Method and apparatus for determining applicability of chicago rain pattern

Abstract

The application relates to the technical field of rain type simulation, in particular to a method and device for determining the application range of a Chicago rain type, wherein the method comprises the following steps: collecting rainfall data of various durations in a target time period, and determining a sample set containing peak rainfall of various durations based on the rainfall data; obtaining a joint probability distribution of peak rainfall of any two durations from the sample set containing peak rainfall of various durations, and obtaining a co-occurrence probability corresponding to the same return period of peak rainfall of any two durations based on the joint probability distribution; and judging whether the co-occurrence probability is greater than a certain threshold value, if the co-occurrence probability is greater than the certain threshold value, the Chicago rain type is applicable, otherwise, the Chicago rain type is not applicable. Therefore, the problems in the prior art that the Chicago rain type often presents a single-peak thin and narrow type feature, does not conform to the actual situation, and cannot quantitatively determine the application range of the Chicago rain type are solved, and quantitative analysis of the application range of the Chicago rain type is realized.

Description

Technical Field

[0001] This application relates to the field of rainfall prediction technology, and in particular to a method and apparatus for determining the applicable range of the Chicago rain pattern. Background Technology

[0002] In recent years, due to global climate change and accelerated human activities, flood disasters have become increasingly frequent and severe worldwide, posing significant challenges to urban flood control and disaster reduction measures. Floods are often unavoidable and unpredictable; however, they can be controlled through appropriate measures to minimize losses and damage. Flood risk assessment is a crucial step in disaster response. The assessment results allow for the identification of potential risk locations and levels, enabling the implementation of engineering and non-engineering measures to control or mitigate disasters.

[0003] In related technologies, rainfall models can be constructed based on numerical analysis methods and artificial intelligence techniques to simulate rainfall. Examples include hydrodynamic models based on physical mechanisms and numerical prediction models based on machine learning. These models lay a solid foundation for flood risk assessment. Among them, the Chicago rainfall pattern is one of the most widely used storm rainfall pattern designs.

[0004] However, in related technologies, the Chicago rain pattern often exhibits a single-peaked, thin shape, which does not conform to reality and cannot quantitatively define the applicable range of the Chicago rain pattern. Therefore, it is urgent to improve the quantitative analysis of the applicable range of the Chicago rain pattern. Summary of the Invention

[0005] This application provides a method and apparatus for determining the applicable range of Chicago rain patterns, in order to solve the problems in related technologies, such as the fact that Chicago rain patterns often exhibit a single-peaked and slender shape, which does not conform to the actual situation and makes it impossible to quantitatively define the applicable range of Chicago rain patterns, thus realizing the quantitative analysis of the applicable range of Chicago rain patterns.

[0006] The first aspect of this application provides a method for determining the applicability of the Chicago rain pattern, comprising the following steps: collecting rainfall data of various durations within a target time period, and determining a sample set containing the various duration peak rainfall amounts based on the rainfall data; obtaining a joint probability distribution of any of the duration peak rainfall amounts based on the sample set containing the various duration peak rainfall amounts, and determining the co-occurrence probability of any two duration peak rainfall amounts corresponding to the same return period based on the joint probability distribution; determining whether the co-occurrence probability is greater than a preset threshold; if the co-occurrence probability is greater than the preset threshold, then the Chicago rain pattern is applicable; otherwise, the Chicago rain pattern is not applicable.

[0007] Optionally, in one embodiment of this application, the step of collecting rainfall data of various durations within a target time period and determining a sample set of various duration peak rainfalls based on the rainfall data includes: obtaining the rainfall events in which the various duration peak rainfalls are located within the target time period based on the rainfall data; obtaining an initial sample set of the various duration peak rainfalls after slippage according to the slippage of the rainfall events with a target time step and a target slippage step; and integrating the initial sample set to obtain the sample set containing various duration peak rainfalls that satisfies the condition of non-repetition.

[0008] Optionally, in one embodiment of this application, obtaining the joint probability distribution of any two of the peak rainfall durations from the sample set containing various peak rainfall durations includes: solving the marginal probability distribution of the various peak rainfall durations using a parameter estimation method based on the sample set; and solving the joint probability distribution of any two peak rainfall durations in the sample set using a copula function based on the marginal probability distribution.

[0009] Optionally, in one embodiment of this application, the expression for the co-occurrence probability may be, but is not limited to, the following:

[0010]

[0011] Where A and B represent the rainfall amounts for two durations under the design return period, F(·,·) is the joint probability distribution function of rainfall amounts for any two durations, and F(·) is the marginal probability distribution function of rainfall amounts for various durations.

[0012] A second aspect of this application provides an apparatus for determining the applicability of the Chicago rain pattern, comprising: a data acquisition module for acquiring rainfall data of various durations within a target time period, and determining a sample set containing the various duration peak rainfall amounts based on the rainfall data; a generation module for obtaining a joint probability distribution of any two duration peak rainfall amounts based on the sample set containing the various duration peak rainfall amounts, and determining the co-occurrence probability of the two duration peak rainfall amounts corresponding to the same return period based on the joint probability distribution; and a judgment module for determining whether the co-occurrence probability is greater than a preset threshold. If the co-occurrence probability is greater than the preset threshold, the Chicago rain pattern is applicable; otherwise, the Chicago rain pattern is not applicable.

[0013] Optionally, in one embodiment of this application, the acquisition module includes: a first generation unit, configured to obtain the rainfall events in which the various duration peak rainfalls occur within the target time period based on the rainfall data; a second generation unit, configured to obtain an initial sample set of the various duration peak rainfalls after slippage according to the slippage of the rainfall events with a target time step and a target slippage step; and a third generation unit, configured to integrate the initial sample set to obtain the sample set containing the various duration peak rainfalls that satisfies the non-repetition condition.

[0014] Optionally, in one embodiment of this application, the generation module includes: a first solving unit, configured to solve the marginal probability distribution of the various duration peak rainfall amounts using a parameter estimation method based on the sample set; and a second solving unit, configured to solve the joint probability distribution of any two duration peak rainfall amounts in the sample set using a copula function based on the marginal probability distribution.

[0015] Optionally, in one embodiment of this application, the expression for the co-occurrence probability may be, but is not limited to, the following:

[0016]

[0017] Where A and B represent the rainfall amounts for two durations under the design return period, F(·,·) is the joint probability distribution function of rainfall amounts for any two durations, and F(·) is the marginal probability distribution function of rainfall amounts for various durations.

[0018] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for determining the applicability of the Chicago rain type as described in the above embodiments.

[0019] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for determining the applicability of the Chicago rain pattern as described above.

[0020] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, implements the method for determining the applicability of the Chicago rain pattern as described above.

[0021] This application's embodiments can determine a sample set containing various duration peak rainfall amounts by collecting rainfall data of various durations within a target time period. This allows for the calculation of the co-occurrence probability of any two duration peak rainfall amounts corresponding to the same return period. If the co-occurrence probability is greater than a certain threshold, the Chicago rainfall pattern is applicable; otherwise, it is not. This provides scientific guidance for the use of the Chicago rainfall pattern and avoids its indiscriminate application. Therefore, it solves the problems in related technologies where the Chicago rainfall pattern often exhibits a single, thin peak characteristic, which does not reflect reality and makes it impossible to quantitatively define the applicable scope of the Chicago rainfall pattern, thus enabling quantitative analysis of the applicable scope of the Chicago rainfall pattern.

[0022] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0024] Figure 1 This is a flowchart illustrating a method for determining the applicability of Chicago rain patterns according to an embodiment of this application;

[0025] Figure 2 This is a probability heatmap of various rainfall duration recurrence periods co-occurring over 2, 3, 4, 5, 10, and 20 years, according to one embodiment of this application.

[0026] Figure 3 A flowchart illustrating the working principle of a method for determining the applicability of Chicago rain patterns according to an embodiment of this application;

[0027] Figure 4 This is a block diagram of a device for determining the applicability of Chicago rain patterns according to an embodiment of this application;

[0028] Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0029] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0030] The following describes a method and apparatus for determining the applicability of Chicago rain patterns according to embodiments of this application, with reference to the accompanying drawings. Addressing the problem mentioned in the background art that Chicago rain patterns often exhibit a single, narrow peak, which is inconsistent with reality and makes it impossible to quantitatively define the applicability of Chicago rain patterns, this application provides a method for determining the applicability of Chicago rain patterns. In this method, a sample set containing various duration peak rainfall amounts can be determined by collecting rainfall data of various durations within a target time period. Then, the co-occurrence probability of any two duration peak rainfall amounts corresponding to the same return period is obtained. If the co-occurrence probability is greater than a certain threshold, the Chicago rain pattern is applicable; otherwise, the Chicago rain pattern is not applicable. This allows for scientific guidance in the use of Chicago rain patterns, avoiding blind application. Therefore, this solves the problems in related technologies where Chicago rain patterns often exhibit a single, narrow peak, which is inconsistent with reality and makes it impossible to quantitatively define the applicability of Chicago rain patterns, thus hindering quantitative analysis of their applicability.

[0031] Specifically, Figure 1 This is a flowchart of a method for determining the applicable range of Chicago rain patterns according to an embodiment of this application.

[0032] like Figure 1 As shown, the method for determining the applicability of this Chicago rain type includes the following steps:

[0033] In step S101, rainfall data of various durations are collected within the target time period, and a sample set of peak rainfall of various durations is determined based on the rainfall data.

[0034] It is understood that the target time period in the embodiments of this application may be, but is not limited to, three months, six months, one year, five years, ten years, etc., and can be set by those skilled in the art according to the actual situation. This application does not impose any specific restrictions.

[0035] Furthermore, the duration of various durations in the embodiments of this application may include, but is not limited to, 5 min, 10 min, 15 min, 20 min, 30 min, 45 min, 60 min, 90 min, 120 min, etc., and can be set by those skilled in the art according to the actual situation. This application does not impose any specific limitations.

[0036] As one possible approach, this application determines a sample set of peak rainfall over various durations by collecting rainfall data of various durations within a target time period.

[0037] Optionally, in one embodiment of this application, collecting rainfall data of various durations within a target time period and determining a sample set of various duration peak rainfalls based on the rainfall data includes: obtaining the rainfall events in which various duration peak rainfalls occur within the target time period based on the rainfall data; obtaining an initial sample set of various duration peak rainfalls after slippage based on the slippage of the target time step and the target slippage step in the rainfall events; and integrating the initial sample set to obtain a sample set of various duration peak rainfalls that satisfies the condition of non-repetition.

[0038] In actual implementation, the embodiments of this application can obtain the rainfall events with various duration peak rainfalls within the target time period based on rainfall data, and then obtain the initial sample set after sliding by sliding, and integrate the initial sample set to obtain a sample set that meets the non-repetition condition.

[0039] For example, this application embodiment can filter the rainfall events with the largest rainfall duration of 5min, 10min, 15min, 20min, 30min, 45min, 60min, 90min, and 120min per year based on the rainfall data of XX station within the past 20 years. For example, the largest rainfall event of 5min is the first event of the first year, and its rainfall duration is 180min.

[0040] Furthermore, in this embodiment, 120 minutes is used as the target time step and 1 minute as the target glide step to glide through the obtained rainfall events, and the 120-minute rainfall data sequence with the largest total rainfall is found as the initial sample. If the total rainfall duration corresponding to a rainfall event is less than 120 minutes, it can be padded with 0 to make up 120 minutes, thereby obtaining the initial sample. In this embodiment, after obtaining 60 initial samples in the first rainfall event with a rainfall duration of 180 minutes, the initial sample set of various duration peak rainfalls after glide can be determined.

[0041] Furthermore, in this embodiment of the application, the initial sample set is integrated according to the principle of non-repetition to obtain sample sets of various duration peak rainfall. For example, if the rainfall events of the maximum 5-minute rainfall and the maximum 10-minute rainfall are exactly the same, then one of the rainfall events can be analyzed to obtain the sample set.

[0042] In step S102, the joint probability distribution of any two duration peak rainfalls is obtained from the sample set containing various duration peak rainfalls, and the co-occurrence probability of any two duration peak rainfalls corresponding to the same return period is obtained based on the joint probability distribution.

[0043] The expression for the co-occurrence probability can be, but is not limited to, as follows:

[0044]

[0045] Where A and B represent the rainfall amounts for two durations under the design return period, F(·,·) is the joint probability distribution function of rainfall amounts for any two durations, and F(·) is the marginal probability distribution function of rainfall amounts for various durations.

[0046] In practical implementation, embodiments of this application can obtain the joint probability distribution of any two peak rainfall durations based on a sample set containing various peak rainfall durations. This allows for the study of the co-occurrence probability of peak rainfall durations of other durations corresponding to the same return period, under certain return period rainfall conditions. The certain return period rainfall conditions can be set by those skilled in the art according to actual circumstances, and this application does not impose specific limitations. Furthermore, the expression for the co-occurrence probability in embodiments of this application can be, but is not limited to, as follows:

[0047]

[0048] Where A and B represent the rainfall amounts for two different durations under the design return period, F(·,·) is the joint probability distribution function of rainfall amounts for various durations, and F(·) is the marginal probability distribution function of rainfall amounts for various durations.

[0049] For example, embodiments of this application can calculate the probability of various rainfall durations occurring simultaneously over 2, 3, 4, 5, 10, and 20 years, specifically as follows: Figure 2 As shown. Among them, Figure 2 (a) A probability heatmap of various rainfall duration recurrence periods occurring in 2-year periods according to an embodiment of this application; Figure 2 (b) A probability heatmap of various rainfall duration recurrence periods occurring in 3-year periods according to an embodiment of this application; Figure 2 (c) A probability heatmap of various rainfall duration recurrence periods occurring in 4-year periods according to an embodiment of this application; Figure 2 (d) is a probability heatmap of various rainfall duration recurrence periods occurring in 5-year periods according to an embodiment of this application; Figure 2 (e) A probability heatmap of various rainfall duration recurrence periods occurring in 10-year periods according to an embodiment of this application; Figure 2 (f) is a probability heatmap of various rainfall durations with the same return period over 20 years according to an embodiment of this application.

[0050] Optionally, in one embodiment of this application, obtaining the joint probability distribution of any two duration peak rainfalls based on a sample set containing various duration peak rainfalls includes: solving the marginal probability distribution of various duration peak rainfalls using a parameter estimation method based on the sample set; and solving the joint probability distribution of any two duration peak rainfalls in the sample set using a copula function based on the marginal probability distribution.

[0051] As one possible implementation, embodiments of this application can use Spearman correlation analysis to calculate the correlation of sample sets of various duration peak rainfall, then use parameter estimation to solve the marginal probability distribution of various duration peak rainfall, and then use the copula function to solve the joint probability distribution of any two various duration peak rainfall.

[0052] The copula function converts random vectors x1, x2, ..., x into their corresponding values. n The joint distribution function F(x1,x2,...,x) n ) and their respective edge functions F1, F2, ..., F n The connection function, i.e., the function C(u1,u2,...,u) n ),make

[0053] F(x1,x2,L x n )=C(F1(x1),F2(x2),L,F n (x n )),

[0054] Furthermore, in the embodiments of this application, the copula function may include, but is not limited to, Frank Copula, Clayton Copula, and Gumbel Copula, etc., and this application does not impose specific limitations.

[0055] In step S103, it is determined whether the co-occurrence probability is greater than a preset threshold. If the co-occurrence probability is greater than the preset threshold, the Chicago rain pattern is applicable; otherwise, the Chicago rain pattern is not applicable.

[0056] As one possible approach, embodiments of this application can determine the applicable duration and recurrence period range for the Chicago rain pattern using a certain threshold. This threshold can be set by those skilled in the art based on actual circumstances, and this application does not impose any specific limitations.

[0057] For example, in this embodiment of the application, a co-occurrence probability of 60% can be used as a certain threshold. If the co-occurrence probability is greater than 60%, the Chicago rain pattern is considered applicable; if the co-occurrence probability is less than 60%, the Chicago rain pattern is considered inapplicable. Accordingly, this embodiment of the application can draw an applicable table of the Chicago rain pattern, as shown in Table 1. Table 1 is an applicable table of the Chicago rain pattern according to an embodiment of this application.

[0058] Table 1

[0059] Duration (min) 120 60 45 30 30

[0060] The working principle of the method for determining the applicability of the Chicago rain pattern proposed in this application will be described in detail below with reference to a specific embodiment.

[0061] in, Figure 3 This is a flowchart illustrating the working principle of a method for determining the applicability of Chicago rain patterns according to an embodiment of this application.

[0062] Step S301: Obtain rainfall data of various durations within the target time period.

[0063] In other words, this application embodiment can filter out the rainfall events with the largest rainfall duration of 5min, 10min, 15min, 20min, 30min, 45min, 60min, 90min, and 120min per year based on rainfall data of XX station within the past 20 years.

[0064] Step S302: Determine an initial sample set containing various historical peak rainfall amounts.

[0065] In other words, in this embodiment of the application, 120 minutes can be used as the target time step and 1 minute as the target glide step to glide through the obtained rainfall events, find the 120-minute rainfall data sequence with the largest total rainfall as the initial sample, and if the total rainfall duration corresponding to the rainfall event is less than 120 minutes, it can be padded with 0 to make up 120 minutes, thereby obtaining the initial sample.

[0066] Step S303: Form a sample set.

[0067] In other words, the embodiments of this application can integrate the sample set in step S302 according to the principle of non-repetition to form a sample set.

[0068] Step S304: Calculate the correlation of various duration peak rainfall amounts.

[0069] In other words, the Spearman correlation analysis method can be used in this application embodiment to calculate the correlation of various duration peak rainfalls in step S303.

[0070] Step S305: Solve for the marginal probability distribution of peak rainfall over various durations.

[0071] In other words, the embodiments of this application can use the parameter estimation method to solve the marginal probability distribution of various duration peak rainfall in step S304.

[0072] Step S306: Solve for the joint probability distribution of any two duration peak rainfall amounts.

[0073] In other words, this application embodiment can use the copula function to solve the joint probability distribution of any two types of peak rainfall durations in step S304, and calculate the co-occurrence probability of peak rainfall durations of other durations corresponding to the same return period under certain return period rainfall conditions. Furthermore, this application embodiment calculates the probability of co-occurrence of various rainfall durations over 2, 3, 4, 5, 10, and 20 years. Figure 2 As shown above, the expression for the co-occurrence probability in the embodiments of this application is as described above, and will not be repeated here.

[0074] Step S307: Set a certain threshold to determine the applicable duration and recurrence period range of the Chicago rain pattern.

[0075] In other words, this application embodiment can use a co-occurrence probability of 60% as a certain threshold. If the co-occurrence probability is greater than 60%, the Chicago rain pattern is considered applicable; if the co-occurrence probability is less than 60%, the Chicago rain pattern is considered inapplicable. Accordingly, this application embodiment can draw an applicable table of the Chicago rain pattern's scope of application, as shown in Table 1.

[0076] The method for determining the applicability of the Chicago rain pattern proposed in this application can determine a sample set containing various peak rainfall amounts over different durations by collecting rainfall data of various durations within a target time period. This allows for the determination of the co-occurrence probability of any two peak rainfall amounts corresponding to the same return period. If the co-occurrence probability is greater than a certain threshold, the Chicago rain pattern is applicable; otherwise, it is not. This scientifically guides the use of the Chicago rain pattern and avoids its indiscriminate application. Therefore, it solves the problems in related technologies where the Chicago rain pattern often exhibits a single, thin peak, which does not reflect reality and makes it impossible to quantitatively define the applicability of the Chicago rain pattern, thus enabling quantitative analysis of its applicability.

[0077] Next, with reference to the accompanying drawings, a device for determining the applicability of the Chicago rain pattern according to an embodiment of this application is described.

[0078] Figure 4 This is a block diagram of a device for determining the applicability of Chicago rain patterns according to an embodiment of this application.

[0079] like Figure 4 As shown, the Chicago rain type applicability determination device 10 includes: a data acquisition module 100, a generation module 200, and a judgment module 300.

[0080] The acquisition module 100 is used to acquire rainfall data of various durations within the target time period and determine a sample set containing various duration peak rainfall based on the rainfall data.

[0081] The generation module 200 is used to obtain the joint probability distribution of any two duration peak rainfalls based on a sample set containing various duration peak rainfalls, and to determine the co-occurrence probability of any two duration peak rainfalls corresponding to the same return period based on the joint probability distribution.

[0082] The judgment module 300 is used to determine whether the co-occurrence probability is greater than a preset threshold. If the co-occurrence probability is greater than the preset threshold, the Chicago rain pattern is applicable; otherwise, the Chicago rain pattern is not applicable.

[0083] Optionally, in one embodiment of this application, the acquisition module 100 includes: a first generation unit, a second generation unit, and a third generation unit.

[0084] The first generation unit is used to obtain the rainfall events with various duration peak rainfalls within the target time period based on rainfall data.

[0085] The second generation unit is used to obtain an initial sample set of various duration peak rainfalls after slippage based on the slippage of the target time step and the target slippage step in the rainfall events.

[0086] The third generation unit is used to integrate the initial sample set to obtain a sample set containing various duration peak rainfall that satisfies the non-repetition condition.

[0087] Optionally, in one embodiment of this application, the generation module 200 includes: a first solving unit and a second solving unit.

[0088] The first solution unit is used to solve the marginal probability distribution of various duration peak rainfalls using parameter estimation methods based on the sample set.

[0089] The second solution unit is used to solve the joint probability distribution of any two duration peak rainfalls in the sample set based on the marginal probability distribution and using the copula function.

[0090] Optionally, in one embodiment of this application, the expression for the co-occurrence probability may be, but is not limited to, the following:

[0091]

[0092] Where A and B represent the rainfall amounts for two durations under the design return period, F(·,·) is the joint probability distribution function of rainfall amounts for any two durations, and F(·) is the marginal probability distribution function of rainfall amounts for various durations.

[0093] It should be noted that the explanation of the aforementioned method for determining the applicable range of the Chicago rain type also applies to the device for determining the applicable range of the Chicago rain type in this embodiment, and will not be repeated here.

[0094] The device for determining the applicability of Chicago rain patterns according to the embodiments of this application can determine a sample set containing various duration peak rainfall amounts by collecting rainfall data of various durations within a target time period. It then obtains the co-occurrence probability of any two duration peak rainfall amounts corresponding to the same return period. If the co-occurrence probability is greater than a certain threshold, the Chicago rain pattern is applicable; otherwise, it is not. This allows for scientific guidance in the use of Chicago rain patterns, avoiding blind application. Therefore, it solves the problems in related technologies where Chicago rain patterns often exhibit a single-peaked, thin shape, which does not conform to reality and makes it impossible to quantitatively define the applicability of Chicago rain patterns, thus achieving quantitative analysis of the applicability of Chicago rain patterns.

[0095] Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. The electronic device may include:

[0096] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0097] When processor 502 executes the program, it implements the method for determining the applicability of the Chicago rain pattern provided in the above embodiments.

[0098] Furthermore, electronic devices also include:

[0099] Communication interface 503 is used for communication between memory 501 and processor 502.

[0100] The memory 501 is used to store computer programs that can run on the processor 502.

[0101] The memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0102] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0103] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0104] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0105] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for determining the applicability of the Chicago rain pattern.

[0106] This application also provides a computer program product, including a computer program that, when executed, implements the method for determining the applicability of the Chicago rain type as described above.

[0107] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0108] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0109] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0110] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0111] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0112] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0113] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0114] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for determining the applicable range of Chicago rain patterns, characterized in that, Includes the following steps: Collect rainfall data of various durations within the target time period, and determine a sample set containing the peak rainfall of each duration based on the rainfall data; Based on the sample set containing various duration peak rainfall amounts, a joint probability distribution of any two duration peak rainfall amounts is obtained, and based on the joint probability distribution, the co-occurrence probability of the two duration peak rainfall amounts corresponding to the same return period is obtained. Determine whether the co-occurrence probability is greater than a preset threshold. If the co-occurrence probability is greater than the preset threshold, the Chicago rain pattern applies; otherwise, the Chicago rain pattern does not apply. Wherein, obtaining the joint probability distribution of any two of the peak rainfall durations based on the sample set containing various peak rainfall durations includes: Based on the sample set, the marginal probability distributions of various duration peak rainfalls are solved using the parameter estimation method. Based on the marginal probability distribution, the joint probability distribution of any two of the time-based peak rainfall amounts in the sample set is solved using the copula function. The expression for the co-occurrence probability is: ， in, , This indicates the rainfall for two different durations within the design return period. Let be the joint probability distribution function of any two durations of rainfall. Let be the marginal probability distribution function for various durations of rainfall.

2. The method for determining the applicable range of the Chicago rain type according to claim 1, characterized in that, The collection of rainfall data of various durations within the target time period, and the determination of the sample set containing various peak rainfall amounts based on the rainfall data, includes: Based on the rainfall data, the rainfall events in which the various duration peak rainfalls occurred within the target time period are obtained; Based on the slippage of the target time step and the target slippage step in the rainfall events, an initial sample set of the various duration peak rainfalls after slippage is obtained; The initial sample set is integrated to obtain a sample set containing various duration peak rainfall that satisfies the non-repetition condition.

3. A device for determining the applicable range of Chicago rain patterns, characterized in that, include: The acquisition module is used to acquire rainfall data of various durations within a target time period, and to determine a sample set containing the peak rainfall of each duration based on the rainfall data. The generation module is used to obtain a joint probability distribution of any two of the peak rainfall durations based on the sample set containing various peak rainfall durations, and to obtain the co-occurrence probability of the two peak rainfall durations corresponding to the same return period based on the joint probability distribution. The judgment module is used to determine whether the co-occurrence probability is greater than a preset threshold. If the co-occurrence probability is greater than the preset threshold, the Chicago rain pattern is applicable; otherwise, the Chicago rain pattern is not applicable. The generation module includes: The first solution unit is used to solve the marginal probability distribution of the various duration peak rainfalls based on the sample set using the parameter estimation method. The second solution unit is used to solve the joint probability distribution of any two of the time-based peak rainfall amounts in the sample set based on the marginal probability distribution and using the copula function. The expression for the co-occurrence probability is: ， in, , This indicates the rainfall for two different durations within the design return period. Let be the joint probability distribution function of any two durations of rainfall. Let be the marginal probability distribution function for various durations of rainfall.

4. The apparatus for determining the applicable range of Chicago rain type according to claim 3, characterized in that, The acquisition module includes: The first generation unit is used to obtain the rainfall events in which the various duration peak rainfalls occur within the target time period based on the rainfall data. The second generation unit is used to obtain an initial sample set of the various duration peak rainfalls after slipping according to the slipping of the target time step and the target slipping step in the rainfall events; The third generation unit is used to integrate the initial sample set to obtain a sample set containing various duration peak rainfall that satisfies the non-repetition condition.

5. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for determining the applicability of the Chicago rain pattern as described in any one of claims 1-2.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for determining the applicability of the Chicago rain pattern as described in any one of claims 1-2.

7. A computer program product, characterized in that, Includes a computer program, which, when executed, is used to implement the method for determining the applicability of the Chicago rain pattern as described in any one of claims 1-2.

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