WO2023246502A1 - 场次洪水识别方法、装置、电子设备及可读存储介质 - Google Patents
场次洪水识别方法、装置、电子设备及可读存储介质 Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Definitions
- the invention relates to the field of data processing technology, and specifically relates to a flood identification method, device, electronic equipment and readable storage medium.
- flood selection refers to extracting the flood process from continuous runoff observation data to obtain information such as peak flow, peak time, start and end time, and flood volume of each flood.
- the traditional flood selection method mainly uses manual empirical selection. The selection results are subjective and the efficiency is low when the amount of data is large.
- embodiments of the present invention provide a flood identification method, device, electronic device, and readable storage medium to solve the problem of subjective and inefficient selection results of manual flood selection.
- an embodiment of the present invention provides a method for identifying floods.
- the method includes:
- N is a positive integer
- using N consecutive first-order difference values in the first-order difference sequence of the runoff time series data to obtain the initial peak appearance time includes:
- the first condition includes: the first first-order The difference value and the N/2-1 consecutive first-order difference values before the first first-order difference value are all greater than or equal to zero, and the N/2 consecutive first-order difference values after the first first-order difference value are all greater than or equal to zero. Less than or equal to zero, N is an even number greater than zero.
- the first condition further includes that the absolute value of the first first-order difference value and the absolute value of the first-order difference value immediately following the first first-order difference value are both greater than a preset threshold.
- using M consecutive first-order difference values in the first-order difference sequence to obtain the initial start and end times includes:
- the second condition includes: the second first-order difference value and M/2-1 consecutive first-order difference values before the second first-order difference value are all less than or equal to zero, and the M/2 consecutive first-order difference values after the second first-order difference value are all greater than or equal to zero, and M is an even number greater than zero.
- filtering out the determined peak occurrence time from the initial peak occurrence time, and filtering out the start and end times corresponding to the peak occurrence time from the initial start and end times including:
- obtaining runoff time series data includes:
- the method further includes:
- the maximum runoff volume is determined to be the peak flow volume, and the peak occurrence time is corrected to the time corresponding to the maximum runoff volume.
- the method further includes:
- the difference multiples between the peak flow and the flow at the flood point are obtained respectively; the difference multiple is the multiple obtained by dividing the first difference by the second difference; for the previous flood, the first difference The value is the difference between the peak flow and the starting flow, the second difference is the difference between the peak flow and the end flow; for the latter flood, the first difference is the The difference between the peak flow and the end flow, the second difference is the difference between the peak flow and the starting flow;
- the two difference multiples are both greater than or equal to the difference multiple threshold, the starting flow of the previous flood is less than or equal to the ending flow, the starting flow of the latter flood is greater than or equal to the ending flow, and the starting flow of the latter flood is greater than or equal to the ending flow. If the difference between the start time and the end time of the previous flood is less than or equal to the average duration of floods determined based on the basin characteristics, the two floods are determined to be compound peak floods.
- an embodiment of the present invention provides a device for identifying floods, including:
- Data acquisition module used to obtain runoff time series data
- the first determination module is used to obtain the initial peak appearance time using N consecutive first-order difference values in the first-order difference sequence of the runoff time series data, where N is a positive integer;
- the second determination module is used to obtain the initial start and end time using M consecutive first-order difference values in the first-order difference sequence, where M is a positive integer;
- the third determination module is used to screen out the determined peak time from the initial peak time, and screen out the start and end times corresponding to the peak time from the initial start and end times.
- an electronic device including:
- a memory and a processor The memory and the processor are communicatively connected to each other.
- the memory is used to store a computer program.
- the computer program is executed by the processor, any one of the first aspects described above is implemented.
- a flood identification method is used.
- embodiments of the present invention provide a computer-readable storage medium.
- the computer-readable storage medium is used to store a computer program.
- the computer program is executed by a processor, Any flood identification method described in the first aspect above.
- the flood identification method, device, electronic equipment and readable storage medium provided by the embodiment of the present invention use N consecutive first-order difference values in the first-order difference sequence of the runoff time series data to obtain the initial peak occurrence time, and use M consecutive first-order difference values in the first-order difference sequence are used to obtain the initial start and end times. Finally, the determined peak time is screened out from the initial peak time, and the peak time is screened out from the initial start and end time. The start and end times corresponding to the current time can be realized to automatically select floods, with high efficiency and high accuracy.
- Figure 1 is a schematic flow chart of a flood identification method provided by an embodiment of the present invention
- Figure 2 is a schematic diagram of original runoff data and preprocessed runoff data provided by an embodiment of the present invention
- Figure 3 is a schematic diagram of runoff data after two-step smoothing of preprocessed runoff data provided by an embodiment of the present invention
- Figure 4 is a schematic diagram of a preliminary selection result of a flood provided by an embodiment of the present invention.
- Figure 5 is a schematic diagram of the flood selection results of a combined complex-peak type flood provided by an embodiment of the present invention.
- FIG. 6 is a schematic structural diagram of a flood identification device provided by an embodiment of the present invention.
- FIG. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
- an embodiment of the present invention provides a method for identifying floods.
- the method includes:
- N is a positive integer
- the first-order difference sequence here refers to the data in the runoff time series data The sequence obtained by performing first-order differences in sequence.
- the first-order difference refers to the next data minus the previous data; among them, the initial peak time is the possible peak time, and the initial peak time can form an initial peak time set;
- S103 Use M consecutive first-order difference values in the first-order difference sequence to obtain the initial start and end times, M is a positive integer; the initial start and end times can form an initial start and end time set;
- the peak flow rate may be the runoff amount corresponding to the peak occurrence time in the runoff time series data.
- the flood identification method uses N consecutive first-order difference values in the first-order difference sequence of the runoff time series data to obtain the initial peak occurrence time, and uses M consecutive first-order difference values in the first-order difference sequence.
- the first-order difference value is used to obtain the initial start and end time, and finally the determined peak time is screened out from the initial peak time, and the start and end time corresponding to the peak time is screened out from the initial start and end time, so that it can be achieved Automatic selection of floods with high efficiency and high accuracy.
- the determined peak occurrence time is screened out from the initial peak occurrence time. After filtering out the start and end times corresponding to the peak occurrence time, it also includes:
- S106 For two consecutive floods, obtain the difference multiples between the peak flow and the flow at the flood point; where, the difference multiple is the multiple obtained by dividing the first difference by the second difference; for the previous flood, the difference multiple The first difference is the difference between the peak flow and the starting flow, and the second difference is the difference between the peak flow and the ending flow; for the latter flood, the first difference The difference is the difference between the peak flow and the end flow, and the second difference is the difference between the peak flow and the starting flow;
- the difference multiple threshold ⁇ between the peak flow rate and the flow rate at the peak and retreat points is used to screen out the complex peak flood type.
- their peak times are recorded as t 1 and t 2 respectively (t 1 ⁇ t 2 ), and the corresponding flood start and end times are st 1 , st 2 and et 1 respectively.
- et 2 the peak flow rates are respectively
- the starting flow and ending flow are respectively T is the average duration of floods determined by the basin characteristics, if it satisfies:
- the two floods are complex peak floods and merged into one flood.
- the starting time of the flood is st 1 and the ending time is et 2 : If Then the peak appearance time of the combined flood is t 1 ; if Then the peak time of merged flood is t 2 .
- the acquisition of runoff time series data includes:
- the original time series data of runoff for example, it can be the original runoff observation data with continuous time series in the study area; the original time series data of runoff can be recorded as x 1 , x 2 ,..., x n , n is the total number of moments;
- the runoff original time series data is preprocessed to obtain the runoff time series data.
- the preprocessing includes eliminating abnormal mutation point data and/or interpolating missing data. Specifically, after eliminating abnormal mutation point data, the eliminated data is marked as missing data, and the interpolation can be linear interpolation.
- the following embodiments of the present invention can be implemented based on the preprocessed runoff time series data.
- the preprocessed runoff time series data can be recorded as qx 1 , qx 2 ,..., qx n .
- the preprocessed runoff time series data qx 1 , qx 2 ,..., qx n can be further smoothed.
- the smoothing process here can be a two-step smoothing process, as follows:
- two-step smoothing is performed on the runoff data, and the smoothing effect is good, thereby improving the accuracy of flood peak selection and avoiding the problem of too long start and end times of floods.
- Performing two-step smoothing processing on runoff data can also make the flood identification method provided by the embodiment of the present invention applicable to fluctuating and oscillating runoff observation data.
- only the original time series data of runoff can be preprocessed, or only the original time series data of runoff can be smoothed.
- the use of N consecutive first-order difference values in the first-order difference sequence of the runoff time series data to obtain the initial peak appearance time includes:
- the first condition includes: the first first-order difference value and the N/2-1 consecutive first-order differences before the first first-order difference value. The values are all greater than or equal to zero, and the N/2 consecutive first-order difference values after the first first-order difference value are all less than or equal to zero, and N is an even number greater than zero.
- the first-order difference is recorded as Therefore, if and Then j is the initial peak appearance time; where, is the first first-order difference value, is the N/2-1 consecutive first-order difference values before the first first-order difference value, It is the N/2 consecutive first-order difference values after the first first-order difference value.
- the first runoff data is other runoff data except the first Eve_Win runoff data and the last Eve_Win runoff data, that is, the value of j above
- smooth data processing will make the front-end Eve_Win runoff data y 2, 1 , y 2 , 2,..., y 2, Eve_Win and the back-end Eve_Win runoff data y 2, n-Eve_Win+1 , y 2, n-Eve_Win+2 ,...,y 2,n distortion.
- the first condition further includes that the absolute value of the first first-order difference value and the absolute value of the first-order difference value immediately after the first first-order difference value are both greater than a predetermined value.
- Set threshold For example, when the preset threshold is 0.01, that is Of course, the preset threshold corresponding to the first first-order difference value and the preset threshold corresponding to the first-order difference value immediately after the first first-order difference value may be different or the same.
- the first condition may also include that the product of the first first-order difference value and the first-order difference value immediately following the first first-order difference value is less than zero, that is,
- the initial peak occurrence time that satisfies the above first condition can be recorded as a set PeakIndex.
- using M consecutive first-order difference values in the first-order difference sequence to obtain the initial start and end times includes:
- the second condition includes: the second first-order difference value and M/2-1 consecutive first-order difference values before the second first-order difference value are all less than or equal to zero, and the M/2 consecutive first-order difference values after the second first-order difference value are all greater than or equal to zero, and M is an even number greater than zero.
- j is the starting time or ending time of the flood; where, is the second first-order difference value, for the sake of The M/2-1 consecutive first-order difference values before the second first-order difference value, are the M/2 consecutive first-order difference values after the first first-order difference value.
- the initial start and end times that satisfy the above second condition can be recorded as a set BottomIndex.
- filtering out the determined peak occurrence time from the initial peak occurrence time, and filtering out the start and end times corresponding to the peak occurrence time from the initial start and end times include:
- obtaining runoff time series data includes:
- the method further includes:
- the maximum runoff volume is determined to be the peak flow volume, and the peak occurrence time is corrected to the time corresponding to the maximum runoff volume.
- the original path is used here.
- the flow data corrects the above-mentioned peak time and peak flow. calculate If the original runoff data corresponds to Then t new is finally regarded as the peak time of the flood, as the corresponding peak flow.
- the original runoff time series data may be preprocessed data. i.e. calculation If the preprocessed runoff data corresponds to Then t new is finally regarded as the peak time of the flood, as the corresponding peak flow.
- the embodiment of the present invention improves the accuracy and efficiency of identifying complex peak floods.
- the following uses the actual runoff data collected at a certain hydrological station as an example to illustrate a flood identification method provided by the embodiment of the present invention.
- PeakIndex ⁇ 176,244,359,440,568,648,764,965,1100,1223,1455,1564,1700,1896 ⁇
- BottomIndex ⁇ 91,219,274,376,535,599,700,858,1072,1147,1380,149 0,1657,1853,1950 ⁇ ;
- the embodiment of the present invention combines the two-step smoothing runoff data processing method, uses the value changes within multiple first-order difference point intervals of the runoff data sequence to determine the flood peak occurrence time and the flood start and end times, and uses the flood peak threshold As well as the original data to accurately identify flood peaks, it combines the multiples of the flow difference between flood peaks and flood points and the original data to determine the complex peak flood, thereby solving the technical problems of non-smooth data series, inaccurate flood peak judgment and difficulty in quickly identifying complex peak floods, effectively Improved the efficiency and accuracy of flood selection.
- An embodiment of the present invention provides a device for identifying floods, including:
- Data acquisition module 601 used to acquire runoff time series data
- the first determination module 602 is used to obtain the initial peak appearance time using N consecutive first-order difference values in the first-order difference sequence of the runoff time series data, where N is a positive integer;
- the second determination module 603 is used to obtain the initial start and end time using M consecutive first-order difference values in the first-order difference sequence, where M is a positive integer;
- the third determination module 604 is configured to filter out the determined peak occurrence time from the initial peak occurrence time, and filter out the start and end times corresponding to the peak occurrence time from the initial start and end times.
- the flood identification device uses N consecutive first-order difference values in the first-order difference sequence of the runoff time series data to obtain the initial peak occurrence time, and uses M consecutive first-order difference values in the first-order difference sequence.
- the first-order difference value is used to obtain the initial start and end time, and finally the determined peak time is screened out from the initial peak time, and the start and end time corresponding to the peak time is screened out from the initial start and end time, so that it can be achieved Automatic selection of floods with high efficiency and high accuracy.
- the first determining module 602 includes:
- the first determination unit is used to determine if the first first-order difference value corresponding to the first runoff data in the runoff time series data and the N-1 first-order difference values consecutive to the first first-order difference value satisfy the first condition, then the time corresponding to the first runoff data is determined to be the initial peak occurrence time; wherein the first condition includes: the first first-order difference value and the consecutive N/ The 2-1 first-order difference values are all greater than or equal to zero, and the N/2 consecutive first-order difference values after the first first-order difference value are all less than or equal to zero, and N is an even number greater than zero.
- the first condition further includes that the absolute value of the first first-order difference value and the absolute value of the first-order difference value immediately following the first first-order difference value are both greater than a preset threshold.
- the second determination module 603 includes:
- the second determination unit is used to determine if the second first-order difference value corresponding to the second runoff data in the runoff time series data and the M-1 first-order difference values continuous with the second first-order difference value satisfy the second condition, then it is determined that the time corresponding to the second runoff data is the initial start and end time; wherein the second condition includes: the second first-order difference value and the continuous M/2 before the second first-order difference value -1 first-order difference values are all less than or equal to zero, and there are M/2 consecutive first-order differences after the second first-order difference value The values are all greater than or equal to zero, and M is an even number greater than zero.
- the third determining module 604 includes:
- the first screening unit is used to screen out the initial peak time whose corresponding runoff is greater than or equal to the preset flood peak threshold as the determined peak time;
- a second screening unit is used to screen out the initial start and end times that are earlier than the peak occurrence time and are closest to the initial start and end times as the start time;
- the third filtering unit is used to filter out the initial start and end time that is later than the peak occurrence time and is closest to it as the end time.
- the data acquisition module 601 includes:
- the original data acquisition unit is used to obtain the original time series data of runoff
- a smoothing processing unit used to smooth the original runoff time series data to obtain the runoff time series data
- the device also includes:
- a selection module configured to obtain part of the original runoff data between the start and end times from the original runoff time series data
- a filtering module for filtering out the maximum runoff volume from the part of the original runoff data
- a correction module configured to determine the maximum runoff amount as the peak flow rate if the maximum runoff amount is greater than or equal to the preset flood peak threshold, and correct the peak occurrence time to the time corresponding to the maximum runoff amount.
- the device further includes:
- a peak flow determination module used to determine the peak flow corresponding to the peak time
- the difference multiple acquisition module is used to obtain the difference multiples of the peak flow and the flood point flow for two consecutive floods; the difference multiple is the multiple obtained by dividing the first difference by the second difference; for the previous flood For a flood, the first difference is the difference between the peak flow and the starting flow, and the second difference is the difference between the peak flow and the ending flow; for the latter flood, the The first difference is the difference between the peak flow and the end flow, and the second difference is the difference between the peak flow and the starting flow;
- the judgment module is used to determine whether the two difference multiples are greater than or equal to the difference multiple threshold in the previous field.
- the starting flow of the flood is less than or equal to the ending flow
- the starting flow of the subsequent flood is greater than or equal to the ending flow
- the difference between the starting time of the latter flood and the ending time of the previous flood is less than or equal to Based on the average duration of floods determined by the basin characteristics, the two floods are determined to be complex-peak floods.
- the embodiments of the present invention are device embodiments based on the same inventive concept as the above-mentioned method embodiments. Therefore, please refer to the above-mentioned method embodiments for specific technical details and corresponding technical effects, and will not be described again here.
- An embodiment of the present invention also provides an electronic device.
- the electronic device may include a processor 71 and a memory 72.
- the processor 71 and the memory 72 may communicate with each other through a bus or other means.
- Figure 7 Take the example of connecting via a bus.
- the processor 71 may be a central processing unit (Central Processing Unit, CPU).
- the processor 71 can also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or Other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components and other chips, or combinations of the above types of chips.
- DSP Digital Signal Processor
- ASIC Application Specific Integrated Circuit
- FPGA Field-Programmable Gate Array
- Other programmable logic devices discrete gate or transistor logic devices, discrete hardware components and other chips, or combinations of the above types of chips.
- the memory 72 can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions/modules corresponding to the flood identification method in the embodiment of the present invention.
- the processor 71 executes the non-transitory software programs, instructions and modules stored in the memory 72 to execute various functional applications and data processing of the processor, that is, to implement the flood identification method in the above method embodiment.
- the memory 72 may include a program storage area and a data storage area, where the program storage area may store an operating system and an application program required for at least one function; the storage data area may store data created by the processor 71 and the like.
- memory 72 may include high-speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device.
- memory 72 optionally includes memory located remotely relative to processor 71 , and these remote memories may be connected to processor 71 through a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.
- the one or more modules are stored in the memory 72 and when executed by the processor 71 When , execute the flood identification method in the embodiment shown in Figure 1-5.
- embodiments of the present invention also provide a computer-readable storage medium.
- the computer-readable storage medium is used to store a computer program.
- the computer program is executed by a processor, each of the above embodiments of the flood identification method is implemented.
- the process can achieve the same technical effect. To avoid repetition, it will not be described again here.
- Computer-readable media includes both persistent and non-volatile, removable and non-removable media that can be implemented by any method or technology for storage of information.
- Information may be computer-readable instructions, data structures, modules of programs, or other data.
- Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), and read-only memory.
- PRAM phase change memory
- SRAM static random access memory
- DRAM dynamic random access memory
- RAM random access memory
- read-only memory read-only memory
- ROM read-only memory
- EEPROM electrically erasable programmable read-only memory
- flash memory or other memory technology
- compact disc read-only memory CD-ROM
- DVD digital versatile disc
- Magnetic tape cassettes tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium can be used to store information that can be accessed by a computing device.
- computer-readable media does not include transitory media, such as modulated data signals and carrier waves.
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Abstract
本发明公开了一种场次洪水识别方法、装置、电子设备及可读存储介质，其中，所述方法包括：获取径流时间序列数据；利用所述径流时间序列数据的一阶差分序列中连续N个一阶差分值，获取初始峰现时间；利用所述一阶差分序列中连续M个一阶差分值，获取初始起止时间；从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间。另外，利用所述峰现时间以及起止时间，获取洪峰流量与涨退点流量，利用洪峰流量与涨退点流量的差异倍数阈值筛选复峰型洪水。本发明提供的技术方案，能够实现场次洪水的自动挑选，且效率和准确性高。
Description
本发明涉及数据处理技术领域，具体涉及一种场次洪水识别方法、装置、电子设备及可读存储介质。
在流域洪水预报、水文模型的参数率定等技术应用中，需要用到多年份、大量的场次洪水。场次洪水挑选是指从连续径流观测数据中提取洪水过程，获得场次洪水的洪峰流量、峰现时间、起止时间及洪量等信息。传统的场次洪水挑选方法主要利用人工进行经验性的挑选，其挑选结果较为主观，且数据量大时效率较低。
发明内容
有鉴于此，本发明实施例提供了一种场次洪水识别方法、装置、电子设备及可读存储介质，以解决人工挑选场次洪水的挑选结果主观、效率低的问题。
根据第一方面，本发明实施例提供了一种场次洪水识别方法，所述方法包括：
获取径流时间序列数据；
利用所述径流时间序列数据的一阶差分序列中连续N个一阶差分值，获取初始峰现时间，N为正整数；
利用所述一阶差分序列中连续M个一阶差分值，获取初始起止时间，M为正整数；
从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间。
可选的，所述利用所述径流时间序列数据的一阶差分序列中连续N个一阶差分值，获取初始峰现时间，包括：
若所述径流时间序列数据中第一径流数据对应的第一一阶差分值以及与所述第一一阶差分值连续的N-1个一阶差分值满足第一条件，则确定所述第一径流数据对应的时间为初始峰现时间；其中，所述第一条件包括：所述第一一阶
差分值以及所述第一一阶差分值之前连续的N/2-1个一阶差分值均大于或等于零，且所述第一一阶差分值之后连续的N/2个一阶差分值均小于或等于零，N为大于零的偶数。
可选的，所述第一条件还包括所述第一一阶差分值的绝对值以及所述第一一阶差分值之后紧邻的一阶差分值的绝对值均大于预设阈值。
可选的，所述利用所述一阶差分序列中连续M个一阶差分值，获取初始起止时间，包括：
若所述径流时间序列数据中第二径流数据对应的第二一阶差分值以及与所述第二一阶差分值连续的M-1个一阶差分值满足第二条件，则确定所述第二径流数据对应的时间为初始起止时间；其中，所述第二条件包括：所述第二一阶差分值以及所述第二一阶差分值之前连续的M/2-1个一阶差分值均小于或等于零，且所述第二一阶差分值之后连续的M/2个一阶差分值均大于或等于零，M为大于零的偶数。
可选的，所述从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间，包括：
筛选出对应的径流量大于或等于预设洪峰阈值的所述初始峰现时间作为确定的所述峰现时间；
筛选出早于所述峰现时间且最接近的所述初始起止时间作为起始时间；
筛选出晚于所述峰现时间且最接近的所述初始起止时间作为结束时间。
可选的，所述获取径流时间序列数据，包括：
获取径流原始时间序列数据；
对所述径流原始时间序列数据进行平滑处理，得到所述径流时间序列数据；
所述从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间之后，还包括：
从所述径流原始时间序列数据中获取所述起止时间之间的部分原始径流数据；
从所述部分原始径流数据中筛选出最大的径流量；
若所述最大的径流量大于或等于预设洪峰阈值，则确定所述最大的径流量为洪峰流量，将所述峰现时间修正为所述最大的径流量对应的时间。
可选的，所述从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间之后，还包括：
确定所述峰现时间对应的洪峰流量；
对于连续的两场洪水，分别获取洪峰流量与涨退点流量的差异倍数；所述差异倍数是第一差值除以第二差值所得的倍数；对于前一场洪水，所述第一差值是所述洪峰流量与起始流量之间的差值，所述第二差值是所述洪峰流量与结束流量之间的差值；对于后一场洪水，所述第一差值是所述洪峰流量与结束流量之间的差值，所述第二差值是所述洪峰流量与起始流量之间的差值；
若两个所述差异倍数均大于或等于差异倍数阈值，前一场洪水的起始流量小于或等于结束流量、后一场洪水的起始流量大于或等于结束流量，且后一场洪水的起始时间与前一场洪水的结束时间之间的差值小于或等于基于流域特性确定的场次洪水平均持续时间，则确定所述两场洪水为复峰型洪水。
根据第二方面，本发明实施例提供了一种场次洪水识别装置，包括：
数据获取模块，用于获取径流时间序列数据；
第一确定模块，用于利用所述径流时间序列数据的一阶差分序列中连续N个一阶差分值，获取初始峰现时间，N为正整数；
第二确定模块，用于利用所述一阶差分序列中连续M个一阶差分值，获取初始起止时间，M为正整数；
第三确定模块，用于从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间。
根据第三方面，本发明实施例提供了一种电子设备，包括：
存储器和处理器，所述存储器和所述处理器之间互相通信连接，所述存储器用于存储计算机程序，所述计算机程序被所述处理器执行时，实现上述第一方面所述的任一种场次洪水识别方法。
根据第四方面，本发明实施例提供了一种计算机可读存储介质，所述计算机可读存储介质用于存储计算机程序，所述计算机程序被处理器执行时，实现
上述第一方面所述的任一种场次洪水识别方法。
本发明实施例提供的场次洪水识别方法、装置、电子设备及可读存储介质，利用所述径流时间序列数据的一阶差分序列中连续N个一阶差分值，获取初始峰现时间，并利用所述一阶差分序列中连续M个一阶差分值，获取初始起止时间，最后从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间，从而能够实现场次洪水的自动挑选，且效率高准确性也高。
通过参考附图会更加清楚的理解本发明的特征和优点，附图是示意性的而不应理解为对本发明进行任何限制，在附图中：
图1为本发明实施例提供的一种场次洪水识别方法的流程示意图；
图2为本发明实施例提供的一种原始径流数据和预处理后的径流数据的示意图；
图3为本发明实施例提供的一种针对预处理后的径流数据进行两步平滑后的径流数据的示意图；
图4为本发明实施例提供的一种场次洪水初步挑选结果示意图；
图5为本发明实施例提供的一种合并复峰型洪水的场次洪水挑选结果示意图；
图6为本发明实施例提供的一种场次洪水识别装置的结构示意图；
图7为本发明实施例提供的一种电子设备的结构示意图。
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
需要说明的是，术语“包括”、“包含”或者其任何其他变体意在涵盖非
排他性的包含，从而使得包括一系列要素的过程、方法、商品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、商品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、商品或者设备中还存在另外的相同要素。此外，术语“第一”、“第二”等仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。在以下各实施例的描述中，“多个”的含义是两个以上，除非另有明确具体的限定。
请参阅图1，本发明实施例提供了一种场次洪水识别方法，所述方法包括：
S101：获取径流时间序列数据；其中，所述径流时间序列数据具体可以是研究区具有连续时间序列的径流观测数据；
S102：利用所述径流时间序列数据的一阶差分序列中连续N个一阶差分值，获取初始峰现时间，N为正整数；这里的一阶差分序列是指对径流时间序列数据中的数据依次做一阶差分得到的序列，一阶差分是指下一个数据减去上一个数据；其中，初始峰现时间是可能的峰现时间，初始峰现时间可以组成一个初始峰现时间集合；
S103：利用所述一阶差分序列中连续M个一阶差分值，获取初始起止时间，M为正整数；初始起止时间可以组成一个初始起止时间集合；
S104：从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间。此处，洪峰流量可以是所述径流时间序列数据中所述峰现时间对应的径流量。
本发明实施例提供的场次洪水识别方法，利用所述径流时间序列数据的一阶差分序列中连续N个一阶差分值，获取初始峰现时间，并利用所述一阶差分序列中连续M个一阶差分值，获取初始起止时间，最后从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间，从而能够实现场次洪水的自动挑选，且效率高准确性也高。
另外，所述从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起
止时间中筛选出与所述峰现时间对应的起止时间之后，还包括：
S105：确定所述峰现时间对应的洪峰流量；
S106：对于连续的两场洪水，分别获取洪峰流量与涨退点流量的差异倍数；其中，所述差异倍数是第一差值除以第二差值所得的倍数；对于前一场洪水，所述第一差值是所述洪峰流量与起始流量之间的差值，所述第二差值是所述洪峰流量与结束流量之间的差值；对于后一场洪水，所述第一差值是所述洪峰流量与结束流量之间的差值，所述第二差值是所述洪峰流量与起始流量之间的差值；
S107：若两个所述差异倍数均大于或等于差异倍数阈值，前一场洪水的起始流量小于或等于结束流量、后一场洪水的起始流量大于或等于结束流量，且后一场洪水的起始时间与前一场洪水的结束时间之间的差值小于或等于基于流域特性确定的场次洪水平均持续时间，则确定所述两场洪水为复峰型洪水。
本发明实施例中，对于连续洪水，利用洪峰流量分别与涨退点流量的差异倍数阈值α筛选复峰型洪水。举例来说，对于连续的两场洪水，其峰现时间分别记为t1、t2(t1＜t2)，对应的洪水起始时间以及结束时间分别为st1、st2、et1、et2，洪峰流量分别为起始流量和结束流量分别为T为流域特性确定的场次洪水平均持续时间，若满足：
则，判定两场洪水为复峰型洪水，合并为一场洪水，洪水起始时间为st1，结束时间为et2：若则合并洪水的峰现时间为t1；若则合并洪水的峰现时间为t2。
具体的，所述获取径流时间序列数据，包括：
获取径流原始时间序列数据；例如可以是研究区具有连续时间序列的原始径流观测数据；径流原始时间序列数据可以记为x1,x2,…,xn，n为时刻总数；
对所述径流原始时间序列数据进行预处理，得到所述径流时间序列数据，所述预处理包括剔除异常突变点数据和/或对缺失数据进行插补。具体的，在剔除异常突变点数据之后将剔除的数据标记为缺失数据，插补可以是线性插补。
本发明的以下实施例可以基于预处理后的所述径流时间序列数据实现，预处理后的所述径流时间序列数据可以记为qx1,qx2,…,qxn。
另外，还可以对预处理后的径流时间序列数据qx1,qx2,…,qxn进一步做平滑处理，这里的平滑处理可以是两步平滑处理，具体如下：
设置两步平滑的窗口大小为win1、win2，分别计算权重系数ω1j、ω2j，公式如下：
则一步滑动平均之后的结果为两步滑动平均之后的结果为其中1≤j≤n。
本发明实施例中，对径流数据做两步平滑处理，平滑处理效果佳，从而提高洪峰选取的准确性，还可以避免场次洪水开始、结束时间过长的问题。对径流数据做两步平滑处理还可以使得本发明实施例提供的场次洪水识别方法可以适用于波动振荡的径流观测数据。
当然，在一些具体的实施方式中，可以只对径流原始时间序列数据做预处理，也可以只对径流原始时间序列数据做平滑处理。
一些可选的具体实施方式中，所述利用所述径流时间序列数据的一阶差分序列中连续N个一阶差分值，获取初始峰现时间，包括：
若所述径流时间序列数据中第一径流数据对应的第一一阶差分值以及与所述第一一阶差分值连续的N-1个一阶差分值满足第一条件，则确定所述第一径流数据对应的时间为初始峰现时间；其中，所述第一条件包括：所述第一一阶差分值以及所述第一一阶差分值之前连续的N/2-1个一阶差分值均大于或等于零，且所述第一一阶差分值之后连续的N/2个一阶差分值均小于或等于零，N为大于零的偶数。
对于两步平滑处理之后的数据y2，j，其一阶差分记为因此，若且
那么j为初始峰现时间；其中，为所述第一一阶差分值，为所述第一一阶差分值之前连续的N/2-1个一阶差分值，为所述第一一阶差分值之后连续的N/2个一阶差分值。
另外，若所述径流时间序列数据是经过两步平滑处理后得到的，那么第一径流数据是除前Eve_Win个径流数据和后Eve_Win个径流数据以外的其他径流数据，也即上述j的取值范围是Eve_Win+1≤j≤n-Eve_Win，其中，Eve_Win＝(win1+win2)/2，win1、win2为两步平滑的窗口大小。这是因为平滑的数据处理会使得前端的Eve_Win个径流数据y2，1，y2，2，…，y2，Eve_Win和后端的Eve_Win个径流数据y2，n-Eve_Win+1，y2，n-Eve_Win+2，…，y2，n失真。
其中一些可选的具体实施方式中，所述第一条件还包括所述第一一阶差分值的绝对值以及所述第一一阶差分值之后紧邻的一阶差分值的绝对值均大于预设阈值。例如，在预设阈值为0.01的情况下，即
当然，第一一阶差分值对应的预设阈值和所述第一一阶差分值之后紧邻的一阶差分值对应的预设阈值可以不同也可以相同。
另外，所述第一条件还可以包括所述第一一阶差分值以及所述第一一阶差分值之后紧邻的一阶差分值的乘积小于零，即
具体的，可以将满足上述第一条件的初始峰现时间记录为集合PeakIndex。
一些具体的实施方式中，所述利用所述一阶差分序列中连续M个一阶差分值，获取初始起止时间，包括：
若所述径流时间序列数据中第二径流数据对应的第二一阶差分值以及与所述第二一阶差分值连续的M-1个一阶差分值满足第二条件，则确定所述第二径流数据对应的时间为初始起止时间；其中，所述第二条件包括：所述第二一阶差分值以及所述第二一阶差分值之前连续的M/2-1个一阶差分值均小于或等于零，且所述第二一阶差分值之后连续的M/2个一阶差分值均大于或等于零，M为大于零的偶数。
也即是说，若且那么j为洪水的起始时间或者结束时间；其中，为所述第二一阶差分值，为所
述第二一阶差分值之前连续的M/2-1个一阶差分值，
为所述第一一阶差分值之后连续的M/2个一阶差分值。
具体的，可以将满足上述第二条件的初始起止时间记录为集合BottomIndex。
一些具体的实施方式中，所述从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间，包括：
筛选出对应的径流量大于或等于预设洪峰阈值的所述初始峰现时间作为确定的所述峰现时间；
筛选出早于所述峰现时间且最接近的所述初始起止时间作为起始时间；
筛选出晚于所述峰现时间且最接近的所述初始起止时间作为结束时间。
本发明实施例中，设置洪峰阈值PeakThreshold，利用就近原则逐场确定洪峰高于PeakThreshold的洪水的峰现时间及其对应的起止时间。具体来说，对于以t为中心作为一场洪水的峰现时间，在BottomIndex中寻找小于t的最近一点st＝max{r：r＜t，r∈BottomIndex}作为该场洪水的起始时间，大于t的最近一点et＝min{r：r＜t，r∈BottomIndex}作为这场洪水的结束时间。
一些具体的实施方式中，所述获取径流时间序列数据，包括：
获取径流原始时间序列数据；
对所述径流原始时间序列数据进行平滑处理，得到所述径流时间序列数据；
所述从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间之后，还包括：
从所述径流原始时间序列数据中获取所述起止时间之间的部分原始径流数据；
从所述部分原始径流数据中筛选出最大的径流量；
若所述最大的径流量大于或等于预设洪峰阈值，则确定所述最大的径流量为洪峰流量，将所述峰现时间修正为所述最大的径流量对应的时间。
本发明实施例中，由于数据平滑可能造成洪峰选取失真，在此利用原始径
流数据对上述峰现时间以及洪峰流量做矫正。计算若原始径流数据对应的则最终将tnew作为该场洪水的峰现时间，作为对应的洪峰流量。
进一步的，所述径流原始时间序列数据可以是经过预处理后的数据。即计算若预处理后的径流数据对应的则最终将tnew作为该场洪水的峰现时间，作为对应的洪峰流量。
本发明实施例提高了复峰型洪水识别的准确性和效率。
下面以收集到的某水文站点的实际径流数据为例说明本发明实施例提供的一种场次洪水识别方法。
(1)径流数据获取。获取某水文站的小时径流数据序列x1,x2,…,xn，n＝2000(请参阅图2中的原始径流数据)。
(2)数据预处理。对径流序列数据人工进行异常突变点检测，剔除异常点，并标识为缺失数据，之后对缺失数据进行线性插补，获得预处理后的数据qx1,qx2,…,qxn(请参阅图2中的预处理后的径流数据)。
(3)对数据进行两步平滑处理。设置平滑窗口win1＝24，win2＝48，获得两步平滑后的数据序列y2，1,y2，2,…,y2，n(请参阅图3中的两步平滑后的径流数据)。
(4)设置M＝12，N＝12，即利用一阶差分前后6个点寻找所有场次洪水可能的峰现时间和起止时间，获得初始峰现时间集合PeakIndex及初始起止时间集合BottomIndex，
PeakIndex＝{176,244,359,440,568,648,764,965,1100,1223,1455,1564,1700,1896}，BottomIndex＝{91,219,274,376,535,599,700,858,1072,1147,1380,1490,1657,1853,1950}；
(5)挑选场次洪水。设置洪峰阈值PeakThreshold＝200，利用就近原则确定逐场洪水的峰现时间以及对应的起止时间，共获得9场洪水，对应的结果(请参阅图4)为：
表1：场次洪水挑选结果
(6)设置洪峰流量分别与涨退点流量的差异倍数阈值α＝1.1，流域特性确定的场次洪水平均持续时间T＝3，识别并合并复峰型洪水，最终挑选出5场洪水(如表2，请参阅图5)。
表2：复峰型洪水挑选结果
综上所述，本发明实施例结合两步平滑的径流数据处理方法，利用径流数据序列多个一阶差分点区间内的取值变化判断洪峰出现时间以及洪水起始和结束时间，利用洪峰阈值以及原始数据准确识别洪峰，结合场次洪水涨退点流量差异倍数以及原始数据判断复峰型洪水，进而解决了数据序列非平滑、洪峰判断不准确以及复峰型洪水难以快速识别的技术问题，有效提高了场次洪水挑选效率和准确性。
相应地，请参考图6，本发明实施例提供一种场次洪水识别装置，包括：
数据获取模块601，用于获取径流时间序列数据；
第一确定模块602，用于利用所述径流时间序列数据的一阶差分序列中连续N个一阶差分值，获取初始峰现时间，N为正整数；
第二确定模块603，用于利用所述一阶差分序列中连续M个一阶差分值，获取初始起止时间，M为正整数；
第三确定模块604，用于从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间。
本发明实施例提供的场次洪水识别装置，利用所述径流时间序列数据的一阶差分序列中连续N个一阶差分值，获取初始峰现时间，并利用所述一阶差分序列中连续M个一阶差分值，获取初始起止时间，最后从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间，从而能够实现场次洪水的自动挑选，且效率高准确性也高。
一些具体的实施方式中，所述第一确定模块602包括：
第一确定单元，用于若所述径流时间序列数据中第一径流数据对应的第一一阶差分值以及与所述第一一阶差分值连续的N-1个一阶差分值满足第一条件，则确定所述第一径流数据对应的时间为初始峰现时间；其中，所述第一条件包括：所述第一一阶差分值以及所述第一一阶差分值之前连续的N/2-1个一阶差分值均大于或等于零，且所述第一一阶差分值之后连续的N/2个一阶差分值均小于或等于零，N为大于零的偶数。
一些具体的实施方式中，所述第一条件还包括所述第一一阶差分值的绝对值以及所述第一一阶差分值之后紧邻的一阶差分值的绝对值均大于预设阈值。
一些具体的实施方式中，所述第二确定模块603包括：
第二确定单元，用于若所述径流时间序列数据中第二径流数据对应的第二一阶差分值以及与所述第二一阶差分值连续的M-1个一阶差分值满足第二条件，则确定所述第二径流数据对应的时间为初始起止时间；其中，所述第二条件包括：所述第二一阶差分值以及所述第二一阶差分值之前连续的M/2-1个一阶差分值均小于或等于零，且所述第二一阶差分值之后连续的M/2个一阶差分
值均大于或等于零，M为大于零的偶数。
一些具体的实施方式中，所述第三确定模块604包括：
第一筛选单元，用于筛选出对应的径流量大于或等于预设洪峰阈值的所述初始峰现时间作为确定的所述峰现时间；
第二筛选单元，用于筛选出早于所述峰现时间且最接近的所述初始起止时间作为起始时间；
第三筛选单元，用于筛选出晚于所述峰现时间且最接近的所述初始起止时间作为结束时间。
一些具体的实施方式中，所述数据获取模块601包括：
原始数据获取单元，用于获取径流原始时间序列数据；
平滑处理单元，用于对所述径流原始时间序列数据进行平滑处理，得到所述径流时间序列数据；
所述装置还包括：
选取模块，用于从所述径流原始时间序列数据中获取所述起止时间之间的部分原始径流数据；
筛选模块，用于从所述部分原始径流数据中筛选出最大的径流量；
修正模块，用于若所述最大的径流量大于或等于预设洪峰阈值，则确定所述最大的径流量为洪峰流量，将所述峰现时间修正为所述最大的径流量对应的时间。
一些具体的实施方式中，所述装置还包括：
洪峰流量确定模块，用于确定所述峰现时间对应的洪峰流量；
差异倍数获取模块，用于对于连续的两场洪水，分别获取洪峰流量与涨退点流量的差异倍数；所述差异倍数是第一差值除以第二差值所得的倍数；对于前一场洪水，所述第一差值是所述洪峰流量与起始流量之间的差值，所述第二差值是所述洪峰流量与结束流量之间的差值；对于后一场洪水，所述第一差值是所述洪峰流量与结束流量之间的差值，所述第二差值是所述洪峰流量与起始流量之间的差值；
判断模块，用于若两个所述差异倍数均大于或等于差异倍数阈值，前一场
洪水的起始流量小于或等于结束流量、后一场洪水的起始流量大于或等于结束流量，且后一场洪水的起始时间与前一场洪水的结束时间之间的差值小于或等于基于流域特性确定的场次洪水平均持续时间，则确定所述两场洪水为复峰型洪水。
本发明实施例是与上述方法实施例基于相同的发明构思的装置实施例，因此具体的技术细节和对应的技术效果请参阅上述方法实施例，此处不再赘述。
本发明实施例还提供了一种电子设备，如图7所示，该电子设备可以包括处理器71和存储器72，其中处理器71和存储器72可以通过总线或者其他方式互相通信连接，图7中以通过总线连接为例。
处理器71可以为中央处理器(Central Processing Unit，CPU)。处理器71还可以为其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等芯片，或者上述各类芯片的组合。
存储器72作为一种非暂态计算机可读存储介质，可用于存储非暂态软件程序、非暂态计算机可执行程序以及模块，如本发明实施例中的场次洪水识别方法对应的程序指令/模块(例如，图6所示的数据获取模块601、第一确定模块602、第二确定模块603和第三确定模块604)。处理器71通过运行存储在存储器72中的非暂态软件程序、指令以及模块，从而执行处理器的各种功能应用以及数据处理，即实现上述方法实施例中的场次洪水识别方法。
存储器72可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；存储数据区可存储处理器71所创建的数据等。此外，存储器72可以包括高速随机存取存储器，还可以包括非暂态存储器，例如至少一个磁盘存储器件、闪存器件、或其他非暂态固态存储器件。在一些实施例中，存储器72可选包括相对于处理器71远程设置的存储器，这些远程存储器可以通过网络连接至处理器71。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
所述一个或者多个模块存储在所述存储器72中，当被所述处理器71执行
时，执行如图1-5所示实施例中的场次洪水识别方法。
上述电子设备具体细节可以对应参阅图1至图5所示的实施例中对应的相关描述和效果进行理解，此处不再赘述。
相应地，本发明实施例还提供一种计算机可读存储介质，所述计算机可读存储介质用于存储计算机程序，所述计算机程序被处理器执行时，实现上述场次洪水识别方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
本说明书中的各个实施例均采用递进的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。尤其，对于系统实施例而言，由于其基本相似于方法实施例，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
以上所述仅为本申请的实施例而已，并不用于限制本申请。对于本领域技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原理之内所作的任何修改、等同替换、改进等，均应包含在本申请的权利要求范围之内。
Claims (9)
- 一种场次洪水识别方法，其特征在于，所述方法包括：获取径流时间序列数据；利用所述径流时间序列数据的一阶差分序列中连续N个一阶差分值，获取初始峰现时间，N为正整数；利用所述一阶差分序列中连续M个一阶差分值，获取初始起止时间，M为正整数；从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间；所述从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间之后，还包括：确定所述峰现时间对应的洪峰流量；对于连续的两场洪水，分别获取洪峰流量与涨退点流量的差异倍数；所述差异倍数是第一差值除以第二差值所得的倍数；对于前一场洪水，所述第一差值是所述洪峰流量与起始流量之间的差值，所述第二差值是所述洪峰流量与结束流量之间的差值；对于后一场洪水，所述第一差值是所述洪峰流量与结束流量之间的差值，所述第二差值是所述洪峰流量与起始流量之间的差值；若两个所述差异倍数均大于或等于差异倍数阈值，前一场洪水的起始流量小于或等于结束流量、后一场洪水的起始流量大于或等于结束流量，且后一场洪水的起始时间与前一场洪水的结束时间之间的差值小于或等于基于流域特性确定的场次洪水平均持续时间，则确定所述两场洪水为复峰型洪水。
- 根据权利要求1所述的方法，其特征在于，所述利用所述径流时间序列数据的一阶差分序列中连续N个一阶差分值，获取初始峰现时间，包括：若所述径流时间序列数据中第一径流数据对应的第一一阶差分值以及与所述第一一阶差分值连续的N-1个一阶差分值满足第一条件，则确定所述第一径流数据对应的时间为初始峰现时间；其中，所述第一条件包括：所述第一一阶差分值以及所述第一一阶差分值之前连续的N/2-1个一阶差分值均大于或等于零，且所述第一一阶差分值之后连续的N/2个一阶差分值均小于或等于零，N为大于零的偶数。
- 根据权利要求2所述的方法，其特征在于，所述第一条件还包括所述第一一阶差分值的绝对值以及所述第一一阶差分值之后紧邻的一阶差分值的绝对值均大于预设阈值。
- 根据权利要求1所述的方法，其特征在于，所述利用所述一阶差分序列中连续M个一阶差分值，获取初始起止时间，包括：若所述径流时间序列数据中第二径流数据对应的第二一阶差分值以及与所述第二一阶差分值连续的M-1个一阶差分值满足第二条件，则确定所述第二径流数据对应的时间为初始起止时间；其中，所述第二条件包括：所述第二一阶差分值以及所述第二一阶差分值之前连续的M/2-1个一阶差分值均小于或等于零，且所述第二一阶差分值之后连续的M/2个一阶差分值均大于或等于零，M为大于零的偶数。
- 根据权利要求1所述的方法，其特征在于，所述从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间，包括：筛选出对应的径流量大于或等于预设洪峰阈值的所述初始峰现时间作为确定的所述峰现时间；筛选出早于所述峰现时间且最接近的所述初始起止时间作为起始时间；筛选出晚于所述峰现时间且最接近的所述初始起止时间作为结束时间。
- 根据权利要求1所述的方法，其特征在于，所述获取径流时间序列数据，包括：获取径流原始时间序列数据；对所述径流原始时间序列数据进行平滑处理，得到所述径流时间序列数据；所述从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间之后，还包括：从所述径流原始时间序列数据中获取所述起止时间之间的部分原始径流数据；从所述部分原始径流数据中筛选出最大的径流量；若所述最大的径流量大于或等于预设洪峰阈值，则确定所述最大的径流量为洪峰流量，将所述峰现时间修正为所述最大的径流量对应的时间。
- 一种场次洪水识别装置，其特征在于，包括：数据获取模块，用于获取径流时间序列数据；第一确定模块，用于利用所述径流时间序列数据的一阶差分序列中连续N个一阶差分值，获取初始峰现时间，N为正整数；第二确定模块，用于利用所述一阶差分序列中连续M个一阶差分值，获取初始起止时间，M为正整数；第三确定模块，用于从所述初始峰现时间中筛选出确定的峰现时间，从所述初始起止时间中筛选出与所述峰现时间对应的起止时间；洪峰流量确定模块，用于确定所述峰现时间对应的洪峰流量；差异倍数获取模块，用于对于连续的两场洪水，分别获取洪峰流量与涨退点流量的差异倍数；所述差异倍数是第一差值除以第二差值所得的倍数；对于前一场洪水，所述第一差值是所述洪峰流量与起始流量之间的差值，所述第二差值是所述洪峰流量与结束流量之间的差值；对于后一场洪水，所述第一差值是所述洪峰流量与结束流量之间的差值，所述第二差值是所述洪峰流量与起始流量之间的差值；判断模块，用于若两个所述差异倍数均大于或等于差异倍数阈值，前一场洪水的起始流量小于或等于结束流量、后一场洪水的起始流量大于或等于结束流量，且后一场洪水的起始时间与前一场洪水的结束时间之间的差值小于或等于基于流域特性确定的场次洪水平均持续时间，则确定所述两场洪水为复峰型洪水。
- 一种电子设备，其特征在于，包括：存储器和处理器，所述存储器和所述处理器之间互相通信连接，所述存储器用于存储计算机程序，所述计算机程序被所述处理器执行时，实现权利要求1至6中任一项所述的场次洪水识别方法。
- 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质用于存储计算机程序，所述计算机程序被处理器执行时，实现权利要求1至6中任一项所述的场次洪水识别方法。
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