A method for selecting key storm surge disaster defense areas based on cumulative frequency method

Abstract

This invention discloses a method for delineating key storm surge disaster prevention areas based on the cumulative frequency method. It avoids the subjective errors often encountered in empirical judgment methods and overcomes the problem of weak research foundations in the standardization of key storm surge disaster prevention areas, which prevents the finding of suitable empirical models for standard determination. The proposed method for determination and zoning not only provides a scientific, reasonable, and effective technical approach for delineating key marine disaster prevention areas in coastal cities of my country, but also provides a valuable standard determination method for other domestic zoning work based on indicator systems. It effectively supplements the commonly used subjective determination methods in my country, improves the scientific rigor of the standard determination results, and can be well applied to my country's current spatial planning field, enhancing the scientific validity and rationality of planning outcomes. This method has good market prospects.

Description

Technical Field

[0001] The invention relates to the technical field of marine disaster data analysis, and in particular to a method for selecting key defense areas for storm surge disasters based on a cumulative frequency method. Background Art

[0002] Storm surge disaster is one of the major marine disasters faced by my country. It is almost everywhere along the coast of China. It has a high frequency and strong disaster intensity, causing heavy human and economic losses. It has been included in the category of major disasters in my country. It is necessary to use quantitative methods to assign values ​​to the selection criteria of key defense areas for storm surge disasters, so that the selection of key defense areas for storm surge disasters can be more scientific and objective.

[0003] The selection of disaster key defense areas currently carried out at home and abroad is basically to make a comprehensive judgment on whether the target unit can be selected as a key defense area through one or more indicators. The usual practice is to first establish a multi-level indicator system and assign values ​​to each indicator, and then map multiple indicators into a comprehensive evaluation indicator by establishing a functional relationship between indicators at different levels. Finally, the comprehensive indicator is used to judge whether the target unit can be selected as a key defense area. Therefore, how to scientifically set the judgment standard of the comprehensive evaluation indicator is the core technology for the selection of key defense areas. At this stage, the commonly used indicator standard assignment methods in my country are mainly:

[0004] 1) Experience judgment method, that is, subjectively setting the standard value of the indicator based on the work experience of the project leader. This method is the easiest to operate, but it is also the most subjective and heavily relies on the experience and level of the project leader. It is suitable for situations where the reliance on objective data is not high and the scientific nature of the assignment results is not required;

[0005] 2) Expert scoring method, that is, determining the standard value of the indicator by consulting a number of experts with rich experience in related fields. This method has the advantages of simplicity, intuitiveness, and simple calculation method, but in general it also reflects subjective consciousness and has strict requirements on the representativeness and professionalism of experts;

[0006] 3) Analogy analysis method, that is, to refer to the standard values ​​of indicators of similar projects with strong correlation as the standard values ​​of indicators for this project. This method has good operability and certain scientificity, but is affected by factors such as the scientificity of the establishment of the standard values ​​of indicators of analogous projects and the similarity between the two projects. In the absence of similar projects or insufficient similarity between the projects, this method cannot be used effectively;

[0007] 4) Statistical analysis method, that is, using statistical principles to organize the obtained indicator data, find certain mathematical rules, and select statistically significant characteristic values ​​as indicator standard values. This method is based on objective data and has good scientificity, but different projects involve data samples with different characteristics, and different data processing methods are required to find characteristic values. Even the same data sample may have different characteristic values ​​obtained by different data processing methods. At this time, it is necessary to verify the rationality of different results and then select the best value. Therefore, this method is suitable for situations with abundant data samples, and the specific data analysis method used may have a strong correlation with the specific project, and there are few generally applicable features.

[0008] Given that the empirical judgment method is the easiest to operate, but also the most subjective, it is heavily dependent on the experience and level of the project leader himself, and is suitable for situations where the reliance on objective data is not high and the scientific nature of the assignment results is not required. The expert scoring method has the advantages of simplicity, strong intuitiveness, and simple calculation method, but in general it also reflects subjective consciousness, and its objectivity depends on the representativeness and professionalism of experts. The analogy analysis method has good operability and certain scientificity, but it is affected by factors such as the scientificity of the establishment of the standard value of the analogy project index and the similarity between the two projects. In the absence of similar projects or insufficient project similarity, this method cannot be effectively used. The statistical analysis method is based on objective data and has good scientificity, but different projects involve different types of data, and different statistical analysis methods are used, and the results obtained will also be different. Therefore, this method has the characteristics of one-on-one discussion. The key is how to choose a suitable statistical method; the storm surge disaster key defense area selection method based on the cumulative frequency method proposed in this patent is a scheme based on statistical analysis methods. At present, no one has applied it to the storm surge disaster key defense area selection and index setting. Summary of the invention

[0009] In view of this, the purpose of the present invention is to propose a method for selecting key storm surge disaster defense areas based on the cumulative frequency method, which is reliable in implementation, has good result guidance and good objectivity.

[0010] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0011] A method for selecting key storm surge disaster defense areas based on cumulative frequency method, comprising:

[0012] S01, obtaining storm surge disaster assessment information corresponding to the area to be selected, and then extracting data according to preset conditions to generate a data set to be processed;

[0013] S02, obtaining a data set to be processed, screening the data set to be processed, obtaining a set of independent judgment indicators and three sets of parallel judgment indicators that are different and classified in parallel, and using them as indicators for selecting key defense areas for storm surge disasters;

[0014] S03. Obtain independent judgment indicators and parallel judgment indicators, perform cumulative frequency analysis on the independent judgment indicators and the parallel judgment indicators according to preset conditions, and determine the cumulative frequencies and classification standards corresponding to the independent judgment indicators and the parallel judgment indicators respectively;

[0015] S04, according to the division standards corresponding to the three groups of parallel judgment indicators, determine the potential key defense areas corresponding to the three groups of parallel judgment indicators according to the preset conditions, then spatially superimpose the potential key defense areas corresponding to the three groups of parallel judgment indicators to obtain the overlapping parts, and output the first key defense area;

[0016] S05. According to the division standard corresponding to the independent judgment index, the key defense area selected corresponding to the independent judgment index is obtained, which is set as the second key defense area. The first key defense area is combined with the second key defense area, and the storm surge disaster key defense area finally selected and determined is output.

[0017] As a possible implementation, further, in S01, the storm surge disaster assessment information includes at least flood depth data, affected population data, direct economic loss data, and indirect economic loss data;

[0018] The data in the data set to be processed include flood depth data, affected population data, direct economic loss data and indirect economic loss data;

[0019] In S02, the flood depth data is screened and set as an independent judgment indicator, and the affected population data, direct economic loss data and indirect economic loss data are screened and set as parallel judgment indicators.

[0020] As a preferred implementation option, preferably, S03, obtaining the independent judgment index and the parallel judgment index, performing cumulative frequency analysis on the independent judgment index and the parallel judgment index according to preset conditions, and determining the cumulative frequencies and division criteria corresponding to the independent judgment index and the parallel judgment index respectively, includes:

[0021] S031. Obtain independent judgment indicators and parallel judgment indicators, and group corresponding sample data in the independent judgment indicators and the parallel judgment indicators according to preset interval units;

[0022] S032. Grouping the sample data according to the independent judgment index and the parallel judgment index, sorting the sample data in ascending order, constructing frequency distribution histograms respectively and constructing cumulative frequency distribution curves on the frequency distribution histograms, wherein the primary ordinate of the histogram indicates the frequency of each data grouping, the secondary ordinate indicates the cumulative frequency of the data grouping, and the abscissa indicates the grouping interval;

[0023] S033. Obtain the frequency distribution histogram and cumulative frequency distribution curve corresponding to the independent judgment index and the parallel judgment index, and determine the cumulative frequency corresponding to each sample data group of the independent judgment index and the parallel judgment index according to preset conditions;

[0024] S034. Obtain a division standard according to the cumulative frequencies corresponding to each sample data grouping of the independent judgment index and the parallel judgment index, and set the division standard as a threshold.

[0025] As a preferred implementation option, preferably, in S031, the independent judgment indicators and the parallel judgment indicators are grouped.

[0026] The unit of the flooded water depth data is "meter" and grouped at intervals of 0.1m;

[0027] The population data affected adopts "10,000 people" as the unit of measurement and is grouped into intervals of 1,000 people;

[0028] The units of direct economic loss data and indirect economic data are both measured in "10,000 yuan" and are grouped in 10,000 yuan.

[0029] As a preferred implementation option, preferably, S04, according to the division standards corresponding to the three groups of parallel judgment indicators, the potential key defense areas corresponding to the three groups of parallel judgment indicators are determined according to preset conditions, and then the potential key defense areas corresponding to the three groups of parallel judgment indicators are spatially superimposed to obtain overlapping parts, and the method of outputting the first key defense area includes:

[0030] S041. According to the division criteria corresponding to the three groups of parallel judgment indicators, the sample data in the parallel judgment indicators are divided into an upper bound part greater than or equal to the threshold and a lower bound part less than the threshold;

[0031] S042, setting the upper bounds corresponding to the three sets of parallel judgment indicators as potential key areas, and setting the lower bounds as non-key areas;

[0032] S043. Spatially superimpose the potential key areas corresponding to the three sets of parallel judgment indicators, take the overlapping parts, and output the first key defense area.

[0033] As a preferred implementation option, preferably, S05, according to the division standard corresponding to the independent judgment index, obtain the key defense area selected corresponding to the independent judgment index, set it as the second key defense area, combine the first key defense area with the second key defense area, and output the storm surge disaster key defense area finally selected and determined. The method includes:

[0034] S051. According to the division standard corresponding to the independent judgment indicator, the sample data in the independent judgment indicator is divided into an upper bound part greater than or equal to the threshold and a lower bound part less than the threshold;

[0035] S052, taking the upper boundary corresponding to the independent judgment index as the selected key defense area, and setting it as the second key defense area;

[0036] S053. Combine the first key defense area with the second key defense area, and output the storm surge disaster key defense area finally selected and determined.

[0037] Based on the above scheme, the present invention also provides a storm surge disaster key defense area selection system based on the cumulative frequency method, which includes:

[0038] A server is used to store storm surge disaster assessment information corresponding to different regions;

[0039] The data scheduling module is used to obtain the storm surge assessment information corresponding to the area to be selected, extract the data according to the preset conditions, and generate the data set to be processed;

[0040] The data processing module is used to obtain the data set to be processed, perform data screening on the data set to be processed, obtain a set of independent judgment indicators and three sets of different and classified parallel judgment indicators, and use them as indicators for selecting key defense areas for storm surge disasters;

[0041] A data analysis module is used to obtain independent judgment indicators and parallel judgment indicators, perform cumulative frequency analysis on the independent judgment indicators and parallel judgment indicators according to preset conditions, and determine the cumulative frequencies and division standards corresponding to the independent judgment indicators and the parallel judgment indicators respectively;

[0042] The data matching module is used to determine the potential key defense areas corresponding to the three sets of parallel judgment indicators according to the division standards corresponding to the three sets of parallel judgment indicators according to preset conditions, and then spatially superimpose the potential key defense areas corresponding to the three sets of parallel judgment indicators to obtain the overlapping parts and output the first key defense area. It is also used to obtain the key defense area corresponding to the independent judgment indicator according to the division standards corresponding to the independent judgment indicator and set it as the second key defense area.

[0043] The spatial selection module is used to combine the first key defense area with the second key defense area and output the storm surge disaster key defense area finally selected and determined.

[0044] Based on the above scheme, the present invention also provides a computer-readable storage medium, in which at least one instruction, at least one program, code set or instruction set is stored. The at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the above-mentioned method for selecting key storm surge disaster defense areas based on the cumulative frequency method.

[0045] Among the commonly used index standard assignment methods, the empirical judgment method is often questioned for its strong subjectivity, and the expert scoring method is often questioned for its lack of representativeness due to factors such as the number of experts, the distribution of research fields, and the depth of professional level. Since the selection of key defense areas for storm surge disasters is just starting, there are no standard values ​​to refer to. The cumulative frequency distribution curve proposed in this patent allows the data to speak and the data to determine the value of the index itself. It has good objectivity. At the same time, the cumulative frequency itself is a statistical method that has been used in many fields and has solved some practical engineering problems. It has a relatively wide recognition and acceptance.

[0046] The principle of the cumulative frequency of this scheme is as follows:

[0047] Cumulative frequency refers to the number of data distributed in each group after the data is grouped according to a certain mark, which is called frequency or number of times. The ratio of the frequency of each group to the sum of all frequencies is called frequency or proportion. For the needs of statistical analysis, it is sometimes necessary to observe the sum of frequencies below or above a certain value, which is called cumulative frequency, or accumulation of frequencies. Accumulating from the side with a small variable value to the side with a large variable value is called upward accumulation, and vice versa is downward accumulation. The final cumulative value of frequency is 100%. The longer the observation period, the larger the sample size and the more reliable its predictability.

[0048] From a mathematical point of view, the cumulative frequency distribution function is expressed as the integral of the probability density function. The probability density function of a continuous random variable is a function that describes the probability of the output value of this random variable near a certain value point. The probability density function of a discrete random variable describes the probability value of the discrete random variable at each value.

[0049] According to the different categories of random variables, the cumulative frequency distribution function can also be divided into discrete and continuous types: the cumulative frequency of continuous random variables is expressed by the formula: It reflects the sum of the probabilities of all events occurring between -∞ and a certain value of the variable.

[0050] In practical applications, discrete cumulative frequency is often used. Suppose discrete random variable X = (x1, x2, ..., x m , ..., x n ), where x1<x2<....<xm <...<x n .

[0051] The cumulative frequency can be expressed as: F(x) = f(x1) + f(x2) + f(x3) + ... + f(x m )

[0052] Among them, f(x i ) represents the sample x i The corresponding probability, F(x) represents the probability from sample x1 to sample x m The cumulative frequency of .

[0053] At present, the cumulative frequency is often represented by a curve. The cumulative frequency curve method refers to the use of a curve graph to represent the upward or downward cumulative frequency. It is often displayed in a permutation diagram ( Figure 1 ). Figure 1 The arrangement diagram in the sample consists of two ordinates, one abscissa, several histograms and a curve (cumulative frequency distribution curve). The ordinate on the left represents the frequency, the ordinate on the right represents the cumulative frequency (0-100%), and the abscissa represents the event group. The high and low cumulative frequency represents the influence of a certain factor in the sample, and the curve represents the cumulative percentage of the influence of each factor. According to the arrangement of primary and secondary factors, they can be divided into three categories: factors with a cumulative frequency of 0-80% are called Class A factors, which are obviously the main factors; factors with a cumulative frequency of 80-90% are called Class B secondary factors; factors with a cumulative frequency of 90-100% are called Class C secondary factors.

[0054] The principle of this scheme's method for determining the standard value based on cumulative frequency is as follows:

[0055] Many research results show that the values ​​of the groups of 50%, 75%, 80% and 90% are usually used as the standard values ​​of the indicators. At the same time, considering the different importance of each indicator and the different locations of the highest frequency, the influence of various aspects needs to be considered comprehensively. A large number of data experiments are needed to determine the adaptability of the cumulative frequency distribution curve method in the standard value setting of storm surge disaster key defense areas. The key to this study is to comprehensively consider the four common frequencies, the highest frequency, and the importance of each indicator.

[0056] There are three points that need to be considered comprehensively when determining the standard value based on the cumulative frequency:

[0057] 1. Select the importance of indicators. The four indicators of flood depth, affected population, direct economic losses, and indirect economic losses in this article are not simply in parallel. The flood depth is the main attribute of setting up key defense areas and is the main basis; the affected population, direct economic losses, and indirect economic losses are loss indicators, and the affected population indicator is more important than the economic loss. Therefore, the importance is: flood depth > affected population > direct economic losses > indirect economic losses. The importance of indicators should be taken into consideration when considering the cumulative frequency.

[0058] 2. Four commonly used cumulative frequencies. The four commonly used cumulative frequencies of 50%, 75%, 80% and 90% have their own statistical significance. The selection of cumulative frequencies in this article will focus on these four commonly used frequencies. If the cumulative frequency is too low, the selected area will not be enough to reflect the importance of the key defense area. If the cumulative frequency is too high, some areas that are severely affected and need key protection may be missed.

[0059] 3. Frequency and highest frequency. Frequency (number of frequencies) is an important parameter for determining cumulative frequency. When determining cumulative frequency, special attention should be paid to the group of high frequency. Usually, if the data contains a prominent high frequency group, it should be considered together with the cumulative frequency to determine the standard value.

[0060] This scheme describes the grouping in the cumulative frequency method:

[0061] When using the cumulative frequency method for analysis, it is key to group the data reasonably. Different groups will have a direct impact on the subsequent cumulative frequency values. Regarding the principles of grouping: First, grouping is related to the requirements and methods of national statistical data. In terms of water depth, statistics are often made with an accuracy of 0.1m, so the flooded water depth data of this study is also grouped at intervals of 0.1m; in terms of the affected population, it is often measured in "persons", while economic losses are measured in "10,000 yuan".

[0062] At the same time, the selection of unit precision also needs to consider the size of the division unit. Different division units (such as counties and cities) require different unit precisions. The smaller the division unit, the higher the unit precision, and vice versa, the lower the precision, which is also in line with the actual situation.

[0063] More precise grouping can make the selection of cumulative frequency more accurate, but too precise grouping will also make statistics and data visualization difficult. Therefore, in the actual grouping process, in addition to considering the above two grouping principles, the actual situation should also be considered to merge or split the groups.

[0064] The present invention uses the cumulative frequency method in statistics for storm surge index determination as a reference for subsequent key defense zone selection, providing an objective basis and method for index determination. In the process of index determination criteria, four indicators, namely, storm surge inundation depth, population impact indicator, direct economic loss indicator, and indirect economic loss indicator, are selected to make a statistical chart of their cumulative frequencies, and the appropriate cumulative frequency is determined in combination with the importance of the indicator and the highest frequency occurrence interval, thereby determining the standard values ​​of various indicators affecting the storm surge key defense zone.

[0065] By adopting the above technical scheme, the present invention has the following beneficial effects compared with the prior art: the storm surge disaster key defense area selection method based on the cumulative frequency method can avoid the common error problem of empirical judgment method, and avoid the problem that the research foundation of storm surge disaster key defense area division standard is weak, and it is difficult to find a suitable empirical model for standard value determination. The value determination and zoning method proposed in this scheme not only provides a scientific, reasonable and effective technical method for the delineation of key defense areas for marine disasters in coastal cities in my country, but also plays a good supporting role in the smooth development of this work in the future. At the same time, it also provides a standard value determination method worthy of reference for the domestic zoning work based on the indicator system, effectively supplements the currently commonly used subjective value determination method in my country, improves the scientific nature of the standard value determination results, and can be better applied to the current spatial planning field in my country, and improves the scientific nature and rationality of planning results. This method has been tried in Tianjin and Zhoushan for internal demonstration, and after a certain adaptability evaluation, it can provide a good case basis for the promotion of this method and has a good market promotion prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0067] Figure 1 It is a simplified schematic diagram of the cumulative frequency distribution curve histogram;

[0068] Figure 2 It is a schematic diagram of a brief implementation process of the method of the present invention;

[0069] Figures 3 to 8 It is a frequency distribution histogram of flooding depths in each typical recurrence period of Putuo District, Zhoushan City in the example of the solution of the present invention, which respectively shows the flooding depth data of 5, 10, 20, 50, 100 and 200 years;

[0070] Fig. 9is a cumulative frequency histogram of the affected population in Putuo District, Zhoushan City in the example of the solution of the present invention;

[0071] Fig.10 is a cumulative frequency histogram of direct economic losses in Putuo District, Zhoushan City in the example of the solution of the present invention;

[0072] Fig.11 is a cumulative frequency histogram of direct economic losses in Putuo District, Zhoushan City in the example of the solution of the present invention;

[0073] Fig.12 It is a simplified module connection diagram of the solution system of the present invention. DETAILED DESCRIPTION

[0074] The present invention will be further described in detail below in conjunction with the accompanying drawings and examples. It is particularly noted that the following examples are only used to illustrate the present invention, but are not intended to limit the scope of the present invention. Similarly, the following examples are only partial embodiments of the present invention rather than all embodiments, and all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0075] like Figure 2 As shown in the figure, this scheme is a method for selecting key storm surge disaster defense areas based on the cumulative frequency method, which includes:

[0076] S01, obtaining storm surge disaster assessment information corresponding to the area to be selected, and then extracting data according to preset conditions to generate a data set to be processed;

[0077] S02, obtaining a data set to be processed, screening the data set to be processed, obtaining a set of independent judgment indicators and three sets of parallel judgment indicators that are different and classified in parallel, and using them as indicators for selecting key defense areas for storm surge disasters;

[0078] S03. Obtain independent judgment indicators and parallel judgment indicators, perform cumulative frequency analysis on the independent judgment indicators and the parallel judgment indicators according to preset conditions, and determine the cumulative frequencies and classification standards corresponding to the independent judgment indicators and the parallel judgment indicators respectively;

[0079] S04, according to the division standards corresponding to the three groups of parallel judgment indicators, determine the potential key defense areas corresponding to the three groups of parallel judgment indicators according to the preset conditions, then spatially superimpose the potential key defense areas corresponding to the three groups of parallel judgment indicators to obtain the overlapping parts, and output the first key defense area;

[0080] S05. According to the division standard corresponding to the independent judgment index, the key defense area selected corresponding to the independent judgment index is obtained, which is set as the second key defense area. The first key defense area is combined with the second key defense area, and the storm surge disaster key defense area finally selected and determined is output.

[0081] In this solution S01, the storm surge disaster assessment information includes at least flooded water depth data, affected population data, direct economic loss data and indirect economic loss data; the data in the data set to be processed includes flooded water depth data, affected population data, direct economic loss data and indirect economic loss data.

[0082] In this solution S02, the flooded water depth data is screened and set as an independent judgment indicator, and the affected population data, direct economic loss data and indirect economic loss data are screened and set as parallel judgment indicators.

[0083] In this solution S03, the method of obtaining the independent judgment index and the parallel judgment index, performing cumulative frequency analysis on the independent judgment index and the parallel judgment index according to preset conditions, and determining the cumulative frequencies and division standards corresponding to the independent judgment index and the parallel judgment index respectively includes:

[0084] S031. Obtain independent judgment indicators and parallel judgment indicators, and group corresponding sample data in the independent judgment indicators and the parallel judgment indicators according to preset interval units;

[0085] S032. Grouping the sample data according to the independent judgment index and the parallel judgment index, sorting the sample data in ascending order, constructing frequency distribution histograms respectively and constructing cumulative frequency distribution curves on the frequency distribution histograms, wherein the primary ordinate of the histogram indicates the frequency of each data grouping, the secondary ordinate indicates the cumulative frequency of the data grouping, and the abscissa indicates the grouping interval;

[0086] S033. Obtain the frequency distribution histogram and cumulative frequency distribution curve corresponding to the independent judgment index and the parallel judgment index, and determine the cumulative frequency corresponding to each sample data group of the independent judgment index and the parallel judgment index according to preset conditions;

[0087] S034. Obtain a division standard according to the cumulative frequencies corresponding to each sample data grouping of the independent judgment index and the parallel judgment index, and set the division standard as a threshold.

[0088] As a preferred implementation of S031, in this solution S031, the independent judgment indicators and the parallel judgment indicators are grouped.

[0089] The unit of the flooded water depth data is "meter" and grouped at intervals of 0.1m;

[0090] The population data affected adopts "10,000 people" as the unit of measurement and is grouped into intervals of 1,000 people;

[0091] The units of direct economic loss data and indirect economic data are both measured in "10,000 yuan" and are grouped in 10,000 yuan.

[0092] In this solution S04, according to the division standards corresponding to the three groups of parallel judgment indicators, the potential key defense areas corresponding to the three groups of parallel judgment indicators are determined according to preset conditions, and then the potential key defense areas corresponding to the three groups of parallel judgment indicators are spatially superimposed to obtain the overlapping parts, and the method of outputting the first key defense area includes:

[0093] S041. According to the division criteria corresponding to the three groups of parallel judgment indicators, the sample data in the parallel judgment indicators are divided into an upper bound part greater than or equal to the threshold and a lower bound part less than the threshold;

[0094] S042, setting the upper bounds corresponding to the three sets of parallel judgment indicators as potential key areas, and setting the lower bounds as non-key areas;

[0095] S043. Spatially superimpose the potential key areas corresponding to the three sets of parallel judgment indicators, take the overlapping parts, and output the first key defense area.

[0096] In this solution S05, according to the division standard corresponding to the independent judgment index, a key defense area selected corresponding to the independent judgment index is obtained, set as the second key defense area, the first key defense area is combined with the second key defense area, and a method for outputting the storm surge disaster key defense area finally selected and determined includes:

[0097] S051. According to the division standard corresponding to the independent judgment indicator, the sample data in the independent judgment indicator is divided into an upper bound part greater than or equal to the threshold and a lower bound part less than the threshold;

[0098] S052, taking the upper boundary corresponding to the independent judgment index as the selected key defense area, and setting it as the second key defense area;

[0099] S053. Combine the first key defense area with the second key defense area, and output the storm surge disaster key defense area finally selected and determined.

[0100] This embodiment takes the method for selecting the key defense area for storm surge disasters in Putuo District, Zhoushan City as a specific example, and uses the cumulative frequency method to perform an example of determining the index value, as follows:

[0101] Submerged water depth indicator

[0102] (1) Data simulation: Numerical simulation of storm surge disasters with a typical return period in the Zhoushan demonstration area was carried out to obtain the corresponding results. The typical return period is 5, 10, 20, 50, 100, and 200 years.

[0103] (2) Data description: There are 22,253 (5-year return period) to 35,881 (200-year return period) flooding depth data. The minimum flooding depth is 0.05 m (the value used to determine whether the simulation cell is flooded), and the maximum flooding depth is 5.3 m (5-year return period) to 7.0 m (200-year return period).

[0104] (3) Sample grouping: Samples were grouped at 0.2 m water depth intervals, with 0.05-0.2 m being divided into a separate group.

[0105] (4) Construction of cumulative frequency distribution curve: Based on the above cumulative frequency distribution curve construction method, a cumulative frequency histogram of flooding depth of storm surge disasters in each typical return period in Putuo District, Zhoushan City was established, where the horizontal axis is the flooding depth, the main vertical axis is the frequency of each group, and the secondary vertical axis is the cumulative frequency. The results are shown in Figures 3 to 8 .

[0106] (5) Standard value: Pass Figures 3 to 8 It can be seen that the value of the group with a cumulative frequency of 80% as the standard of the flooding depth index will increase as the return period increases. Therefore, it is necessary to first determine which typical return period to use as the simulation scenario of the flooding depth. Considering that the general protection level of the seawall in Zhoushan City is 20-50 years, the simulation results of the flooding depth of storm surge disasters under the 100-year return period scenario are considered as the data source of the flooding depth index of Putuo District, Zhoushan City. Based on this, a water depth of 2.6m is recommended as the flooding depth index standard for the storm surge disaster key defense area in Putuo District, Zhoushan City.

[0107] Impact Population Indicators

[0108] (1) Data simulation: Conduct assessment simulation on the affected population and obtain corresponding results.

[0109] (2) Data description: There are 6447 corresponding flood depth data, with a value range of 0.1-0.85 million people.

[0110] (3) Sample grouping: divided into five groups: 0.1, 0.25, 0.35, 0.5 and 0.85, with the unit being 10,000 people.

[0111] (4) Construction of cumulative frequency distribution curve: Based on the above-mentioned cumulative frequency distribution curve construction method, a cumulative frequency histogram of the affected population in Putuo District, Zhoushan City was established, where the horizontal axis is the affected population, the main vertical axis is the frequency of each group, and the secondary vertical axis is the cumulative frequency. The results are shown in Fig. 9 .

[0112] (5) Standard value: Pass Fig. 9It can be seen that the group with the highest frequency is the 3,500 person group, with a cumulative frequency of nearly 70% (69.77%), followed by the 5,000 person group, with a cumulative frequency of nearly 95% (94.81%). Taking into account that the affected population indicator is more important than the economic loss indicator, the value of the group with a cumulative frequency of nearly 70% is recommended as the affected population indicator standard for the storm surge key defense area in Putuo District, Zhoushan City, and the result is 3,500 people.

[0113] Direct economic loss index

[0114] (1) Data simulation: Conduct assessment and simulation of direct economic losses to obtain corresponding results.

[0115] (2) Data description: There are 6447 corresponding flooding depth data, with a value range of 0.0001-6813195 yuan.

[0116] (3) Sample grouping: According to data characteristics, the samples are grouped into 0.0001-10, 10-100, 100-1000, 1000-10000, 10000-100000 (each 10000 is a group), 100000-1000000 (each 100000 is a group), and 1000000-10000000.

[0117] (4) Construction of cumulative frequency distribution curve: Based on the above cumulative frequency distribution curve construction method, a cumulative frequency histogram of direct economic losses in Putuo District, Zhoushan City was established, where the horizontal axis is the direct economic losses, the main vertical axis is the frequency of each group, and the secondary vertical axis is the cumulative frequency. The results are shown in Fig.10 .

[0118] (5) Standard value: Pass Fig.10 It can be seen that the group with the highest frequency is the 10,000 yuan group, with a cumulative frequency of nearly 25% (23.82%), followed by the 300,000 yuan group, with a cumulative frequency of nearly 80% (79.2%). Considering that the direct economic loss index is not as important as the affected population index, the value of the group with a cumulative frequency of nearly 80% is recommended as the affected population index standard for the storm surge key defense area in Putuo District, Zhoushan City, and the result is 300,000 yuan.

[0119] Indirect economic loss index

[0120] (1) Data simulation: Conduct assessment and simulation of indirect economic losses to obtain corresponding results.

[0121] (2) Data description: There are 6446 corresponding flooding depth data, with a value range of 0.01-3058989 yuan.

[0122] (3) Sample grouping: According to data characteristics, the samples are grouped into 0.01-10, 10-100, 100-1000, 1000-10000 (each 1000 is a group), 10000-100000 (each 10000 is a group), 100000-1000000, and 1000000-10000000.

[0123] (4) Construction of cumulative frequency distribution curve: Based on the above cumulative frequency distribution curve construction method, a cumulative frequency histogram of indirect economic losses in Putuo District, Zhoushan City was established, where the horizontal axis is the indirect economic losses, the main vertical axis is the frequency of each group, and the secondary vertical axis is the cumulative frequency. The results are shown in Fig.11 .

[0124] (5) Standard value: Pass Fig.11 It can be seen that the group with the highest frequency is the 1,000 yuan group, with a cumulative frequency of nearly 25% (22.31%), followed by the 40,000 yuan group, with a cumulative frequency of nearly 75% (76.07%). Considering that the indirect economic loss indicator is the weakest, the value of the group with a cumulative frequency of nearly 75% is recommended as the index standard for the population affected by the storm surge key defense area in Putuo District, Zhoushan City, and the result is 40,000 yuan.

[0125] In the above example, the storm surge water increase index needs to be set. The water depth is generally grouped at intervals of 0.1m. First, the choice of the recurrence period is determined to be 100 years based on the protection level of the general seawall in Zhoushan City. Since the frequency difference between each group is not large, the water depth with 80% frequency is selected as the index standard, and the water depth of 2.6m is obtained as the flooding water depth index standard for the storm surge disaster key defense area in Putuo District, Zhoushan City.

[0126] In the affected population indicator, the range of the affected population is 0.1-0.85 thousand people. According to the research report related to the data characteristics, the population standard is divided into 5 groups. The cumulative frequency corresponding to 0.35 thousand people is nearly 70%, and the frequency of the 0.35 thousand people group is the largest. The cumulative frequency of the 0.5 thousand people group is 94.81% (nearly 95%). The affected population indicator is more important, so 0.35 thousand people are used as the affected population standard.

[0127] Among the direct economic loss indicators, the groups with the highest frequency are the 10,000 yuan group and the 300,000 yuan group, with cumulative frequencies of 23.82% and 79.2% respectively. Since the direct economic loss indicator is lower than the affected population indicator, 300,000 yuan is selected as the direct loss standard.

[0128] The selection of indirect loss indicators is based on similar direct economic loss standards. The value of the group with a cumulative frequency of 75% is used as the standard, and 40,000 yuan is obtained as the indirect loss standard.

[0129] After determining the division criteria corresponding to the flooded water depth data, affected population data, direct economic loss data, and indirect economic loss data, the data in the upper bound can be obtained, and then the corresponding potential disaster key defense areas can be known. As for how to select the disaster key defense areas corresponding to the independent judgment data (flooded water depth data) and the parallel judgment data (affected population data, direct economic loss data, and indirect economic loss data) according to the preset conditions, this scheme has been mentioned in the previous steps and will not be repeated here.

[0130] like Fig.12 As shown, based on the above scheme, the present invention also provides a storm surge disaster key defense area selection system based on the cumulative frequency method, which includes:

[0131] A server is used to store storm surge disaster assessment information corresponding to different regions;

[0132] The data scheduling module is used to obtain the storm surge assessment information corresponding to the area to be selected, extract the data according to the preset conditions, and generate the data set to be processed;

[0133] The data processing module is used to obtain the data set to be processed, perform data screening on the data set to be processed, obtain a set of independent judgment indicators and three sets of different and classified parallel judgment indicators, and use them as indicators for selecting key defense areas for storm surge disasters;

[0134] A data analysis module is used to obtain independent judgment indicators and parallel judgment indicators, perform cumulative frequency analysis on the independent judgment indicators and parallel judgment indicators according to preset conditions, and determine the cumulative frequencies and division standards corresponding to the independent judgment indicators and the parallel judgment indicators respectively;

[0135] The data matching module is used to determine the potential key defense areas corresponding to the three sets of parallel judgment indicators according to the division standards corresponding to the three sets of parallel judgment indicators according to preset conditions, and then spatially superimpose the potential key defense areas corresponding to the three sets of parallel judgment indicators to obtain the overlapping parts and output the first key defense area. It is also used to obtain the key defense area corresponding to the independent judgment indicator according to the division standards corresponding to the independent judgment indicator and set it as the second key defense area.

[0136] The spatial selection module is used to combine the first key defense area with the second key defense area and output the storm surge disaster key defense area finally selected and determined.

[0137] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0138] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0139] The above descriptions are only some embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any equivalent device or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for selecting key storm surge disaster defense areas based on the cumulative frequency method, characterized in that: include: S01, obtaining storm surge disaster assessment information corresponding to the area to be selected, and then extracting data according to preset conditions to generate a data set to be processed; S02, obtaining a data set to be processed, screening the data set to be processed, obtaining a set of independent judgment indicators and three sets of parallel judgment indicators that are different and classified in parallel, and using them as indicators for selecting key defense areas for storm surge disasters; S03. Obtain independent judgment indicators and parallel judgment indicators, perform cumulative frequency analysis on the independent judgment indicators and the parallel judgment indicators according to preset conditions, and determine the cumulative frequencies and classification standards corresponding to the independent judgment indicators and the parallel judgment indicators respectively; S04, according to the division standards corresponding to the three groups of parallel judgment indicators, determine the potential key defense areas corresponding to the three groups of parallel judgment indicators according to the preset conditions, then spatially superimpose the potential key defense areas corresponding to the three groups of parallel judgment indicators to obtain the overlapping parts, and output the first key defense area; S05. According to the division standard corresponding to the independent judgment index, a key defense area selected corresponding to the independent judgment index is obtained, which is set as the second key defense area. The first key defense area is combined with the second key defense area to output the storm surge disaster key defense area finally selected and determined; Wherein, in S01, the storm surge disaster assessment information includes at least flood depth data, affected population data, direct economic loss data and indirect economic loss data; The data in the data set to be processed include flood depth data, affected population data, direct economic loss data and indirect economic loss data; In S02, the flood depth data is screened and set as an independent judgment indicator, and the affected population data, direct economic loss data and indirect economic loss data are screened and set as parallel judgment indicators; S03. The method of obtaining the independent judgment index and the parallel judgment index, performing cumulative frequency analysis on the independent judgment index and the parallel judgment index according to preset conditions, and determining the cumulative frequencies and division standards corresponding to the independent judgment index and the parallel judgment index respectively includes: S031. Obtain independent judgment indicators and parallel judgment indicators, and group corresponding sample data in the independent judgment indicators and the parallel judgment indicators according to preset interval units; S032. Grouping the sample data according to the independent judgment index and the parallel judgment index, sorting the sample data in ascending order, constructing frequency distribution histograms respectively and constructing cumulative frequency distribution curves on the frequency distribution histograms, wherein the primary ordinate of the histogram indicates the frequency of each data grouping, the secondary ordinate indicates the cumulative frequency of the data grouping, and the abscissa indicates the grouping interval; S033. Obtain the frequency distribution histogram and cumulative frequency distribution curve corresponding to the independent judgment index and the parallel judgment index, and determine the cumulative frequency corresponding to each sample data group of the independent judgment index and the parallel judgment index according to preset conditions; S034. Obtain a division standard according to the cumulative frequencies corresponding to each sample data grouping of the independent judgment index and the parallel judgment index, and set the division standard as a threshold.

2. The method for selecting key storm surge disaster defense areas based on the cumulative frequency method according to claim 1, characterized in that: In S031, the independent judgment indicators and the parallel judgment indicators are grouped. The unit of the flooded water depth data is "meter" and grouped at intervals of 0.1m; The population data affected adopts "10,000 people" as the unit of measurement and is grouped into intervals of 1,000 people; The units of direct economic loss data and indirect economic data are both measured in "10,000 yuan" and grouped in 10,000 yuan.

3. The method for selecting key storm surge disaster defense areas based on the cumulative frequency method according to claim 1 or 2, characterized in that: S04. According to the division standards corresponding to the three groups of parallel judgment indicators, the potential key defense areas corresponding to the three groups of parallel judgment indicators are determined according to preset conditions, and then the potential key defense areas corresponding to the three groups of parallel judgment indicators are spatially superimposed to obtain overlapping parts, and the method of outputting the first key defense area includes: S041. According to the division criteria corresponding to the three groups of parallel judgment indicators, the sample data in the parallel judgment indicators are divided into an upper bound part greater than or equal to the threshold and a lower bound part less than the threshold; S042, setting the upper bounds corresponding to the three sets of parallel judgment indicators as potential key areas, and setting the lower bounds as non-key areas; S043. Spatially superimpose the potential key areas corresponding to the three sets of parallel judgment indicators, take the overlapping parts, and output the first key defense area.

4. The method for selecting key storm surge disaster defense areas based on the cumulative frequency method according to claim 3, characterized in that: S05. According to the division standard corresponding to the independent judgment index, a key defense area selected corresponding to the independent judgment index is obtained, set as the second key defense area, and the first key defense area is combined with the second key defense area to output the storm surge disaster key defense area finally selected and determined. The method includes: S051. According to the division standard corresponding to the independent judgment indicator, the sample data in the independent judgment indicator is divided into an upper bound part greater than or equal to the threshold and a lower bound part less than the threshold; S052, taking the upper boundary corresponding to the independent judgment index as the selected key defense area, and setting it as the second key defense area; S053. Combine the first key defense area with the second key defense area, and output the storm surge disaster key defense area finally selected and determined.

5. A storm surge disaster key defense area selection system based on the cumulative frequency method, which uses the storm surge disaster key defense area selection method based on the cumulative frequency method as claimed in any one of claims 1 to 4, characterized in that: It includes: A server is used to store storm surge disaster assessment information corresponding to different regions; The data scheduling module is used to obtain the storm surge assessment information corresponding to the area to be selected, extract the data according to the preset conditions, and generate the data set to be processed; The data processing module is used to obtain the data set to be processed, screen the data set to be processed, obtain a set of independent judgment indicators and three sets of different and classified parallel judgment indicators, and use them as indicators for selecting key defense areas for storm surge disasters; A data analysis module is used to obtain independent judgment indicators and parallel judgment indicators, perform cumulative frequency analysis on the independent judgment indicators and parallel judgment indicators according to preset conditions, and determine the cumulative frequencies and division standards corresponding to the independent judgment indicators and the parallel judgment indicators respectively; The data matching module is used to determine the potential key defense areas corresponding to the three sets of parallel judgment indicators according to the division standards corresponding to the three sets of parallel judgment indicators according to preset conditions, and then spatially superimpose the potential key defense areas corresponding to the three sets of parallel judgment indicators to obtain the overlapping parts and output the first key defense area. It is also used to obtain the key defense area corresponding to the independent judgment indicator according to the division standards corresponding to the independent judgment indicator and set it as the second key defense area. The spatial selection module is used to combine the first key defense area with the second key defense area and output the storm surge disaster key defense area finally selected and determined.

6. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the method for selecting key storm surge disaster defense areas based on the cumulative frequency method as described in one of claims 1 to 4.

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