Optimization design method and optimization design device for joint recurrence period

Abstract

The invention provides an optimization design method and an optimization design device for a joint return period. The optimization design method comprises the steps that a multi-dimensional environment variable data set of a target area is acquired, and a to-be-solved joint return period is determined; determining an extreme value of a marginal distribution variable corresponding to a first set in the multi-dimensional environment variable data set in the joint recurrence period; sorting the first set and dividing the sorted first set into an extreme part and a non-extreme part based on the extreme value of the marginal distribution variable; probability distribution fitting is carried out on the extreme part and the non-extreme part to obtain a first fitting result of the first set, and evaluation of a first environment isoline evaluation index is carried out on the first fitting result; and in response to the fact that the evaluation result of the first fitting result does not reach the standard, optimizing the first fitting result to obtain a first fitting optimization result of the first set. According to the optimization design method disclosed by the embodiment of the invention, the marginal distribution fitting precision of the multi-dimensional environment variable data can be improved.

Description

Technical Field

[0001] The present application relates to the field of marine engineering and marine environment, and more specifically, to an optimization design method and an optimization design device for a joint recurrence period. Background Art

[0002] Derivation of the joint return period is crucial for marine engineering, water resources management, urban planning and infrastructure design. It enables engineers to accurately respond to situations where multiple environmental variables reach extreme values ​​simultaneously, such as storm surges and waves. This is essential to ensure the safety and functionality of offshore platforms, seawalls, flood control measures and other critical structures during extreme climate events, thereby protecting people's lives and property and significantly reducing economic losses and casualties caused by disasters.

[0003] The Environmental Contour Method (ECM) has become an effective tool for predicting such extreme ocean conditions. This method is recommended by the International Electrotechnical Commission (IEC) and predicts possible extreme conditions in the future by analyzing limited historical meteorological and oceanographic data. Compared with traditional extreme value analysis based on a single parameter, ECM analyzes the relationship between various environmental parameters in more detail and constructs multidimensional spatial contours that can reflect the same return period. These contours can more accurately represent the joint probability of wind speed and wave height, thereby fully capturing the extreme conditions that may affect structural design and safety.

[0004] The environmental contour method derived by the traditional IEC method has the following shortcomings:

[0005] When fitting marginal distributions, the commonly used single fitting function is difficult to effectively capture the distribution characteristics of data under extreme conditions. This method often leads to large deviations in results when extrapolating the marginal distribution function to a specific return period, thus affecting the overall accuracy. Summary of the invention

[0006] One of the purposes of the present disclosure is to provide an optimization design method that can effectively capture the distribution characteristics of multi-dimensional environmental variable data under extreme conditions.

[0007] According to a first aspect of the present disclosure, there is provided an optimization design method for a joint recurrence period, the optimization design method comprising: obtaining a multidimensional environmental variable data set of a target area and determining a joint recurrence period to be solved; determining a marginal distribution variable corresponding to a first set in the multidimensional environmental variable data set under the joint recurrence period; sorting the first set and dividing the sorted first set into an extreme part and a non-extreme part based on the marginal distribution variable; performing probability distribution fitting on the extreme part and the non-extreme part, respectively, to obtain a first fitting result of the first set, and evaluating the first fitting result with a first environmental contour line evaluation index; in response to the evaluation result of the first fitting result not being up to standard, optimizing the first fitting result to obtain a first fitting optimization result of the first set.

[0008] According to an embodiment of the present disclosure, optimizing the first fitting result to obtain the first set of first fitting optimization results may include: determining the first fitting optimization result based on a connection point between the fitting results of the extreme part and the fitting results of the extreme part.

[0009] According to an embodiment of the present disclosure, the step of determining a first fitting optimization result based on the fitting result of the extreme part and the connection point of the fitting result of the extreme part may include: fitting the non-extreme part to obtain the non-extreme part fitting result, and fitting the extreme part to obtain the extreme part fitting result; determining the connection point of the non-extreme part fitting result and the extreme part fitting result; determining the first fitting optimization result of the first set based on the non-extreme part fitting result, the extreme part fitting result and the connection point.

[0010] According to an embodiment of the present disclosure, the step of determining the first fitting optimization result of the first set based on the non-extreme part fitting results, the extreme part fitting results and the connection point may include: taking the combination of the extreme part fitting results before the connection point and the extreme part fitting results after the connection point as the first fitting optimization result.

[0011] According to an embodiment of the present disclosure, the connection point may be a point where the extreme part fitting goodness is the highest among the intersections of the non-extreme part fitting result and the extreme part fitting result.

[0012] According to an embodiment of the present disclosure, the step of fitting the extreme part to obtain the extreme part fitting result may include: using lognormal distribution as the fitting model and fitting the extreme part with weighted least squares method, and the weight of the weighted least squares method may be determined by a genetic algorithm.

[0013] According to an embodiment of the present disclosure, the optimization design method may also include: dividing the sorted first set into multiple segments, and obtaining the conditional probability distribution of the second set in the multidimensional environmental variable data set in each segment; solving the first environmental contour line results of the marginal distribution variables corresponding to the first set and the second variables corresponding to the second set under the joint recurrence period based on the conditional probability distribution and the first fitting optimization result.

[0014] According to an embodiment of the present disclosure, the optimization design method may further include: performing a second environment contour line index evaluation on the first environment contour line result; in response to the second environment contour line index evaluation meeting the standard, using the first environment contour line result as the final environment contour line result.

[0015] According to an embodiment of the present disclosure, the first environmental contour line evaluation index may include at least one of the following indexes: the determination coefficient corresponding to the extreme part, the root mean square error corresponding to the extreme part, and the extreme value envelope rate of the environmental contour line; the second environmental contour line evaluation index may include the relative error between the maximum value of the marginal distribution variable based on the extrapolation of the environmental contour line and the extreme value obtained by the EVA method.

[0016] According to an embodiment of the present disclosure, the step of dividing the sorted first set into extreme parts and non-extreme parts based on marginal distribution variables may include: calculating the cumulative exceedance probability based on the marginal distribution variables, and dividing the first set into extreme parts and non-extreme parts according to the cumulative exceedance probability.

[0017] According to an embodiment of the present disclosure, the multidimensional environmental variable data set may be a multidimensional ocean environmental variable data set, and may include a significant wave height data set and a spectrum peak period data set, and the joint recurrence period may be a two-dimensional variable joint recurrence period.

[0018] According to a second embodiment of the present disclosure, there is provided an optimization design device for a joint recurrence period, the optimization design device comprising: a first calculation unit, which obtains a multidimensional environmental variable data set of a target area and determines a joint recurrence period to be solved; a second calculation unit, which determines a marginal distribution variable corresponding to a first set in the multidimensional environmental variable data set under the joint recurrence period; an extreme event separation unit, which sorts the first set and divides the sorted first set into an extreme part and a non-extreme part based on the marginal distribution variable; a fitting unit, which performs probability distribution fitting on the extreme part and the non-extreme part, respectively, to obtain a first fitting result of the first set, and evaluates the first fitting result according to a first environmental contour evaluation index; an optimization unit, which optimizes the first fitting result in response to the evaluation result of the first fitting result not being up to standard, so as to obtain a first fitting optimization result of the first set.

[0019] According to a third aspect of the present disclosure, a computing device is provided, comprising a memory and a processor, wherein the memory stores instructions or programs, and when the instructions or programs are executed by the processor, the processor is prompted to execute the above-mentioned optimization design method.

[0020] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is prompted to execute the above-mentioned optimization design method.

[0021] According to the optimization design method of the embodiment of the present disclosure, the accuracy of marginal distribution fitting of multidimensional environmental variable data can be improved by combining connection points for marginal distribution fitting, and the result deviation can be effectively reduced.

[0022] The optimization design method according to the embodiment of the present disclosure can enhance the objective evaluation capability of environmental contour results by evaluating the contour results using preset rating indicators, thereby improving accuracy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart showing an optimization design method for joint return periods according to a first embodiment of the present disclosure.

[0024] Figure 2 is a flow chart illustrating an optimization design method for joint return periods according to a second embodiment of the present disclosure.

[0025] Figure 3 is a flowchart showing specific optimization steps of a first fitting optimization result according to an embodiment of the present disclosure.

[0026] Figure 4 is a flow chart illustrating an overall optimization design method according to an embodiment of the present disclosure.

[0027] Figure 5 It is the hindcast result of hourly significant wave height and spectral peak period based on numerical simulation of ocean mode of offshore wind farm according to an embodiment of the present disclosure.

[0028] Figure 6 It is the comparison result between the probability density obtained by different fitting models and fitting methods and the probability density obtained by numerical simulation of ocean mode.

[0029] Figure 7 It is the comparison result between the probability density obtained based on the non-extreme part and the extreme part and the probability density obtained by numerical simulation of the ocean model.

[0030] Figure 8 Graph showing the comparison results between the probability density obtained by using the three-parameter Weibull distribution and the method of the present disclosure and the probability density obtained by model numerical simulation.

[0031] Fig. 9 2 is a diagram showing the environmental contour lines obtained by the optimization design method according to an embodiment of the present disclosure and the environmental contour lines obtained by the IEC method.

[0032] Fig.10 is a block diagram showing an optimization design apparatus for joint return period according to the present disclosure. DETAILED DESCRIPTION

[0033] The following detailed description is provided to help gain a comprehensive understanding of the methods, devices and / or systems described herein. However, the order of operations described herein is only an example and is not limited to those orders set forth herein, but may be equivalently replaced or changed except for operations that must occur or be performed in a specific order. In addition, for greater clarity and simplicity, the description of content known in the art will be omitted or simplified.

[0034] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by a person of ordinary skill in the art to which the present disclosure belongs after understanding the present disclosure. Unless explicitly defined as such herein, terms (such as those defined in a general dictionary) should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal manner.

[0035] Unless otherwise specified, the same reference numerals generally refer to the same elements (e.g., components, steps, and methods). Reference numerals described in the previous embodiments may be omitted if they appear again in the subsequent embodiments. In addition, the technical features described in different or the same embodiments may be combined in any manner, as long as the combined embodiments or technical solutions are complete and can solve the technical problems of the present application or achieve the technical effects described or not described in the present disclosure but can be determined based on the above complete technical solutions. The terms used in the present disclosure are explained below.

[0036] Environmental contour line: also called environmental outline, is a probability contour line drawn in a multidimensional parameter space where one or more environmental variables (such as wind speed, wave height, flow velocity, wave period, etc.) reach or exceed a certain threshold.

[0037] EVA: That is, the traditional extreme value statistical method, which is a statistical method used to evaluate and model rare events, especially extreme events in meteorology, hydrology, engineering, financial risk management and other fields. EVA focuses on understanding very rare extreme data points that deviate greatly from the average level, rather than the central trend of the distribution. Extreme value analysis first requires selecting samples from the time series data, then fitting the samples to obtain the optimal extreme value distribution function, and then analyzing the extreme values ​​under different recurrence periods based on this function.

[0038] For the probability of occurrence of a specific event, the extreme value analysis uses empirical probability calculation, as shown in the following formula (1):

[0039]

[0040] Where n is the total number of extreme value samples, m is the sample number arranged in descending order, and the value range of m is an integer from 1 to n. The observed extreme value sequence is fitted to the theoretical extreme value probability distribution function to establish the extreme value model.

[0041] R 2 : Coefficient of determination is a statistical indicator used in regression analysis to measure the degree of fit between the original sample and the fitted sample. Its calculation formula is: Where n is the number of samples, y i and Represent the cumulative probability density of the original sample and the fitted distribution function, respectively. Represents the mean of the original sample cumulative probability density.

[0042] RMSE: Root mean square error, a standard measure of the difference between the original sample and the fitted sample. Its calculation formula is The symbols in the formula have the same meaning as above.

[0043] ELR: Envelope rate, used to measure the degree of envelopment of the environmental contour line of a specific return period to the distance between data points within the return period. Its calculation formula is: Where ∑ n and∑ N They represent the number of data points enclosed by the environmental contour line and the total number of data points respectively.

[0044] RE: Relative error, used to determine the degree of deviation between the extreme value derived from the environmental contour line of a specific return period and the extreme value derived from the traditional extreme value statistical method. The calculation formula is: Where Y EC and Y EVA They represent the extreme values ​​of the extrapolated environmental contours and the extreme values ​​of the extrapolated EVA method respectively.

[0045] Environmental contour method: Based on the inverse first-order reliability method (IFORM), environmental probability contours are defined in the environmental parameter space according to the relevant probability distribution. Short-term load and response calculations are performed along the contours for limited critical sea conditions, and then the specific return period response of the structure is estimated by selecting appropriate short-term response quantiles, which can significantly reduce simulation time and cost. Its solution process meets the requirements of IEC 61400-3-1-2019.

[0046] The optimization design method of the present disclosure can be executed or implemented by various computing devices such as computers.

[0047] In view of the above-mentioned deficiencies in the prior art, the present disclosure provides an optimization design method for a joint return period. In addition, the optimization design method of the present disclosure can also accurately estimate the joint return period of two-dimensional variables based on environmental contours, can optimize the marginal distribution fitting function, and use the IFORM principle and Rosenblatt transformation to obtain the environmental contour results of the specified joint return period, and evaluate the results through the constructed uncertainty evaluation scheme, thereby achieving accurate output of environmental conditions for the specified joint return period. The following is a detailed description in conjunction with the drawings of the present disclosure.

[0048] Figure 1 is a flow chart showing a method for optimizing the joint return period according to a first embodiment of the present disclosure, Figure 2 is a flow chart showing a method for optimizing the joint return period according to a second embodiment of the present disclosure, Figure 3 is a flowchart showing specific optimization steps of a first fitting optimization result according to an embodiment of the present disclosure, Figure 4 is a flow chart showing an overall optimization design method according to an embodiment of the present disclosure, Figure 5 is the hourly significant wave height and spectrum peak period hindcast result based on the numerical simulation of the ocean mode of the offshore wind farm according to the embodiment of the present disclosure, Figure 6 is the comparison result between the probability density obtained by different fitting models and fitting methods and the probability density obtained by numerical simulation of ocean models. Figure 7 It is the comparison result of the probability density obtained based on the non-extreme part and the extreme part and the probability density obtained by numerical simulation of the ocean model. Figure 8 : is a comparison result showing the probability density obtained by using the three-parameter Weibull distribution and the method disclosed in the present invention and the probability density obtained by the numerical simulation of the model, Fig. 9 2 is a diagram showing the environmental contour lines obtained by the optimization design method according to an embodiment of the present disclosure and the environmental contour lines obtained by the IEC method.

[0049] Reference Figure 1According to an embodiment of the present disclosure, the optimization design method of the joint return period may include step S110, step S120, step S130, step S140 and step S150.

[0050] In step S110 , a multidimensional environmental variable data set of a target area is obtained and a joint recurrence period to be solved is determined.

[0051] The multidimensional environmental variable data set here can be a multidimensional marine environmental variable data set, for example, it can include a significant wave height data set and a spectrum peak period data set. Based on this, the joint recurrence period can be a two-dimensional variable joint recurrence period, that is, the multidimensional marine environmental variable includes both the significant wave height and the spectrum peak period. However, this is only an example, and the multidimensional environmental variable data set can also include wind speed, current speed, etc. The present disclosure only describes the significant wave height and the spectrum peak period as examples.

[0052] The multidimensional environmental variable data set here can be obtained from a third party such as an ocean data center, or can be obtained through an ocean observation station equipped with various measuring instruments. For example, multidimensional environmental variable data for nearly 40 years can be obtained and the joint return period to be solved is determined to be 50 years. However, this is only an example.

[0053] In step S120, the marginal distribution variables corresponding to the first set in the multidimensional environmental variable data set under the joint return period are determined.

[0054] Here, the marginal distribution variable is the significant wave height and the first set is the significant wave height data set. However, this is only an example, and other variables such as wind speed can also be used as marginal distribution variables.

[0055] The significant wave height corresponding to the significant wave height data set in the two-dimensional environmental variable data set under the joint return period can be determined as a marginal distribution variable. In addition, the extreme value of the significant wave height under the joint return period can be deduced based on the existing EVA method. The extreme value of the marginal distribution variable extrapolated by the EVA method here can be used for the evaluation of subsequent evaluation indicators. Specifically, according to the characteristics of the significant wave height data, a suitable probability distribution model can be selected to fit the data, and after obtaining the optimal extreme value distribution function, the extreme value under the return period can be obtained based on the function.

[0056] Reference Figure 5 , Figure 5 The long series of hindcast data of an offshore wind farm through ocean model numerical simulation is shown. The results include 44 years of ocean hydrological environmental parameters such as wind speed, significant wave height (Hs) and spectral peak period (Tp) with an interval of 1h. The joint recurrence period to be solved is determined to be once in 50 years. Figure 5The 50-year Hs obtained by the EVA method is 11.23m, and the environmental contour line category is the Hs-Tp environmental contour line.

[0057] In step S130 , the first set is sorted and the sorted first set is divided into an extreme part and a non-extreme part based on the marginal distribution variable.

[0058] As an example, the significant wave height data set as the first set can be sorted from large to small, and then the cumulative exceedance probability of each Hs as the marginal distribution variable is calculated, and the Hs with a cumulative exceedance probability of 99.8% is determined to be 5.4m. That is to say, the part with a cumulative exceedance probability greater than 99.8% is defined as an extreme part or an extreme event, and the part that does not exceed 99.8% is defined as a non-extreme part or a non-extreme event, that is, the data with a significant wave height greater than 5.4m accounts for 0.2%, that is, the non-extreme part accounts for 99.8%, and the extreme part accounts for 0.2%. Therefore, the step of dividing the sorted first set into extreme parts and non-extreme parts based on marginal distribution variables includes: calculating the cumulative exceedance probability based on the marginal distribution variables, and dividing the first set into extreme parts and non-extreme parts according to the cumulative exceedance probability.

[0059] As mentioned above, elements with percentiles exceeding 99.8% are defined as extreme events. For example, assuming that the significant wave height corresponding to the 99.8% quantile is 5.40m, any moment when the wave height exceeds 5.40m is considered an extreme event. This method can more accurately evaluate and analyze the impact of different fitting methods and optimization correction methods on environmental contours.

[0060] As described above, according to the embodiments of the present disclosure, attention is paid to the fitting of extreme events and their data envelopment characteristics. It is worth noting that if in the analysis of R 2 If the data set used for the RMSE and ELR indicators contains all the events that occurred, the results obtained by different fitting methods may be very different, thereby weakening the sensitivity of the fitting method to extreme events. Therefore, the embodiments of the present disclosure can only consider the data set of extreme events when calculating the evaluation indicators.

[0061] In step S140, probability distribution fitting is performed on the extreme part and the non-extreme part respectively to obtain a first set of first fitting results, and the first environmental contour evaluation index is evaluated on the first fitting results.

[0062] The multidimensional environmental variables as mentioned above can be marine hydrological environmental parameters or variables. Commonly used marine hydrological environmental parameters or variable distribution functions include three-parameter Weibull, lognormal, exponential-Weibull and Rayleigh distribution, and commonly used fitting methods include maximum likelihood method (MLE), least squares method (LS) and genetic algorithm (GA). According to different data characteristics, the results obtained by different combinations of distribution functions and fitting methods are quite different. The specific fitting process of fitting models such as three-parameter Weibull, lognormal, exponential-Weibull and Rayleigh distribution and fitting methods such as maximum likelihood method (MLE), least squares method (LS) and genetic algorithm (GA) can all adopt existing methods.

[0063] According to an embodiment of the present disclosure, the same data source can first be fitted based on a combination of different distribution functions and different fitting methods by cross-combining different distribution functions in the above-mentioned distribution functions with different fitting methods, and then the optimal combination method can be determined based on a preset objective function to achieve an optimized combination function of the fitting functions and methods.

[0064] As an example, we can first fit 12 different combinations of the same data source by cross-combining 4 distribution functions with 3 fitting methods. Then, with the minimization of overall error and extreme value error as the objective function, we can determine the optimal combination method and realize the optimization combination function of fitting functions and methods.

[0065] The fitting results can be shown in Table 1 and Figure 6 shown.

[0066] Table 1 Probability distribution fitting results of Hs by different fitting models and fitting methods

[0067]

[0068] The first environmental contour evaluation index may include at least one of the following indexes: the determination coefficient R corresponding to the extreme part 2 (Evaluation index 1), the root mean square error RMSE corresponding to the extreme part (Evaluation index 2) and the extreme envelope rate ELR of the environmental contour line (Evaluation index 3).

[0069] As an example, it can be determined whether R 2The three constraints of >0.90, RMSE<0.05 and RE<1% are met, and the marginal distribution result as the first fitting result meets the standard. If not met, the marginal distribution result as the first fitting result does not meet the standard.

[0070] In step S150, in response to the evaluation result of the first fitting result not meeting the standard, the first fitting result may be optimized to obtain a first set of first fitting optimization results.

[0071] As an example, optimizing the first fitting result to obtain the first set of first fitting optimization results may include: determining the first fitting optimization result based on a connection point (or anchor point) between the fitting results of the extreme part and the fitting results of the extreme part.

[0072] When there are multiple connection points, the point with the highest extreme part goodness of fit among the intersections of the non-extreme part fitting result and the extreme part fitting result can be selected as the connection point (i.e., intersection point) for determining the first fitting optimization result. Taking the example that the non-extreme part fitting curve as the non-extreme part fitting result and the extreme part fitting curve as the extreme part fitting result have two intersections (i.e., connection points), the point with the smallest deviation from the real data among the two intersections can be selected as the connection point for selecting the fitting curve.

[0073] Reference Figure 3 The step of determining the first fitting optimization result based on the fitting result of the extreme part and the connection point of the fitting result of the extreme part includes step S1511, step S1512 and step S1513.

[0074] In step S1511, the non-extreme part is fitted to obtain a non-extreme part fitting result, and the extreme part is fitted to obtain an extreme part fitting result.

[0075] As an example, for the fitting of non-extreme events, different fitting models and fitting methods can be combined. There are four fitting models to choose from, namely three-parameter Weibull, exponential-Weibull, lognormal and Rayleigh distribution. There are three fitting methods to choose from, namely least squares method (LS), maximum likelihood method (MLE) and artificial intelligence genetic algorithm (GA). The combination that best fits the non-extreme part of the variable is selected as the fitting result of this part.

[0076] The step of fitting the extreme part to obtain the extreme part fitting result includes: using lognormal distribution as a fitting model and fitting the extreme part by weighted least square method, and the weight of the weighted least square method is determined by genetic algorithm.

[0077] As an example, in order to ensure that the extrapolated extreme value of the return period is as close as possible to the result of the EVA method under the premise of effectively improving the goodness of fit of the extreme value part, the optimization design method according to the embodiment of the present disclosure can compare the extrapolated extreme value of the return period with the extreme value of the return period obtained by the EVA method, and use the minimum error as the objective function, the weight of each sample as the decision variable, and automatically optimize the parameters based on the NSGA-Ⅱ algorithm (non-dominated sorting genetic algorithm with elite strategy) to find the weight under the global optimal state.

[0078] That is to say, Lognormal can be selected for the fitting of extreme events. The fitting method adopts weighted least squares method and sets the weight value of each extreme event sample. The larger the weight value, the more important the information corresponding to the sample. The fitting goals are as follows: to ensure that the fitting results are as close as possible to the distribution characteristics of the extreme event data; the extrapolated return period extreme value is as close as possible to the result obtained by the EVA method.

[0079] As an example, a genetic algorithm can be used to perform weight optimization, taking the R of extreme events as 2 The three objective functions of maximum, RMSE and RE minimum, the constraint range of each weight value is limited between 0 and 1, and 5 intervals or bins can be set. The final parameters can be: when Hs∈[5.40,6.35), weight = 0.111; when Hs∈[6.35,7.30), weight = 0.137; when Hs∈[7.30,8.26), weight = 0.089; when Hs∈[8.26,9.21), weight = 0.107; when Hs≥9.21, weight = 0.555.

[0080] In step S1512, the connection point between the non-extreme part fitting result and the extreme part fitting result is determined.

[0081] Reference Figure 7 The fitting functions of the non-extreme part and the extreme part form two intersection points (connection points). The point with higher fitting goodness of extreme events is selected as the connection point of the marginal distribution function, that is, Hs = 3.5m. The Hs probability distribution function obtained is shown in the attached figure. Figure 8 shown.

[0082] In step S1513, a first set of first fitting optimization results is determined according to the non-extreme part fitting results, the extreme part fitting results and the connection points.

[0083] As an example, the step of determining the first fitting optimization result of the first set based on the non-extreme part fitting results, the extreme part fitting results and the connection point includes: taking the combination of the extreme part fitting results before the connection point and the extreme part fitting results after the connection point as the first fitting optimization result.

[0084] That is to say, before the connection point, the fitting result of the non-extreme part is used, and after the connection point, the fitting result of the extreme part is used. The connection point is selected from the intersection of the two fitting results. If there are multiple intersections, the intersection with higher extreme event fitting goodness can be selected as the connection point of the marginal distribution function as described above.

[0085] The above optimization method can make up for the lack of grasp of extreme events when fitting the marginal distribution function, improve the extrapolation accuracy of the marginal distribution function, and then improve the accuracy of solving the joint working conditions of the environmental contour line.

[0086] Reference Figure 2 According to the optimization design method of the present disclosure, the optimization design method may further include step S160 and step S170.

[0087] In step S160 , the sorted first set is divided into a plurality of sections, and the conditional probability distribution of the second set in the multidimensional environmental variable data set in each section is obtained.

[0088] As an example, the significant wave height data set can be divided into several segments with equal intervals, the number of samples of another set (for example, spectral peak period) can be counted within each segment, and the conditional probability distribution within each segment can be obtained using log-normal distribution. However, this is only an example, and the conditional probability distribution can be obtained using other methods.

[0089] In step S170, based on the conditional probability distribution and the first fitting optimization result, the first environmental contour results of the marginal distribution variables corresponding to the first set and the second variables corresponding to the second set under the joint return period are solved.

[0090] Here, the method of obtaining environmental contour results based on conditional probability distribution, marginal distribution, etc. can adopt existing means, and environmental contours can be obtained based on the IRORM principle and Rosenblatt transformation. The brief process is as follows: according to the failure probability, the set in the standard normal space (U space) is converted into environmental variables in the physical space (X space) through the inverse Rosenblatt transformation, thereby obtaining environmental contours. The following set Figure 4 The overall optimization design method of the present invention is described.

[0091] Reference Figure 4 In step S10, a long sequence data set of significant wave height and spectrum peak period is obtained, the joint recurrence period is determined, and the extreme value under the joint recurrence period is deduced based on the EVA method.

[0092] In step S20, extreme events are separated. As described above, the extreme part and the non-extreme part can be separated by cumulative exceedance probability.

[0093] In step S30, marginal distribution fitting can be performed using the above-mentioned fitting model and fitting method.

[0094] When evaluation index 1, evaluation index 2 and evaluation index 3 are all not up to standard, marginal distribution optimization may be performed in step S40, and the specific optimization method is as described above.

[0095] In step S50, conditional distribution fitting is performed. As an example, the significant wave height data set can be divided into several segments at equal intervals, the number of samples of another set (for example, spectral peak period) is counted within each segment, and the conditional probability distribution within each segment is obtained using log-normal distribution. However, this is only an example, and the conditional probability distribution can be obtained by other methods.

[0096] S70: Solving environmental contours based on IFORM and Rosenblatt transformation. The set in the standard normal space (U space) can be converted into environmental variables in the physical space (X space) through the inverse Rosenblatt transformation, thereby obtaining environmental contours.

[0097] Comparison of the probability distribution fitting results of Hs by the IEC method and the optimization design method according to the present disclosure is shown in Table 2 below.

[0098] Table 2 Probability distribution fitting results of Hs by traditional IEC method and this method

[0099]

[0100] As shown in Table 2, the traditional IEC method using the combination of Weibull and maximum likelihood methods does not have an ideal fitting effect in this embodiment, especially in the extreme part. Figure 7 According to the distribution trend of data points, the traditional IEC method has a significant underestimation of the specified return period, which will undoubtedly affect the design safety of offshore structures. However, the present disclosure adopts a piecewise function approach, and the fitting effect in both extreme and non-extreme areas is relatively ideal, and the extrapolated 50-year extreme value is consistent with the EVA method, which meets the requirements of environmental contour evaluation indicators.

[0101] In addition, refer to Fig. 9The red dots represent the long sequence hindcast data of the ocean model numerical simulation, and the black line represents the 50-year environmental contour line obtained by the traditional IEC method. From the above analysis, it can be seen that the marginal distribution function fitted by the traditional method is not ideal in the extreme value area, and the extrapolated 50-year Hs has a significant underestimate. Therefore, the environmental contour line obtained based on this misses a large number of large Hs data points. For the extreme events defined in this disclosure, the ELR index is even only 0%. Therefore, according to the environmental contour line evaluation index defined in this disclosure, the results obtained by the traditional method are not up to standard. The results obtained by using the method of the present invention to deduce the environmental contour line are as shown in the attached figure. Fig. 9 As shown by the blue line. As can be seen from the above analysis, the Hs cumulative probability distribution obtained by the method of the present disclosure can better capture extreme events, and the extrapolated 50-year extreme value is completely consistent with the EVA method. At the same time, the envelope rate of the environmental contour line for extreme events obtained based on this also meets the evaluation index set by the present disclosure. Therefore, the result can be considered to be up to standard and can be used for the design and construction of offshore wind turbines. Therefore, the optimization design method according to the present disclosure may also include the step of designing and / or constructing offshore wind turbines according to the environmental contour line.

[0102] According to the embodiment of the present disclosure, the optimization design method may further include: performing a second environment contour line index evaluation on the first environment contour line result; in response to the second environment contour line index evaluation meeting the standard, using the first environment contour line result as the final environment contour line result.

[0103] The second environmental contour evaluation index may include a relative error between a maximum value of a marginal distribution variable based on environmental contour extrapolation and an extreme value obtained by an EVA method.

[0104] As an example, determine whether the relative errors of the maximum values ​​of the two variables extrapolated by the environmental contour line and the extreme values ​​derived by the EVA method both meet the condition of RE < 1%. If so, the environmental contour line result is valid; if not, the result is invalid.

[0105] If there is more than one environmental contour line result that meets the conditions, the result with the smallest relative error RE is selected as the optimal conclusion.

[0106] The requirements for the first environmental contour index and the second environmental contour index can be described as follows:

[0107] Evaluation indicator 1: R of extreme events 2 Should be greater than 0.90;

[0108] Evaluation indicator 2: The RMSE of extreme events should be less than 0.05;

[0109] Evaluation indicator 3 compliance conditions: If the time length of the input data series is less than 20% of the recurrence period length, the ELR of the extreme value part of the environmental contour line should be greater than 95%.

[0110] Evaluation index 4 compliance conditions: Under the same recurrence period, the RE of the maximum value of the variable extrapolated by the environmental contour line and the extreme value derived by the EVA method should be less than 1%.

[0111] The above evaluation indicators can be used to generate more accurate environmental contour lines, thereby obtaining more accurate specified joint return period environmental conditions, which can further reduce the design and construction costs while ensuring structural safety in the fields of water resources management, flood risk assessment, urban planning and marine engineering.

[0112] In addition, the optimization design method disclosed in the present invention has high universality and is applicable not only to marine hydrological environment parameters, but also to multidimensional variables to be solved in other fields.

[0113] Fig.10 is a block diagram showing an optimization design apparatus for joint return period according to the present disclosure.

[0114] According to an embodiment of the present disclosure, the optimization design device 1000 for joint return period includes a first calculation unit 1100 , a second calculation unit 1200 , an extreme event separation unit 1300 , a fitting unit 1400 and an optimization unit 1500 .

[0115] The first calculation unit 1100 may obtain a multidimensional environmental variable data set of the target area and determine a joint recurrence period to be solved. In addition, the first calculation unit 1100 may also determine an extreme value of the first variable under the joint recurrence period.

[0116] The second calculation unit 1200 may determine the marginal distribution variables corresponding to the first set in the multidimensional environmental variable data set for the joint return period under the joint return period. As described above, the significant wave height may be determined as the marginal distribution variable.

[0117] The extreme event separation unit 1300 may sort the first set for the joint return period and divide the sorted first set into an extreme part and a non-extreme part based on extreme values ​​of the marginal distribution variables for the joint return period.

[0118] The fitting unit 1400 may perform probability distribution fitting for the extreme part and the non-extreme part of the joint return period respectively to obtain a first fitting result for the first set of joint return periods, and evaluate the first environmental contour evaluation index for the first fitting result for the joint return period. The fitting model and fitting method sampled by the fitting unit 1400 may be as described above, and will not be repeated here.

[0119] In response to the evaluation result of the first fitting result not meeting the standard, the optimization unit 1500 may optimize the first fitting result for the joint return period to obtain a first fitting optimization result for the first set of joint return periods.

[0120] As an example, the optimization unit 1500 may determine the first fitting optimization result based on the fitting result of the extreme part and the connection point of the fitting result of the extreme part.

[0121] The optimization unit 1500 can fit the non-extreme part to obtain the non-extreme part fitting result, and fit the extreme part to obtain the extreme part fitting result, determine the connection point between the non-extreme part fitting result and the extreme part fitting result, and determine the first fitting optimization result of the first set based on the non-extreme part fitting result, the extreme part fitting result and the connection point.

[0122] In one example, the optimization unit 1500 may use a combination of the extreme part fitting result before the connection point and the extreme part fitting result after the connection point as the first fitting optimization result. The connection point here is the point where the extreme part fitting goodness is the highest among the intersections of the non-extreme part fitting result and the extreme part fitting result.

[0123] In addition, the first calculation unit 1100, the second calculation unit 1200, the extreme event separation unit 1300, and the fitting unit 1400 may execute the corresponding steps described above, which will not be described in detail here.

[0124] When the systems, units or modules shown in the drawings are implemented in software, firmware, middleware or microcode, the program code or code segments for performing the corresponding operations may be stored in a computer-readable medium such as a storage medium, so that at least one processor or at least one computing device may perform the corresponding operations by reading and running the corresponding program code or code segments. In addition, the computer-readable medium or storage medium may cause the processor to execute the above-mentioned optimization design method when the computer program is executed by the processor.

[0125] For example, according to an exemplary embodiment of the present disclosure, a computer device including a readable medium storing computer program instructions may be provided, wherein the instructions, when executed by at least one computing device, prompt the at least one computing device to perform at least one of the above steps.

[0126] The computing device according to the embodiment of the present disclosure includes a memory and a processor. The memory stores instructions or programs, and when the instructions or programs are executed by the processor, the processor is prompted to execute the above-mentioned optimization design method.

[0127] According to an embodiment of the present disclosure, a computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor is prompted to execute the above-mentioned optimization design method.

[0128] The instructions stored in the computer-readable storage medium can be executed in an environment deployed in a computer device such as a client, a host, an agent device, a server, etc. It should be noted that the instructions can also be used to perform additional steps in addition to the above steps or perform more specific processing when performing the above steps. The contents of these additional steps and further processing have been described in detail in reference to Figures 1 to 10 It is mentioned in the description of related systems and methods, so it will not be repeated here to avoid repetition.

[0129] It should be noted that the control method according to the exemplary embodiment of the present disclosure can completely rely on the operation of computer programs or instructions to realize the corresponding functions, that is, each device corresponds to each step in the functional architecture of the computer program, so that the entire system is called through a special software package (for example, lib library) to realize the corresponding function.

[0130] The optimization design method for the joint return period according to the embodiment of the present disclosure can make up for the shortcomings of the traditional IEC method in fitting the marginal distribution function for extreme events, improve the extrapolation accuracy of the marginal distribution function, and thus improve the accuracy of solving the joint working conditions by environmental contour lines.

[0131] The optimization design method for the joint return period according to the embodiment of the present disclosure has high universality and is applicable not only to marine hydrological environmental parameters, but also to multidimensional environmental variables to be solved in other fields.

[0132] The optimization design method for the joint return period according to the embodiment of the present disclosure can generate more accurate environmental contour lines, thereby obtaining more accurate environmental conditions for the specified joint return period. It can further reduce the design and construction costs while ensuring the safety of the structure, and is of great significance in the fields of water resource management, flood risk assessment, urban planning and marine engineering.

[0133] The specific implementation methods of the present disclosure are described in detail above. Although some embodiments have been shown and described, those skilled in the art should understand that these embodiments can be combined, modified and improved without departing from the principles and spirit of the present disclosure whose scope is defined by the claims and their equivalents. These combinations, modifications and improvements should also be within the scope of protection of the present disclosure.

Claims

1. A method for optimizing the joint return period, characterized in that: The optimization design method comprises: Obtaining a multidimensional environmental variable data set for a target area and determining a joint return period to be solved; determining marginal distribution variables corresponding to a first set in the multidimensional environmental variable data set under the joint return period; sorting the first set and dividing the sorted first set into an extreme part and a non-extreme part based on the marginal distribution variable; Performing probability distribution fitting on the extreme part and the non-extreme part respectively to obtain a first fitting result of the first set, and evaluating the first environmental contour evaluation index on the first fitting result; In response to an evaluation result of the first fitting result not meeting a standard, the first fitting result is optimized to obtain a first fitting optimization result of the first set.

2. The optimization design method for joint return period according to claim 1, characterized in that: Optimizing the first fitting result to obtain the first set of first fitting optimization results includes: determining the first fitting optimization result based on the connection point between the fitting result of the extreme part and the fitting result of the extreme part.

3. The optimization design method for joint return period according to claim 2, characterized in that: The step of determining the first fitting optimization result based on the fitting result of the extreme part and the connection point of the fitting result of the extreme part comprises: Fitting the non-extreme part to obtain a non-extreme part fitting result, and fitting the extreme part to obtain an extreme part fitting result; Determine a connection point between the non-extreme part fitting result and the extreme part fitting result; A first fitting optimization result of the first set is determined according to the fitting result of the non-extreme part, the fitting result of the extreme part and the connection point.

4. The optimization design method for joint return period according to claim 3, characterized in that: The step of determining the first fitting optimization result of the first set according to the fitting result of the non-extreme part, the fitting result of the extreme part and the connection point comprises: A combination of the extreme part fitting result before the connection point and the extreme part fitting result after the connection point is used as the first fitting optimization result.

5. The optimization design method for joint return period according to claim 4, characterized in that: The connection point is a point where the extreme part fitting goodness is the highest among the intersections of the non-extreme part fitting result and the extreme part fitting result.

6. The optimization design method for joint return period according to claim 4, characterized in that: The step of fitting the extreme part to obtain the extreme part fitting result includes: using lognormal distribution as a fitting model and fitting the extreme part by weighted least squares method, and the weight of the weighted least squares method is determined by a genetic algorithm.

7. The optimization design method for joint return period according to claim 1, characterized in that: The optimization design method also includes: Divide the sorted first set into a plurality of sections, and obtain a conditional probability distribution of a second set in the multidimensional environmental variable data set in each section; Based on the conditional probability distribution and the first fitting optimization result, first environmental contour results of marginal distribution variables corresponding to the first set and second variables corresponding to the second set under the joint return period are solved.

8. The optimization design method for joint return period according to claim 7, characterized in that: The optimization design method also includes: Performing a second environment contour index evaluation on the first environment contour result; In response to the second environmental contour line indicator evaluation being up to standard, the first environmental contour line result is used as the final environmental contour line result.

9. The optimization design method for joint return period according to claim 8, characterized in that: The first environmental contour line evaluation index includes at least one of the following indexes: the determination coefficient corresponding to the extreme part, the root mean square error corresponding to the extreme part, and the extreme value envelope rate of the environmental contour line; the second environmental contour line evaluation index includes the relative error between the maximum value of the marginal distribution variable based on the extrapolation of the environmental contour line and the extreme value obtained by the EVA method.

10. The optimization design method for joint return period according to claim 1, characterized in that: The step of dividing the sorted first set into an extreme part and a non-extreme part based on the marginal distribution variable comprises: calculating a cumulative exceedance probability based on the marginal distribution variable, and dividing the first set into an extreme part and a non-extreme part according to the cumulative exceedance probability.

11. The optimization design method for joint return period according to claim 1, characterized in that: The multidimensional environmental variable data set is a multidimensional marine environmental variable data set, and includes a significant wave height data set and a spectrum peak period data set, and the joint recurrence period is a two-dimensional variable joint recurrence period.

12. An optimization design device for joint return period, characterized in that: The optimization design device comprises: A first computing unit acquires a multidimensional environmental variable data set of a target area and determines a joint recurrence period to be solved; A second computing unit determines a marginal distribution variable corresponding to a first set in the multidimensional environmental variable data set under the joint return period; an extreme event separation unit, which sorts the first set and divides the sorted first set into an extreme part and a non-extreme part based on the marginal distribution variable; A fitting unit, performing probability distribution fitting on the extreme part and the non-extreme part respectively to obtain a first fitting result of the first set, and evaluating the first environmental contour evaluation index on the first fitting result; The optimization unit optimizes the first fitting result in response to the evaluation result of the first fitting result not meeting the standard, so as to obtain a first fitting optimization result of the first set.

13. A computing device, characterized in that: The invention comprises a memory and a processor, wherein the memory stores instructions or programs, and when the instructions or programs are executed by the processor, the processor is prompted to execute the optimization design method according to any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to execute the optimization design method according to any one of claims 1 to 11.

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