A flood risk prediction system and method based on multi-source data

Abstract

The application discloses a flood risk prediction system and method based on multi-source data, and relates to the technical field. The system comprises a site module generation module, a historical database construction module, a to-be-optimized index value analysis module, an early warning site determination module, an associated response model construction module and an optimal optimization site information output module. The site module generation module is used for generating a site module by using corresponding monitoring site information of all monitoring sites in the same to-be-monitored area. The historical database construction module is used for constructing a historical database for implementing prediction by the flood risk prediction system. The to-be-optimized index value analysis module is used for analyzing and evaluating to-be-optimized index values of various monitoring sites in the site module of the same to-be-monitored area. The early warning site determination module is used for outputting a monitoring site type needing early warning in the to-be-monitored area as an early warning site. The associated response model construction module is used for constructing an associated response model of various early warning sites and contrast monitoring sites.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of risk prediction, in particular to a flood risk prediction system and method based on multi-source data. BACKGROUND

[0002] Global climate change has led to frequent occurrence of extreme rainfall events. Accurate and timely flood monitoring and early warning are crucial to reducing disaster losses, and scientific and reasonable monitoring station layout is the basis for efficient monitoring. At present, most of the existing flood monitoring stations are laid out based on historical disaster data, topographic features and experience. However, with the acceleration of urbanization, the underlying surface conditions in cities have changed significantly, and the increase in impervious area has intensified the risk of waterlogging. At the same time, factors such as water conservancy construction and land use changes in the basin have also changed the rules of flood evolution. These dynamic changes have gradually exposed many problems in the traditional monitoring station layout mode based on fixed experience. On the one hand, the number of monitoring stations in some high-risk areas (such as urban low-lying areas and river bends) is insufficient, which cannot capture key flood data in time, resulting in delayed disaster warning. On the other hand, there are phenomena of excessive concentration or unreasonable location of monitoring stations in some areas, causing waste of monitoring resources and difficulty in covering flood risk points comprehensively.

[0003] In addition, the existing monitoring station layout lacks effective combination of real-time data and dynamic risk assessment. The traditional layout method cannot adapt to the monitoring needs under different rainfall conditions and flood conditions, and cannot dynamically optimize and adjust the number and location of stations according to real-time monitoring data and flood risk evolution trends. This makes the reliability and accuracy of the monitoring system greatly discounted when facing sudden extreme flood disasters, and cannot provide accurate and effective data support for flood control decision-making.

[0004] Therefore, how to scientifically optimize the number and location of the laid flood monitoring stations based on multi-source data and the layout method effectively predicting the event records in the historical database has become a technical problem to be solved to improve the flood monitoring and early warning capability and enhance the regional flood control and disaster reduction level. SUMMARY

[0005] The purpose of the present application is to provide a flood risk prediction system and method based on multi-source data to solve the problems in the prior art.

[0006] To achieve the above purpose, the present application provides the following technical scheme: a flood risk prediction method based on multi-source data, the method comprising:

[0007] Step S100: Extract all monitoring station information laid in the to-be-monitored area, the monitoring station information including the function type and location of the monitoring station; generate a station module with the corresponding monitoring station information for all monitoring stations in the same to-be-monitored area;

[0008] Step S200: Construct a historical database based on the flood risk prediction system to implement prediction, and the historical database stores effective prediction events of the flood risk, where the effective prediction event refers to an event in which the deviation between the prediction result of an index of the prediction system and the actual monitoring data is less than the corresponding index deviation threshold;

[0009] Step S300: Extract the monitoring data recorded by the site module in the to-be-monitored area, match the monitoring area in the historical database that meets the similar conditions when the site module is independently analyzed as a control monitoring area, extract the monitoring data recorded by each type of monitoring station in the control monitoring area as control data, and analyze and evaluate the to-be-optimized index values of each type of monitoring station in the site module of the same to-be-monitored area;

[0010] Step S400: Based on the to-be-optimized index values of each type of monitoring station, comprehensively analyze the abnormal early warning values of the corresponding to-be-monitored area and perform early warning response based on the abnormal early warning values; and output the monitoring station type that needs to be warned in the to-be-monitored area as a warning station.

[0011] Step S500: Construct an associated response model of each type of warning station and the control monitoring station; filter the optimization information of the warning station based on the associated response model; and traverse all types of warning stations in the same to-be-monitored area to output the corresponding best optimization station information.

[0012] Further, all monitoring stations in the same to-be-monitored area are generated into a site module with corresponding monitoring station information, including the following steps:

[0013] The same type of monitoring station is taken as the same type of monitoring station, and a rectangular coordinate system is established with the center point of the to-be-monitored area; different types of monitoring stations are distinguished and marked in the rectangular coordinate system, and the same type of monitoring station is taken as a group of stations, and the straight-line distance of adjacent monitoring stations in the coordinate system is obtained from any coordinate axis direction of the coordinate system; the adjacent monitoring stations are connected in turn, and the straight-line distance is marked on the connecting line of the adjacent stations to form a site module of the same type of monitoring station in the corresponding to-be-monitored area; the site module also records the position coordinates and the number of the same type of monitoring station.

[0014] Further, step S300 includes the following specific process:

[0015] Step S310: The monitoring data refers to various target data required to be monitored by the corresponding site module; a site module of a certain type is determined as a target analysis station, and other types of monitoring stations in the same to-be-monitored area are determined as to-be-analyzed stations; the similar conditions are met as follows:

[0016] First, the types of all monitoring stations in the control monitoring area are the same as the types of the stations in the area to be monitored, and the number of the stations to be analyzed is the same as the number of the same type of stations in the corresponding control monitoring area; second, the stations of the same type as the stations to be analyzed in the control monitoring area respectively satisfy that the position similarity of the corresponding station module is greater than the similarity threshold; the position similarity refers to the distance similarity of adjacent stations and the similarity formed by the azimuth angle similarity in the coordinate system;

[0017] Both of the above conditions are met to satisfy the similarity condition;

[0018] Step S320: The area to be monitored is divided according to the grid, the number of corresponding target analysis stations in each grid is counted, the flood risk area planning map stored by the system is superimposed, the high-risk area is marked, the number N1 of target analysis stations in the corresponding grid of the high-risk area is obtained, and the total number N0 of target analysis stations in the area to be monitored is obtained, and the risk matching degree K1 of the corresponding target analysis stations in the area to be monitored is calculated by using the formula: K1=(N1 / S1)-(N0 / S0), wherein S1 represents the area in the high-risk area, and S0 represents the total area of the area to be monitored;

[0019] A risk matching degree threshold K0 is set, the risk matching degree K2 of the same type of monitoring stations of each control monitoring area and the target analysis station is calculated, when K1≤K0, the first characteristic value of the target analysis station is output as 1; when K1>K0, the difference value of |K1-K2| is calculated, the number N2 of control monitoring areas with a difference value greater than a first difference threshold from the target analysis station is extracted, the first characteristic value P1 is calculated, P1=N2 / N3, N3 represents the number of all control monitoring areas corresponding to the target analysis station; when the first characteristic value is output as 1, it indicates that the target analysis station arranged in the high-risk area does not meet the requirements, the density is low, and optimization is needed; when greater than the threshold K0, the relationship with the control monitoring area is further analyzed in order to quantify the deviation relationship of the risk matching degree of the monitoring data under different distribution states of the target analysis station of this type of monitoring station;

[0020] Step S330: The data peak event of the target analysis station recorded in the historical database in the flood occurrence event is an effective monitoring event, the total number M1 of flood occurrence events monitored by the target analysis station in the historical database is extracted, the extreme event capture rate Z1 of the target analysis station is calculated, Z1=M0 / M1, wherein M0 represents the number of effective monitoring events;

[0021] An extreme event capture rate threshold Z0 is set, and an extreme event capture rate Z2 of each control monitoring area and a same type monitoring station of the target analysis station is calculated. When Z1≤Z0, the second characteristic value of the target analysis station is output as 1; when Z1>Z0, a difference value of |Z1-Z2| is calculated, the number M2 of control monitoring areas with a difference value greater than a second difference value threshold from the target analysis station is extracted, and a second characteristic value P2 is calculated, P2=M2 / N3;

[0022] Step S340: Based on the first characteristic value P1 and the second characteristic value P2, a to-be-optimized index value Q of the corresponding type target analysis station in each station module of the to-be-monitored area is calculated, Q=a1*P1+a2*P2; wherein a1 and a2 represent corresponding reference coefficients.

[0023] The greater the to-be-optimized index, the greater the possibility that the target analysis station has an unreasonable layout; and the historical database control variable is used to realize the targeted analysis of each type of monitoring station, and the rationality of the layout of the same type monitoring station of the control monitoring area is evaluated in the calculation of the to-be-optimized index, while in the foregoing direct judgment of the characteristic value, it is indicated that the layout of the output station is unreasonable.

[0024] Further, step S400 includes the following:

[0025] A to-be-optimized index value threshold Q0 is set, and the to-be-optimized index values of each type of monitoring station in the same to-be-monitored area are compared with the to-be-optimized index value threshold, and the corresponding type monitoring station with Q≥Q0 is marked as an abnormal station; an abnormal early warning value U of the to-be-monitored area is calculated, U=m1 / m2; m1 represents the type number of the abnormal station, and m2 represents the total type number of the monitoring station of the to-be-monitored area;

[0026] When U=0, no early warning is performed; when U≠0, the corresponding type abnormal station is early warned;

[0027] The monitoring station that needs to be early warned is the marked abnormal station.

[0028] Further, step S500 includes the following:

[0029] Step S510: The type number n2 of the control monitoring station recorded in the analysis of the first characteristic of the early warning station and the corresponding risk matching degree difference g1=|K1-K2| are obtained, the minimum area d1 formed by all stations of the same type corresponding to the early warning station is marked, and the station density f1 of the area where the early warning station is located is calculated, f1=h1 / d1, wherein h1 represents the total number of all stations of the same type corresponding to the early warning station; the station density f2 of the area station formed by each control monitoring station and the early warning station of the same type is calculated; the density difference z1 is obtained, z1=|f1-f2|.

[0030] A first data set B1 formed by the early warning site and each control monitoring site is sequentially corresponded, B1=(z1, g1), and a data set formed by the density difference value of the early warning site and each control monitoring site and the risk matching degree difference value can effectively and clearly quantify the monitoring analysis from the data influence level under different layout information, so as to evaluate whether the layout of the early warning site is reasonable and the optimization direction; the correlation response model r1 of the early warning site and the n2 types of control monitoring sites is calculated,

[0031] r1=∑[(z 1i- z 10 )( g1i -g 10 )] / [∑(z 1i -z 10 ) 2 ∑(g 1i -g 10 ) 2 ] 1 / 2 ; wherein z 1i , g 1i represent the i-th density difference value and the risk matching degree difference value in the first data set B formed by the early warning site and each control monitoring site, z 10 , g 10 represent the average value of the corresponding density difference value and the average value of the risk matching degree difference value in all data sets;

[0032] Step S520: Similarly, the correlation response model r2 of the early warning site and the corresponding q2 types of control monitoring sites is calculated when analyzing the second feature; r1 and r2 are compared, and the control monitoring site corresponding to the maximum correlation response model output value and greater than the correlation coefficient threshold value is selected as the monitoring station for investigation; the layout information corresponding to the monitoring station for investigation is used as the optimization information;

[0033] The purpose of analyzing the correlation coefficient which is the maximum and greater than the threshold value is to effectively screen out which control monitoring sites have the layout density influence evaluation index value size relationship with the early warning site; the greater the correlation coefficient, the greater the influence of the layout information on the index evaluation; the more accurate and directional the optimization is;

[0034] Step S530: The monitoring area where each monitoring station for investigation is located is marked as an investigation area, and each type of early warning site corresponds to a set of investigation areas; all early warning sites are traversed to generate a corresponding set of investigation areas; if there is an intersection between each set of investigation areas, the optimization information of the investigation area record corresponding to the intersection is output as the best optimization site information;

[0035] If there is no intersection, the optimization information corresponding to the least number of variable sites is selected as the best optimization site information for each type of early warning site corresponding to the investigation area; if the correlation response model output value is less than or equal to the correlation coefficient threshold value, only the early warning response of the early warning site type is performed.

[0036] A flood risk prediction system based on multi-source data, the system comprising a site module generation module, a historical database construction module, a to-be-optimized index value analysis module, a warning site determination module, a correlation response model construction module, and a best optimization site information output module;

[0037] The site module generation module is configured to generate a site module from all monitoring sites in a same to-be-monitored region based on corresponding monitoring site information;

[0038] The historical database construction module is configured to construct a historical database based on flood risk prediction system implementation prediction,

[0039] The to-be-optimized index value analysis module is configured to analyze and evaluate to-be-optimized index values of various types of monitoring sites in a site module of a same to-be-monitored region;

[0040] The warning site determination module is configured to comprehensively analyze abnormal warning values of a corresponding to-be-monitored region and perform a warning response based on the abnormal warning values; and output a monitoring site type that needs to be warned in the to-be-monitored region as a warning site;

[0041] The correlation response model construction module is configured to construct a correlation response model of various types of warning sites and a control monitoring site;

[0042] The best optimization site information output module is configured to traverse all types of warning sites in a same to-be-monitored region and output corresponding best optimization site information.

[0043] Further, the to-be-optimized index value analysis module comprises a site type determination unit, a first characteristic value calculation unit, a second characteristic value calculation unit, and a to-be-optimized index value calculation unit;

[0044] The site type determination unit is configured to determine a certain type of site module as a target analysis site, and other types of monitoring sites in a same to-be-monitored region as to-be-analyzed sites;

[0045] The first characteristic value calculation unit is configured to calculate a first characteristic value of a target analysis site and a control monitoring region based on a risk matching degree;

[0046] The second characteristic value calculation unit is configured to calculate a second characteristic value of a target analysis site and a control monitoring region based on an extreme event capture rate;

[0047] The to-be-optimized index value calculation unit is configured to calculate a to-be-optimized index value based on the first characteristic value and the second characteristic value.

[0048] Further, the correlation response model construction module comprises a density difference value calculation unit, a characteristic value difference calculation unit, and a correlation response model output comparison unit;

[0049] The density difference calculation unit calculates the station density of the regional station of the same type as the control monitoring station and the early warning station, and calculates the density difference;

[0050] The characteristic value difference calculation unit is used for calculating the difference based on the risk matching degree and the extreme event capture rate;

[0051] The correlation response model output comparison unit is used for calculating the correlation coefficient based on the data set, selecting the control monitoring station corresponding to the analysis as the monitoring station for investigation when the correlation response model output value is maximum and greater than the correlation coefficient threshold, taking the layout information corresponding to the monitoring station for investigation as the optimization information, and comparing and analyzing the output best optimization station information.

[0052] Compared with the prior art, the beneficial effects of the present application are:

[0053] 1. The present application can accurately identify the deficiencies of the site layout in quantity and position by extracting the site information of the region to be monitored to generate a site module, matching the control monitoring region with the effective prediction events in the historical database, and analyzing the to-be-optimized index value.

[0054] 2. The historical database constructed in the present application stores effective prediction events, and can dynamically evaluate the rationality of the site layout based on multi-source data by comparing and analyzing the data of the region to be monitored with the data of the control region. When the environment of the region to be monitored changes, the control region can be matched again, the to-be-optimized index can be calculated, and then the optimization scheme can be determined, so that the site layout can adapt to the change of the flood evolution law of the basin, and the problem of dynamic adjustment of the traditional layout can be solved, and the adaptability of the monitoring system to environmental changes can be enhanced.

[0055] 3. The present application can identify the sites with unreasonable layout by evaluating the to-be-optimized index value. For the case that the site density of the high-risk region is insufficient, it is suggested to add new sites; for the redundant sites with low contribution degree, it is suggested to migrate or replace, so as to optimize the resource allocation while ensuring the monitoring effect. For example, by calculating the correlation coefficient of the site density difference and the risk matching degree difference, the optimal optimization information is screened out, the best monitoring effect is achieved with the least number of sites, resource waste is avoided, and the overall efficiency of the monitoring system is improved. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 It is a flowchart of the flood risk prediction method based on multi-source data. DETAILED DESCRIPTION

[0057] Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present application.

[0058] Embodiment: As Figure 1As shown, the present application provides a flood risk prediction method based on multi-source data, the method comprising:

[0059] Step S100: Extract all monitoring station information laid in the to-be-monitored area, the monitoring station information including the functional type and location of the monitoring station; generate a station module with the corresponding monitoring station information for all monitoring stations in the same to-be-monitored area;

[0060] Step S200: Construct a historical database for the flood risk prediction system to implement prediction, the historical database storing effective prediction events of flood risk, an effective prediction event being an event in which the deviation between the prediction result of the index of the prediction system and the actual monitoring data is less than the corresponding index deviation threshold;

[0061] Step S300: Extract the monitoring data recorded by the station module in the to-be-monitored area, match the monitoring area in the historical database that meets the similar conditions when the station module is independently analyzed as a control monitoring area, extract the monitoring data recorded by each type of monitoring station in the control monitoring area as control data, and analyze and evaluate the to-be-optimized index value of each type of monitoring station in the station module of the same to-be-monitored area;

[0062] Step S400: Based on the to-be-optimized index value of each type of monitoring station, comprehensively analyze the abnormal early warning value of the corresponding to-be-monitored area and perform early warning response based on the abnormal early warning value; output the monitoring station type that needs to be warned in the to-be-monitored area as a warning station;

[0063] Step S500: Construct an associated response model of each type of warning station and the control monitoring station; filter the optimization information of the warning station based on the associated response model; traverse all types of warning stations in the same to-be-monitored area, and output the corresponding best optimization station information.

[0064] Generating a station module with the corresponding monitoring station information for all monitoring stations in the same to-be-monitored area comprises the following steps:

[0065] Monitoring stations of the same functional type are regarded as the same type of monitoring station, a rectangular coordinate system is established with the center point of the to-be-monitored area; different types of monitoring stations are distinguished and marked in the rectangular coordinate system, and the same type of monitoring station is taken as a group of stations; the straight-line distance of adjacent monitoring stations in the coordinate system is obtained from any coordinate axis direction of the coordinate system; the adjacent monitoring stations are connected in turn, and the straight-line distance is marked on the connecting line of the adjacent stations to form a station module of the same type in the corresponding to-be-monitored area; the station module also records the position coordinates and number of the same type of monitoring station.

[0066] The adjacent monitoring stations are obtained from any coordinate axis direction of the coordinate system, which means that the coordinates of the same type of monitoring stations are recorded as a(-2, 3), b(1, 5) and c(3, -1) from the negative direction of the x-axis, and the connection order is abc.

[0067] The step S300 includes the following specific processes:

[0068] Step S310: The monitoring data refers to various target data required to be monitored by the corresponding station module; a certain type of station module is determined as a target analysis station, and other types of monitoring stations in the same to-be-monitored area are determined as to-be-analyzed stations; the similar conditions are met as follows:

[0069] First, the number of to-be-analyzed stations is the same as the number of the same type of stations in the corresponding monitoring area; second, the same type of stations in the corresponding monitoring area meets the position similarity of the corresponding station module greater than the similarity threshold; the position similarity refers to the distance similarity of adjacent stations and the azimuth angle similarity formed in the coordinate system.

[0070] Both of the above conditions meet the similar conditions.

[0071] Step S320: The to-be-monitored area is divided according to the grid, the number of corresponding target analysis stations in each grid is counted, the flood risk area planning map stored in the system is superimposed, the high-risk area is marked, the number N1 of target analysis stations in the corresponding grid of the high-risk area is obtained, and the total number N0 of target analysis stations in the to-be-monitored area is obtained, and the formula K1=(N1 / S1)-(N0 / S0) is used to calculate the risk matching degree K1 of the corresponding target analysis stations in the to-be-monitored area, wherein S1 represents the area in the high-risk area, and S0 represents the total area of the to-be-monitored area.

[0072] The risk matching degree threshold K0 is set, the risk matching degree K2 of the same type of monitoring stations of each corresponding monitoring area and the target analysis station is calculated, when K1≤K0, the first characteristic value of the target analysis station is output as 1; when K1>K0, the difference value of |K1-K2| is calculated, the number N2 of the corresponding monitoring areas whose difference value with the target analysis station is greater than the first difference threshold is extracted, the first characteristic value P1 is calculated, P1=N2 / N3, N3 represents the number of all corresponding monitoring areas of the target analysis station; when the first characteristic value is output as 1, it means that the target analysis station arranged in the high-risk area does not meet the requirements, the density is low, and optimization is required; when the threshold K0 is greater than the threshold K0, the relationship with the corresponding monitoring area is further analyzed, in order to quantify the deviation relationship of the risk matching degree of the monitoring data under different distribution states of the target analysis station.

[0073] Step S330: The event in which the data peak in the flood occurrence event recorded in the historical database of the target analysis site is an effective monitoring event. The total number M1 of flood occurrence events monitored by the target analysis site in the historical database is extracted. The extreme event capture rate Z1 of the target analysis site is calculated, Z1=M0 / M1, where M0 represents the number of effective monitoring events; the definition of effective monitoring event is that according to actual demand, if any one of the actual same type monitoring stations captures the data peak in the flood occurrence event, the event is an effective monitoring event; the target analysis site is a group of stations and is not independent;

[0074] The extreme event capture rate threshold Z0 is set. The extreme event capture rate Z2 of the same type monitoring station of the target analysis site in each control monitoring area is calculated. When Z1≤Z0, the second characteristic value of the target analysis site is output as 1. When Z1>Z0, the difference value of |Z1-Z2| is calculated. The number M2 of control monitoring areas with a difference value greater than the second difference value threshold from the target analysis site is extracted. The second characteristic value P2 is calculated, P2=M2 / N3.

[0075] Step S340: Based on the first characteristic value P1 and the second characteristic value P2, the optimization index value Q of the corresponding type target analysis site in each station module of the to-be-monitored area is calculated, Q=a1*P1+a2*P2; where a1 and a2 represent corresponding reference coefficients.

[0076] The larger the optimization index is, the greater the possibility of unreasonable layout of the target analysis site is. The control variables in the historical database are used to realize the targeted analysis of each type of monitoring station. The rationality of the layout of the same type monitoring station in the control monitoring area is evaluated in the calculation of the optimization index. In the foregoing direct judgment of the characteristic value, the layout of the output station is unreasonable.

[0077] Step S400 includes the following:

[0078] The optimization index value threshold Q0 is set, and the optimization index value thresholds of different monitoring stations are the same. Generally, Q0=1. The optimization index value of each type of monitoring station in the same to-be-monitored area is compared with the optimization index value threshold. The type of monitoring station corresponding to Q≥Q0 is marked as an abnormal station. The abnormal early warning value U of the to-be-monitored area is calculated, U=m1 / m2; m1 represents the type number of abnormal stations, and m2 represents the total type number of monitoring stations in the to-be-monitored area.

[0079] When U=0, no early warning is performed; when U≠0, the corresponding type of abnormal station is warned;

[0080] The monitoring station that needs to be warned refers to the marked abnormal station.

[0081] Step S500 includes the following:

[0082] Step S510: Obtain the contrast monitoring station type number n2 and the corresponding risk matching degree difference g1=|K1-K2| recorded by the early warning station when analyzing the first feature, mark the minimum area d1 of the region formed by all stations of the same type corresponding to the early warning station, and calculate the station density f1 of the region where the early warning station is located, f1=h1 / d1, where h1 represents the total number of all stations of the same type corresponding to the early warning station; calculate the station density f2 of the region formed by the same type of each contrast monitoring station and the early warning station; obtain the density difference z1, z1=|f1-f2|;

[0083] The first data set B1 formed by the early warning station and each contrast monitoring station is sequentially corresponded, B1=(z1, g1), and the density difference of the early warning station and each contrast monitoring station corresponds to the risk matching degree difference to form a data set, which can effectively and clearly quantify the monitoring analysis from the data impact level under different layout information, so as to evaluate whether the layout of the early warning station is reasonable and the optimization direction; calculate the correlation response model r1 of the early warning station and n2 types of contrast monitoring stations,

[0084] r1=∑[(z 1i- z 10 )( g1i -g 10 )] / [∑(z 1i -z 10 ) 2 ∑(g 1i -g 10 ) 2 ] 1 / 2 ; wherein z 1i , g 1i represent the i-th density difference and risk matching degree difference in the first data set B formed by the early warning station and each contrast monitoring station, z 10 , g 10 represent the average value of the corresponding density difference and the average value of the risk matching degree difference in all data sets;

[0085] Step S520: Similarly, calculate the correlation response model r2 of the early warning station and q2 types of contrast monitoring stations when analyzing the second feature; compare r1 and r2, and select the contrast monitoring station corresponding to the maximum correlation response model output value and greater than the correlation threshold value as the monitoring station for investigation; the layout information corresponding to the monitoring station for investigation is used as the optimization information.

[0086] The purpose of analyzing the correlation coefficient that is maximum and greater than the threshold value is to effectively screen which control monitoring sites have layout density influence on the evaluation index value of the early warning site; the greater the correlation coefficient, the greater the influence of layout information on the evaluation of the index; the stronger the direction and accuracy of optimization;

[0087] As shown in the embodiment: obtaining the type q2 of the control monitoring site recorded by the early warning site in analyzing the second feature and the corresponding extreme event capture rate difference g2=|Z1-Z2|, calculating the site density f3 of each control monitoring site and the same type site of the early warning site; obtaining the density difference z2, z2=|f1-f3|; the second data set B2 formed by the early warning site and each control monitoring site is sequentially corresponding, B2=(z2, g2), and the correlation coefficient r2 of the early warning site and the q2 type control monitoring site is calculated;

[0088] Step S530: marking the monitoring area where each investigation monitoring site is located as an investigation area, and each type of early warning site corresponds to a set of investigation areas; traversing all early warning sites to generate corresponding sets of investigation areas; if there is an intersection between each set of investigation areas, output the optimization information of the intersection corresponding to the investigation area record as the best optimization site information; the optimization information refers to the position layout relationship of each type of monitoring site in the area record, such as the information recorded by the site module analyzed above.

[0089] If there is no intersection, for each type of early warning site corresponding to the investigation area, select the optimization information corresponding to the least number of variable sites as the best optimization site information; if the output values of the correlation response model are all less than or equal to the correlation coefficient threshold value, only the early warning response of the early warning site type is performed.

[0090] A flood risk prediction system based on multi-source data, the system comprising a site module generation module, a historical database construction module, a to-be-optimized index value analysis module, an early warning site determination module, a correlation response model construction module, and a best optimization site information output module;

[0091] The site module generation module is used to generate a site module by using corresponding monitoring site information of all monitoring sites in the same to-be-monitored area;

[0092] The historical database construction module is used to construct a historical database based on the flood risk prediction system for implementation of prediction,

[0093] The to-be-optimized index value analysis module is used to analyze and evaluate the to-be-optimized index values of each type of monitoring site in the site module of the same to-be-monitored area;

[0094] The early warning site determination module is used to comprehensively analyze the abnormal early warning values of the corresponding to-be-monitored area and perform an early warning response based on the abnormal early warning values; the type of monitoring site that needs to be early warned in the to-be-monitored area is an early warning site.

[0095] The correlation response model construction module is configured to construct a correlation response model of each type of early warning station and the control monitoring station;

[0096] The optimal optimization station information output module is configured to traverse all types of early warning stations in the same to-be-monitored region and output corresponding optimal optimization station information.

[0097] The to-be-optimized index value analysis module includes a station type determination unit, a first characteristic value calculation unit, a second characteristic value calculation unit, and a to-be-optimized index value calculation unit.

[0098] The station type determination unit is configured to determine a certain type of station module as a target analysis station and other types of monitoring stations in the same to-be-monitored region as to-be-analyzed stations.

[0099] The first characteristic value calculation unit is configured to calculate a first characteristic value of the target analysis station and the control monitoring region based on a risk matching degree.

[0100] The second characteristic value calculation unit is configured to calculate a second characteristic value of the target analysis station and the control monitoring region based on an extreme event capture rate.

[0101] The to-be-optimized index value calculation unit is configured to calculate a to-be-optimized index value based on the first characteristic value and the second characteristic value.

[0102] The correlation response model construction module includes a density difference calculation unit, a characteristic value difference calculation unit, and a correlation response model output comparison unit.

[0103] The density difference calculation unit calculates the station density of the region station formed by the same type of each control monitoring station and early warning station and calculates a density difference.

[0104] The characteristic value difference calculation unit is configured to calculate a difference based on a risk matching degree and a difference based on an extreme event capture rate.

[0105] The correlation response model output comparison unit is configured to calculate a correlation coefficient based on a data group, select a control monitoring station corresponding to an analysis of a correlation response model output value being the largest and greater than a correlation coefficient threshold value as an investigation monitoring station, take layout information corresponding to the investigation monitoring station as optimization information, and compare and analyze to output optimal optimization station information.

[0106] Finally, it should be noted that the above only describes the preferred embodiments of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art will appreciate that the technical solutions described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalent ones. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for flood risk prediction based on multi-source data, characterized in that: The method comprises: Step S100: extracting all monitoring station information arranged in the to-be-monitored region, the monitoring station information comprising a functional type and a position of the monitoring station; and generating a station module with the corresponding monitoring station information for all monitoring stations in the same to-be-monitored region; Step S200: constructing a historical database based on a flood risk prediction system for implementation of prediction, the historical database storing effective prediction events of the flood risk, the effective prediction event being an event in which a deviation between a prediction result of an index of the prediction system and actual monitoring data is less than a corresponding index deviation threshold; Step S300: extracting monitoring data recorded by the station module in the to-be-monitored region, matching a monitoring region in the historical database that meets a similar condition when the station module is independently analyzed as a control monitoring region, extracting monitoring data recorded by all types of monitoring stations in the control monitoring region as control data, and analyzing and evaluating to-be-optimized index values of all types of monitoring stations in the station module of the same to-be-monitored region; Step S400: comprehensively analyzing abnormal early warning values of the corresponding to-be-monitored region based on the to-be-optimized index values of all types of monitoring stations and performing early warning response based on the abnormal early warning values; and outputting a monitoring station type that needs early warning in the to-be-monitored region as an early warning station; Step S500: constructing an associated response model of all types of early warning stations and control monitoring stations; screening optimization information of the early warning station based on the associated response model; and traversing all types of early warning stations in the same to-be-monitored region to output corresponding best optimization station information.

2. The flood risk prediction method based on multi-source data according to claim 1, characterized in that: The generation of the station module with the corresponding monitoring station information for all monitoring stations in the same to-be-monitored region comprises the following steps: monitoring stations of the same functional type are taken as the same type of monitoring stations, a rectangular coordinate system is established with a center point of the to-be-monitored region; different types of monitoring stations are distinguished and marked in the rectangular coordinate system, and the same type of monitoring stations is taken as a group of stations, a straight-line distance of adjacent monitoring stations on the rectangular coordinate system is obtained from any coordinate axis direction of the rectangular coordinate system; adjacent monitoring stations are connected in sequence, and the straight-line distance is marked on the connecting line of the adjacent stations to form a station module of the same type of monitoring stations in the corresponding to-be-monitored region; the station module further records position coordinates and a number of the same type of monitoring stations.

3. The method of claim 1, wherein: The step S300 comprises the following specific process: Step S310: the monitoring data refers to all types of target data required to be monitored by the corresponding station module; a certain type of station module is determined as a target analysis station, and other types of monitoring stations in the same to-be-monitored region are determined as to-be-analyzed stations; the similar condition is met as follows: first, all types of monitoring stations in the control monitoring region are of the same type as the to-be-monitored region, and the number of to-be-analyzed stations is the same as the number of the same type of stations in the corresponding control monitoring region; second, the same type of stations as the to-be-analyzed stations in the control monitoring region respectively meet a position similarity of the corresponding station module being greater than a similarity threshold; the position similarity refers to a distance similarity of adjacent stations and a bearing angle similarity formed in the rectangular coordinate system; both of the above two conditions are met as the similar condition is met. Step S320: The to-be-monitored region is divided according to a grid, the number of corresponding target analysis sites in each grid is counted, a flood risk area planning map stored by the system is superimposed, a high-risk area is marked, the number N1 of target analysis sites in the corresponding grid of the high-risk area is obtained, and the total number N0 of target analysis sites in the to-be-monitored region is obtained, and the risk matching degree K1 of the to-be-monitored region corresponding to the target analysis site is calculated by using the formula: K1=(N1 / S1)-(N0 / S0), wherein S1 represents the area of the high-risk area, and S0 represents the total area of the to-be-monitored region; A risk matching degree threshold K0 is set, the risk matching degree K2 of the same type of monitoring site of the target analysis site in each control monitoring region is calculated, when K1≤K0, the first characteristic value of the target analysis site is output as 1; when K1>K0, the difference value of |K1-K2| is calculated, the number N2 of control monitoring regions with a difference value greater than a first difference threshold from the target analysis site is extracted, the first characteristic value P1 is calculated, P1=N2 / N3, and N3 represents the number of all control monitoring regions corresponding to the target analysis site; Step S330: The event in which the data peak of the target analysis site recorded in the historical database in the flood occurrence event is an effective monitoring event, the total number M1 of flood occurrence events monitored by the target analysis site in the historical database is extracted, the extreme event capture rate Z1 of the target analysis site is calculated, Z1=M0 / M1, wherein M0 represents the number of effective monitoring events; An extreme event capture rate threshold Z0 is set, the extreme event capture rate Z2 of the same type of monitoring site of the target analysis site in each control monitoring region is calculated, when Z1≤Z0, the second characteristic value of the target analysis site is output as 1; when Z1>Z0, the difference value of |Z1-Z2| is calculated, the number M2 of control monitoring regions with a difference value greater than a second difference threshold from the target analysis site is extracted, the second characteristic value P2 is calculated, P2=M2 / N3; Step S340: Based on the first characteristic value P1 and the second characteristic value P2, the to-be-optimized index value Q of the corresponding type of target analysis site in each site module of the to-be-monitored region is calculated, Q=a1*P1+a2*P2; wherein a1 and a2 represent corresponding reference coefficients.

4. The flood risk prediction method based on multi-source data according to claim 3, characterized in that: The step S400 includes the following: A to-be-optimized index value threshold Q0 is set, the size of the to-be-optimized index value of each type of monitoring site in the same to-be-monitored region and the to-be-optimized index value threshold is compared, and the corresponding type of monitoring site with Q≥Q0 is marked as an abnormal site; an abnormal early warning value U of the to-be-monitored region is calculated, U=m1 / m2; m1 represents the number of types of abnormal sites, and m2 represents the total number of types of monitoring sites in the to-be-monitored region; When U=0, no early warning is performed; When U≠0, the corresponding type of abnormal site is early warned; The monitoring site that needs to be early warned is the marked abnormal site.

5. The flood risk prediction method based on multi-source data according to claim 3, characterized in that: The step S500 includes the following: Step S510: Obtain the contrast monitoring station type number n2 recorded by the early warning station when analyzing the first feature and the corresponding risk matching degree difference g1=|K1-K2|, mark the minimum area d1 of the early warning station corresponding to all stations of the same type, and calculate the station density f1 of the area where the early warning station is located, f1=h1 / d1, where h1 represents the total number of all stations of the same type corresponding to the early warning station; calculate the station density f2 of the area where each contrast monitoring station and the early warning station of the same type are located; obtain the density difference z1, z1=|f1-f2|; correspond to each contrast monitoring station, B1=(z1, g1), calculate the correlation response model r1 of the early warning station and the n2 types of contrast monitoring stations, r1 =∑[(z 1i- -z 10 ) 1i -g 10 )] / [∑(z 1i -z 10 ) 2 ∑(g 1i -g 10 ) 2 ] 1 / 2 ; wherein z 1i , g 1i denote the i-th density difference value and the risk matching degree difference value in the first data set B, z 10 , g 10 denote the average value of the corresponding density difference value and the average value of the risk matching degree difference value in all data sets; Step S520: Similarly, calculate the correlation response model r2 of the early warning station and the q2 types of contrast monitoring stations when analyzing the second feature; compare r1 and r2, and select the contrast monitoring station corresponding to the analysis with the maximum correlation response model output value and greater than the correlation threshold value as the monitoring station for investigation; the layout information corresponding to the monitoring station for investigation is used as the optimization information; Step S530: Mark the monitoring area where each monitoring station for investigation is located as an investigation area, and each type of early warning station corresponds to a set of investigation areas; Traverse all early warning stations to generate corresponding sets of investigation areas; if there is an intersection between each set of investigation areas, output the optimization information of the investigation area recorded in the intersection as the best optimization station information; If there is no intersection, for each type of early warning station corresponding to the investigation area, select the optimization information corresponding to the least number of changed stations as the best optimization station information; if the correlation response model output value is less than or equal to the correlation threshold value, only the early warning station type is used for early warning response.

6. A flood risk prediction system based on multi-source data, using the flood risk prediction method based on multi-source data according to any one of claims 1-5, characterized in that: The system comprises a station module generation module, a historical database construction module, a to-be-optimized index value analysis module, an early warning station determination module, a correlation response model construction module, and a best optimization station information output module; The station module generation module is used to generate a station module with corresponding monitoring station information from all monitoring stations in the same to-be-monitored area; The historical database construction module is used to construct a historical database based on the flood risk prediction system for implementation of prediction, The to-be-optimized index value analysis module is used to analyze and evaluate the to-be-optimized index values of each type of monitoring station in the station module of the same to-be-monitored area; The early warning station determination module is used to comprehensively analyze the abnormal early warning values of the corresponding to-be-monitored area and perform early warning response based on the abnormal early warning values; The monitoring station type that needs to be early warned in the to-be-monitored area is the early warning station; The correlation response model construction module is used to construct the correlation response model of each type of early warning station and contrast monitoring station; The best optimization station information output module is used to traverse all types of early warning stations in the same to-be-monitored area and output the corresponding best optimization station information.

7. The flood risk prediction system based on multi-source data according to claim 6, characterized in that: The to-be-optimized index value analysis module comprises a station type determination unit, a first feature value calculation unit, a second feature value calculation unit, and a to-be-optimized index value calculation unit; The station type determining unit is configured to determine a station module of a certain type as a target analysis station, and other monitoring stations of different types in the same to-be-monitored area as to-be-analyzed stations; The first feature value calculating unit is configured to calculate a first feature value of the target analysis station and the control monitoring area based on the risk matching degree; The second feature value calculating unit is configured to calculate a second feature value of the target analysis station and the control monitoring area based on the extreme event capture rate; The to-be-optimized index value calculating unit is configured to calculate a to-be-optimized index value based on the first feature value and the second feature value.

8. The flood risk prediction system based on multi-source data according to claim 7, characterized in that: The correlation response model constructing module comprises a density difference calculating unit, a feature value difference calculating unit, and a correlation response model output comparing unit; The density difference calculating unit calculates the station density of the area stations of the same type composed of each control monitoring station and the early warning station, and calculates the density difference; The feature value difference calculating unit is configured to calculate the difference based on the risk matching degree and the difference based on the extreme event capture rate; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model output comparing unit is configured to calculate the correlation coefficient based on the data set; The correlation response model

Images