Method, device and equipment for predicting influence of maximum rainfall intensity time history distribution on flood peak flow and medium

Abstract

The invention discloses a method, a device, equipment and a medium for predicting influence of maximum rainfall intensity time history distribution on flood peak flow, and relates to the field of hydrological analysis and meteorological monitoring. The method comprises the following steps: firstly, carrying out rainfall session division on hourly rainfall data of a historical time period to obtain hourly rainfall data of different historical rainfall sessions, and constructing a time sequence database of maximum rainfall intensity; based on the time sequence database of the maximum rainfall intensity, constructing a probability density function of maximum rainfall intensity time history distribution, performing multiple times of peak flood flow simulation to obtain peak flood flow data of different maximum rainfall intensity positions, and based on the peak flood flow data, constructing a probability density function of the peak flood flow; and calculating the occurrence probability of the flood disaster event. According to the method, the time history distribution of the maximum rainfall intensity is dynamically analyzed, the influence of the maximum rainfall intensity on the flood peak flow can be accurately evaluated, the prediction accuracy of the flood peak flow is improved, the risk of flood disasters is reduced, and good social benefits and economic values are achieved.

Description

Technical Field

[0001] The present application relates to the fields of hydrological analysis and meteorological monitoring, and in particular to a method, device, equipment and medium for predicting the impact of the maximum rainfall intensity time distribution on flood peak flow. Background Art

[0002] The magnitude of the flood peak flow is closely related to the temporal distribution of rainfall intensity. Traditional flood prediction models mostly use assumed uniform distribution, triangular distribution and other rainfall time curves, ignoring the actual temporal changes of rainfall, and are unable to accurately assess the impact of the maximum rainfall intensity on the flood peak flow, resulting in low flood prediction accuracy. Summary of the invention

[0003] The purpose of this application is to provide a prediction method, device, equipment and medium for the impact of maximum rainfall intensity time distribution on flood peak flow, so as to accurately evaluate the impact of maximum rainfall intensity on flood peak flow and improve the accuracy of flood prediction.

[0004] To achieve the above objectives, this application provides the following solutions.

[0005] In a first aspect, the present application provides a method for predicting the impact of the maximum rainfall intensity time-course distribution on the flood peak flow, comprising:

[0006] Get hourly rainfall data for historical time periods;

[0007] The hourly rainfall data of the historical time period is divided into rainfall events to obtain the hourly rainfall data of different historical rainfall events;

[0008] Extract the maximum rainfall intensity and its corresponding time from the hourly rainfall data of each historical rainfall event, and build a time series database of maximum rainfall intensity;

[0009] Based on the time series database of maximum rainfall intensity, the probability density function of the maximum rainfall intensity time series distribution is constructed;

[0010] Based on the probability density function of the maximum rainfall intensity time-history distribution, multiple flood peak flow simulations were performed to obtain flood peak flow data at different maximum rainfall intensity locations;

[0011] Based on the peak flow data at different maximum rainfall intensity locations, the probability density function of the peak flow is constructed;

[0012] The probability of flood disaster events is calculated based on the probability density function of flood peak flow.

[0013] In a second aspect, the present application provides a prediction device for the influence of the maximum rainfall intensity time-series distribution on the flood peak flow, wherein the prediction device for the influence of the maximum rainfall intensity time-series distribution on the flood peak flow applies the above-mentioned prediction method for the influence of the maximum rainfall intensity time-series distribution on the flood peak flow, and the prediction device for the influence of the maximum rainfall intensity time-series distribution on the flood peak flow comprises:

[0014] The rainfall data acquisition module is used to obtain hourly rainfall data for historical time periods;

[0015] The rainfall event division module is used to divide the hourly rainfall data of the historical time period into rainfall events, and obtain the hourly rainfall data of different historical rainfall events;

[0016] The maximum rainfall intensity time series database construction module is used to extract the maximum rainfall intensity and its corresponding time from the hourly rainfall data of each historical rainfall event, and to construct a maximum rainfall intensity time series database;

[0017] The probability density function construction module of the maximum rainfall intensity time series distribution is used to construct the probability density function of the maximum rainfall intensity time series distribution based on the time series database of the maximum rainfall intensity;

[0018] The flood peak flow simulation module is used to perform multiple flood peak flow simulations based on the probability density function of the maximum rainfall intensity time distribution to obtain flood peak flow data at different maximum rainfall intensity locations;

[0019] A probability density function construction module for peak flow, which is used to construct a probability density function of peak flow based on peak flow data at different maximum rainfall intensity locations;

[0020] The probability calculation module is used to calculate the probability of flood disaster events based on the probability density function of the flood peak flow.

[0021] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for predicting the impact of the maximum rainfall intensity time distribution on the peak flow.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for predicting the impact of the maximum rainfall intensity time distribution on the peak flow.

[0023] According to the specific embodiments provided in this application, this application has the following technical effects.

[0024] The present application provides a prediction method, device, equipment and medium for the influence of the maximum rainfall intensity time series distribution on the peak flow. The present application first divides the hourly rainfall data of the historical time period into rainfall events, and obtains the hourly rainfall data of different historical rainfall events; extracts the maximum rainfall intensity and its corresponding time in the hourly rainfall data of each historical rainfall event, and constructs a time series database of the maximum rainfall intensity; then, based on the time series database of the maximum rainfall intensity, constructs a probability density function of the maximum rainfall intensity time series distribution; performs multiple peak flow simulations based on the probability density function of the maximum rainfall intensity time series distribution, and obtains the peak flow data at different maximum rainfall intensity locations; constructs the probability density function of the peak flow based on the peak flow data at different maximum rainfall intensity locations; and calculates the probability of the occurrence of a flood disaster event based on the probability density function of the peak flow. The present application can accurately evaluate the influence of the maximum rainfall intensity on the peak flow by dynamically analyzing the time series distribution of the maximum rainfall intensity, significantly improve the prediction accuracy of the peak flow, and reduce the risk of flood disasters, which has good social benefits and economic value. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A flowchart of a method for predicting the impact of the maximum rainfall intensity temporal distribution on flood peak flow is provided in one embodiment of the present application.

[0027] Figure 2 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] In an exemplary embodiment, a method for predicting the impact of the maximum rainfall intensity time series distribution on the flood peak flow is provided, such as Figure 1As shown, the process includes the following steps 101 to 106.

[0031] Step 101, obtaining hourly rainfall data for a historical period.

[0032] Step 102, dividing the hourly rainfall data of the historical time period into rainfall events, and obtaining hourly rainfall data of different historical rainfall events.

[0033] Step 103, extracting the maximum rainfall intensity and its corresponding time from the hourly rainfall data of each historical rainfall event, and constructing a time series database of the maximum rainfall intensity.

[0034] Step 104, based on the time series database of maximum rainfall intensity, construct a probability density function of the time series distribution of maximum rainfall intensity.

[0035] Step 105, performing multiple flood peak flow simulations based on the probability density function of the maximum rainfall intensity time-history distribution to obtain flood peak flow data at different maximum rainfall intensity locations.

[0036] Step 106, constructing a probability density function of the peak flow based on the peak flow data at different maximum rainfall intensity locations.

[0037] Step 107, calculating the probability of a flood disaster event occurring based on the probability density function of the flood peak flow.

[0038] Implementing the above steps 101 to 107 can accurately evaluate the impact of the maximum rainfall intensity on the peak flow, significantly improve the prediction accuracy of the peak flow, reduce the risk of flood disasters, and have good social benefits and economic value.

[0039] In another exemplary embodiment of the present application, in the above step 101, the present application embodiment collects hourly rainfall data of a historical period (e.g., the past 30 years) to ensure that the data is representative and accurate. The data sources include meteorological monitoring stations, remote sensing monitoring, etc. The data is organized into a time series for subsequent analysis.

[0040] In another exemplary embodiment of the present application, the above step 102 can be replaced by the following steps 201 to 205.

[0041] Step 201, initializing a target sequence based on hourly rainfall data of a historical time period.

[0042] Step 202, traverse the target sequence, and determine the time in the target sequence that meets the following conditions as the start time of the historical rainfall event.

[0043] T start =min(h) when R(h)>R min and

[0044] Among them, T start is the start time of the historical rainfall event, R min is the rainfall threshold, R(h) is the rainfall in hour h, R(h+T i ) is hours h+T i The rainfall, T i is the time interval threshold.

[0045] Step 203, starting from the start time, traverse the target sequence and determine the time in the target sequence that meets the following conditions as the end time of the historical rainfall event.

[0046] T end =max(h) when R(h) <R min and

[0047] Among them, T end This is the end time of the historical rainfall event.

[0048] Step 204: extract the hourly rainfall data between the start time and the end time of the historical rainfall events in the target sequence to form the hourly rainfall data of the historical rainfall events.

[0049] Step 205, delete the hourly rainfall data before the end time of the historical rainfall session in the target sequence, and return to the step of "traversing the target sequence, determining the moment in the target sequence that meets the following conditions as the start time of the historical rainfall session", until the time length of the target sequence is less than the time interval threshold.

[0050] The specific implementation of the above steps 201 to 205 is as follows:

[0051] 1. Initialization parameters.

[0052] Set rainfall threshold: Determine a rainfall threshold R min (e.g. 0.1mm) is used to determine whether rainfall has occurred.

[0053] Set time interval threshold: Set a time interval threshold T i (e.g. 30 minutes) to determine whether the rainfall has ended.

[0054] Create an empty rainfall event list Rainfall_Events.

[0055] Set the current rainfall event Current_Event = none.

[0056] Initialize the rainfall start time Rain_Start_Time=none.

[0057] 2. Traverse the hourly rainfall R(h).

[0058] When R(h)>R min If Current_Event = none, create a new rainfall event and record the rainfall start time. Otherwise, record the current rainfall amount.

[0059] When R(h)≤R t If Current_Event≠none, start the timer T=Current_Time-Rain_Start_Time. Otherwise, if T>T i , end the current rainfall event and add it to Rainfall_Events, resetting the current rainfall event.

[0060] 3. Output rainfall events Rainfall_Events.

[0061] In another exemplary embodiment, after dividing the rainfall events, the maximum rainfall intensity I is extracted from each rainfall event. max , and record the corresponding occurrence time T max , specifically including the following steps 301-303.

[0062] Step 301: for each rainfall event, find the maximum rainfall in the event and use it as the maximum rainfall intensity.

[0063] Step 302, storing the maximum rainfall intensity and its corresponding time in each year into time series data.

[0064] Step 303, constructing a time series database of maximum rainfall intensity through the time series data of maximum rainfall intensity to support subsequent flood prediction and analysis.

[0065] In another exemplary embodiment, in the above step 104, in order to accurately describe the time-course distribution characteristics of the maximum rainfall intensity, a Gamma distribution model is used for fitting, and the specific steps include the following steps 401 to 405.

[0066] Step 401: Prepare data.

[0067] 1. Extract the maximum rainfall intensity data set from the constructed maximum rainfall intensity time series database, denoted as: {x1, x2,…, x n}, where n is the number of data points.

[0068] 2. Clean the data, handle missing values ​​and outliers, and ensure that all data are positive.

[0069] Step 402: Select a Gamma distribution model.

[0070] The probability density function (PDF) of the Gamma distribution is:

[0071]

[0072] Among them, α>0 is the shape parameter, β>0 is the scale parameter, and Γ(α) is the Gamma function.

[0073] Step 403: Estimate model parameters using the maximum likelihood method.

[0074] 1. Calculate the sample mean.

[0075]

[0076] in, is the average value of all maximum rainfall intensity data in the maximum rainfall intensity dataset, x i is the ith maximum rainfall intensity data in the maximum rainfall intensity data set, and n is the number of maximum rainfall intensity data in the maximum rainfall intensity data set.

[0077] 2. Calculate the sample log mean.

[0078]

[0079] in, It is the average of the logarithms of all maximum rainfall intensity data in the maximum rainfall intensity dataset.

[0080] 3. Initial estimate of shape parameters.

[0081]

[0082] Among them, α0 is the initial shape parameter.

[0083] 4. Use the Newton-Raphson method to iteratively solve the shape parameter α and scale parameter β.

[0084]

[0085] Among them, α k+1 is the shape parameter of the k+1th iteration, α k is the shape parameter of the kth iteration, ψ( ) is the Digamma function, ψ'( ) is the Trigamma function, β k+1 is the scale parameter for the k+1th iteration.

[0086] Step 404: Information criterion evaluation.

[0087] 1. AIC criterion: AIC = 2k-2lnLmax.

[0088] 2. BIC criterion: BIC=kln n-2lnLmax.

[0089] Among them, Lmax is the maximum log-likelihood value.

[0090] In another exemplary embodiment, a flood analysis process is also provided for calculating the 24-hour rainfall process distribution to verify the accuracy of the probability density function of the maximum rainfall intensity time series distribution, which specifically includes the following steps 501 to 506.

[0091] Step 501: Given a rainstorm attenuation index n 6,24 and 24-hour accumulated rainfall H 24 , calculate the 6-hour accumulated rainfall H6.

[0092]

[0093] Step 502: Given a rainstorm attenuation index n 1,6 And the 6-hour accumulated rainfall H6, calculate the 1-hour accumulated rainfall H1.

[0094]

[0095] Step 503, calculating the accumulated rainfall in each time period.

[0096] (1)t i = Between 1 and 6 hours: Among them, H i is the accumulated rainfall up to time period i, t i is the total duration of time period i.

[0097] (2)t i = Between 6 and 24 hours:

[0098] Step 504, calculate the rainfall t for each hour period i .

[0099] ΔH i =H i -H i-1 , i=2,3,…24

[0100] ΔH1=H1

[0101] Where ΔH i is the rainfall in period i.

[0102] Step 505: Use the obtained probability density function (PDF) to sample the maximum rainfall intensity occurrence time t max .

[0103] Step 506, rainfall distribution by time period.

[0104] (1) The maximum rainfall in the period is placed at the time point t where the maximum rainfall intensity obtained by sampling occurs. max .

[0105] (2) The rainfall in the remaining periods ensures that the entire 24-hour rainfall sequence is continuous and follows the max The rainfall intensity increases successively to the maximum rainfall intensity at t max Then decrease successively.

[0106] Based on the above analysis, it can be seen that the maximum rainfall intensity time-series distribution obtained by analyzing the probability density function of the maximum rainfall intensity time-series distribution based on this application is consistent with the maximum rainfall intensity time-series distribution obtained by actual calculation. The probability density function of the maximum rainfall intensity time-series distribution obtained in this application can accurately obtain the peak flow data at different maximum rainfall intensity locations.

[0107] In another exemplary embodiment, the above step 105 performs a probability forecast of peak flow based on the Monte Carlo method, which specifically includes the following steps 601-602.

[0108] Step 601, Monte Carlo sampling, i.e., Monte Carlo sampling is performed on the probability density function of the maximum rainfall intensity time history distribution to obtain multiple maximum rainfall intensity positions, and the 24-hour rainfall data of each maximum rainfall intensity position is reconstructed, specifically including:

[0109] (1) Randomly select the location of the maximum rainfall intensity. According to the probability density function of the maximum rainfall intensity time distribution, Monte Carlo sampling is performed to randomly generate multiple samples of the maximum rainfall intensity occurrence time (for example, 1000 times or more).

[0110] (2) Reconstruct the 24-hour rainfall process. For the maximum rainfall intensity position of each sampling, the 24-hour rainfall process is rearranged according to the known rainfall distribution rules.

[0111] Step 602, using a hydrological model to simulate the peak flow, inputting the 24-hour rainfall data of each maximum rainfall intensity location into the hydrological model, and obtaining the peak flow data of each maximum rainfall location, specifically including:

[0112] (1) Input 24-hour rainfall data. Input each reconstructed rainfall time course into the hydrological model (such as Xinanjiang model, unit line method, etc.) and set the basic parameters of the model.

[0113] (2) Calculate the peak flow. Run the hydrological model and record the corresponding peak flow in each simulation.

[0114] (3) Repeat the simulation. Repeat the simulation multiple times (e.g., 1,000 times) to generate peak flow results under different maximum rainfall intensity locations.

[0115] In another exemplary embodiment, the above step 106 collects statistics of all simulated peak flow data and plots their probability density functions (ie, probability density function of peak flow, PDF) to reflect the probability distribution of peak flow.

[0116] In another exemplary embodiment, the above step 107 calculates the probability of occurrence of the peak flow according to the probability density function (PDF) of the peak flow, which specifically includes the following two situations.

[0117] Case 1: Setting a peak flow threshold Q extreme (e.g., a 10-year or 50-year peak flow), the probability of its occurrence is calculated as follows:

[0118]

[0119] Among them, P(Q>Q extreme ) is the peak flow Q greater than the peak flow threshold Q extreme The probability of a flood disaster event occurring; PDF() is the probability density function of the peak flow.

[0120] Scenario 2: The probability of occurrence of different flow rates is predicted based on the probability density function (PDF), that is, the probability of occurrence of peak flow rates in different intervals can be predicted through PDF. For example, the probability of flood occurrence within a certain flow range is calculated to assess the risk of a specific flow level.

[0121] Assuming that the peak flow range of concern is [Q1, Q2], the probability of occurrence in this interval can be calculated by integration.

[0122]

[0123] Among them, P(Q1≤Q≤Q2) is the probability of a flood disaster event occurring when the peak flow Q is in the peak flow range [Q1,Q2], and Q1 and Q2 are the minimum and maximum values ​​of the peak flow range, respectively.

[0124] Based on the same inventive concept, the embodiment of the present application also provides a prediction device for the effect of the time-series distribution of maximum rainfall intensity on the peak flow rate, which is used to implement the prediction method of the effect of the time-series distribution of maximum rainfall intensity on the peak flow rate involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the prediction device for the effect of the time-series distribution of maximum rainfall intensity on the peak flow rate provided below can refer to the limitations of the prediction method for the effect of the time-series distribution of maximum rainfall intensity on the peak flow rate above, and will not be repeated here.

[0125] In an exemplary embodiment, a prediction device for the influence of the maximum rainfall intensity time-history distribution on the flood peak flow is provided, comprising:

[0126] The rainfall data acquisition module is used to obtain hourly rainfall data for historical time periods.

[0127] The rainfall event division module is used to divide the hourly rainfall data of the historical time period into rainfall events, and obtain the hourly rainfall data of different historical rainfall events.

[0128] The maximum rainfall intensity time series database construction module is used to extract the maximum rainfall intensity and its corresponding time from the hourly rainfall data of each historical rainfall event, and construct a maximum rainfall intensity time series database.

[0129] The probability density function construction module of the maximum rainfall intensity time series distribution is used to construct the probability density function of the maximum rainfall intensity time series distribution based on the time series database of the maximum rainfall intensity.

[0130] The flood peak flow simulation module is used to perform multiple flood peak flow simulations based on the probability density function of the maximum rainfall intensity time distribution to obtain flood peak flow data at different maximum rainfall intensity locations.

[0131] The probability density function construction module of the peak flow is used to construct the probability density function of the peak flow based on the peak flow data at different maximum rainfall intensity locations.

[0132] The probability calculation module is used to calculate the probability of flood disaster events based on the probability density function of the flood peak flow.

[0133] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 2 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for predicting the influence of the maximum rainfall intensity time distribution on the peak flow is implemented.

[0134] Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0135] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0136] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0137] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0138] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0139] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0140] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for predicting the impact of the maximum rainfall intensity time series distribution on the flood peak flow, characterized in that: include: Get hourly rainfall data for historical time periods; The hourly rainfall data of the historical time period is divided into rainfall events to obtain the hourly rainfall data of different historical rainfall events; Extract the maximum rainfall intensity and its corresponding time from the hourly rainfall data of each historical rainfall event, and build a time series database of maximum rainfall intensity; Based on the time series database of maximum rainfall intensity, the probability density function of the maximum rainfall intensity time series distribution is constructed; Based on the probability density function of the maximum rainfall intensity time-history distribution, multiple flood peak flow simulations were performed to obtain flood peak flow data at different maximum rainfall intensity locations; Based on the peak flow data at different maximum rainfall intensity locations, the probability density function of the peak flow is constructed; The probability of flood disaster events is calculated based on the probability density function of flood peak flow.

2. The method for predicting the impact of the maximum rainfall intensity time series distribution on the flood peak flow according to claim 1 is characterized in that: The hourly rainfall data of the historical time period is divided into rainfall events to obtain the hourly rainfall data of different historical rainfall events, including: Initialize the target sequence based on hourly rainfall data of historical time periods; Traversing the target sequence, determining a moment in the target sequence that satisfies the following conditions as the start time of the historical rainfall event; T start =min(h) when R(h)>R min and Among them, T start is the start time of the historical rainfall event, R min is the rainfall threshold, R(h) is the rainfall in hour h, R(h+T i ) is hours h+T i The rainfall, T i is the time interval threshold; From the start time, traverse the target sequence and determine the time in the target sequence that meets the following conditions as the end time of the historical rainfall event; T end =max(h) when R(h) <R min and Among them, T end The end time of the historical rainfall event; Extract the hourly rainfall data between the start time and the end time of the historical rainfall events in the target sequence to form the hourly rainfall data of the historical rainfall events; The hourly rainfall data before the end time of the historical rainfall session in the target sequence is deleted, and the step of "traversing the target sequence and determining the moment in the target sequence that meets the following conditions as the start time of the historical rainfall session" is returned until the time length of the target sequence is less than the time interval threshold.

3. The method for predicting the impact of the maximum rainfall intensity time series distribution on the flood peak flow according to claim 1 is characterized in that: Based on the time series database of maximum rainfall intensity, the probability density function of the maximum rainfall intensity time series distribution is constructed, including: Construct the probability density function of the Gamma distribution; Based on the time series database of maximum rainfall intensity, the maximum likelihood method is used to estimate the parameters of the probability density function of the Gamma distribution, and the probability density function of the maximum rainfall intensity time series distribution is obtained.

4. The method for predicting the impact of the maximum rainfall intensity time series distribution on the flood peak flow according to claim 3 is characterized in that: Based on the time series database of maximum rainfall intensity, the maximum likelihood method is used to estimate the parameters of the probability density function of the Gamma distribution, and the probability density function of the maximum rainfall intensity time series distribution is obtained, which includes: Extract the maximum rainfall intensity data from the time series database of maximum rainfall intensity and construct a maximum rainfall intensity data set; According to the maximum rainfall intensity data set, the shape parameter of the probability density function of the Gamma distribution is initialized using the following formula; Among them, α0 is the initial shape parameter, is the average value of all maximum rainfall intensity data in the maximum rainfall intensity dataset, is the average value of the logarithm of all maximum rainfall intensity data in the maximum rainfall intensity data set, and n is the number of maximum rainfall intensity data in the maximum rainfall intensity data set; Use the following formula to iteratively update the shape parameter and scale parameter of the probability density function of the Gamma distribution until the probability density function of the Gamma distribution meets the information criterion evaluation standard; Among them, α k+1 is the shape parameter of the k+1th iteration, α k is the shape parameter of the kth iteration, ψ() is the Digamma function, ψ'( ) is the Trigamma function, β k+1 is the scale parameter for the k+1th iteration.

5. The method for predicting the impact of the maximum rainfall intensity time series distribution on the flood peak flow according to claim 4 is characterized in that: The information criterion evaluation criteria include the AIC criterion and the BIC criterion.

6. The method for predicting the impact of the maximum rainfall intensity time series distribution on the flood peak flow according to claim 1 is characterized in that: Based on the probability density function of the maximum rainfall intensity time-history distribution, multiple flood peak flow simulations were performed to obtain flood peak flow data at different maximum rainfall intensity locations, including: Monte Carlo sampling is performed on the probability density function of the maximum rainfall intensity time history distribution to obtain multiple maximum rainfall intensity locations; Reconstruct 24-hour rainfall data for each location of maximum rainfall intensity; The 24-hour rainfall data at each maximum rainfall intensity location are input into the hydrological model to obtain the peak flow data at each maximum rainfall location.

7. The method for predicting the impact of the maximum rainfall intensity time series distribution on the flood peak flow according to claim 1 is characterized in that: The formula for calculating the probability of flood disaster events based on the probability density function of flood peak flow is: and / or, Among them, P(Q>Q extreme ) is the peak flow Q greater than the peak flow threshold Q extreme The probability of a flood disaster event occurring; PDF() is the probability density function of the peak flow, P(Q1≤Q≤Q2) is the probability of a flood disaster event occurring when the peak flow Q is in the peak flow range [Q1,Q2], and Q1 and Q2 are the minimum and maximum values ​​of the peak flow range, respectively.

8. A prediction device for the influence of the maximum rainfall intensity time series distribution on the flood peak flow, characterized in that: The prediction device for the influence of the maximum rainfall intensity time-course distribution on the flood peak flow applies the prediction method for the influence of the maximum rainfall intensity time-course distribution on the flood peak flow as described in any one of claims 1 to 7, and the prediction device for the influence of the maximum rainfall intensity time-course distribution on the flood peak flow comprises: The rainfall data acquisition module is used to obtain hourly rainfall data for historical time periods; The rainfall event division module is used to divide the hourly rainfall data of the historical time period into rainfall events, and obtain the hourly rainfall data of different historical rainfall events; The maximum rainfall intensity time series database construction module is used to extract the maximum rainfall intensity and its corresponding time from the hourly rainfall data of each historical rainfall event, and to construct a maximum rainfall intensity time series database; The probability density function construction module of the maximum rainfall intensity time series distribution is used to construct the probability density function of the maximum rainfall intensity time series distribution based on the time series database of the maximum rainfall intensity; The flood peak flow simulation module is used to perform multiple flood peak flow simulations based on the probability density function of the maximum rainfall intensity time distribution to obtain flood peak flow data at different maximum rainfall intensity locations; A probability density function construction module for peak flow, which is used to construct a probability density function of peak flow based on peak flow data at different maximum rainfall intensity locations; The probability calculation module is used to calculate the probability of flood disaster events based on the probability density function of the flood peak flow.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting the impact of the maximum rainfall intensity time-series distribution on the flood peak flow as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the influence of the maximum rainfall intensity time-series distribution on the flood peak flow described in any one of claims 1 to 7 is implemented.

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