Mountain torrent disaster grading early warning method and system based on multi-source data analysis

Abstract

The invention discloses a mountain torrent disaster grading early warning method and system based on multi-source data analysis, and particularly relates to the field of mountain torrent disaster grading early warning, through establishing a mountain torrent disaster risk area soil anti-erosion capability evaluation model, the soil anti-erosion capability of each monitoring early warning area is evaluated, and the mountain torrent disaster risk area soil anti-erosion capability evaluation model is established. The water flow diffusivity of the mountain torrents is evaluated based on the water flow monitoring network data of the mountain torrent disaster early-warning area, and the debris flow transformation tropism of the mountain torrent disaster early-warning area is evaluated according to the real-time soil erosion resistance of the mountain torrent disaster early-warning area in combination with the regional topographic data. And comprehensively evaluating an expected disaster influence level based on debris flow transformation tropism and water flow diffusivity, setting a disaster influence graded early warning gradient interval, and performing graded early warning of disaster influence expectation on a monitoring area.

Description

Technical Field

[0001] The present invention relates to the technical field of graded early warning of flash flood disasters, and more specifically, to a graded early warning method and system for flash flood disasters based on multi-source data analysis. Background Art

[0002] Flash floods refer to the sudden floods that occur in mountainous areas. Flash floods are sudden, with concentrated water volume, high flow velocity, strong destructive power, and mud and even stones in the water flow, which often cause local floods. The soil loss rate assessment caused by flash floods needs to comprehensively consider multiple factors, including rainfall intensity, slope, vegetation cover, soil type, etc.

[0003] Flash floods are highly destructive and sudden. Some areas are prone to flash floods due to their meteorological, soil and topographical characteristics. If the expected destructive impact of flash floods cannot be assessed and warned in time when flash floods occur in these areas, flash floods may cause significant loss of life and property. In addition, if there is no accurate disaster warning classification, it may cause unreasonable allocation of disaster relief and governance resources, leading to further expansion of disaster impacts. Therefore, how to realize a method that can provide timely disaster classification warnings based on multi-source regional characteristic data monitoring in the area when flash floods occur in the area is an urgent problem to be solved.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a flash flood disaster graded warning method and system based on multi-source data analysis to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] S1: Mark the flash flood disaster risk areas and set up a water flow monitoring network in the flash flood disaster risk areas;

[0008] S2: Collect soil information in areas at risk of flash floods and establish a soil anti-scouring capacity assessment model in areas at risk of flash floods;

[0009] S3: Mark the flash flood disaster warning area, obtain the water flow monitoring network data of the flash flood disaster warning area, and evaluate the water flow diffusion capacity of the flash flood based on the monitoring data;

[0010] S4: Calculate the real-time soil erosion resistance in the flash flood warning area, and evaluate the transformation trend of debris flow in the flash flood warning area by combining regional terrain data;

[0011] S5: Set up disaster impact classification gradient intervals, comprehensively assess the expected disaster impact level based on the transformation tendency of debris flow and the water flow diffusion capacity, and issue graded warnings for flash flood disasters.

[0012] In a preferred embodiment, in S1, marking the flash flood disaster risk area, and setting up a water flow monitoring network in the flash flood disaster risk area specifically includes:

[0013] Based on historical meteorological information, the areas where flash floods have occurred are screened out and marked as flash flood disaster areas;

[0014] Set the flash flood disaster monitoring time window length, calculate the ratio of the number of flash flood disasters in all flash flood disaster areas within the window length to the window length, and use it as the flash flood disaster frequency in the area;

[0015] Mark the areas where the frequency of flash flood disasters exceeds the set frequency threshold as flash flood disaster risk areas, and obtain the critical value of flash flood precipitation in each flash flood disaster risk area based on meteorological data;

[0016] Set up equidistant circular monitoring points in areas at risk of flash floods, arrange standard grid water flow sensors, and establish a water flow monitoring network.

[0017] In a preferred embodiment, in S2, collecting soil information in flash flood disaster risk areas and establishing a soil anti-scour capacity assessment model in flash flood disaster risk areas specifically includes:

[0018] Collect soil information data in flash flood risk areas at a set frequency. The soil information data includes: soil shear strength, particle content, moisture content, internal friction angle and void ratio;

[0019] Obtain the soil loss rate after historical flash floods in flash flood risk areas, calculate the mean historical soil loss rate of the region, and adjust the inverse of the mean soil loss rate of all flash flood risk areas to the set interval according to the same scale ratio as the assessment value of the soil anti-scouring capacity of the region;

[0020] A regional soil anti-scour capacity assessment model was established based on soil information data and historical soil loss rate averages in flash flood risk areas;

[0021] Specifically: Where E k is the regional soil anti-scouring capacity assessment value of the k-th flash flood disaster risk area, S k , P ks , P kf , W k ,θ k , G kare the shear strength, coarse particle ratio, fine particle ratio, moisture content, internal friction angle, and porosity ratio of the soil in the kth flash flood disaster risk area, respectively; α, β, γ, λ, and μ are the undetermined coefficients of linear regression, and k' is an adjustable proportional constant, where the undetermined coefficients of linear regression are calculated using the least squares method;

[0022] Set the model update cycle, and refit and optimize the model parameters based on the latest collected data in each update cycle.

[0023] In a preferred embodiment, marking the flash flood disaster warning area in S3, obtaining the water flow monitoring network data of the flash flood disaster warning area, and evaluating the water flow diffusion capacity of the flash flood based on the monitoring data specifically include:

[0024] Based on the real-time precipitation, the areas where the precipitation exceeds the critical value of flash floods are screened and marked as flash flood disaster warning areas;

[0025] Obtain real-time monitoring data from the water flow monitoring network in flash flood disaster warning areas, including the direction and intensity of water flow;

[0026] The monitored water flow directions of all water flow monitoring points are converted into unit vectors and then vector synthesis is performed to calculate the average synthetic vector of all unit vectors, specifically: Where R is the average composite vector, θ i is the angle between the water flow direction monitored by the ith monitoring point and the line connecting the monitoring point to the center of the ring monitoring point, and n is the number of monitoring points set up in the water flow monitoring network;

[0027] The water flow intensity deviation of adjacent monitoring points is calculated, and the water flow intensity balance index is calculated by combining the maximum, minimum and average water flow intensities of all monitoring points. The specific calculation method of the water flow intensity balance index is: Where L is the water flow intensity equilibrium index,

[0029] σ high 2 , σ low 2 are the variance of water flow intensity between the highest ring monitoring point and the lowest ring monitoring point, ST avg , ST max , ST min are the mean, maximum, and minimum values ​​of water flow intensity at all monitoring points;

[0030] The product of the average synthetic vector of the water flow direction and the water flow intensity equilibrium index is taken as the evaluation value of the water flow diffusion capacity.

[0031] In a preferred embodiment, in S4, calculating the real-time soil erosion resistance in the flash flood disaster warning area, combining regional terrain data, and evaluating the tendency of debris flow transformation in the flash flood disaster warning area specifically includes:

[0032] Obtain the latest soil information data and water flow monitoring network data collected in flash flood disaster warning areas;

[0033] The soil information data and the undetermined coefficients of the linear regression obtained by fitting are brought into the regional soil anti-scour capacity assessment model to calculate the soil scour resistance and mark it as the real-time soil scour resistance of the area;

[0034] Obtain the real-time cumulative precipitation in the flash flood disaster warning area, the overflow of the flash flood critical precipitation, and the current unit time precipitation. Calculate the accumulated gravitational potential energy of the overflow precipitation based on the overflow of precipitation and the current unit time precipitation. Specifically: Among them, EP over is the gravitational potential energy of the precipitation overflow at the last collection time, is the differential of the gravitational potential energy of the real-time precipitation;

[0035] The real-time soil erosion resistance, overflow precipitation gravitational potential energy accumulation and average water flow intensity monitored by the water flow monitoring network in the flash flood disaster warning area are normalized and then comprehensively calculated to calculate the debris flow tendency expansion coefficient in the flash flood disaster warning area, which is used to evaluate the debris flow transformation tendency in the area. Specifically, where TR deb is the debris flow expansion coefficient in the area, EP sum , ST avg 、E new are the accumulated gravitational potential energy of overflow precipitation, the average water flow intensity and the real-time soil erosion resistance, ω1, ω2 and ω3 are EP sum , ST avg 、E new The adjustable weight coefficient.

[0036] In a preferred embodiment, in S5, the disaster impact classification gradient interval is set, and the expected disaster impact level is comprehensively evaluated according to the transformation tendency of debris flow and the water flow diffusion capacity, and the graded warning of flash flood disasters specifically includes:

[0037] Set disaster impact warning levels and set default warning gradient intervals for each disaster impact level;

[0038] Obtain all debris flow tendency expansion coefficients and water flow diffusion capacity assessment values, and perform weighted summation of debris flow tendency expansion coefficients and water flow diffusion capacity assessment values ​​to calculate the expected disaster impact index;

[0039] The scale value of the warning gradient interval is adjusted according to the expected disaster index, and the upper limit value of each gradient interval after adjustment is used as the threshold value of the expected disaster impact index corresponding to the warning level, so that the expected disaster impact index of all flash flood disaster warning areas falls within the warning gradient interval of the disaster impact level;

[0040] Disaster impact warning grades are set according to the warning gradient intervals of the disaster impact level, and graded warnings of expected disaster impacts are issued for flash flood disaster warning areas.

[0041] On the other hand, the present invention provides a flash flood disaster classification warning system based on multi-source data analysis, including a regional data acquisition module, a soil anti-scouring capacity assessment module, a water flow diffusion capacity assessment module, a debris flow conversion tendency assessment module, and a disaster classification warning module:

[0042] Regional data collection module: classify and mark the monitoring and early warning areas, set up detection devices, and collect soil, meteorological, and terrain information data of the monitoring and early warning areas according to actual needs and settings;

[0043] Soil anti-scour capacity assessment module: A regional soil anti-scour capacity assessment model is established based on soil information data and historical soil loss rate averages in flash flood risk areas, and model parameters are refitted and optimized based on the latest data collected regularly at a set period;

[0044] Water flow diffusion capacity assessment module: Based on the direction and intensity of the monitored water flow, the module analyzes the dispersion of the water flow direction and the balance of the water flow intensity, and assesses the diffusion capacity of the water flow;

[0045] Debris flow conversion tendency assessment module: Based on the real-time soil scour resistance and the accumulated gravity potential energy of overflow precipitation in the region, combined with the average value of the monitored water flow intensity, the debris flow tendency expansion coefficient is comprehensively calculated to assess the debris flow conversion tendency in the region;

[0046] Disaster graded warning module: Comprehensively evaluate the expected disaster impact based on the transformation trend of debris flow and the diffusion capacity of water flow, set graded warning gradient intervals based on the expected disaster impact, and conduct graded warnings for flash flood disaster warning areas.

[0047] The technical effects and advantages of the flash flood disaster classification early warning method and system based on multi-source data analysis of the present invention are as follows:

[0048] Collect soil information in flash flood disaster risk areas, establish a soil anti-scour capacity assessment model for flash flood disaster risk areas. The soil anti-scour capacity assessment helps to understand the blocking capacity of the soil in the area when flash floods occur, and is also one of the assessment factors for the possibility of flash floods turning into debris flows. Obtain water flow monitoring network data in the flash flood disaster warning area. Evaluating the water flow diffusion capacity of flash floods based on monitoring data helps to judge the extensiveness of the current flash flood impact area, so that hidden dangers such as the increase in the number of affected people, economic losses and social impacts can be discovered in time during disaster relief work. Based on real-time soil scour resistance and combined with regional terrain data, the tendency of debris flow conversion in the flash flood disaster warning area is assessed, which helps to know in advance the possibility of flash floods turning into debris flows and causing greater disaster impacts. According to the transformation tendency of debris flow and the water flow diffusion capacity, the expected disaster impact level is comprehensively assessed, and graded warnings for flash flood disasters are given, which helps to improve the timeliness and rationality of resource allocation, and also improve the accuracy of disaster relief plan selection by rescue personnel, reducing the loss of life and property in the affected areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of a flash flood disaster classification early warning method based on multi-source data analysis according to the present invention;

[0050] Figure 2 The present invention is a schematic structural diagram of a graded early warning system for flash flood disasters based on multi-source data analysis. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Example 1

[0053] Figure 1 The present invention provides a flash flood disaster classification early warning method based on multi-source data analysis, which includes the following steps:

[0054] S1: Mark the flash flood disaster risk areas and set up a water flow monitoring network in the flash flood disaster risk areas;

[0055] S2: Collect soil information in areas at risk of flash floods and establish a soil anti-scouring capacity assessment model in areas at risk of flash floods;

[0056] S3: Mark the flash flood disaster warning area, obtain the water flow monitoring network data of the flash flood disaster warning area, and evaluate the water flow diffusion capacity of the flash flood based on the monitoring data;

[0057] S4: Calculate the real-time soil erosion resistance in the flash flood warning area, and evaluate the transformation trend of debris flow in the flash flood warning area by combining regional terrain data;

[0058] S5: Set up disaster impact classification gradient intervals, comprehensively assess the expected disaster impact level based on the transformation tendency of debris flow and the water flow diffusion capacity, and issue graded warnings for flash flood disasters.

[0059] In S1, flash flood disaster risk areas are marked and a water flow monitoring network is set up in the flash flood disaster risk areas.

[0060] Based on historical meteorological information, the areas where flash floods have occurred are screened out and marked as flash flood disaster areas;

[0061] Set the flash flood disaster monitoring time window length, calculate the ratio of the number of flash flood disasters in all flash flood disaster areas within the window length to the window length, and use it as the flash flood disaster frequency in the area;

[0062] Mark the areas where the frequency of flash flood disasters exceeds the set frequency threshold as flash flood disaster risk areas, and obtain the critical value of flash flood precipitation in each flash flood disaster risk area based on meteorological data;

[0063] Set up equidistant circular monitoring points in areas at risk of flash floods, arrange standard grid water flow sensors, and establish a water flow monitoring network.

[0064] The frequency threshold of flash flood disasters can be set by professionals in the field based on real-time meteorological information and the sensitivity of disaster warning and prevention, which will not be elaborated here. Screening out areas where flash flood disasters frequently occur as risk areas and focusing on monitoring risk areas will help improve the regional targeting of flash flood disaster prevention.

[0065] In S2, soil information of flash flood disaster risk areas is collected, and a soil anti-scour capacity assessment model of flash flood disaster risk areas is established. Soil information data of flash flood disaster risk areas are collected at a set frequency, and the soil information data includes: soil shear strength, particle content, moisture content, internal friction angle and void ratio;

[0066] The soil shear strength is the structural strength of the soil, which is usually expressed by its shear strength (Shear Strength), which is the ability of the soil to resist shear failure. The shear strength mainly depends on the internal friction and cohesion of the soil. The commonly used method to evaluate the structural strength of the soil is the Mohr-Coulomb theory, which is calculated by the shear strength formula: τ=c+σtan(θ), where τ, c, σ, and tan(θ) are the shear strength of the soil, the cohesion of the soil, the effective normal stress of the soil, and the tangent of the internal friction angle of the soil, respectively.

[0067] Obtain the soil loss rate after historical flash floods in flash flood risk areas, calculate the mean historical soil loss rate of the region, and adjust the inverse of the mean soil loss rate of all flash flood risk areas to the set interval at the same scale ratio as the assessment value of the soil anti-scouring capacity of the region. For example, for the convenience of calculation, the scale can be adjusted to between 0 and 1.

[0068] A regional soil anti-scour capacity assessment model was established based on soil information data and historical soil loss rate averages in flash flood risk areas;

[0069] Specifically: Where E k is the regional soil anti-scouring capacity assessment value of the k-th flash flood disaster risk area, S k , P ks , P kf , W k ,θ k , G k are the shear strength, coarse particle ratio, fine particle ratio, moisture content, internal friction angle, and porosity ratio of the soil in the kth flash flood disaster risk area, respectively; α, β, γ, λ, and μ are the undetermined coefficients of linear regression, and k' is an adjustable proportional constant, where the undetermined coefficients of linear regression are calculated using the least squares method;

[0070] Set a model update cycle, refit and optimize the model parameters based on the latest collected data in each update cycle, and regularly optimize and adjust the model parameters to help improve the accuracy of the model evaluation results.

[0071] Mark the flash flood disaster warning area in S3, obtain the water flow monitoring network data of the flash flood disaster warning area, and evaluate the water flow diffusion capacity of the flash flood based on the monitoring data.

[0072] Based on the real-time precipitation, the areas where the precipitation exceeds the critical value of flash floods are screened and marked as flash flood disaster warning areas;

[0073] Obtain real-time monitoring data from the water flow monitoring network in flash flood disaster warning areas, including the direction and intensity of water flow;

[0074] The monitored water flow directions of all water flow monitoring points are converted into unit vectors and then vector synthesis is performed to calculate the average synthetic vector of all unit vectors, specifically: Where R is the average composite vector, θ i is the angle between the water flow direction monitored by the ith monitoring point and the line connecting the monitoring point to the center of the ring monitoring point, and n is the number of monitoring points set up in the water flow monitoring network;

[0075] The value range of |R| is between (0,1). The closer it is to 0, the more dispersed the water flow direction is. Conversely, the closer it is to 1, the more concentrated the water flow direction is.

[0076] The water flow intensity deviation of adjacent monitoring points is calculated, and the water flow intensity balance index is calculated by combining the maximum, minimum and average water flow intensities of all monitoring points. The specific calculation method of the water flow intensity balance index is: Where L is the water flow intensity equilibrium index,

[0078] σ high 2 , σ low 2 are the variance of water flow intensity between the highest ring monitoring point and the lowest ring monitoring point, ST avg , ST max , ST min are the mean, maximum, and minimum values ​​of water flow intensity at all monitoring points;

[0079] The product of the average composite vector of the water flow direction and the water flow intensity balance index is used as the evaluation value of the water flow diffusion capacity. When the direction of the water flow is more dispersed and the intensity is more balanced, the theoretical impact range will be larger. Otherwise, the impact range will be smaller, but the destructiveness of its unidirectional mountain torrent runoff will increase.

[0080] In S4, the real-time soil erosion resistance of the flash flood warning area is calculated, and combined with the regional terrain data, the transformation tendency of debris flow in the flash flood warning area is evaluated. Compared with flash floods, debris flow contains a large amount of soil, stones and other solid materials, with greater density, heavier weight and stronger impact. Debris flow often expands during the sliding process and can destroy all obstacles along the way, including buildings, roads, bridges and vegetation. The destructive power of flash floods is mainly concentrated in the low-lying areas where the water flows. In addition, debris flow often forms in a short period of time and is difficult to predict. Once it occurs, people have very little time to react, and the damage caused is often more serious.

[0081] Obtain the latest soil information data and water flow monitoring network data collected in flash flood disaster warning areas;

[0082] The soil information data and the undetermined coefficients of the linear regression obtained by fitting are brought into the regional soil anti-scour capacity assessment model to calculate the soil scour resistance and mark it as the real-time soil scour resistance of the area;

[0083] Obtain the real-time cumulative precipitation in the flash flood disaster warning area, the overflow of the flash flood critical precipitation, and the current unit time precipitation. Calculate the accumulated gravitational potential energy of the overflow precipitation based on the overflow of precipitation and the current unit time precipitation. Specifically: Among them, EP over is the gravitational potential energy of the precipitation overflow at the last collection time, is the differential of the gravitational potential energy of the real-time precipitation;

[0084] The real-time total gravitational potential energy corresponding to the precipitation overflow continues to grow over time and will eventually be converted into the kinetic energy of mountain torrents. The rainfall depth is converted into rainfall volume, and the gravitational potential energy differential of the real-time precipitation is decomposed and calculated through the real-time change of volume. Specifically: where ρ, g, h are the water density, gravitational acceleration, and height of the rainfall area, respectively, and V(t) is the rainfall volume that varies with time.

[0085] The real-time soil erosion resistance, overflow precipitation gravitational potential energy accumulation and average water flow intensity monitored by the water flow monitoring network in the flash flood disaster warning area are normalized and then comprehensively calculated to calculate the debris flow tendency expansion coefficient in the flash flood disaster warning area, which is used to evaluate the debris flow transformation tendency in the area. Specifically, where TR deb is the debris flow expansion coefficient in the area, EP sum , ST avg 、E new are the accumulated gravitational potential energy of overflow precipitation, the average water flow intensity and the real-time soil erosion resistance, ω1, ω2 and ω3 are EP sum , ST avg 、E new The adjustable weight coefficient.

[0086] In S5, the disaster impact classification gradient interval is set, and the expected disaster impact level is comprehensively evaluated based on the transformation tendency of debris flow and the water flow diffusion capacity, and graded warnings are issued for flash flood disasters.

[0087] Set disaster impact warning levels and set default warning gradient intervals for each disaster impact level;

[0088] Obtain all debris flow tendency expansion coefficients and water flow diffusion capacity assessment values, and perform weighted summation of debris flow tendency expansion coefficients and water flow diffusion capacity assessment values ​​to calculate the expected disaster impact index, which is calculated as follows: DG = q*EXP(δ1TR deb +δ2(L*R)), where DG is the expected disaster impact index, q is the adjustable scaling factor, and δ1 and δ2 are the weight coefficients of the debris flow tendency expansion coefficient and the water flow diffusion capacity assessment value, respectively.

[0089] The scale value of the warning gradient interval is adjusted according to the expected disaster index, and the upper limit value of each gradient interval after adjustment is used as the threshold of the expected disaster impact index corresponding to the warning level, so that the expected disaster impact index of all flash flood disaster warning areas falls within the disaster impact level warning gradient interval.

[0090] The scale adjustment interval is set to 0-1 by default, and can also be set based on a comprehensive assessment based on the expected density of the disaster impact index distribution and the number of disaster impact warning levels that are set, which will not be elaborated here.

[0091] According to the disaster impact warning gradient interval, the disaster impact warning level is set accordingly, and the expected disaster impact warning is carried out in the flash flood disaster warning area. The graded warning can help relevant departments formulate different emergency measures according to the severity of the disaster. When a high-level warning is issued, a higher-level emergency response can be immediately initiated, and more resources can be mobilized for rescue, ensuring that effective actions are taken in the shortest time to reduce the loss of life and property caused by the disaster.

[0092] Example 2

[0093] The difference between Example 2 of the present invention and Example 1 is that this example introduces a graded early warning system for mountain torrent disasters based on multi-source data analysis.

[0094] Figure 2 A structural schematic diagram of a flash flood disaster classification warning system based on multi-source data analysis of the present invention is given. A flash flood disaster classification warning system based on multi-source data analysis includes a regional data acquisition module, a soil anti-scouring capacity assessment module, a water flow diffusion capacity assessment module, a debris flow conversion tendency assessment module, and a disaster classification warning module:

[0095] Regional data collection module: classify and mark the monitoring and early warning areas, set up detection devices, and collect soil, meteorological, and terrain information data of the monitoring and early warning areas according to actual needs and settings;

[0096] Soil anti-scour capacity assessment module: A regional soil anti-scour capacity assessment model is established based on soil information data and historical soil loss rate averages in flash flood risk areas, and model parameters are refitted and optimized based on the latest data collected regularly at a set period;

[0097] Water flow diffusion capacity assessment module: Based on the direction and intensity of the monitored water flow, the module analyzes the dispersion of the water flow direction and the balance of the water flow intensity, and assesses the diffusion capacity of the water flow;

[0098] Debris flow conversion tendency assessment module: Based on the real-time soil scour resistance and the accumulated gravity potential energy of overflow precipitation in the region, combined with the average value of the monitored water flow intensity, the debris flow tendency expansion coefficient is comprehensively calculated to assess the debris flow conversion tendency in the region;

[0099] Disaster graded warning module: Comprehensively evaluate the expected disaster impact based on the transformation trend of debris flow and the diffusion capacity of water flow, set graded warning gradient intervals based on the expected disaster impact, and conduct graded warnings for flash flood disaster warning areas.

[0100] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0101] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0102] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0103] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0104] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0105] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0107] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0108] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0109] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A graded early warning method for flash flood disasters based on multi-source data analysis. It is characterized in that The steps include: S1: Mark the flash flood disaster risk areas and set up a water flow monitoring network in the flash flood disaster risk areas; S2: Collect soil information in areas at risk of flash floods and establish a soil anti-scouring capacity assessment model in areas at risk of flash floods; S3: Mark the flash flood disaster warning area, obtain the water flow monitoring network data of the flash flood disaster warning area, and evaluate the water flow diffusion capacity of the flash flood based on the monitoring data; S4: Calculate the real-time soil erosion resistance in the flash flood warning area, and evaluate the transformation trend of debris flow in the flash flood warning area by combining regional terrain data; S5: Set up disaster impact classification gradient intervals, comprehensively evaluate the expected disaster impact level based on the transformation tendency of debris flow and the water flow diffusion capacity, and issue graded warnings for flash flood disasters.

2. A flash flood disaster classification early warning method based on multi-source data analysis according to claim 1, It is characterized in that In S1, the flash flood disaster risk areas are marked, and the water flow monitoring network is set up in the flash flood disaster risk areas, including: Based on historical meteorological information, the areas where flash floods have occurred are screened out and marked as flash flood disaster areas; Set the flash flood disaster monitoring time window length, calculate the ratio of the number of flash flood disasters in all flash flood disaster areas within the window length to the window length, and use it as the flash flood disaster frequency in the area; Mark the areas where the frequency of flash flood disasters exceeds the set frequency threshold as flash flood disaster risk areas, and obtain the critical value of flash flood precipitation in each flash flood disaster risk area based on meteorological data; Set up equidistant circular monitoring points in areas at risk of flash floods, arrange standard grid water flow sensors, and establish a water flow monitoring network.

3. A flash flood disaster classification early warning method based on multi-source data analysis according to claim 2, It is characterized in that In S2, soil information of flash flood disaster risk areas is collected, and a soil anti-scour capacity assessment model for flash flood disaster risk areas is established, which specifically includes: Collect soil information data in flash flood risk areas at a set frequency. The soil information data includes: soil shear strength, particle content, moisture content, internal friction angle and void ratio; Obtain the soil loss rate after historical flash floods in flash flood risk areas, calculate the mean historical soil loss rate of the region, and adjust the inverse of the mean soil loss rate of all flash flood risk areas to the set interval according to the same scale ratio as the assessment value of the soil anti-scouring capacity of the region; A regional soil anti-scour capacity assessment model was established based on soil information data and historical soil loss rate averages in flash flood risk areas; Specifically: Where E k is the regional soil anti-scouring capacity assessment value of the k-th flash flood disaster risk area, S k , P ks , P kf , W k ,θ k , G k are the shear strength, coarse particle ratio, fine particle ratio, moisture content, internal friction angle, and porosity ratio of the soil in the kth flash flood disaster risk area, respectively; α, β, γ, λ, and μ are the undetermined coefficients of linear regression, and k' is an adjustable proportional constant, where the undetermined coefficients of linear regression are calculated using the least squares method; Set the model update cycle, and refit and optimize the model parameters based on the latest collected data in each update cycle.

4. A flash flood disaster classification early warning method based on multi-source data analysis according to claim 3, It is characterized in that Mark the flash flood disaster warning area in S3, obtain the water flow monitoring network data of the flash flood disaster warning area, and evaluate the water flow diffusion capacity of the flash flood based on the monitoring data, including: Based on the real-time precipitation, the areas where the precipitation exceeds the critical value of flash floods are screened and marked as flash flood disaster warning areas; Obtain real-time monitoring data from the water flow monitoring network in flash flood disaster warning areas, including the direction and intensity of water flow; The monitored water flow directions of all water flow monitoring points are converted into unit vectors and then vector synthesis is performed to calculate the average synthetic vector of all unit vectors, specifically: Where R is the average composite vector, θ i is the angle between the water flow direction monitored by the ith monitoring point and the line connecting the monitoring point to the center of the ring monitoring point, and n is the number of monitoring points set up in the water flow monitoring network; The water flow intensity deviation of adjacent monitoring points is calculated, and the water flow intensity balance index is calculated by combining the maximum, minimum and average water flow intensities of all monitoring points. The specific calculation method of the water flow intensity balance index is: Where L is the water flow intensity equilibrium index, σ high 2 , σ low 2 are the variance of water flow intensity between the highest ring monitoring point and the lowest ring monitoring point, ST avg 、ST max 、ST min are the mean, maximum, and minimum values ​​of water flow intensity at all monitoring points; The product of the average synthetic vector of the water flow direction and the water flow intensity equilibrium index is taken as the evaluation value of the water flow diffusion capacity.

5. A flash flood disaster classification early warning method based on multi-source data analysis according to claim 4, It is characterized in that In S4, the real-time soil erosion resistance in the flash flood disaster warning area is calculated, and the transformation tendency of debris flow in the flash flood disaster warning area is evaluated by combining the regional terrain data. Specifically, the following are included: Obtain the latest soil information data and water flow monitoring network data collected in flash flood disaster warning areas; The soil information data and the undetermined coefficients of the linear regression obtained by fitting are brought into the regional soil anti-scour capacity assessment model to calculate the soil scour resistance and mark it as the real-time soil scour resistance of the area; Obtain the real-time cumulative precipitation in the flash flood disaster warning area, the overflow of the flash flood critical precipitation, and the current unit time precipitation. Calculate the accumulated gravitational potential energy of the overflow precipitation based on the overflow of precipitation and the current unit time precipitation. Specifically: Among them, EP over is the gravitational potential energy of the precipitation overflow at the last collection time, is the differential of the gravitational potential energy of the real-time precipitation; The real-time soil erosion resistance, overflow precipitation gravitational potential energy accumulation and average water flow intensity monitored by the water flow monitoring network in the flash flood disaster warning area are normalized and then comprehensively calculated to calculate the debris flow tendency expansion coefficient in the flash flood disaster warning area, which is used to evaluate the debris flow transformation tendency in the area. Specifically, where TR deb is the debris flow expansion coefficient in the area, EP sum 、ST avg 、E new are the accumulated gravitational potential energy of overflow precipitation, the average water flow intensity and the real-time soil erosion resistance, ω 1 ,ω 2 ,ω 3 EP sum , ST avg 、E new The adjustable weight coefficient.

6. A flash flood disaster classification early warning method based on multi-source data analysis according to claim 5, It is characterized in that In S5, the disaster impact classification gradient interval is set, and the expected disaster impact level is comprehensively evaluated based on the transformation tendency of debris flow and the water flow diffusion capacity. The graded warning of flash flood disasters includes: Set disaster impact warning levels and set default warning gradient intervals for each disaster impact level; Obtain all debris flow tendency expansion coefficients and water flow diffusion capacity assessment values, and perform weighted summation of debris flow tendency expansion coefficients and water flow diffusion capacity assessment values ​​to calculate the expected disaster impact index; The scale value of the warning gradient interval is adjusted according to the expected disaster index, and the upper limit value of each gradient interval after adjustment is used as the threshold value of the expected disaster impact index corresponding to the warning level, so that the expected disaster impact index of all flash flood disaster warning areas falls within the warning gradient interval of the disaster impact level; Disaster impact warning grades are set according to the warning gradient intervals of the disaster impact level, and graded warnings of expected disaster impacts are issued for flash flood disaster warning areas.

7. A flash flood disaster classification early warning system based on multi-source data analysis, used to implement a flash flood disaster classification early warning method based on multi-source data analysis as described in any one of claims 1 to 6, It is characterized in that It includes regional data collection module, soil anti-scour capacity assessment module, water flow diffusion capacity assessment module, debris flow transformation tendency assessment module, and disaster classification warning module: Regional data collection module: classify and mark the monitoring and early warning areas, set up detection devices, and collect soil, meteorological, and terrain information data of the monitoring and early warning areas according to actual needs and settings; Soil anti-scour capacity assessment module: Establish a regional soil anti-scour capacity assessment model based on soil information data and historical soil loss rate averages in flash flood risk areas, and re-fit and optimize model parameters based on the latest data collected regularly at a set period; Water flow diffusion capacity assessment module: Based on the direction and intensity of the monitored water flow, the module analyzes the dispersion of the water flow direction and the balance of the water flow intensity, and assesses the diffusion capacity of the water flow; Debris flow conversion tendency assessment module: Based on the real-time soil scour resistance and overflow precipitation gravity potential energy accumulation in the region, combined with the average value of the monitored water flow intensity, the debris flow tendency expansion coefficient is comprehensively calculated to assess the debris flow conversion tendency in the region; Disaster graded warning module: Comprehensively evaluate the expected disaster impact based on the transformation tendency of debris flow and the diffusion capacity of water flow, set graded warning gradient intervals based on the expected disaster impact, and conduct graded warnings for flash flood disaster warning areas.

Images