Methods, devices, media and equipment for determining flood change patterns

Abstract

The present invention provides a method, device, medium, and equipment for determining the law of flood changes. The method includes: obtaining a candidate factor database related to the climate of a hydrological station, a parameter set related to a control basin, and a first reservoir index; establishing a regression equation based on at least one prediction factor, and determining the climate factor covariate according to the calculation result of the regression equation; determining the second reservoir index based on the first reservoir index and the parameters in the parameter set; constructing and using a time-varying distribution model based on the climate factor covariate, the first reservoir index, and the second reservoir index to determine the distribution law of flood influencing factors related to the hydrological station. By obtaining a candidate factor database related to the climate of the hydrological station and a first reservoir index of a parameter set related to the control basin of the hydrological station, a time-varying distribution model with low-frequency climate factors and reservoir index as covariates is determined to be constructed, and then the inconsistency characteristics of the flood are analyzed, providing a basic tool for related engineering design and disaster prevention and mitigation.

Description

Technical Field

[0001] The present invention relates to the field of engineering hydrology technology, and in particular to a method, device, medium and equipment for determining flood change rules. Background Art

[0002] The study of long-term flood sequence changes is very necessary for the practice of medium- and long-term planning of hydropower development and verification of flood control standards. In recent years, the research angles are mostly: trend, mutation, cycle and causal relationship. The Mann-Kendall method, sliding t method and Yamamoto method are used to analyze the trend changes of the flow of multiple hydrological stations in the upper reaches of the Yangtze River. The results show that the annual runoff of multiple hydrological stations shows a significant increase, slow decrease and significant decrease trend, and there are mutation points.

[0003] In the related technology, the frequency analysis of the minimum monthly flow and annual average flow of the hydrological station is carried out based on the time-varying moment model. The annual average flow shows a linear decreasing trend, but when the distribution parameter is a constant, the result obtained still conforms to the "consistency hypothesis", while the distribution position parameters of the annual minimum monthly flow sequence show the inconsistency characteristics of nonlinear transformation. Summary of the invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the defect in the prior art that the law of inconsistent changes in floods cannot be accurately determined, thereby providing a method, device, medium, and equipment for determining the law of flood changes.

[0005] In a first aspect, an embodiment of the present invention provides a method for determining the law of flood change, including: obtaining a candidate factor database related to the climate of a hydrological station, a parameter set related to the watershed controlled by the hydrological station, and a first reservoir index, the candidate factor database including at least one prediction factor, and the parameter set being used to characterize the flood season flow of the hydrological station; establishing a regression equation based on at least one prediction factor, and determining the climate factor covariate based on the calculation result of the regression equation; determining the second reservoir index based on the first reservoir index and the parameters in the parameter set; constructing a time-varying distribution model based on the climate factor covariate, the first reservoir index and the second reservoir index; and determining the distribution law of flood influencing factors related to the hydrological station using the time-varying distribution model.

[0006] In combination with the first aspect, in a possible implementation manner of the first aspect, the regression equation is expressed as:

[0007] y=β i x i +ε

[0008] Among them, y represents the result of regression equation calculation, x i represents the i-th predictor, β i represents the coefficient and ε represents the residual.

[0009] In combination with the first aspect, in another possible implementation of the first aspect, determining the climate factor covariate based on the calculation results of the regression equation also includes: determining the correlation coefficient and confidence level of the calculation results of the regression equation; and judging the significance of the climate factor covariate based on the correspondence between the correlation coefficient, confidence level and a preset significance threshold.

[0010] In combination with the first aspect, in another possible implementation of the first aspect, the parameters in the parameter set include: flood control reservoir capacity, average runoff during flood season, and the second reservoir index is determined based on the first reservoir index and the parameters in the parameter set, including: replacing the first reservoir index based on the flood control reservoir capacity and the average runoff during flood season to obtain an improved first reservoir index, and using the improved first reservoir index as the second reservoir index.

[0011] In combination with the first aspect, in another possible implementation of the first aspect, the second reservoir index is expressed as:

[0012]

[0013] Among them, MRI represents the second reservoir index, N represents the number of reservoirs in the basin controlled by the hydrological station, and A k represents the catchment area of ​​the kth reservoir in the control basin, A T represents the controlled basin area of ​​the hydrological station, B k represents the flood control storage capacity, B T Represents the average runoff during the flood season.

[0014] In combination with the first aspect, in another possible implementation manner of the first aspect, the first reservoir index is expressed as:

[0015]

[0016] Among them, RI represents the first reservoir index, N represents the number of reservoirs in the basin controlled by the hydrological station, and A k represents the catchment area of ​​the kth reservoir in the control basin, A T represents the controlled catchment area of ​​the hydrological station, C k represents the total storage capacity of the kth reservoir in the control basin, C T Represents the average runoff of the hydrological station.

[0017] In combination with the first aspect, in another possible implementation of the first aspect, the time-varying distribution model includes: location parameters and scale parameters. Based on the climate factor covariate, the first reservoir index and the second reservoir index, a time-varying distribution model is constructed, including: constructing a function of the location parameters and scale parameters changing with the climate factor covariate; and, constructing a function of the location parameters and scale parameters changing with the climate factor covariate and the first reservoir index; and, constructing a function of the location parameters and scale parameters changing with the climate factor covariate and the second reservoir index.

[0018] In the second aspect, an embodiment of the present invention provides a device for determining the law of flood change, including: an acquisition unit, used to acquire a candidate factor database related to the climate of a hydrological station, a parameter set related to the watershed controlled by the hydrological station, and a first reservoir index, the candidate factor database including at least one prediction factor, and the parameter set being used to characterize the flood season flow of the hydrological station; a climate factor covariate determination unit, used to establish a regression equation based on at least one prediction factor, and determine the climate factor covariate according to the calculation result of the regression equation; a second reservoir index determination unit, used to determine the second reservoir index based on the first reservoir index and the parameters in the parameter set; a time-varying distribution model construction unit, used to construct a time-varying distribution model based on the climate factor covariate, the first reservoir index and the second reservoir index; a distribution law determination unit, used to use the time-varying distribution model to determine the distribution law of flood influencing factors related to the hydrological station.

[0019] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, a method for determining a flood change law as in any implementation of the first aspect is implemented.

[0020] In a fourth aspect, an embodiment of the present invention provides a computer device comprising at least one processor; and a memory communicatively connected to the at least one processor; the memory stores computer program instructions, and when the instructions are executed by the at least one processor, a method for determining a flood change pattern as in any embodiment of the first aspect is implemented.

[0021] The technical solution of the present invention has the following advantages:

[0022] The present invention provides a method, device, medium and equipment for determining the law of flood change. The method determines the climate factor covariate and the second reservoir index by obtaining a candidate factor database related to the climate of a hydrological station and a parameter set related to the watershed controlled by the hydrological station, and constructs a time-varying distribution model with low-frequency climate factors and reservoir indexes as covariates, and then analyzes and determines the distribution law of flood influencing factors related to the hydrological station, thereby determining the inconsistency characteristics of the flood, and providing a basic tool for related engineering design and disaster prevention and mitigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A flowchart of a specific example of a method for determining a flood change law provided by an embodiment of the present invention;

[0025] Figure 2 A flowchart of a regression equation of a method for determining flood change rules provided by an embodiment of the present invention;

[0026] Figure 3 An example diagram of significance judgment of climate factor covariates in a method for determining flood variation rules provided by an embodiment of the present invention;

[0027] Figure 4 An example diagram of significance judgment of climate factor covariates in another method for determining flood variation law provided by an embodiment of the present invention;

[0028] Figure 5 An example diagram of comparison between a first reservoir index and a second reservoir index in a method for determining a flood variation law provided by an embodiment of the present invention;

[0029] Figure 6 A principle block diagram of a specific example of a device for determining a flood change law provided by an embodiment of the present invention;

[0030] Figure 7 A diagram showing an example structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0033] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0034] This embodiment provides a method for determining the flood change law, such as Figure 1 As shown, the following steps are included:

[0035] S101, obtaining a candidate factor database related to the climate of the hydrological station, a parameter set related to the watershed controlled by the hydrological station, and a first reservoir index, wherein the candidate factor database includes at least one prediction factor, and the parameter set is used to characterize the flood season flow of the hydrological station.

[0036] Specifically, the candidate factor database related to the climate of hydrological stations includes: Arctic Oscillation (AO), North Pacific Oscillation (NPO), Pacific Decadal Oscillation (PDO), Southern Oscillation (SOI) and El Nino Southern Oscillation (ENSO3.4, ENSO3.4, ENSO4) or other predictive factors used to reflect the impact of low-frequency climate change on floods.

[0037] In practical applications, since low-frequency climate indices have a lag effect on floods, the low-frequency climate indices from January to June of the current year and the previous two years are generally used as candidate prediction factor databases.

[0038] Specifically, the parameter set related to the watershed controlled by the hydrological station includes: flood control storage capacity, average runoff during the flood season, or other parameters used to reflect the impact of the flood control storage capacity of the reservoir of the hydrological station on floods during the flood season.

[0039] S102, establishing a regression equation based on at least one prediction factor, and determining a climate factor covariate according to a calculation result of the regression equation.

[0040] Specifically, establishing a regression equation based on at least one prediction factor and determining the climate factor covariates according to the calculation results of the regression equation means establishing regression equations according to each prediction factor, determining the calculation results of each regression equation, judging whether each calculation result passes the test, and selecting a preset number of prediction factors that pass the test as climate factor covariates according to the calculation results.

[0041] S103. Determine a second reservoir index based on the first reservoir index and the parameters in the parameter set.

[0042] Specifically, based on the first reservoir index and the parameters in the parameter set, determining the second reservoir index means replacing the first reservoir index according to the parameters in the parameter set to obtain the second reservoir index.

[0043] In practical applications, the second reservoir index is an improvement on the first reservoir index. By replacing the original parameters of the first reservoir index with parameters that can reflect the impact of the reservoir flood control capacity of the hydrological station on floods during the flood season, the second reservoir index can accurately reflect the impact of the reservoir flood control capacity on floods during the flood season.

[0044] S104. Construct a time-varying distribution model based on the climate factor covariate, the first reservoir index and the second reservoir index.

[0045] Specifically, constructing a time-varying distribution model means controlling the changes in location parameters and scale parameters of the time-varying distribution model by using climate factor covariates, the first reservoir index, and the second reservoir index as inputs.

[0046] In practical applications, the generalized additive model GAMLSS is used as the time-varying distribution model, and the functional relationship between the location parameter, scale parameter, climate factor covariate, the first reservoir index and the second reservoir index is fitted by cubic spline function.

[0047] S105. Determine the distribution pattern of flood influencing factors related to the hydrological station using a time-varying distribution model.

[0048] Specifically, the distribution law of flood influencing factors related to hydrological stations is obtained by fitting the location parameters, scale parameters, and the functional relationship between the climatic factor covariates, the first reservoir index and the second reservoir index through a time-varying distribution model to obtain the optimal distribution type of each hydrological station and the flood influencing factors related to the location parameters and scale parameters.

[0049] In practical applications, as shown in Table 1, the fitting results of the optimal distribution are exemplarily shown.

[0050] Table 1

[0051] Site Optimal Distribution <![CDATA[θ1]]> <![CDATA[θ2]]> Zhutuo LOGNO SOI+AO+MRI ct Cuntan LONGNO SOI+cs(AO,1)+MRI ct Yichang NO AO+cs(NPO,1)+MRI ct Beibei WEI NAO+SOI+MRI cs(NPO,1) Wulong WEI NPO+AO+MRI cs(NAO,2) Wuxi WEI cs(NPO,2) ct

[0052] Among them, ct indicates that the distribution parameter is a constant, cs(,i) represents the cubic spline function with i degrees of freedom, LOGNO is the abbreviation of Lognormal distribution, WEI is the abbreviation of Weibull distribution, NO is the abbreviation of Normal distribution, θ1 represents the location function, θ2 represents the scale function, and MRI represents the second reservoir index.

[0053] In practical applications, the fitting results show that the θ2 of the time-varying distribution model is a constant with the hydrological stations Zhutuo, Cuntan and Yichang, a linear function with Beibei and Wuxi, and a nonlinear function with Wulong. The first reservoir index and the second reservoir index have a linear function relationship with the θ1 of Cuntan, Zhutuo, Yichang, Beibei and Wulong stations with more upstream cascade reservoirs, but have no obvious effect on Wuxi station with fewer upstream reservoirs. The low-frequency climate indexes AO, SOI and NPO have a wide range of influences on θ1, showing linear and nonlinear relationships, and NAO has the second largest influence on the distribution parameter θ1; for the distribution parameter θ2, only Beibei and Wulong stations show nonlinear relationships with NPO and NAO respectively, and θ2 of other stations is a constant.

[0054] By implementing this embodiment, by obtaining a candidate factor database related to the climate of the hydrological station and the first reservoir index of the parameter set related to the watershed controlled by the hydrological station, the climate factor covariate and the second reservoir index are determined, and a time-varying distribution model with low-frequency climate factors and reservoir indices as covariates is constructed, and then the distribution pattern of flood influencing factors related to the hydrological station is analyzed and determined, thereby determining the inconsistency characteristics of the flood, and providing basic tools for related engineering design and disaster prevention and mitigation.

[0055] In an alternative embodiment, Figure 2 As shown in the figure, according to the regression equation calculation results, the climate factor covariates are determined, including:

[0056] (1) Establish the regression equation for each predictor.

[0057] In an optional implementation, the regression equation is expressed as:

[0058] y=β i x i +ε

[0059] Among them, y represents the result of regression equation calculation, x i represents the i-th predictor, β i represents the coefficient and ε represents the residual.

[0060] (2) Determine the calculation results of each regression equation.

[0061] In practical applications, the calculation results of each regression equation are used to characterize the degree of correlation with the flood sequence. The higher the calculation result, the higher the degree of correlation.

[0062] (3) Determine whether the calculation result passes the test.

[0063] In an optional implementation, determining the climate factor covariate according to the regression equation calculation result also includes:

[0064] Determine the correlation coefficient and confidence level of the regression equation calculation results.

[0065] Based on the correspondence between the correlation coefficient, confidence level and the preset significance threshold, the significance of the climate factor covariate is judged.

[0066] Specifically, based on the corresponding relationship between the correlation coefficient, the confidence level and the preset significance threshold, the significance of the climate factor covariate is judged to determine whether the calculation result passes the test. It should be understood that the calculation of the correlation coefficient and the confidence level is a relatively mature technology, and the present invention will not repeat it in detail.

[0067] Specifically, the correlation coefficient of the regression equation settlement result is used to characterize the fitting effect. When the correlation coefficient meets the value range of the correlation coefficient in the preset significance threshold and the confidence level is less than the confidence level threshold in the preset significance threshold, the climate factor covariate is considered to be significant and the corresponding calculation result passes the test.

[0068] In practical applications, the range of the correlation coefficient in the preset significance threshold is -1 to 1. The closer it is to -1 or 1, the better the fitting effect. The confidence threshold in the preset significance threshold can be 0.05, 0.06 or other values. 0.05 is usually selected as the confidence threshold. Figure 3 The calculation results of Zhutuo site and climate factor covariate SOI are shown. Figure 4 The calculation results of Yichang station and climate factor covariate AO are shown. Among them, r represents the correlation coefficient and p represents the confidence level. It can be seen that Figure 3 and Figure 4 All the cases shown are significant, that is, the corresponding calculation results pass the test.

[0069] In practical applications, a preset number of prediction factors corresponding to the calculation results that have passed the test are usually selected as climate factor covariates. The standard for selecting the preset number of prediction factors is to select the first preset number of prediction factors with the highest calculation results. The preset number can be 2, 3 or other values, which can be selected according to actual working conditions, and the present invention does not make specific limitations on this.

[0070] By implementing this embodiment, a regression equation is established with prediction factors through a candidate factor database related to the climate of the hydrological station, and the process of determining the climate factor covariates based on the calculation results of the regression equation is to determine the prediction factors that reflect the high degree of impact of low-frequency climate change on floods through the regression equation, thereby providing a data basis for the subsequent construction of a time-varying distribution model, and then analyzing and determining the distribution law of flood influencing factors related to the hydrological station, and determining the inconsistent characteristics of the flood.

[0071] In an optional implementation, the parameters in the parameter set include: flood control storage capacity, average runoff during the flood season, and the second reservoir index is determined based on the first reservoir index and the parameters in the parameter set, including:

[0072] The first reservoir index is replaced based on the flood control storage capacity and the average runoff during the flood season to obtain an improved first reservoir index, and the improved first reservoir index is used as the second reservoir index.

[0073] In an optional embodiment, the first reservoir index is expressed as:

[0074]

[0075] Among them, RI represents the first reservoir index, N represents the number of reservoirs in the basin controlled by the hydrological station, and A k represents the catchment area of ​​the kth reservoir in the control basin, A T represents the controlled catchment area of ​​the hydrological station, C k represents the total storage capacity of the kth reservoir in the control basin, C T Represents the average runoff of the hydrological station.

[0076] In an optional embodiment, the second reservoir index is expressed as:

[0077]

[0078] Where MRI represents the second reservoir index, B k represents the flood control storage capacity, B T Represents the average runoff during the flood season.

[0079] Specifically, the first reservoir index is replaced based on the flood control storage capacity and the average runoff during the flood season to obtain the improved first reservoir index, and the improved first reservoir index is used as the second reservoir index, which means that the A in the first reservoir index is replaced by k With A T Replace with B k With B T , thus obtaining the second reservoir index.

[0080] In practical applications, considering that it is the flood control storage capacity of the reservoir rather than the total storage capacity that regulates floods, and the large annual distribution of runoff, C T It cannot accurately reflect the flood volume during the flood season. Therefore, it is necessary to modify the first reservoir index so that it can accurately reflect the impact of the reservoir's flood control capacity on floods during the flood season. i and B T Replace C i and C T , and obtain the modified reservoir index (MRI). In practical applications, such as Figure 5 As shown, Figure 5 (a) shows the calculation results of the first reservoir index RI corresponding to each station. Figure 5 (b) shows the calculation results of the second reservoir index MRI corresponding to each station. It can be seen that the second reservoir index can more clearly reflect the impact of the reservoir flood control capacity of the hydrological station on floods during the flood season.

[0081] By implementing this embodiment, the first reservoir index is replaced based on the flood control storage capacity and the average runoff during the flood season to determine the second reservoir index, so that the second reservoir index can more accurately reflect the flood volume of the site during the flood season, thereby providing a data basis for the subsequent construction of a time-varying distribution model, and further analyzing and determining the distribution law of flood influencing factors related to the hydrological station, and determining the inconsistency characteristics of the flood.

[0082] In an optional implementation, the time-varying distribution model includes: a location parameter and a scale parameter, and based on the climate factor covariate, the first reservoir index and the second reservoir index, the time-varying distribution model is constructed, including:

[0083] Construct the function of location parameter and scale parameter changing with the climate factor covariate. And,

[0084] Construct the function of location parameter and scale parameter with the change of climate factor covariate and the first reservoir index. And,

[0085] Construct the function of location parameters and scale parameters changing with the climate factor covariate and the second reservoir index.

[0086] In practical applications, the time-varying distribution model is expressed as:

[0087]

[0088] Among them, ~1 indicates that the location parameter or scale parameter is a constant value, CI represents the low-frequency climate factor, RI represents the first reservoir index, MRI represents the second reservoir index, cs(,i) represents the cubic spline function with i degrees of freedom, and m is the number of low-frequency climate factors in the corresponding model.

[0089] In practical applications, in order to prevent overfitting, i takes the value of 0, 1, 2 or 3.

[0090] In practical applications, the time-varying distribution model is a generalized additive model GAMLSS, which allows time-varying modeling of distribution location parameters and scale parameters, that is, a functional relationship is established through climate factor covariates, the first reservoir index and the second reservoir index, such as linear, exponential, and spline functions. The calculation of GAMLSS is a relatively mature technology, and the present invention will not elaborate on it.

[0091] The embodiment of the present invention provides a device for determining the flood change law, such as Figure 6As shown, it includes: an acquisition unit 21, a climate factor covariate determination unit 22, a second reservoir index determination unit 23, a time-varying distribution model construction unit 24, and a distribution law determination unit 25.

[0092] The acquisition unit 21 is used to acquire a candidate factor database related to the climate of the hydrological station, a parameter set related to the watershed controlled by the hydrological station, and a first reservoir index, wherein the candidate factor database includes at least one prediction factor, and the parameter set is used to characterize the flood season flow of the hydrological station. The specific process can be referred to the relevant description of step S101 in the above embodiment, which will not be repeated here.

[0093] The climate factor covariate determination unit 22 is used to establish a regression equation based on at least one prediction factor, and determine the climate factor covariate according to the calculation result of the regression equation. The specific process can be found in the description of step S102 in the above embodiment, which will not be repeated here.

[0094] The second reservoir index determination unit 23 is used to determine the second reservoir index based on the first reservoir index and the parameters in the parameter set. The specific process can refer to the relevant description of step S103 in the above embodiment, which will not be repeated here.

[0095] The time-varying distribution model building unit 24 is used to build a time-varying distribution model based on the climate factor covariate, the first reservoir index and the second reservoir index. The specific process can be found in the description of step S104 in the above embodiment, which will not be repeated here.

[0096] The distribution law determination unit 25 is used to determine the distribution law of flood impact factors related to the hydrological station using the time-varying distribution model. The specific process can be found in the description of step S105 in the above embodiment, which will not be repeated here.

[0097] The device obtains a candidate factor database related to the climate of the hydrological station and a first reservoir index of a parameter set related to the watershed controlled by the hydrological station through an acquisition unit, determines the climate factor covariate and the second reservoir index through a climate factor covariate determination unit and a second reservoir index determination unit, and constructs a time-varying distribution model with low-frequency climate factors and reservoir index as covariates through a time-varying distribution model construction unit, and then determines the distribution law of flood influencing factors related to the hydrological station through a distribution law determination unit, thereby determining the inconsistency characteristics of the flood, and providing a basic tool for related engineering design and disaster prevention and mitigation.

[0098] One embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions can execute the method for determining the flood change law in any of the above method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memory.

[0099] An embodiment of the present invention further provides a computer device, such as Figure 7 As shown, Figure 7 It is a structural diagram of a computer device provided by an optional embodiment of the present invention, and the computer device may include at least one processor 31, at least one communication interface 32, at least one communication bus 33 and at least one memory 34, wherein the communication interface 32 may include a display screen (Display), a keyboard (Keyboard), and the optional communication interface 32 may also include a standard wired interface and a wireless interface. The memory 34 may be a high-speed RAM memory (Random Access Memory, volatile random access memory) or a non-volatile memory (non-volatile memory), such as at least one disk storage. The memory 34 may also be at least one storage device located away from the aforementioned processor 31. The processor 31 may be combined with Figure 6 In the described device, the memory 34 stores an application program, and the processor 31 calls the program code stored in the memory 34 to execute the steps of the method for determining the flood change law described in any of the above method embodiments.

[0100] The communication bus 33 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The communication bus 33 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0101] Among them, the memory 34 may include a volatile memory (volatile memory), such as a random-access memory (RAM); the memory may also include a non-volatile memory (non-volatile memory), such as a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); the memory 34 may also include a combination of the above types of memory.

[0102] The processor 31 may be a central processing unit (CPU), a network processor (NP) or a combination of a CPU and a NP.

[0103] The processor 31 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0104] Optionally, the memory 34 is also used to store program instructions. The processor 31 can call the program instructions to implement the method for determining the flood change law described in any embodiment of the present invention.

[0105] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for determining the law of flood change, characterized in that: The method comprises: Acquire a candidate factor database related to the climate of a hydrological station, a parameter set related to a watershed controlled by the hydrological station, and a first reservoir index, wherein the candidate factor database includes at least one prediction factor, and the parameter set is used to characterize the flood season flow of the hydrological station; Establishing a regression equation based on the at least one prediction factor, and determining a climate factor covariate according to a calculation result of the regression equation; determining a second reservoir index based on the first reservoir index and a parameter in the parameter set; constructing a time-varying distribution model based on the climate factor covariate, the first reservoir index, and the second reservoir index; Determine the distribution law of flood influencing factors related to the hydrological station by using the time-varying distribution model; The time-varying distribution model includes: a location parameter and a scale parameter. The time-varying distribution model is constructed based on the climate factor covariate, the first reservoir index and the second reservoir index, including: Constructing functions of location and scale parameters as a function of climate factor covariates; and, Constructing functions of location and scale parameters as a function of climate factor covariates and the first reservoir index; and, Construct the function of location parameter and scale parameter with the change of climate factor covariate and the second reservoir index; In the time-varying distribution model, the location and scale parameters vary with The changes are expressed as: Position and scale parameters and The changes are expressed as: Position and scale parameters CI and RI The changes are expressed as: Among them, ~1 means that the location parameter or scale parameter is a constant value. represents the low-frequency climate factor, represents the first reservoir index, represents the second reservoir index, The degrees of freedom are The cubic spline function of is the number of low-frequency climate factors in the corresponding model, Indicates location, Indicates scale.

2. The method according to claim 1, characterized in that The regression equation is expressed as: in, represents the result of regression equation calculation, represents the i-th predictor, represents the coefficient, Represents the residual.

3. The method according to claim 1, wherein determining the climate factor covariate according to the regression equation calculation result further comprises: Determine the correlation coefficient and confidence level of the regression equation calculation result; Based on the corresponding relationship between the correlation coefficient, the confidence level and the preset significance threshold, the significance of the climate factor covariate is judged.

4. The method according to claim 1, characterized in that The parameters in the parameter set include: flood control storage capacity, average runoff during flood season, and the second reservoir index is determined based on the first reservoir index and the parameters in the parameter set, including: The first reservoir index is replaced based on the flood control storage capacity and the average runoff during the flood season to obtain an improved first reservoir index, and the improved first reservoir index is used as the second reservoir index.

5. The method according to claim 1, characterized in that The second reservoir index is expressed as: Among them, MRI represents the second reservoir index, N represents the number of reservoirs in the basin controlled by the hydrological station, and A k represents the catchment area of ​​the kth reservoir in the control basin, A T represents the controlled basin area of ​​the hydrological station, B k represents the flood control storage capacity, B T Represents the average runoff during the flood season.

6. The method according to claim 1, characterized in that The first reservoir index is expressed as: Among them, RI represents the first reservoir index, N represents the number of reservoirs in the basin controlled by the hydrological station, and A k represents the catchment area of ​​the kth reservoir in the control basin, A T represents the controlled catchment area of ​​the hydrological station, C k represents the total storage capacity of the kth reservoir in the control basin, C T Represents the average runoff of the hydrological station.

7. A device for determining the law of flood change, characterized in that: The device comprises: An acquisition unit, used for acquiring a candidate factor database related to the climate of the hydrological station, a parameter set related to the watershed controlled by the hydrological station, and a first reservoir index, wherein the candidate factor database includes at least one prediction factor, and the parameter set is used for characterizing the flood season flow of the hydrological station; A climate factor covariate determination unit, configured to establish a regression equation based on the at least one prediction factor, and determine the climate factor covariate according to a calculation result of the regression equation; A second reservoir index determining unit, configured to determine a second reservoir index based on the first reservoir index and the parameters in the parameter set; A time-varying distribution model building unit, used to build a time-varying distribution model based on the climate factor covariate, the first reservoir index and the second reservoir index; A distribution law determination unit, used to determine the distribution law of flood influencing factors related to the hydrological station by using the time-varying distribution model; The time-varying distribution model construction unit is specifically used to construct a function in which the location parameter and the scale parameter change with the climate factor covariate; and, construct a function in which the location parameter and the scale parameter change with the climate factor covariate and the first reservoir index; and, construct a function in which the location parameter and the scale parameter change with the climate factor covariate and the second reservoir index; in the time-varying distribution model, the location and scale parameters change with The changes are expressed as: Position and scale parameters and The changes are expressed as: Position and scale parameters CI and RI The changes are expressed as: Among them, ~1 means that the location parameter or scale parameter is a constant value. represents the low-frequency climate factor, represents the first reservoir index, represents the second reservoir index, The degrees of freedom are The cubic spline function of is the number of low-frequency climate factors in the corresponding model, Indicates location, Indicates scale.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method for determining a flood variation law according to any one of claims 1 to 6 is implemented.

9. A computer device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to perform the method for determining flood change patterns according to any one of claims 1 to 6.

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