Mountain torrent disaster comprehensive risk prediction method and system

Abstract

The embodiment of the invention relates to a mountain torrent disaster comprehensive risk prediction method and system. The method comprises the following steps: receiving partition division of a target area, and reading information of each partition; calculating a first class index value according to a preset first class index; receiving weight coefficient setting of each index value, and establishing a mountain torrent risk evaluation hierarchical structure model according to the first type of index values; calculating to obtain a risk score of each partition; calculating a second-class index value according to a preset second-class index; receiving second-class weight coefficient setting of each index value, and establishing a mountain torrent susceptibility evaluation hierarchical structure model according to the second-class index values; calculating to obtain a susceptibility score of each partition; obtaining a risk score according to the product of the risk score and the susceptibility score; and grading the risk score, and obtaining the risk grade of the corresponding partition according to the grade.

Description

Technical Field

[0001] Multiple embodiments of this specification relate to, specifically, a method and system for comprehensive risk prediction of flash flood disasters. Background Art

[0002] At present, the understanding of the laws and inducing factors of flash floods is relatively thorough, but the research on the impact of comprehensive factors on flash floods is still insufficient. Due to the differences in topography, landforms and climate conditions, there are great differences in the susceptibility and risk assessment methods in different regions. Therefore, customized research is needed based on the actual conditions of different regions. Therefore, although the previous conclusions are universal and representative, they are not targeted and applicable enough. In the issue of flash flood disaster zoning, due to the large regional differences and the wide range of flash flood disasters, disaster maps such as susceptibility zoning maps, danger zoning maps, and risk zoning maps should be prepared according to local conditions to lay the foundation for flash flood disaster prevention and control work. Summary of the invention

[0003] This specification describes a method and system for predicting the comprehensive risk of flash flood disasters in multiple embodiments.

[0004] In a first aspect, the embodiments of this specification provide a method for predicting the comprehensive risk of flash flood disasters, including the following steps: Receive the partition division of the target area and read the information of each partition; According to the preset first category indicators, calculate the first category indicator value; receiving a weight coefficient setting for each index value, and establishing a flash flood hazard assessment hierarchical model according to the first type of index values; Calculate the risk score of each partition according to the normalized value of the first type of indicator value and the weight coefficient; According to the preset second category indicators, calculate the second category indicator value; receiving a second type of weight coefficient setting for each index value, and establishing a flash flood susceptibility evaluation hierarchical structure model according to the second type of index value; Calculate the susceptibility score of each partition according to the normalized value of the second type of indicator value and the second type of weight coefficient; Obtaining a risk score according to the product of the hazard score and the susceptibility score; The mean and standard deviation of the risk scores are calculated, the risk scores are divided into grades according to the mean and standard deviation, and the risk grades of the corresponding partitions are obtained according to the grades.

[0005] In a second aspect, the embodiments of this specification provide a system for predicting the comprehensive risk of flash flood disasters, including: A receiving module receives the partition division of the target area and reads the information of each partition; The first index module calculates the first type of index values according to the preset first type of indexes; The first establishment module receives the setting of the weight coefficients of each index value and establishes a mountain flood hazard assessment hierarchical structure model according to the first type of index values; The first scoring module calculates the hazard scores of each partition according to the normalized values of the first type of index values and the weight coefficients; The second index module calculates the second type of index values according to the preset second type of indexes; The second establishment module receives the setting of the second type of weight coefficients of each index value and establishes a mountain flood susceptibility assessment hierarchical structure model according to the second type of index values; The second scoring module calculates the susceptibility scores of each partition according to the normalized values of the second type of index values and the second type of weight coefficients; The third scoring module obtains the risk scores according to the product of the hazard scores and the susceptibility scores; The partitioning module calculates the mean and standard deviation of the risk scores, divides the risk scores into levels according to the mean and standard deviation, and obtains the risk levels of the corresponding partitions according to the levels.

[0006] In a third aspect, an embodiment of the present specification provides an electronic device, including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method described in any of the above aspects.

[0007] In a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method described in any of the above aspects is implemented.

[0008] In a fifth aspect, an embodiment of the present specification provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method described in any of the above aspects is implemented.

[0009] The beneficial effects brought by the technical solutions provided by some embodiments of the present specification at least include: In multiple embodiments of this specification, the provided comprehensive risk prediction method for mountain flood disasters conducts quantitative analysis through hazard scoring and susceptibility scoring, and can provide more accurate reference information in disaster prevention, mitigation, and rescue. According to various factors such as geographical environment, meteorology, and hydrology, the potential danger levels of mountain flood disasters are quantified and zoned to provide guidance for disaster prevention and control in different zones, which can help effectively prevent and control mountain flood disasters and ensure the safety of the region. The standard deviation method divides the distribution characteristics of data into four risk levels based on the mean and standard deviation, quantifies the risk into specific numerical values, making risk management more comparable and quantitative, and helping to reduce the influence of subjective factors.

[0010] Other features and advantages of multiple embodiments of this specification will be further revealed in the following detailed implementation manners and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] To more clearly illustrate the technical solutions in the embodiments of this specification, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of this specification. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0012] Figure 1 It is a schematic diagram of the application scenario of the comprehensive risk prediction method for mountain flood disasters provided in this specification.

[0013] Figure 2 It is a schematic diagram of the application architecture of the comprehensive risk prediction method for mountain flood disasters provided in this specification.

[0014] Figure 3 It is a schematic diagram of the interaction interface of the comprehensive risk prediction method for mountain flood disasters provided in this specification.

[0015] Figure 4 It is a schematic diagram of the process of the comprehensive risk prediction method for mountain flood disasters provided in this specification.

[0016] Figure 5 It is a schematic diagram of the comprehensive risk prediction system for mountain flood disasters provided in this specification.

[0017] Figure 6 It is a schematic diagram of the NDVI value distribution provided in this specification.

[0018] Figure 7 It is a schematic diagram of the slope value distribution provided in this specification.

[0019] Figure 8 It is a schematic diagram of the land use type value distribution provided in this specification.

[0020] Figure 9The schematic diagram of the SPI value distribution provided in this specification.

[0021] Figure 10 The schematic diagram of the geological type value distribution provided in this specification.

[0022] Figure 11 The schematic diagram of the distribution of the risk score provided in this specification.

[0023] Figure 12 The schematic diagram of the DEM value distribution provided in this specification.

[0024] Figure 13 Another schematic diagram of the NDVI value distribution provided in this specification.

[0025] Figure 14 The schematic diagram of the night light index value distribution provided in this specification.

[0026] Figure 15 The schematic diagram of the distribution of the susceptibility score provided in this specification.

[0027] Figure 16 The schematic diagram of the distribution of the risk score provided in this specification.

[0028] Figure 17 The schematic diagram of the electronic device provided in the embodiments of this specification. Detailed implementation manners

[0029] Next, the technical solutions of the embodiments of this specification will be explained and illustrated with reference to the accompanying drawings of the embodiments of this specification. However, the following embodiments are only the preferred embodiments of this specification, not all of them. Based on the embodiments in the implementation manners, other embodiments obtained by those skilled in the art without creative efforts all fall within the protection scope of this specification.

[0030] The terms "first", "second", "third", etc. in the specification, claims and the above accompanying drawings of this specification are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0031] In the following description, terms such as "inner", "outer", "upper", "lower", "left", "right", etc., which indicate orientation or positional relationship, are only for the convenience of describing embodiments and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to this specification.

[0032] The data involved in this application are all information and data authorized by users or fully authorized by all parties, and the collection of relevant data complies with relevant laws, regulations and standards of relevant countries and regions.

[0033] Before introducing the technical solutions described in this specification, the application scenarios of the technical solutions and related technologies are introduced.

[0034] At present, the laws and inducing factors of mountain flood disasters are relatively well understood, but the research on the impact of comprehensive factors on mountain floods is still insufficient. Due to the differences in various topographies and climatic conditions, there are significant differences in the evaluation methods of their susceptibility and risk. Therefore, customized research needs to be carried out according to the actual situation of different regions. Therefore, although the previous conclusions have high universality and representativeness, their pertinence and applicability are insufficient. In the issue of mountain flood disaster zoning, due to the large regional differences and the wide impact range of mountain flood disasters, disaster maps such as susceptibility zoning maps, hazard zoning maps, and risk zoning maps should be made according to local conditions to lay a foundation for mountain flood disaster prevention and control work.

[0035] For this reason, this specification provides a comprehensive risk prediction method and system for mountain flood disasters. Please refer to the appendix Figure 1 , which is a schematic diagram of the application scenario of the embodiments described in this specification. This embodiment is used to divide the risk levels of mountain flood disaster risks within a certain area and provide a reference for flood prevention.

[0036] The method provided in this application is applied to the system architecture as shown in Figure 2 shown, Figure 2 which is a schematic diagram of one of the system architectures in the embodiments of this application. As shown in Figure 2 shown, the system architecture includes a server 10 and a terminal device 20. Among them, an interaction interface 21 is set on the terminal device 20. Please refer to the appendix Figure 3, the interactive interface 21 can run on the terminal device 20 in the form of a browser, or can also run on the terminal device 20 in the form of an independent application (APP), etc. For the specific display form of the interactive interface 21, no limitation is made here. The server 10 involved in this application can be an independent physical server, can also be a server cluster or distributed system composed of multiple physical servers, and can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms. The terminal device 20 can be a smart phone, a tablet computer, a laptop computer, a handheld computer, a personal computer, a smart speaker, a smart TV, a smart watch, a vehicle-mounted device, a wearable device, etc., but is not limited thereto. The terminal device 20 and the server can be directly or indirectly connected through wired or wireless communication methods, and no limitation is made in this application. The number of the server 10 and the terminal device 20 is also not limited.

[0037] Embodiment 1

[0038] A comprehensive risk prediction method for mountain flood disasters provided in this specification, please refer to the appendix Figure 4 , including the steps: Step S101) Receive the partition division of the target area and read the information of each partition. The partition division can be an administrative division, can also be a division made by experts according to geological landforms and precipitation, or can also be a division made according to the division of geological disaster control responsibilities. As a recommended method, it is to make a division according to geological landforms and precipitation, which can more accurately reflect the risk situation of flood disasters.

[0039] Step S102) Calculate the first type of index value according to the preset first type of index.

[0040] The first type of index includes the Normalized Difference Vegetation Index, slope, land use type, Standardized Precipitation Index, and geological lithology.

[0041] The method for calculating the first type of index value includes: Calculate the Normalized Difference Vegetation Index NDVI value, ,

[0042] wherein, is the reflectivity of ground objects in the near-infrared light band, is the reflectance of the ground objects in the red light band. Vegetation data obtained by satellite remote sensing were selected to analyze the relationship between disaster occurrence and vegetation coverage in typical study areas of flash flood disasters in the study area. The normalized difference vegetation index (NDVI) can be used to represent the vegetation coverage in the study area.

[0043] Calculate the slope value. ,

[0044] in, Indicates the change in horizontal direction. Indicates the change in the vertical direction. It indicates the change in surface elevation. Slope refers to the angle between the tangent plane at any point on the surface and the horizontal plane, which is used to indicate the slope of the slope. The slope is closely related to the climate, hydrology, surface conditions and other factors of the area where it is located. Therefore, slope is one of the important factors in evaluating flash flood disasters.

[0045] According to the land use type of the partition, the preset land use type and its value, the corresponding land use type value of the partition is obtained. The land use type data is mainly used to describe the basic attributes such as the state, characteristics, dynamic changes, distribution characteristics and regional land use structure of the land use system and various elements within the region. In this embodiment, the land use types include seven main land use types such as farmland, forest, grassland, desert, impervious surface, water body and wetland. Its values ​​are numbers 1 to 7 respectively.

[0046] Calculate the Standardized Precipitation Index (SPI) value. ,

[0047] Among them, SCA represents the runoff per unit area and slope represents the slope. The Standardized Precipitation Index (SPI) is used to describe the characteristics of meteorological drought on a range of time scales. On short time scales, SPI is closely related to soil moisture, while on long time scales, SPI can be related to groundwater and reservoir storage. SPI has good temporal and spatial comparability and can be compared in areas with significantly different climates. Higher SPI values ​​indicate concentrated runoff that may lead to soil erosion.

[0048] According to the geological lithology of the sub-district, the preset land use type and its proxy value, the corresponding geological lithology value of the sub-district is obtained. Different geological lithologies have different water storage capacity, weathering resistance and erosion resistance, so different geological rock layers have different effects on flash flood disasters.

[0049] Step S103) Receive the setting of the weight coefficient for each index value, and establish a mountain flood hazard assessment hierarchical structure model based on the first type of index values.

[0050] Among them, the method of receiving the setting of the weight coefficient for each index value and establishing a mountain flood hazard assessment hierarchical structure model based on the first type of index values includes: Construct a hierarchical structure model according to three levels: the target layer, the factor layer, and the factor sub - layer; Construct a judgment matrix , indicating the relative importance value of index i to index j; Calculate the product of each row factor of the judgment matrix respectively , and then calculate the fifth - root of, normalize the vector W to obtain the eigenvector F; Calculate the maximum eigenvalue of the judgment matrix , ,

[0051] where T is a preset transposed vector, indicating the weight coefficient of index i; Since the calculation results are affected by objective things and subjective judgments, it is necessary to check whether the consistency and randomness of the calculation results are reasonable. Calculate the consistency index CI of the judgment matrix, ,

[0052] Calculate the random consistency index CR of the judgment matrix, ,

[0053] where CI is the judgment matrix consistency index, N is the order of the judgment matrix, and RI is the average random consistency index of the judgment matrix; When CR ≥ 0.1, re - receive the setting of the weight coefficient for each index value until the calculated CR < 0.1.

[0054] Step S104) Calculate the hazard score for each partition according to the normalized value of the first type of index values and the weight coefficient.

[0055] Specifically include: Use the following calculation formula to calculate the hazard score S, ,

[0056] Among them, represents the normalized difference vegetation index NDVI value, represents the slope value, represents the land use type value, represents the Standardized Precipitation Index SPI value, represents the geological lithology value, , , , , are respectively weights. Before calculating the hazard score S, all are normalized.

[0057] Step S105) Calculate the second - type index value according to the preset second - type index.

[0058] The second - type index includes vegetation coverage, night - time light index and dynamic factor. The method for calculating the second - type index value includes: Calculate the vegetation coverage value FVC, ,

[0059] where NDVI is the Normalized Difference Vegetation Index, is the NDVI value of pure bare soil, is the NDVI value of pure vegetation. High vegetation density means strong soil water storage and sand - fixing ability, and the risk of flash flood disasters is relatively low.

[0060] The night - time light index includes the total light index value TNLI and the average light index value ANLI. Divide the area into m grids, and calculate the total light index value TNLI and the average light index value ANLI, ,

[0061] where, is the light radiation value of each grid cell in the area, and m is the number of grids in the area, .

[0062] Calculate the dynamic factor value. Use the elevation model of the area to obtain the height difference of the area as the dynamic factor value. In this embodiment, DEM is used as the dynamic factor value. Digital Elevation Model, abbreviated as DEM, is a digital simulation of the ground terrain through limited terrain elevation data (i.e., the digital expression of the terrain surface morphology). The height difference of the area refers to the difference between the highest and lowest elevation values in the area. Factors such as the elevation, slope and terrain undulation of the terrain directly affect the distribution of loose accumulations and the collection of precipitation in flash flood disasters. At the same time, low - lying areas are more vulnerable to flash floods.

[0063] Step S106) Receive the setting of the second - type weight coefficient of each index value, and establish a flash - flood susceptibility evaluation hierarchical structure model according to the second - type index value.

[0064] The method for setting the weight coefficient of each index value and establishing a mountain flood susceptibility evaluation hierarchical model according to the second type of index value includes: Construct a hierarchical model according to three levels: the target layer, the factor layer, and the factor layer; Construct the second judgment matrix , indicating the relative importance value of index i to index j; Calculate the product of each row factor of the second judgment matrix respectively , and then calculate the cube root of, and normalize the vector to obtain the eigenvector ; Calculate the maximum eigenvalue of the second judgment matrix , ,

[0065] where T is a preset transposed vector, indicating the weight coefficient of index i; Calculate the consistency index CI' of the second judgment matrix, ,

[0066] Calculate the random consistency index CR' of the second judgment matrix, ,

[0067] where CI' is the consistency index of the second judgment matrix, N' is the order of the second judgment matrix, and RI' is the average random consistency index of the second judgment matrix; When CR'≥0.1, re-receive the weight coefficient setting of each index value until the calculated CR'<0.1.

[0068] Step S107) Calculate the susceptibility score of each partition according to the normalized value of the second type of index value and the second type of weight coefficient.

[0069] Among them, the method for calculating the susceptibility score of each partition according to the normalized value of the second type of index value and the weight coefficient includes: Use the following calculation formula to calculate the susceptibility score V, ,

[0070] where, represents the vegetation coverage value, represents the FVC night light index, represents the dynamic factor value, are respectively Weight.

[0071] Step S108) Obtain a risk score according to the product of the hazard score and the susceptibility score.

[0072] The method for obtaining a risk score according to the product of the hazard score and the susceptibility score includes: Calculate the risk score R, R = S × V where S is the hazard score and V is the susceptibility score.

[0073] Step S109) Calculate the mean and standard deviation of the risk scores, divide the risk scores into grades according to the mean and standard deviation, and obtain the risk level of the corresponding partition according to the grades.

[0074] Calculate the mean of the risk scores R of all partitions and the standard deviation , according to the mean and the standard deviation , the method for dividing the risk scores into grades and obtaining the risk level of the corresponding partition according to the grades includes: When , the risk level of the partition is extremely high risk, when , the risk level of the partition is high risk, when , the risk level of the partition is medium risk, when , the risk level of the partition is low risk.

[0075] On the other hand, this specification provides a comprehensive risk prediction system for mountain flood disasters, including: A receiving module 100, which receives the partition division of the target area and reads the information of each partition; A first index module 200, which calculates the first type of index value according to the preset first type of index; A first establishment module 300, which receives the setting of the weight coefficient of each index value and establishes a mountain flood hazard evaluation hierarchical model according to the first type of index value; A first scoring module 400, which calculates and obtains the hazard score of each partition according to the normalized value of the first type of index value and the weight coefficient; A second index module 500, which calculates the second type of index value according to the preset second type of index; A second establishment module 600, which receives the setting of the second weight coefficient of each index value and establishes a mountain flood susceptibility evaluation hierarchical model according to the second type of index value; The second scoring module 700 calculates the susceptibility score for each partition based on the normalized value of the second type of index value and the second type of weight coefficient. The third scoring module 800 obtains the risk score based on the product of the hazard score and the susceptibility score. The partitioning module 900 calculates the mean and standard deviation of the risk scores, divides the risk scores into grades based on the mean and standard deviation, and obtains the risk level of the corresponding partition according to the grades.

[0076] Embodiment 2

[0077] In this embodiment, the satellite remote sensing images and other data of the XX Lake area are used as examples to divide the comprehensive risk degree of mountain flood disasters.

[0078] Select satellite remote sensing data, and obtain five factors including the normalized difference vegetation index, slope, land use type, standardized precipitation index, and geological lithology in the XX Lake area as the evaluation indicators for the hazard of mountain flood disasters in the XX Lake area. Figure 6 For the distribution of the normalized difference vegetation index value Figure 7 For the distribution of the slope value Figure 8 For the distribution of the land use type value Figure 9 For the distribution of the standardized precipitation index value Figure 10 For the distribution of the geological lithology value.

[0079] Use the analytic hierarchy process to establish an evaluation index system for the hazard of mountain flood disasters in the XX Lake area, obtain the weight coefficient of each hazard factor of mountain flood disasters, and the final hazard score S is: ,

[0080] Obtain the value of the hazard score S.

[0081] According to the hazard score S, the hazard evaluation of mountain flood disasters in the XX Lake area is divided into 4 grades, from high to low are high-risk area, medium-risk area, low-risk area, and safe area. The division results are as shown in the appendix Figure 11 as shown.

[0082] Select the vegetation coverage, night light index, and dynamic factor, and calculate the second type of index value. Figure 12 For the distribution of the dynamic factor value Figure 13 For the distribution of the vegetation coverage value Figure 14 For the distribution of the night light index.

[0083] Use the analytic hierarchy process to establish an evaluation index system for the susceptibility of mountain flood disasters in the XX Lake area, and determine the weight coefficient of each susceptibility evaluation factor of mountain flood disasters. The susceptibility score V is: ,

[0084] Obtain the value of the susceptibility score V. According to the susceptibility score V, the risk assessment of mountain flood disasters in the XX Lake area is divided into 4 levels, from high to low are high-risk area, medium-risk area, low-risk area, and safe area. The division results are as attached Figure 15 shown

[0085] Calculate the risk score R of mountain flood disasters. The calculation formula is: R = S×V. The calculation results of the risk score R of mountain flood disasters are as attached Figure 16 shown. Calculate the mean value and standard deviation of the risk scores R of all partitions. According to the mean value and standard deviation of the risk scores R of all partitions, the method for dividing the risk scores into levels and obtaining the risk level of the corresponding partition according to the levels includes: when , the risk level of the partition is extremely high risk. When , the risk level of the partition is high risk. When , the risk level of the partition is medium risk. When , the risk level of the partition is low risk. Please refer to the attachment again for the division results Figure 1 . The figure shows the division results of the risk score R of mountain flood disasters in the XX Lake area

[0086] Please refer to Figure 17 the schematic structural diagram of an electronic device provided in the embodiment of the present specification shown

[0087] As Figure 17As shown in the figure, the electronic device 1100 may include: at least one processor 1101, at least one network interface 1104, a user interface 1103, a memory 1105, and at least one communication bus 1102. Among them, the communication bus 1102 can be used to realize the connection and communication of the above-mentioned components. Among them, the user interface 1103 may include buttons, and the optional user interface may further include a standard wired interface and a wireless interface. Among them, the network interface 1104 may, but is not limited to, include a Bluetooth module, an NFC module, a Wi-Fi module, etc. Among them, the processor 1101 may include one or more processing cores. The processor 1101 connects various parts within the entire electronic device 1100 through various interfaces and lines, and by running or executing instructions, programs, code sets or instruction sets stored in the memory 1105, and calling data stored in the memory 1105, it executes various functions of the routing device and processes data. Optionally, the processor 1101 may be implemented in at least one hardware form of DSP, FPGA, or PLA. The processor 1101 may integrate one or several combinations of a CPU, a GPU, and a modem, etc. Among them, the CPU mainly processes the operating system, the user interface, and application programs, etc.; the GPU is responsible for the rendering and drawing of the content to be displayed on the display screen; the modem is used to process wireless communication.

[0088] It can be understood that the above-mentioned modem may not be integrated into the processor 1101 and may be implemented separately by a single chip.

[0089] Among them, the memory 1105 may include RAM and may also include ROM. Optionally, the memory 1105 includes a non-transitory computer-readable medium. The memory 1105 can be used to store instructions, programs, code, code sets or instruction sets. The memory 1105 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store the data involved in the above-mentioned method embodiments. Optionally, the memory 1105 may further be at least one storage device located far from the aforementioned processor 1101. The memory 1105, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and application programs. The processor 1101 can be used to call the application programs stored in the memory 1105 and execute the methods in the above-mentioned multiple embodiments.

[0090] The embodiments of this specification also provide a computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer or a processor, the computer or the processor is caused to execute multiple steps in the above embodiments. If each component module of the above electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in the computer-readable storage medium.

[0091] The embodiments of this specification also provide a computer program product, including a computer program. When the computer program is executed by a processor, multiple steps in the above embodiments are implemented.

[0092] Without conflict, the technical features in this embodiment and the implementation solutions can be combined arbitrarily.

[0093] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes multiple computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this specification are 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 through the computer-readable storage medium. The computer instructions can be transmitted from a website, a computer, a server, or a data center to another website, a computer, a server 10, or a data center in a wired manner (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that integrates multiple available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a Digital Versatile Disc (DVD)), or a semiconductor medium (for example, a Solid State Disk (SSD)), etc.

[0094] When implemented by hardware or firmware, the foregoing method flow is programmed into a hardware circuit to obtain a corresponding hardware circuit structure and implement corresponding functions. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is an integrated circuit whose logic function is determined by a user's programming of the device. A designer can program on their own to "integrate" a digital system onto a PLD, without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a Hardware Description Language (HDL), and there are not only one but many kinds of HDLs. Those skilled in the art should also be clear that by simply performing a little logical programming on the method flow using the above-mentioned several hardware description languages and programming it into an integrated circuit, it is easy to obtain a hardware circuit that implements the logical method flow.

[0095] The embodiments described above are only described in a preferred embodiment manner of this specification, and do not limit the scope of this specification. Without departing from the design spirit of this specification, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of this specification shall fall within the protection scope determined by the claims of this specification.

Claims

1. A method for predicting the comprehensive risk of flash flood disasters, characterized in that: Includes steps: Receive the partition division of the target area and read the information of each partition; According to the preset first category indicators, calculate the first category indicator value; receiving a weight coefficient setting for each index value, and establishing a flash flood hazard assessment hierarchical model according to the first type of index values; Calculate the risk score of each partition according to the normalized value of the first type of indicator value and the weight coefficient; According to the preset second category indicators, calculate the second category indicator value; receiving a second type of weight coefficient setting for each index value, and establishing a flash flood susceptibility evaluation hierarchical structure model according to the second type of index value; Calculate the susceptibility score of each partition according to the normalized value of the second type of indicator value and the second type of weight coefficient; Obtaining a risk score according to the product of the hazard score and the susceptibility score; The mean and standard deviation of the risk scores are calculated, the risk scores are divided into grades according to the mean and standard deviation, and the risk grades of the corresponding partitions are obtained according to the grades.

2. A method for predicting the comprehensive risk of flash flood disasters according to claim 1, characterized in that: The first type of indicators include normalized difference vegetation index, slope, land use type, standardized precipitation index and geological lithology. The method for calculating the first type of indicator value includes: Calculate the Normalized Difference Vegetation Index NDVI value, ， in, is the reflectivity of the ground in the near-infrared band, is the reflectivity of ground objects in the red light band; Calculate the slope value. ， in, Indicates the change in horizontal direction. Indicates the change in the vertical direction. It represents the change value of surface elevation; According to the land use type of the partition, the preset land use type and its value, the corresponding land use type value of the partition is obtained; the standardized precipitation index SPI value is calculated, ， Among them, SCA represents the flow per unit area, and slope represents the slope; According to the geological lithology of the partition, the preset land use type and its proxy value, the corresponding geological lithology value of the partition is obtained.

3. A method for predicting the comprehensive risk of flash flood disasters according to claim 1 or 2, characterized in that: The method of receiving the weight coefficient setting of each index value and establishing a flash flood hazard assessment hierarchical structure model according to the first type of index value includes: Construct a hierarchical model according to the three levels of target layer, factor layer and factor layer; Constructing a judgment matrix , It represents the relative importance of indicator i to indicator j; Calculate the product of the factors of each row of the judgment matrix separately , and then calculate The fifth root of , normalize the vector W and obtain the eigenvector F; Calculate the maximum eigenvalue of the judgment matrix , ， Where T is the preset transposed vector, represents the weight coefficient of indicator i; Calculate the consistency index CI of the judgment matrix, ， Calculate the random consistency index CR of the judgment matrix, ， Among them, CI is the consistency index of the judgment matrix, N is the order of the judgment matrix, and RI is the average random consistency index of the judgment matrix; When CR≥0.1, the weight coefficient setting of each indicator value is received again until the calculated CR<0.

1.

4. A method for predicting the comprehensive risk of flash flood disasters according to claim 3, characterized in that: The method of calculating the risk score of each partition according to the normalized value of the first type of indicator value and the weight coefficient includes: Use the following formula to calculate the risk score S: ， in, Represents the Normalized Difference Vegetation Index NDVI value, Indicates the slope value. Represents the land use type value, Represents the Standardized Precipitation Index SPI value, Indicates geological lithology value, , , , , They are The weight of .

5. A method for predicting the comprehensive risk of flash flood disasters according to claim 1 or 2, characterized in that: The method of obtaining the risk score according to the product of the hazard score and the susceptibility score includes: Calculate the risk score R, R=S×V, Among them, S is the risk score, and V is the susceptibility score; Calculate the mean risk score R of all partitions and standard deviation , according to the mean and standard deviation , the risk score is divided into levels, and the method of obtaining the risk level of the corresponding partition according to the level includes: when When the risk level of the partition is extremely high, When the risk level of the partition is high risk, When the risk level of the partition is medium risk, , the risk level of the partition is low risk.

6. A flash flood disaster comprehensive risk prediction system, characterized in that: include: A receiving module receives the partition division of the target area and reads the information of each partition; The first indicator module calculates the first indicator value according to the preset first indicator; A first establishing module receives a weight coefficient setting for each index value and establishes a flash flood risk assessment hierarchical structure model according to the first type of index values; A first scoring module, calculating a risk score for each partition according to the normalized value of the first category indicator value and the weight coefficient; The second indicator module calculates the second indicator value according to the preset second indicator; A second establishment module receives a second type of weight coefficient setting for each index value, and establishes a flash flood susceptibility evaluation hierarchical structure model according to the second type of index value; A second scoring module calculates the susceptibility score of each partition according to the normalized value of the second type of indicator value and the second type of weight coefficient; A third scoring module, obtaining a risk score according to the product of the hazard score and the susceptibility score; The partitioning module calculates the mean and standard deviation of the risk score, divides the risk score into levels according to the mean and standard deviation, and obtains the risk level of the corresponding partition according to the level.

7. An electronic device, characterized in that: including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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