A flood control risk analysis method and system for dispatching plans by coupling Gaussian and extreme value distribution

Abstract

The present invention belongs to the technical field related to flood control risk analysis, and discloses a method and system for analyzing flood control risks of scheduling plans coupled with Gaussian and extreme value distributions. The method comprises the following steps: determining the flood control objects in the basin to be processed and the evaluation index of each flood control object; calculating the risk assessment index threshold of each evaluation index under each risk level; calculating and obtaining the comprehensive risk value corresponding to each scheduling plan to be analyzed; constructing a cloud model, inputting the risk assessment threshold corresponding to different risk levels and the comprehensive risk value of each scheduling plan to be analyzed into the cloud model respectively, outputting the standard cloud map corresponding to different risk levels and the comprehensive risk assessment cloud map corresponding to each scheduling plan to be analyzed respectively, judging the similarity between the comprehensive risk assessment cloud map and the standard cloud map, and thereby determining the risk level of the scheduling plan to be analyzed. Through the present invention, the problem of insufficient accuracy in risk level analysis of the plan to be analyzed in flood control risk is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field related to flood control risk analysis, and more specifically, relates to a method and system for analyzing flood control risks of a scheduling plan coupled with Gaussian and extreme value distributions. Background Art

[0002] In recent years, with the continuous changes in the global climate and the increasing conflicts between man and nature, extreme climate events have occurred frequently, and the frequency and intensity of flood disasters have become increasingly severe. In particular, flood disasters caused by extreme rainfall are a kind of natural disaster with strong suddenness and serious harm.

[0003] Flood risk is usually affected by multiple random factors. According to the central limit theorem, when the number of these random factors is large enough, the overall distribution of flood risk can be approximated to a normal distribution. However, the probability distribution of floods usually shows asymmetry and thick tails. Flood events are often extreme, and the probability of a major flood event is higher than the probability predicted by the Gaussian distribution. Therefore, in order to overcome the fact that the distribution of flood risk does not completely conform to the Gaussian distribution, the Gaussian distribution and the extreme value distribution are coupled to construct a flood risk analysis model for scheduling plans, improve the accuracy of risk threshold division, better reflect the law of flood risk distribution, more accurately calculate the comprehensive risk value of the scheduling plan, and more systematically evaluate the risk level of the scheduling plan, so that the flood risk analysis is closer to reality. At present, there are still problems in flood risk assessment research that need to be solved, such as incomplete indicator system and insufficient accuracy in risk level threshold division. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides a method and system for analyzing flood control risks of scheduling plans by coupling Gaussian and extreme value distributions, which solves the problem of insufficient accuracy in risk level analysis of plans to be analyzed in flood control risks.

[0005] To achieve the above object, according to one aspect of the present invention, a method for analyzing flood control risks of a dispatch plan by coupling Gaussian and extreme value distributions is provided, the method comprising the following steps:

[0006] For the river basin to be processed, determine the flood control objects and evaluation indicators of each flood control object in the river basin to be processed; divide the risk levels, and calculate the risk assessment indicator threshold of each evaluation indicator under each risk level;

[0007] Using the evaluation index of each flood control object at the highest water level in the to-be-processed basin, a risk analysis model for calculating the comprehensive risk value is constructed, and the evaluation index of each flood control object at the highest water level in each dispatching plan to be analyzed is input into the risk analysis model, so as to calculate and obtain the comprehensive risk value corresponding to each dispatching plan to be analyzed;

[0008] A cloud model is constructed with risk value as input and risk assessment cloud map as output, and the risk assessment thresholds corresponding to different risk levels and the comprehensive risk value of each scheduling plan to be analyzed are respectively input into the cloud model, and standard cloud maps corresponding to different risk levels and comprehensive risk assessment cloud maps corresponding to each scheduling plan to be analyzed are respectively output, and the similarity between the comprehensive risk assessment cloud map of each scheduling plan to be analyzed and the standard cloud maps of different risk levels is judged, and the risk level corresponding to the most similar standard cloud map is the risk level corresponding to the scheduling plan to be analyzed.

[0009] Further preferably, the risk assessment indicator threshold is calculated according to the following steps:

[0010] The probability density function of the Gaussian distribution is constructed using the values ​​of each evaluation index at different times; the cumulative distribution function of the Gaussian distribution and its inverse function are obtained using the probability density function, so as to calculate the Gaussian index thresholds corresponding to the cumulative probabilities of different risk levels in the cumulative distribution function of the Gaussian distribution;

[0011] The probability density function of the extreme value distribution is constructed using the values ​​of each evaluation indicator at different times; the extreme value indicator thresholds corresponding to the cumulative probabilities of different risk levels in the cumulative distribution function of the extreme value distribution are calculated using the probability density function and the cumulative distribution function of the extreme value distribution;

[0012] The Gaussian index threshold and the extreme value index threshold are used to construct a relationship between risk assessment index thresholds, and the risk assessment index thresholds corresponding to each evaluation index are calculated using the relationship.

[0013] Further preferably, the relationship between the risk assessment index threshold is as follows:

[0014] x p =βn p +(1-β)g p

[0015] Among them, x p is the risk assessment index threshold when the cumulative probability is p, n p is the Gaussian index threshold when the cumulative probability is p, g p is the extreme value indicator threshold when the cumulative probability is p, and β is the method coefficient.

[0016] Further preferably, the relationship of the risk analysis model is as follows:

[0017]

[0018] Among them, r is the comprehensive risk value of the dispatch plan to be analyzed, ω i is the weight of the i-th flood control object, ω j is the weight of the j-th risk assessment indicator, xij is the value of the jth risk assessment index of the ith flood control object in the dispatch plan to be analyzed, x jmax is the maximum threshold of the j-th risk assessment indicator, x jmin is the minimum threshold of the jth risk assessment index, n is the total number of flood control objects, and m is the total number of risk assessment indicators.

[0019] Further preferably, the flood control objects include reservoirs, hydrological stations and flood storage and detention areas; the evaluation indicators of the reservoirs are water level, discharge flow, flood control storage capacity utilization and super-flood control high water level time; the evaluation indicators of the hydrological stations are station water level, super-warning duration and super-protection duration; the evaluation indicators of the flood storage and detention areas are flooded area, flooded depth, flooded duration and affected population.

[0020] Further preferably, the process of the cloud model outputting the risk assessment cloud map is as follows:

[0021] (1) Calculate the expected Ex, entropy En and super entropy He of the input risk value;

[0022] (2) Generate a network with En as the expectation and He 2 is the normal random number with variance En1;

[0023] (3) Generate a normal random number x with Ex as the expectation and En1 as the standard deviation;

[0024] (4) x is a cloud droplet, and the membership of the cloud droplet μ(x) is calculated;

[0025] (5) Repeat steps (2) to (4) to obtain N cloud droplets and the membership degrees corresponding to the N cloud droplets, and use the N cloud droplets and their corresponding membership degrees to draw a cloud map, which is the risk assessment cloud map of the input risk value.

[0026] Further preferably, when the input risk value is the risk assessment threshold corresponding to different risk levels, the relationship between the expected Ex, entropy En and super entropy He is calculated as follows:

[0027]

[0028]

[0029]

[0030]

[0031] He=0.1×En

[0032] Among them, Ex, En, and He are the expectation, entropy, and hyperentropy parameters of the standard cloud map of risk level k, respectively. k+1 , νk-1 are the upper and lower limits of the risk level k, respectively, i is the weight of the i-th flood control object, ω j is the weight of the j-th risk assessment indicator, x j,k+1 is the threshold value of the jth risk assessment index of the ith flood control object at risk level k+1, x j,k-1 is the threshold value of the jth risk assessment index of the ith flood control object at risk level k+1, x jmax is the maximum threshold of the j-th risk assessment indicator; x jmin is the minimum threshold of the jth risk assessment index, n is the total number of flood control objects, and m is the total number of risk assessment indicators.

[0033] Further preferably, when the input risk value is the comprehensive risk value of each scheduling plan to be analyzed, the relationship between the expected Ex, entropy En and super entropy He is calculated as follows:

[0034] Ex=r

[0035]

[0036]

[0037]

[0038] Among them, Ex, En, and He are the expectation, entropy, and super entropy parameters of the comprehensive risk assessment cloud map of the scheduling plan to be analyzed, r is the comprehensive risk value of the scheduling plan to be analyzed, ω j is the weight of the jth risk assessment indicator, m is the total number of risk assessment indicators, n is the total number of flood control objects, x ij is the value of the jth risk assessment index of the ith flood control object in the dispatch plan to be analyzed; jmax is the maximum threshold of the j-th risk assessment indicator; x jmin is the minimum threshold of the j-th risk assessment indicator, S 2 is the variance.

[0039] Further preferably, the relationship formula for calculating the membership of the cloud droplet is as follows:

[0040]

[0041] Among them, x is a normal random number generated during the operation of the cloud model, μ(x) is the membership degree of x, Ex is the expectation of the risk value of the input cloud model, and En is the entropy of the risk value of the input cloud model.

[0042] According to another aspect of the present invention, a system for risk assessment using the above-mentioned method for analyzing flood control risks of scheduling plans coupled with Gaussian and extreme value distributions is provided, the system comprising a data acquisition module, a risk assessment index threshold calculation module, a comprehensive risk value calculation module and a risk assessment cloud map module, wherein:

[0043] The data acquisition module is used to determine the flood control objects in the basin to be processed and the evaluation index of each flood control object, and to collect the evaluation indexes of different flood control objects at different times;

[0044] The risk assessment indicator threshold calculation module is used to calculate the risk assessment indicator threshold of each evaluation indicator;

[0045] The comprehensive risk value calculation module is used to calculate the comprehensive risk value corresponding to each scheduling plan to be analyzed;

[0046] The risk assessment cloud map module is used to draw a standard cloud map and a comprehensive risk assessment cloud map, and judge the similarity between the comprehensive risk assessment cloud map and the standard cloud map of each risk level, so as to determine the risk level corresponding to the comprehensive risk assessment cloud map.

[0047] In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:

[0048] 1. The flood control risk analysis method provided by the present invention performs risk analysis on multiple dispatching plans to be analyzed in the treated basin, determines flood control objects and evaluation indicators, calculates risk assessment indicator thresholds, constructs a dispatching plan flood control risk analysis model and draws a risk assessment cloud map, improves the accuracy of risk threshold division, better reflects the flood risk distribution law, more accurately calculates the comprehensive risk value of the dispatching plan, and more systematically evaluates the risk level of the dispatching plan, so that the flood control risk analysis is closer to reality, realizes the qualitative and quantitative system transformation of flood risks, and provides a feasible new method for flood control risk analysis;

[0049] 2. Compared with the traditional flood control risk analysis method, the flood control risk analysis method provided by the present invention constructs a multi-level evaluation index system for flood control risks of dispatching plans. According to different floods, different flood control object dispatching plans and the dangerous situations they occur, it does not only consider a single flood control object, but comprehensively considers flood control objects such as reservoirs, hydrological stations and flood storage areas, and determines the evaluation indicators in a more systematic and scientific manner. The indicators are quantitative and easy to obtain, and the values ​​of each indicator can be quickly determined for evaluating the flood control risks of dispatching plans;

[0050] 3. The cloud model of the risk assessment cloud map provided by the present invention takes the risk value as input, compares the comprehensive risk assessment cloud map of the dispatch plan with the risk level standard cloud map, and intuitively presents the risk level of the dispatch plan;

[0051] 4. The present invention couples Gaussian distribution and extreme value distribution to calculate the threshold of the evaluation index. The probability distribution of floods usually shows asymmetry and thick tails. Flood events are often extreme. The probability of a major flood event is higher than the probability predicted by Gaussian distribution. Extreme value distribution is a right-skewed probability distribution that can be used to fit various natural phenomena and actual data. It has the characteristic of maximum value stability, can reflect the serious harm of extreme value risk events, and describe extreme flood risk events. Therefore, in order to overcome the fact that the flood risk distribution does not fully conform to the Gaussian distribution, combined with the characteristics of extreme value distribution, the threshold of the evaluation index is calculated by coupling Gaussian distribution and extreme value distribution, so that flood risk analysis is closer to reality. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of a flood control risk analysis method for a dispatch plan coupled with Gaussian and extreme value distributions constructed according to a preferred embodiment of the present invention;

[0053] Figure 2 It is a multi-level evaluation index system diagram of flood control risk of dispatching plan constructed according to the preferred embodiment of the present invention;

[0054] Figure 3 It is a risk level standard cloud map constructed according to the preferred embodiment of the present invention;

[0055] Figure 4 It is a comprehensive risk assessment cloud map of flood prevention plans constructed according to the preferred embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0057] A flood control risk analysis method for dispatching plans coupled with Gaussian and extreme value distributions includes the following steps:

[0058] S1 determines the flood control objects and evaluation indicators of each flood control object in the river basin to be processed; divides the risk level, and calculates the risk assessment indicator threshold of each evaluation indicator under each risk level; the river basin to be processed includes multiple dispatching plans to be analyzed;

[0059] S2 uses the evaluation index of each flood control object at the highest water level of the to-be-processed basin to construct a risk analysis model for calculating the comprehensive risk value, and inputs the evaluation index of each flood control object at the highest water level in each scheduling plan to be analyzed into the risk analysis model, thereby calculating and obtaining the comprehensive risk value corresponding to each scheduling plan to be analyzed;

[0060] According to the risk level threshold and comprehensive risk value, S3 draws the risk level standard cloud map and the comprehensive risk assessment cloud map of the dispatch plan based on the cloud model, intuitively presenting the risk level of the dispatch plan.

[0061] In one embodiment of the present invention, a multi-level evaluation index system for flood control risk of dispatching plans is constructed based on different floods, dispatching plans for different flood control objects and the dangerous situations that occur. The multi-level evaluation index system for flood control risk of dispatching plans comprehensively considers reservoirs, hydrological stations and flood storage and detention areas, and uses water level, downstream flow, flood control storage capacity utilization, and super-flood control high water level time as evaluation indicators for reservoirs; uses station water level, super-warning duration, and super-protection duration as evaluation indicators for hydrological stations; and uses flooded area, flooded depth, flooded duration, and affected population as evaluation indicators for flood storage and detention areas.

[0062] In one embodiment of the present invention, the risk levels are divided, and the risk assessment indicator threshold of each evaluation indicator under each risk level is calculated. The basic steps are as follows:

[0063] (1) Based on the data obtained in the study area, the mean and standard deviation of the historical basic data are calculated, and the probability density function corresponding to the basic data is fitted according to the Gaussian distribution probability density function. The Gaussian distribution probability density function is as follows:

[0064]

[0065] Where f(x) is the Gaussian distribution probability density function, μ is the mean, σ is the standard deviation, and x is the indicator variable.

[0066] (2) The corresponding index threshold n when the output ratio is p is calculated by using the cumulative distribution function of Gaussian distribution and its inverse function p ;

[0067] (3) Based on the data obtained in the study area, an extreme value type I distribution (i.e., Gumbel distribution) is fitted. The probability density function of the Gumbel distribution is as follows:

[0068]

[0069] E=μ+γσ

[0070]

[0071] Where μ is the location parameter, σ is the scale parameter, γ is the Euler constant, γ≈0.57721, E is the data mean, V is the data variance, and x is the indicator variable.

[0072] (4) Calculate the corresponding index threshold g when the proportion is p based on the extreme value distribution probability density function and the cumulative distribution function p ;

[0073] (5) Calculate the flood risk assessment index threshold x p ,as follows:

[0074] x p =βn p +(1-β)g p

[0075] Where: x p is the flood risk assessment index threshold under the proportion p, n p is the index threshold with a proportion of p under Gaussian distribution, g p is the indicator threshold with a proportion of p under extreme value distribution, β is the method coefficient, and 0.5 is taken if the two methods are equally important.

[0076] In one embodiment of the present invention, for the evaluation index of each flood control object at the highest water level of the basin to be processed, the highest water level corresponding to different plans may be different. Due to the regulation of floods by reservoirs (water storage, water discharge, etc.), the highest water level of the reservoir, the highest water level of the hydrological station, etc., may be different due to different dispatching plans.

[0077] (1) Each evaluation index x corresponding to the moment is standardized to eliminate the influence of different dimensions, as follows:

[0078]

[0079] Where n ij is the quantitative value after standardization of the indicator; x ij is the original value of the jth evaluation index of the i-th flood control object before standardization; x jmax is the maximum threshold of the evaluation index j; x jmin is the minimum threshold of the evaluation index j.

[0080] (2) Assign weights to each indicator. The weights of each indicator can be calculated and determined by subjective weighting method, objective weighting method or combined weighting method. The comprehensive risk value of each flood control object is obtained by weighting as follows:

[0081]

[0082] In the formula, m i is the comprehensive risk value of flood control object i, ω jis the weight of the jth indicator, n ij It is the quantitative value after standardization of the indicator.

[0083] (3) According to the comprehensive risk value of each flood control object and the weight assigned to each flood control object, the weight of each flood control object can be calculated and determined by subjective weighting method, objective weighting method or combined weighting method to calculate the comprehensive risk value of the dispatch plan as follows:

[0084]

[0085] In the formula, r is the comprehensive risk value of the dispatch plan, m i is the comprehensive risk value of flood control object i, ω i is the weight of the i-th flood control object.

[0086] Based on the multi-level evaluation index system for flood control risks of dispatching plans and the flood control risk analysis model of dispatching plans, the risk level threshold of each indicator and the comprehensive risk value of the dispatching plan are calculated with the flood process and the rehearsal process as input.

[0087] In one embodiment of the present invention, according to the risk level threshold and the comprehensive risk value, a risk level standard cloud map and a comprehensive risk assessment cloud map of the scheduling plan are drawn based on the cloud model to intuitively present the risk level of the scheduling plan. The basic steps are as follows:

[0088] (1) Based on the cloud model, the thresholds of each risk level are used as data input to calculate the Ex, En, and He of the standard cloud of each risk level as follows:

[0089]

[0090]

[0091]

[0092]

[0093] He=0.1×En

[0094] Among them, Ex, En, and He are the expectation, entropy, and hyperentropy parameters of the standard cloud map of risk level k, respectively. k+1 , ν k-1 are the upper and lower limits of the risk level k, respectively, i is the weight of the i-th flood control object, ω j is the weight of the j-th risk assessment indicator, x j,k+1 is the threshold value of the jth risk assessment index of the ith flood control object at risk level k+1, x j,k-1 is the threshold value of the jth risk assessment index of the ith flood control object at risk level k+1, xjmax is the maximum threshold of the j-th risk assessment indicator; x jmin is the minimum threshold of the jth risk assessment index, n is the total number of flood control objects, and m is the total number of risk assessment indicators.

[0095] (2) Generate a network with En as the expectation and He 2 is the normal random number with variance En1;

[0096] (3) Generate a graph with Ex as the expectation and En as the i1 is a normal random number x with standard deviation;

[0097] (4) x is a cloud droplet, and the membership of the cloud droplet μ(x) is calculated as follows:

[0098]

[0099] (5) Repeat the above steps (2) to (4) N times to generate N cloud droplets, which form a cloud map, thereby obtaining a standard cloud map of risk level i;

[0100] (6) Repeat the above steps (1) to (5) to draw all risk level standard cloud maps in one coordinate system;

[0101] (7) Based on the cloud model, the risk value m of each flood control object in the dispatch plan is i As data input, the Ex, En, and He of the comprehensive risk assessment cloud of the dispatch plan are obtained as follows:

[0102] Ex=r

[0103]

[0104]

[0105]

[0106] Among them, Ex, En, and He are the expectation, entropy, and super entropy parameters of the comprehensive risk assessment cloud map of the scheduling plan to be analyzed, r is the comprehensive risk value of the scheduling plan to be analyzed, ω j is the weight of the jth risk assessment indicator, m is the total number of risk assessment indicators, n is the total number of flood control objects, x ij is the value of the jth risk assessment index of the ith flood control object in the dispatch plan to be analyzed; jmax is the maximum threshold of the j-th risk assessment indicator; x jmin is the minimum threshold of the j-th risk assessment indicator, S 2 is the variance.

[0107] (8) Taking Ex, En, and He of the dispatch plan as data input, on the basis of the risk level standard cloud map, repeat the above steps (2) to (4) N times to generate N cloud droplets. The N cloud droplets form a cloud map, thereby obtaining a comprehensive risk assessment cloud map of the dispatch plan.

[0108] (9) Compare the comprehensive risk assessment cloud map of the dispatch plan with the risk level standard cloud map to intuitively present the risk level of the dispatch plan. Specifically: Compare the comprehensive risk assessment cloud map of the dispatch plan with the standard cloud maps of each risk level. If the comprehensive risk assessment cloud map of the dispatch plan is closer to the risk level k standard cloud map, the risk level of the dispatch plan is determined to be level k.

[0109] The present invention will be further described below with reference to specific embodiments.

[0110] The following takes the Yangtze River mainstream section from the Three Gorges to Chenglingji in the Yangtze River Basin in 2020 as an example to illustrate the method provided by the present invention, which specifically includes the following steps:

[0111] (1) Based on the data obtained in the study area, according to the scheduling plans of different floods, different flood control objects and possible dangerous situations, and following the principles of objectivity, scientificity, and availability, a multi-level evaluation index system for flood control risks of scheduling plans is constructed, such as Figure 2 As shown in the figure, the multi-level evaluation index system of flood control risk in the dispatching plan comprehensively considers three objects: reservoirs, hydrological stations and flood storage and detention areas. The water level, discharge flow, flood storage capacity utilization and time exceeding the flood control high water level are used as evaluation indicators for reservoirs; the water level of the station, duration of exceeding the warning level and duration exceeding the protection level are used as evaluation indicators for hydrological stations; the flooded area, depth of flooding, duration of flooding and population affected are used as evaluation indicators for flood storage and detention areas. The reservoirs include one reservoir, including the Three Gorges Reservoir; the hydrological stations include five hydrological stations, including Yichang, Zhicheng, Shashi, Jianli and Lianhuatang; the flood storage and detention areas include 31 flood storage and detention areas, including the Jingjiang flood diversion area, Qianlianghu embankment, Gongshuangcha embankment and Datonghu East embankment.

[0112] (2) Based on the multi-level evaluation index system of flood control risk of dispatching plans, a flood control risk analysis model of dispatching plans that couples Gaussian and extreme value distributions is constructed.

[0113] (3) Based on the multi-level evaluation index system of flood control risk of dispatching plan and the flood control risk analysis model of dispatching plan, with the flood process and the rehearsal process as input, the risk level threshold of each indicator and the comprehensive risk value of the dispatching plan are calculated. Specifically:

[0114] (3.1) Risks are divided into four risk levels in advance: low risk (level IV), medium risk (level III), high risk (level II), and extremely high risk (level I). Based on the 1 standard deviation, 2 standard deviations, and 3 standard deviations of the normal distribution and the 95% confidence level principle, the percentages of level IV, level III, level II, and level I are predetermined to be 2.3%, 22%, 84%, and 95%, respectively;

[0115] (3.2) Based on the multi-level evaluation index system of flood control risks in the dispatching plan, the actual data set of each indicator in the study area in 2020 was collected, the mean and standard deviation of the data set were calculated, and the basic data were fitted into a Gaussian distribution;

[0116] (3.2) The corresponding index threshold n when the output proportion is p (2.3%, 22%, 84%, 95% respectively) is calculated by the cumulative distribution function of Gaussian distribution and its inverse function p ;

[0117] (3.3) Based on the data obtained in the study area, the maximum value data set of daily indicator data in 2020 is fitted into an extreme value type I distribution;

[0118] (3.4) According to the extreme value distribution probability density function and the cumulative distribution function, the corresponding index threshold g when the proportion is p (2.3%, 22%, 84%, 95% respectively) is calculated. p ;

[0119] (3.5) Taking the method coefficient β as 0.6, calculate the flood risk assessment index threshold x p The water level index thresholds of each hydrological station are shown in Table 1. The water level index thresholds of each hydrological station are close to the characteristic water levels such as the defense water level, warning water level and guaranteed water level of the corresponding hydrological station. This shows that the risk index threshold division is more accurate, which improves the accuracy of risk threshold division. The risk index threshold division results are consistent with the actual project and better reflect the flood risk distribution law.

[0120] Table 1 Calculation results of water level thresholds at hydrological stations (unit: m)

[0121]

[0122] (3.6) Combined with the threshold values ​​of each indicator, the indicator data x is standardized to eliminate the influence of different dimensions.

[0123] (3.7) The weight of each indicator is determined by the subjective weighting method, and the comprehensive risk value of each object is obtained by combining the weights of each indicator, as shown in Table 2.

[0124] Table 2 Calculation results of comprehensive risk values ​​for each flood control object

[0125]

[0126] (3.8) The comprehensive risk value of each object is determined by the subjective weighting method, and the comprehensive risk value of the scheduling plan is calculated by combining the weights assigned to each object, which is 0.3805.

[0127] (4) According to the risk level threshold and the comprehensive risk value, a risk level standard cloud map and a comprehensive risk assessment cloud map of the dispatch plan are drawn based on the cloud model to intuitively present the risk level of the dispatch plan. Specifically:

[0128] (4.1) Based on the cloud model, the thresholds of each indicator under risk level IV are used as data input to calculate Ex, En, and He of the standard cloud at risk level IV;

[0129] (4.2) Generate a network with En as the expectation and He 2 is the normal random number with variance En1;

[0130] (4.3) Generate a normal random number x with Ex as expectation and En1 as standard deviation;

[0131] (4.4) x is a cloud droplet, calculate the membership of the cloud droplet μ(x);

[0132] (4.5) Repeat the above steps (2) to (4) N times to generate N cloud droplets, which form a cloud map, thereby obtaining a standard cloud map of risk level IV;

[0133] (4.6) Repeat the above steps (1) to (5) three times to draw the standard cloud diagrams of risk levels III, II, and I respectively. All risk level standard cloud diagrams are drawn in one coordinate system, such as Figure 3 As shown, Figure 3 In the figure, the standard cloud charts from left to right represent risk levels IV, III, II, and I respectively;

[0134] (4.7) Based on the cloud model, the risk value m of each object in the scheduling plan i As data input, the Ex, En, and He of the comprehensive risk assessment cloud of the dispatch plan are obtained;

[0135] (4.8) Taking Ex, En, and He of the dispatch plan as data input, on the basis of the risk level standard cloud map, repeat the above steps (2) to (4) N times to generate N cloud drops, and N cloud drops form a cloud map, thereby obtaining a comprehensive risk assessment cloud map of the dispatch plan, as shown in Figure 4 shown.

[0136] (4.9) Compare the comprehensive risk assessment cloud map of the dispatch plan with the risk level standard cloud map to intuitively present the risk level of the dispatch plan. Specifically:

[0137] like Figure 4 In the figure, the comprehensive risk assessment cloud map of the dispatching plan is compared with the standard cloud maps of each risk level. It is easy to see that the comprehensive risk assessment cloud map of the dispatching plan is close to the standard cloud map of risk level III, so the risk level of the dispatching plan is level III.

[0138] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A flood control risk analysis method for dispatching plans by coupling Gaussian and extreme value distributions, characterized in that: The method comprises the following steps: For the river basin to be processed, determine the flood control objects and evaluation indicators of each flood control object in the river basin to be processed; divide the risk levels, and calculate the risk assessment indicator threshold of each evaluation indicator under each risk level; Using the evaluation index of each flood control object at the highest water level in the to-be-processed basin, a risk analysis model for calculating the comprehensive risk value is constructed, and the evaluation index of each flood control object at the highest water level in each dispatching plan to be analyzed is input into the risk analysis model, so as to calculate and obtain the comprehensive risk value corresponding to each dispatching plan to be analyzed; Construct a cloud model with risk value as input and risk assessment cloud map as output, input the risk assessment thresholds corresponding to different risk levels and the comprehensive risk value of each scheduling plan to be analyzed into the cloud model respectively, output the standard cloud maps corresponding to different risk levels and the comprehensive risk assessment cloud map corresponding to each scheduling plan to be analyzed respectively, judge the similarity between the comprehensive risk assessment cloud map of each scheduling plan to be analyzed and the standard cloud maps of different risk levels, and the risk level corresponding to the most similar standard cloud map is the risk level corresponding to the scheduling plan to be analyzed; The risk assessment indicator threshold is calculated according to the following steps: The probability density function of the Gaussian distribution is constructed using the values ​​of each evaluation index at different times; the cumulative distribution function of the Gaussian distribution and its inverse function are obtained using the probability density function, so as to calculate the Gaussian index thresholds corresponding to the cumulative probabilities of different risk levels in the cumulative distribution function of the Gaussian distribution; The probability density function of the extreme value distribution is constructed using the values ​​of each evaluation indicator at different times; the extreme value indicator thresholds corresponding to the cumulative probabilities of different risk levels in the cumulative distribution function of the extreme value distribution are calculated using the probability density function and the cumulative distribution function of the extreme value distribution; Using the Gaussian index threshold and the extreme value index threshold, a relationship between the risk assessment index threshold is constructed, and the risk assessment index threshold corresponding to each evaluation index is calculated using the relationship; The relationship between the risk assessment index threshold is as follows: x p =βn p +(1-β)g p Among them, x p is the risk assessment index threshold when the cumulative probability is p, n p is the Gaussian index threshold when the cumulative probability is p, g p is the extreme value indicator threshold when the cumulative probability is p, and β is the method coefficient; The relationship of the risk analysis model is as follows: Among them, r is the comprehensive risk value of the dispatch plan to be analyzed, ω i is the weight of the i-th flood control object, ω j is the weight of the j-th risk assessment indicator, x ij is the value of the jth risk assessment index of the ith flood control object in the dispatch plan to be analyzed, x jmax is the maximum threshold of the j-th risk assessment indicator, x jmin is the minimum threshold of the jth risk assessment index, n is the total number of flood control objects, and m is the total number of risk assessment indicators.

2. A method for analyzing flood risk of scheduling plans by coupling Gaussian and extreme value distributions according to claim 1, characterized in that: The flood control objects include reservoirs, hydrological stations and flood storage and detention areas; the evaluation indicators of the reservoirs are water level, discharge flow, flood control storage capacity utilization and time exceeding flood control high water level; the evaluation indicators of the hydrological stations are station water level, duration of exceeding warning level and duration of exceeding protection level; the evaluation indicators of the flood storage and detention areas are flooded area, flooded depth, flooded duration and affected population.

3. The method for analyzing flood risk of scheduling plans by coupling Gaussian and extreme value distributions according to claim 1, characterized in that: The process of outputting the risk assessment cloud map by the cloud model is as follows: (1) Calculate the expected Ex, entropy En and super entropy He of the input risk value; (2) Generate a network with En as the expectation and He 2 is the normal random number with variance En1; (3) Generate a normal random number x with Ex as the expectation and En1 as the standard deviation; (4) x is a cloud droplet, and the membership of the cloud droplet μ(x) is calculated; (5) Repeat steps (2) to (4) to obtain N cloud droplets and the membership degrees corresponding to the N cloud droplets, and use the N cloud droplets and their corresponding membership degrees to draw a cloud map, which is the risk assessment cloud map of the input risk value.

4. The method for analyzing flood risk of scheduling plans by coupling Gaussian and extreme value distributions according to claim 1, characterized in that: When the input risk value is the risk assessment threshold corresponding to different risk levels, the relationship between the expected Ex, entropy En and super entropy He of the input risk value is calculated as follows: He=0.1×En Among them, Ex, En, and He are the expectation, entropy, and hyperentropy parameters of the standard cloud map when the risk level k is used as the input risk value, respectively, and ν k+1 , ν k-1 are the upper and lower limits of the risk level k, respectively, i is the weight of the i-th flood control object, ω j is the weight of the j-th risk assessment indicator, x j,k+1 is the threshold value of the jth risk assessment index of the ith flood control object at risk level k+1, x j,k-1 is the threshold value of the jth risk assessment index of the ith flood control object at risk level k+1, x jmax is the maximum threshold of the j-th risk assessment indicator; x jmin is the minimum threshold of the jth risk assessment index, n is the total number of flood control objects, and m is the total number of risk assessment indicators.

5. The method for analyzing flood risk of scheduling plans by coupling Gaussian and extreme value distributions according to claim 1, characterized in that: When the input risk value is the comprehensive risk value of each scheduling plan to be analyzed, the relationship between the expected Ex, entropy En and super entropy He of the input risk value is calculated as follows: Ex=r Among them, Ex, En, and He are the expectation, entropy, and super entropy parameters of the evaluation cloud map when the comprehensive risk value of the scheduling plan to be analyzed is used as the input risk value, r is the comprehensive risk value of the scheduling plan to be analyzed, ω j is the weight of the jth risk assessment indicator, m is the total number of risk assessment indicators, n is the total number of flood control objects, x ij is the value of the jth risk assessment index of the ith flood control object in the dispatch plan to be analyzed; jmax is the maximum threshold of the j-th risk assessment indicator; x jmin is the minimum threshold of the j-th risk assessment indicator, S 2 is the variance.

6. A method for analyzing flood risk of scheduling plans by coupling Gaussian and extreme value distributions as claimed in claim 3, characterized in that: The relationship for calculating the membership of the cloud droplet is as follows: Among them, x is a normal random number generated during the operation of the cloud model, μ(x) is the membership degree of x, Ex is the expectation of the risk value of the input cloud model, and En is the entropy of the risk value of the input cloud model.

7. A system for risk assessment using the flood control risk analysis method for scheduling plans coupled with Gaussian and extreme value distributions as described in any one of claims 1 to 6, characterized in that: The system includes a data acquisition module, a risk assessment index threshold calculation module, a comprehensive risk value calculation module and a risk assessment cloud map module, among which: The data acquisition module is used to determine the flood control objects in the basin to be processed and the evaluation index of each flood control object, and to collect the evaluation indexes of different flood control objects at different times; The risk assessment indicator threshold calculation module is used to calculate the risk assessment indicator threshold of each evaluation indicator; The comprehensive risk value calculation module is used to calculate the comprehensive risk value corresponding to each scheduling plan to be analyzed; The risk assessment cloud map module is used to draw a standard cloud map and a comprehensive risk assessment cloud map, and judge the similarity between the comprehensive risk assessment cloud map and the standard cloud map of each risk level, so as to determine the risk level corresponding to the comprehensive risk assessment cloud map.

Images