤sti) = F(SPEI) - C(F(SPEI),F(STI)); SPEI is the random variable X, and STI is the random variable Y. Random variables are assumed theoretical values. For example, data follow a normal distribution, but there are actual deviations. Spei and sti are observed values, namely the obtained standardized precipitation evapotranspiration index and standardized temperature index.

[0061] Among them, the preset formula is: SCDHI=φ -1 [G(P)]; where SCDHI represents the standardized composite dry heat index, φ -1 (·) represents the inverse function of the standard normal distribution cumulative distribution function, and G(·) represents the Gringorten empirical frequency distribution.

[0062] S3. Identify the composite dry heat event level corresponding to the standardized composite dry heat index according to the standardized composite dry heat index threshold.

[0063] The standardized composite dry heat index threshold includes: a first threshold, a second threshold, a third threshold, and a fourth threshold, which are successively smaller. Specifically, S3 includes:

[0064] When the normalized composite dry heat index is not less than the first threshold, determining the composite dry heat event level as no composite dry heat event;

[0065] When the normalized composite dry heat index is less than the first threshold value and not less than the second threshold value, the composite dry heat event level is determined as a mild composite dry heat event;

[0066] When the normalized composite dry heat index is less than the second threshold value and not less than the third threshold value, the composite dry heat event level is determined as a moderate composite dry heat event;

[0067] When the normalized composite dry heat index is less than the third threshold value and not less than the fourth threshold value, determining the composite dry heat event level as a severe composite dry heat event;

[0068] When the normalized composite dry heat index is less than the fourth threshold, the composite dry heat event level is determined to be an extreme composite dry heat event.

[0069] The first threshold value defaults to -0.5, the second threshold value defaults to -0.8, the third threshold value defaults to -1.3, and the fourth threshold value defaults to -1.6. It should be noted that in this embodiment, the specific value of the standardized composite dry heat index threshold is determined by referring to the threshold definition method of the standardized composite event index (SCEI).

[0070] In an optional manner, the method further includes:

[0071] A plurality of preset Copula functions are used to respectively construct the joint distribution models corresponding to the optimal index combination, and the model fitting goodness of each joint distribution model is evaluated through a variety of indicator evaluation methods.

[0072] The preset Copula function corresponding to the joint distribution model with the best model fit goodness of fit is determined as the optimal Copula function.

[0073] Among them, multiple preset copula functions include the Frank copula function, the T copula function, and the Gaussian copula function. Various evaluation metrics include the Akaike Information Criterion (AIC), the Bayesian Criterion (BIC), and the root mean square error (RMSE). The Gaussian copula function achieved the lowest AIC of -83.27, the BIC of -82.25, and the RMSE of 0.11, indicating that the Gaussian copula function had the best model fit. Therefore, the Gaussian copula function was determined to be the optimal copula function.

[0074] Figure 3 and Figure 4The following figure shows an example of using the composite dry heat event identification method provided in this embodiment and the standardized composite event index (SCEI) method to monitor the spatial range, frequency, severity, intensity, and duration of composite dry heat events. A comparison shows that the two methods show high consistency in spatial range, frequency, severity, intensity, and duration, with a correlation coefficient R 2 All are greater than 0.5. It can be seen that the composite dry heat event identification method provided in this embodiment is reliable in identifying and monitoring composite dry heat events. In addition, Figure 5 An example diagram shows the difference in monitoring results between the composite dry heat event identification method provided by this embodiment and the SCEI method in vegetation areas. As the normalized difference vegetation index (NDVI) increases, the difference between SCDHI and SCEI becomes larger, indicating that SCDHI can better capture the impact of composite dry heat events on vegetation. In particular, in areas with good vegetation conditions, SCDHI can identify affected areas that SCEI fails to detect. The NDVI in these areas drops significantly during composite dry heat events. Figure 5 It can be seen that the composite dry-heat event identification method provided in this embodiment performs better in identifying and monitoring composite dry-heat events, especially in areas with high vegetation coverage.

[0075] The technical solution of this embodiment can solve the problem of inaccurate monitoring of composite dry-heat events in vegetation areas due to neglect of evapotranspiration in existing methods, improve the monitoring accuracy of the impact range and severity of composite dry-heat events, and has broad application prospects.

[0076] Figure 6 FIG. 2 shows a schematic structural diagram of an embodiment of a composite dry heat event identification system 200 that considers the influence of evapotranspiration provided by the present invention. Figure 6 As shown, the system 200 includes: a determination module 210, a generation module 220 and an identification module 230;

[0077] The determination module 210 is used to obtain standardized precipitation evapotranspiration indexes and standardized temperature indices at multiple time scales for the vegetation area to be detected, calculate the Pearson correlation coefficient between each pair of index combinations, and determine the index combination with the highest absolute value of the Pearson correlation coefficient and passing the significance check as the optimal index combination; wherein each pair of index combinations includes the standardized precipitation evapotranspiration index of one time scale and the standardized temperature index of one time scale;

[0078] The generating module 220 is configured to calculate the joint probability of the composite dry heat event in the vegetation area to be detected based on the optimal Copula function and the optimal index combination and in combination with a conditional probability formula, and input the joint probability of the composite dry heat event into a preset formula to generate a standardized composite dry heat index for the vegetation area to be detected;

[0079] The identification module 230 is configured to identify a composite dry heat event level corresponding to the standardized composite dry heat index according to a standardized composite dry heat index threshold.

[0080] In an optional manner, the method further includes: a screening module; the screening module is configured to:

[0081] Using multiple preset Copula functions to construct joint distribution models corresponding to the optimal index combination, and evaluating the model fit goodness of each joint distribution model through multiple indicator evaluation methods;

[0082] The preset Copula function corresponding to the joint distribution model with the best model fit goodness of fit is determined as the optimal Copula function.

[0083] In an optional manner, the multiple preset Copula functions include: Frank Copula function, TCopula function and Gaussian Copula function; the multiple indicator evaluation methods include: Akaike Information Criterion, Bayesian Criterion and Root Mean Square Error.

[0084] In an optional manner, the conditional probability formula is: P=F(SPEI)-C(F(SPEI), F(STI));

[0085] Wherein, P represents the joint probability of the composite dry-heat event, C(·) represents the optimal copula function, F(·) represents the marginal distribution function, SPEI represents the optimal standardized precipitation evapotranspiration index in the optimal index combination, and STI represents the optimal standardized temperature index in the optimal index combination.

[0086] In an optional manner, the preset formula is: SCDHI=φ -1 [G(P)];

[0087] Wherein, the SCDHI represents the standardized composite dry heat index, φ -1 (·) represents the inverse function of the standard normal distribution cumulative distribution function, and G(·) represents the Gringorten empirical frequency distribution.

[0088] In an optional manner, the standardized composite dry heat index threshold includes: a first threshold, a second threshold, a third threshold, and a fourth threshold whose values ​​decrease in sequence; the identification module 230 is specifically configured to:

[0089] When the normalized composite dry heat index is not less than the first threshold, determining the composite dry heat event level as no composite dry heat event;

[0090] When the normalized composite dry heat index is less than the first threshold value and not less than the second threshold value, the composite dry heat event level is determined as a mild composite dry heat event;

[0091] When the normalized composite dry heat index is less than the second threshold value and not less than the third threshold value, the composite dry heat event level is determined as a moderate composite dry heat event;

[0092] When the normalized composite dry heat index is less than the third threshold value and not less than the fourth threshold value, determining the composite dry heat event level as a severe composite dry heat event;

[0093] When the normalized composite dry heat index is less than the fourth threshold, the composite dry heat event level is determined to be an extreme composite dry heat event.

[0094] It should be noted that the beneficial effects of the composite dry heat event identification system 200 considering the influence of evapotranspiration provided in the above embodiment are the same as the beneficial effects of the composite dry heat event identification method considering the influence of evapotranspiration, and will not be repeated here. In addition, when implementing its functions, the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0095] The composite dry heat event identification system 200 considering the influence of evapotranspiration of the present invention may be a computer program (including program code) running in a computer device. For example, the composite dry heat event identification system considering the influence of evapotranspiration of the present invention is an application software that can be used to execute the corresponding steps of the composite dry heat event identification method considering the influence of evapotranspiration of the present invention.

[0096] In some embodiments, the composite dry heat event identification system 200 considering the influence of evapotranspiration of the present invention can be implemented using a combination of software and hardware. As an example, the composite dry heat event identification system considering the influence of evapotranspiration of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the composite dry heat event identification method considering the influence of evapotranspiration of the present invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0097] The modules described in the embodiments of the present invention may be implemented in software or hardware, and the name of a module does not necessarily limit the module itself.

[0098] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the aforementioned methods for identifying a composite dry heat event that considers the influence of evapotranspiration is implemented. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is configured to store the computer program; and the processor is configured to execute, by calling the computer program, any of the aforementioned methods for identifying a composite dry heat event that considers the influence of evapotranspiration as described in any of the embodiments of the present invention.

[0099] In an alternative embodiment, an electronic device is provided, such as Figure 7 As shown, Figure 7 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data exchange between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the number of transceivers 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0100] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the present disclosure. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0101] Bus 4002 may include a path for transmitting information between the above components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 In the figure, only one thick line is used to represent the bus 4002, but this does not mean that there is only one bus or one type of bus.

[0102] The memory 4003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0103] The memory 4003 is used to store application code (computer program) for executing the solution of the present invention, and is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0104] Among them, the electronic device can also be a terminal device, and the terminal device can be any terminal device that can install applications and access web pages through applications, including at least one of a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, smart TV, and smart car-mounted device.

[0105] It should be noted that Figure 7 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0106] A computer-readable storage medium according to an embodiment of the present invention stores a computer program, which, when executed by a processor, implements any of the above-mentioned methods for identifying composite dry heat events that consider the influence of evapotranspiration.

[0107] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, or the like.

[0108] In an exemplary embodiment, a computer program product or computer program is also provided. The computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the aforementioned composite dry heat event identification method that considers the influence of evapotranspiration.

[0109] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0110] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0111] The computer-readable storage medium provided in the embodiments of the present invention may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or component.

[0112] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0113] The above description is merely a preferred embodiment of the present invention and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the present invention.

[0114] It should be noted that the terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and to define a specific order or precedence. Where appropriate, the order used for similar objects may be interchanged, such that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0115] Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention may be implemented in the form of a computer program product embodied in one or more computer-readable media containing computer-readable program code.

[0116] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A composite dry heat event identification method considering the influence of evapotranspiration, characterized in that: include: Obtain standardized precipitation evapotranspiration index and standardized temperature index at multiple time scales for the vegetation area to be tested, calculate the Pearson correlation coefficient between each pair of index combinations, and determine the index combination with the highest absolute value of the Pearson correlation coefficient and passing the significance test as the optimal index combination; wherein each pair of index combinations includes the standardized precipitation evapotranspiration index of one time scale and the standardized temperature index of one time scale; Based on the optimal Copula function and the optimal index combination, and in combination with the conditional probability formula, the joint probability of the composite dry heat event in the vegetation area to be detected is calculated, and the joint probability of the composite dry heat event is input into a preset formula to generate a standardized composite dry heat index for the vegetation area to be detected; According to the standardized composite dry heat index threshold, a composite dry heat event level corresponding to the standardized composite dry heat index is identified.

2. The composite dry heat event identification method considering the influence of evapotranspiration according to claim 1 is characterized in that: Also includes: Using multiple preset Copula functions to construct joint distribution models corresponding to the optimal index combination, and evaluating the model fit goodness of each joint distribution model through multiple indicator evaluation methods; The preset Copula function corresponding to the joint distribution model with the best model fit goodness of fit is determined as the optimal Copula function.

3. The composite dry heat event identification method considering the influence of evapotranspiration according to claim 2 is characterized in that: The multiple preset Copula functions include: Frank Copula function, T Copula function and Gaussian Copula function; the multiple indicator evaluation methods include: Akaike Information Criterion, Bayesian Criterion and Root Mean Square Error.

4. The composite dry heat event identification method considering the influence of evapotranspiration according to claim 1 is characterized in that: The conditional probability formula is: P = F (SPEI) - C (F (SPEI), F (STI)); Wherein, P represents the joint probability of the composite dry-heat event, C(·) represents the optimal copula function, F(·) represents the marginal distribution function, SPEI represents the optimal standardized precipitation evapotranspiration index in the optimal index combination, and STI represents the optimal standardized temperature index in the optimal index combination.

5. The composite dry heat event identification method considering the influence of evapotranspiration according to claim 4 is characterized in that: The preset formula is: SCDHI=φ -1 [G(P)]; Wherein, the SCDHI represents the standardized composite dry heat index, φ -1 (·) represents the inverse function of the standard normal distribution cumulative distribution function, and G(·) represents the Gringorten empirical frequency distribution.

6. The method for identifying a composite dry heat event considering the influence of evapotranspiration according to any one of claims 1 to 5, characterized in that: The standardized composite dry heat index threshold includes: a first threshold, a second threshold, a third threshold, and a fourth threshold, which have values ​​decreasing in sequence; and the step of identifying the composite dry heat event level corresponding to the standardized composite dry heat index according to the standardized composite dry heat index threshold includes: When the normalized composite dry heat index is not less than the first threshold, determining the composite dry heat event level as no composite dry heat event; When the normalized composite dry heat index is less than the first threshold value and not less than the second threshold value, the composite dry heat event level is determined as a mild composite dry heat event; When the normalized composite dry heat index is less than the second threshold value and not less than the third threshold value, the composite dry heat event level is determined as a moderate composite dry heat event; When the normalized composite dry heat index is less than the third threshold value and not less than the fourth threshold value, determining the composite dry heat event level as a severe composite dry heat event; When the normalized composite dry heat index is less than the fourth threshold, the composite dry heat event level is determined to be an extreme composite dry heat event.

7. A composite dry heat event identification system considering the influence of evapotranspiration, characterized in that: include: Determine modules, generate modules, and recognize modules; The determination module is used to obtain standardized precipitation evapotranspiration index and standardized temperature index of the vegetation area to be detected at multiple time scales, calculate the Pearson correlation coefficient between each pair of index combinations, and determine the index combination with the highest absolute value of the Pearson correlation coefficient and passing the significance check as the optimal index combination; wherein each pair of index combinations includes the standardized precipitation evapotranspiration index of one time scale and the standardized temperature index of one time scale; The generation module is used to calculate the joint probability of the composite dry heat event in the vegetation area to be detected based on the optimal Copula function and the optimal index combination, in combination with the conditional probability formula, and input the joint probability of the composite dry heat event into a preset formula to generate a standardized composite dry heat index for the vegetation area to be detected; The identification module is used to identify the composite dry heat event level corresponding to the standardized composite dry heat index according to the standardized composite dry heat index threshold.

8. The composite dry heat event identification system considering the influence of evapotranspiration according to claim 7 is characterized in that: Also includes: Screening module; the screening module is used to: Using multiple preset Copula functions to construct joint distribution models corresponding to the optimal index combination, and evaluating the model fit goodness of each joint distribution model through multiple indicator evaluation methods; The preset Copula function corresponding to the joint distribution model with the best model fit goodness of fit is determined as the optimal Copula function.

9. An electronic device, characterized in that: The electronic device includes a processor coupled to a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the composite dry heat event identification method considering the influence of evapotranspiration as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor so that the computer-readable storage medium implements the composite dry heat event identification method considering the influence of evapotranspiration according to any one of claims 1 to 6.

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