Disaster prediction layer generation method, device, equipment and storage medium

Abstract

The present invention relates to artificial intelligence technology and discloses a method for generating a disaster prediction layer, comprising: performing spatiotemporal data transformation processing on original disaster reference data to obtain initial disaster reference data; calculating the vulnerability index and hazard index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and hazard index algorithm; inputting the vulnerability index and the hazard index into a preset disaster hazard index calculation formula to obtain a disaster hazard index; and visualizing the disaster hazard index using a preset convolution kernel algorithm to generate a disaster prediction layer. In addition, the present invention also relates to blockchain technology, and the disaster hazard index can be stored in a node of the blockchain. The present invention also proposes a disaster prediction layer generation device, an electronic device, and a storage medium. The present invention can improve the accuracy of disaster prediction layer generation.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a disaster prediction layer generation method, device, electronic device and computer-readable storage medium. Background Art

[0002] Currently, common disasters such as geological disasters and floods are highly destructive, sudden, and difficult to prevent. They cause numerous casualties and enormous property losses in China each year, making geological disaster prediction and forecasting urgent. Disaster prediction and forecasting are typically performed using disaster prediction layers. Existing methods for generating disaster prediction layers typically obtain and standardize the relevant hazard factors to create the layer. This approach fails to consider the impact of these factors on disaster risk, resulting in inaccurate prediction layers. Summary of the Invention

[0003] The present invention provides a disaster prediction layer generation method, device and computer-readable storage medium, the main purpose of which is to improve the accuracy of disaster prediction layer generation.

[0004] To achieve the above-mentioned purpose, the present invention provides a method for generating a disaster prediction layer, comprising:

[0005] Obtaining original disaster reference data, and performing spatiotemporal data transformation processing on the original disaster reference data to obtain initial disaster reference data;

[0006] Calculating the vulnerability index and hazard index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and hazard index algorithm;

[0007] Inputting the vulnerability index and the hazard index into a preset disaster risk index calculation formula to obtain a disaster risk index;

[0008] The disaster risk index is visualized using a preset convolution kernel algorithm to generate a disaster prediction layer.

[0009] Optionally, the visualizing the disaster risk index using a preset convolution kernel algorithm to generate a disaster prediction layer includes:

[0010] Perform downscaling calculation on the pre-acquired grid layer to obtain a downscaled layer;

[0011] Performing focus calculation on the downscaled layer to obtain a focus grid layer;

[0012] The disaster risk index is smoothly distributed on the focal grid layer using a preset convolution function to obtain a disaster prediction layer.

[0013] Optionally, performing downscaling calculation on the pre-acquired grid layer to obtain the downscaled layer includes:

[0014] Obtaining preset resolution parameters and interpolation functions, and identifying the layer resolution of the grid layer;

[0015] The layer resolution of the grid layer is converted into the resolution parameter based on the interpolation function to obtain a downscaled layer.

[0016] Optionally, performing focus calculation on the downscaled layer to obtain a focus grid layer includes:

[0017] Dividing the downscaled layer into multiple sublayers according to a preset partition size;

[0018] Performing focus positioning on the multiple sub-layers using a preset focus positioning function to obtain multiple focus sub-layers;

[0019] The plurality of focus sub-layers are merged to obtain a focus grid layer.

[0020] Optionally, the calculating the vulnerability index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm includes:

[0021] Extracting population reference data, economic reference data, and land reference data from the initial disaster reference data;

[0022] Performing dimensionless transformation on the population reference data and the economic reference data respectively to obtain normalized population data and normalized economic data;

[0023] The normalized population data, the normalized economic data, and the land reference data are input into a preset vulnerability index calculation formula to obtain a vulnerability index.

[0024] Optionally, the preset vulnerability index calculation formula is:

[0025]

[0026] Wherein, V is the vulnerability index, POP is the normalized population data, ECO is the normalized economic data, and LC is the land reference data.

[0027] Optionally, performing spatiotemporal data transformation processing on the original disaster reference data to obtain initial disaster reference data includes:

[0028] Using a preset missing value detection statement to detect whether there are missing values ​​in the original disaster reference data;

[0029] When there are missing values ​​in the original disaster reference data, the missing values ​​are supplemented to obtain supplementary reference data;

[0030] The supplementary reference data is mapped to a preset projection coordinate system, and the supplementary reference data on the projection coordinate system is mirrored and rotated to obtain initial disaster reference data.

[0031] In order to solve the above problems, the present invention further provides a disaster prediction layer generation device, the device comprising:

[0032] A data transformation module is used to obtain original disaster reference data, perform spatiotemporal data transformation processing on the original disaster reference data, and obtain initial disaster reference data;

[0033] A reference index generation module, configured to calculate the vulnerability index and hazard index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and hazard index algorithm;

[0034] a hazard index calculation module, configured to input the vulnerability index and the hazard index into a preset disaster hazard index calculation formula to obtain a disaster hazard index;

[0035] The visualization module is used to use a preset convolution kernel algorithm to visualize the disaster risk index and generate a disaster prediction layer.

[0036] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0037] at least one processor; and,

[0038] a memory communicatively connected to the at least one processor; wherein,

[0039] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the disaster prediction layer generation method described above.

[0040] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned disaster prediction layer generation method.

[0041] The embodiment of the present invention performs spatiotemporal data transformation on the acquired original disaster reference data, so that the initial disaster reference data obtained after the transformation can be easily used as the basis for index calculation. The vulnerability index and hazard index corresponding to the initial disaster reference data are calculated based on the preset vulnerability index algorithm and hazard index algorithm, and combined with the preset disaster hazard index calculation formula, a disaster hazard index is obtained, which involves both vulnerability and hazard, making the calculated disaster hazard index more comprehensive. The disaster hazard index is visualized using a preset convolution kernel algorithm to generate a disaster prediction layer. The visualization processing realizes the fusion and display of data and layers, and improves the accuracy of the disaster prediction layer generation method. Therefore, the disaster prediction layer generation method, device, electronic device and computer-readable storage medium proposed in the present invention can solve the problem of insufficient accuracy in disaster prediction layer generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of a flow chart of a method for generating a disaster prediction layer according to an embodiment of the present invention;

[0043] Figure 2 A functional module diagram of a disaster prediction layer generation device provided by one embodiment of the present invention;

[0044] Figure 3 A schematic structural diagram of an electronic device for implementing the disaster prediction layer generation method provided in one embodiment of the present invention.

[0045] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0046] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] The embodiment of the present application provides a method for generating a disaster prediction layer. The execution subject of the disaster prediction layer generation method includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the disaster prediction layer generation method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides 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 networks (CDNs), and big data and artificial intelligence platforms.

[0048] Reference Figure 1 FIG. 1 is a flow chart of a method for generating a disaster prediction layer according to an embodiment of the present invention. In this embodiment, the method for generating a disaster prediction layer includes:

[0049] S1. Obtain original disaster reference data, perform spatiotemporal data transformation on the original disaster reference data, and obtain initial disaster reference data.

[0050] In the embodiment of the present invention, the original disaster reference data includes, but is not limited to, precipitation forecast data, HFS live reanalysis data, statistical yearbook data, hydrological bureau flow data, earthquake excitation coefficients, landslide and debris flow coefficients, and disaster-prone environmental data. The original disaster reference data is primarily derived from official public and open-source datasets.

[0051] Specifically, performing spatiotemporal data transformation processing on the original disaster reference data to obtain initial disaster reference data includes:

[0052] Using a preset missing value detection statement to detect whether there are missing values ​​in the original disaster reference data;

[0053] When there are missing values ​​in the original disaster reference data, the missing values ​​are supplemented to obtain supplementary reference data;

[0054] The supplementary reference data is mapped to a preset projection coordinate system, and the supplementary reference data on the projection coordinate system is mirrored and rotated to obtain initial disaster reference data.

[0055] Specifically, the preset missing value detection statement can be a missing value detection Java statement. When the missing value detection statement is used to detect the presence of missing values ​​in the original disaster reference data, the existing missing value filling method can be used to complete the missing value processing of the original disaster reference data. The completed reference data is subjected to spatiotemporal data conversion using GIS (Geographic Information System) software. The main spatiotemporal data conversion methods used include mirroring and rotating the projected coordinate system. The generated initial disaster reference data includes a 3D precipitation data grid with a resolution of 3km*3km and disaster-induced data.

[0056] The source data for the spatiotemporal data transformation is data of various resolutions: precipitation data is gridded at 3 km resolution, and parameter coefficient data is rasterized at 5 km resolution. Spatiotemporal data transformation is performed using the ArcGis framework, using methods such as spatial flipping, spatial mirroring, and spatial translation to preprocess the 3D grid data into 1 km resolution precipitation raster data and induced dynamic index data.

[0057] In detail, existing missing value filling methods include but are not limited to filling default values, mean, mode, KNN filling, etc.

[0058] S2. Calculate the vulnerability index and hazard index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and hazard index algorithm.

[0059] In an embodiment of the present invention, the calculating of the vulnerability index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm includes:

[0060] Extracting population reference data, economic reference data, and land reference data from the initial disaster reference data;

[0061] Performing dimensionless transformation on the population reference data and the economic reference data respectively to obtain normalized population data and normalized economic data;

[0062] The normalized population data, the normalized economic data, and the land reference data are input into a preset vulnerability index calculation formula to obtain a vulnerability index.

[0063] Specifically, the initial disaster reference data includes multiple types of reference data, and population reference data, economic reference data, and land reference data are extracted from the initial disaster reference data. The population reference data refers to population exposure data, the economic reference data refers to economic exposure data, and the land reference data refers to land cover type. Dimensionless transformation refers to normalization processing.

[0064] Specifically, the inputting of the normalized population data, the normalized economic data, and the land reference data into a preset vulnerability index calculation formula to obtain the vulnerability index includes:

[0065] The preset vulnerability index calculation formula is:

[0066]

[0067] Wherein, V is the vulnerability index, POP is the normalized population data, ECO is the normalized economic data, and LC is the land reference data.

[0068] Furthermore, the calculating of the hazard index corresponding to the initial disaster reference data based on a preset hazard index algorithm includes:

[0069] Extracting environmental reference data and disaster induction coefficients from the initial disaster reference data;

[0070] The environmental reference data and the disaster inducing coefficient are input into a preset hazard index calculation formula to obtain a hazard index.

[0071] In detail, the environmental reference data refers to water disaster-prone environmental data and water disaster-inducing dynamic data, and the disaster-inducing coefficient includes an earthquake excitation coefficient and a disturbance correction coefficient.

[0072] Specifically, inputting the environmental reference data and the disaster inducing coefficient into a preset hazard index calculation formula includes:

[0073] The preset risk index calculation formula is:

[0074] D=HFE×MHE×ECC×DC

[0075] Wherein, D is the hazard index, HFE is the water disaster-prone environmental data in the environmental reference data, MHE is the water disaster-induced dynamic data in the environmental reference data, ECC is the earthquake excitation coefficient in the disaster-induced coefficient, and DC is the disturbance correction coefficient in the disaster-induced coefficient.

[0076] S3. Input the vulnerability index and the hazard index into a preset disaster risk index calculation formula to obtain a disaster risk index.

[0077] In an embodiment of the present invention, the step of inputting the vulnerability index and the hazard index into a preset disaster risk index calculation formula to obtain the disaster risk index includes:

[0078] The preset disaster risk index calculation formula is:

[0079] R=D×V

[0080] Wherein, R is the disaster risk index, D is the hazard index, and V is the vulnerability index.

[0081] In detail, the area where water disaster risk may exist, the scale of risk, and the probability of risk occurrence can be determined based on the hazard index and the vulnerability index, so that the calculated disaster risk index is more accurate and reliable.

[0082] S4. Use a preset convolution kernel algorithm to visualize the disaster risk index and generate a disaster prediction layer.

[0083] In an embodiment of the present invention, the disaster risk index can reflect the potential impact of mountain disasters on a local area. In order to facilitate viewing of the mountain disaster index of various locations, the disaster risk index can be visualized to generate a disaster prediction layer.

[0084] Specifically, the visualization processing of the disaster risk index using a preset convolution kernel algorithm to generate a disaster prediction layer includes:

[0085] Perform downscaling calculation on the pre-acquired grid layer to obtain a downscaled layer;

[0086] Performing focus calculation on the downscaled layer to obtain a focus grid layer;

[0087] The disaster risk index is smoothly distributed on the focal grid layer using a preset convolution function to obtain a disaster prediction layer.

[0088] Furthermore, performing downscaling calculation on the pre-acquired grid layer to obtain the downscaled layer includes:

[0089] Obtaining preset resolution parameters and interpolation functions, and identifying the layer resolution of the grid layer;

[0090] The layer resolution of the grid layer is converted into the resolution parameter based on the interpolation function to obtain a downscaled layer.

[0091] In detail, the resolution parameter may be 250m, and the interpolation function may be an interpolation function.

[0092] Specifically, performing focus calculation on the downscaled layer to obtain a focus grid layer includes:

[0093] Dividing the downscaled layer into multiple sublayers according to a preset partition size;

[0094] Performing focus positioning on the multiple sub-layers using a preset focus positioning function to obtain multiple focus sub-layers;

[0095] The plurality of focus sub-layers are merged to obtain a focus grid layer.

[0096] Wherein, the focus positioning function may be a GIS function.

[0097] The embodiment of the present invention performs spatiotemporal data transformation processing on the original disaster reference data obtained, so that the initial disaster reference data obtained after the transformation can be used as the basis for index calculation. The vulnerability index and hazard index corresponding to the initial disaster reference data are calculated based on the preset vulnerability index algorithm and hazard index algorithm, and combined with the preset disaster hazard index calculation formula, a disaster hazard index is obtained, which involves both vulnerability and hazard, making the calculated disaster hazard index more comprehensive. The disaster hazard index is visualized using a preset convolution kernel algorithm to generate a disaster prediction layer. The visualization processing realizes the fusion and display of data and layers, and improves the accuracy of the disaster prediction layer generation method. Therefore, the disaster prediction layer generation method proposed in the present invention can solve the problem of insufficient accuracy in disaster prediction layer generation.

[0098] like Figure 2 FIG. 1 is a functional module diagram of a disaster prediction layer generating device provided by an embodiment of the present invention.

[0099] The disaster prediction layer generation device 100 described in the present invention can be installed in an electronic device. Depending on the functionality to be implemented, the disaster prediction layer generation device 100 may include a data conversion module 101, a reference index generation module 102, a hazard index calculation module 103, and a visualization module 104. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These are stored in the electronic device's memory.

[0100] In this embodiment, the functions of each module / unit are as follows:

[0101] The data transformation module 101 is used to obtain original disaster reference data and perform spatiotemporal data transformation on the original disaster reference data to obtain initial disaster reference data;

[0102] The reference index generating module 102 is configured to calculate the vulnerability index and hazard index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and hazard index algorithm;

[0103] The risk index calculation module 103 is used to input the vulnerability index and the risk index into a preset disaster risk index calculation formula to obtain a disaster risk index;

[0104] The visualization module 104 is used to perform visualization processing on the disaster risk index using a preset convolution kernel algorithm to generate a disaster prediction layer.

[0105] In detail, the specific implementation of each module of the disaster prediction layer generation device 100 is as follows:

[0106] Step 1: Obtain original disaster reference data, perform spatiotemporal data transformation on the original disaster reference data, and obtain initial disaster reference data.

[0107] In the embodiment of the present invention, the original disaster reference data includes, but is not limited to, precipitation forecast data, HFS live reanalysis data, statistical yearbook data, hydrological bureau flow data, earthquake excitation coefficients, landslide and debris flow coefficients, and disaster-prone environmental data. The original disaster reference data is primarily derived from official public and open-source datasets.

[0108] Specifically, performing spatiotemporal data transformation processing on the original disaster reference data to obtain initial disaster reference data includes:

[0109] Using a preset missing value detection statement to detect whether there are missing values ​​in the original disaster reference data;

[0110] When there are missing values ​​in the original disaster reference data, the missing values ​​are supplemented to obtain supplementary reference data;

[0111] The supplementary reference data is mapped to a preset projection coordinate system, and the supplementary reference data on the projection coordinate system is mirrored and rotated to obtain initial disaster reference data.

[0112] Specifically, the preset missing value detection statement can be a missing value detection Java statement. When the missing value detection statement is used to detect the presence of missing values ​​in the original disaster reference data, the existing missing value filling method can be used to complete the missing value processing of the original disaster reference data. The completed reference data is subjected to spatiotemporal data conversion using GIS (Geographic Information System) software. The main spatiotemporal data conversion methods used include mirroring and rotating the projected coordinate system. The generated initial disaster reference data includes a 3D precipitation data grid with a resolution of 3km*3km and disaster-induced data.

[0113] The source data for the spatiotemporal data transformation is data of various resolutions: precipitation data is gridded at 3 km resolution, and parameter coefficient data is rasterized at 5 km resolution. Spatiotemporal data transformation is performed using the ArcGis framework, using methods such as spatial flipping, spatial mirroring, and spatial translation to preprocess the 3D grid data into 1 km resolution precipitation raster data and induced dynamic index data.

[0114] In detail, existing missing value filling methods include but are not limited to filling default values, mean, mode, KNN filling, etc.

[0115] Step 2: Calculate the vulnerability index and hazard index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and hazard index algorithm.

[0116] In an embodiment of the present invention, the calculating of the vulnerability index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm includes:

[0117] Extracting population reference data, economic reference data, and land reference data from the initial disaster reference data;

[0118] Performing dimensionless transformation on the population reference data and the economic reference data respectively to obtain normalized population data and normalized economic data;

[0119] The normalized population data, the normalized economic data, and the land reference data are input into a preset vulnerability index calculation formula to obtain a vulnerability index.

[0120] Specifically, the initial disaster reference data includes multiple types of reference data, and population reference data, economic reference data, and land reference data are extracted from the initial disaster reference data. The population reference data refers to population exposure data, the economic reference data refers to economic exposure data, and the land reference data refers to land cover type. Dimensionless transformation refers to normalization processing.

[0121] Specifically, the inputting of the normalized population data, the normalized economic data, and the land reference data into a preset vulnerability index calculation formula to obtain the vulnerability index includes:

[0122] The preset vulnerability index calculation formula is:

[0123]

[0124] Wherein, V is the vulnerability index, POP is the normalized population data, ECO is the normalized economic data, and LC is the land reference data.

[0125] Furthermore, the calculating of the hazard index corresponding to the initial disaster reference data based on a preset hazard index algorithm includes:

[0126] Extracting environmental reference data and disaster induction coefficients from the initial disaster reference data;

[0127] The environmental reference data and the disaster inducing coefficient are input into a preset hazard index calculation formula to obtain a hazard index.

[0128] In detail, the environmental reference data refers to water disaster-prone environmental data and water disaster-inducing dynamic data, and the disaster-inducing coefficient includes an earthquake excitation coefficient and a disturbance correction coefficient.

[0129] Specifically, inputting the environmental reference data and the disaster inducing coefficient into a preset hazard index calculation formula includes:

[0130] The preset risk index calculation formula is:

[0131] D=HFE×MHE×ECC×DC

[0132] Wherein, D is the hazard index, HFE is the water disaster-prone environmental data in the environmental reference data, MHE is the water disaster-induced dynamic data in the environmental reference data, ECC is the earthquake excitation coefficient in the disaster-induced coefficient, and DC is the disturbance correction coefficient in the disaster-induced coefficient.

[0133] Step 3: Input the vulnerability index and the hazard index into a preset disaster risk index calculation formula to obtain a disaster risk index.

[0134] In an embodiment of the present invention, the step of inputting the vulnerability index and the hazard index into a preset disaster risk index calculation formula to obtain the disaster risk index includes:

[0135] The preset disaster risk index calculation formula is:

[0136] R=D×V

[0137] Wherein, R is the disaster risk index, D is the hazard index, and V is the vulnerability index.

[0138] In detail, the area where water disaster risk may exist, the scale of risk, and the probability of risk occurrence can be determined based on the hazard index and the vulnerability index, so that the calculated disaster risk index is more accurate and reliable.

[0139] Step 4: Use a preset convolution kernel algorithm to visualize the disaster risk index and generate a disaster prediction layer.

[0140] In an embodiment of the present invention, the disaster risk index can reflect the potential impact of mountain disasters on a local area. In order to facilitate viewing of the mountain disaster index of various locations, the disaster risk index can be visualized to generate a disaster prediction layer.

[0141] Specifically, the visualization processing of the disaster risk index using a preset convolution kernel algorithm to generate a disaster prediction layer includes:

[0142] Perform downscaling calculation on the pre-acquired grid layer to obtain a downscaled layer;

[0143] Performing focus calculation on the downscaled layer to obtain a focus grid layer;

[0144] The disaster risk index is smoothly distributed on the focal grid layer using a preset convolution function to obtain a disaster prediction layer.

[0145] Furthermore, performing downscaling calculation on the pre-acquired grid layer to obtain the downscaled layer includes:

[0146] Obtaining preset resolution parameters and interpolation functions, and identifying the layer resolution of the grid layer;

[0147] The layer resolution of the grid layer is converted into the resolution parameter based on the interpolation function to obtain a downscaled layer.

[0148] In detail, the resolution parameter may be 250m, and the interpolation function may be an interpolation function.

[0149] Specifically, performing focus calculation on the downscaled layer to obtain a focus grid layer includes:

[0150] Dividing the downscaled layer into multiple sublayers according to a preset partition size;

[0151] Performing focus positioning on the multiple sub-layers using a preset focus positioning function to obtain multiple focus sub-layers;

[0152] The plurality of focus sub-layers are merged to obtain a focus grid layer.

[0153] Wherein, the focus positioning function may be a GIS function.

[0154] The embodiment of the present invention performs spatiotemporal data transformation processing on the original disaster reference data obtained, so that the initial disaster reference data obtained after the transformation can be used as the basis for index calculation. The vulnerability index and hazard index corresponding to the initial disaster reference data are calculated based on the preset vulnerability index algorithm and hazard index algorithm, and combined with the preset disaster hazard index calculation formula, a disaster hazard index is obtained, which involves both vulnerability and hazard, making the calculated disaster hazard index more comprehensive. The disaster hazard index is visualized using a preset convolution kernel algorithm to generate a disaster prediction layer. The visualization processing realizes the fusion and display of data and layers, and improves the accuracy of the disaster prediction layer generation method. Therefore, the disaster prediction layer generation device proposed in the present invention can solve the problem of insufficient accuracy in disaster prediction layer generation.

[0155] like Figure 3 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing a method for generating a disaster prediction layer according to an embodiment of the present invention.

[0156] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a disaster prediction layer generation program.

[0157] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and lines, and executing the programs or modules stored in the memory 11 (for example, executing the disaster prediction layer generation program, etc.), as well as calling the data stored in the memory 11, to execute various functions of the electronic device and process data.

[0158] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of the disaster prediction layer generation program, but can also be used to temporarily store data that has been output or is to be output.

[0159] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0160] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0161] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0162] For example, although not shown, the electronic device may further include a power source (such as a battery) for powering various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management device, thereby implementing functions such as charge management, discharge management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0163] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0164] The disaster prediction layer generation program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve the following:

[0165] Obtaining original disaster reference data, and performing spatiotemporal data transformation processing on the original disaster reference data to obtain initial disaster reference data;

[0166] Calculating the vulnerability index and hazard index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and hazard index algorithm;

[0167] Inputting the vulnerability index and the hazard index into a preset disaster risk index calculation formula to obtain a disaster risk index;

[0168] The disaster risk index is visualized using a preset convolution kernel algorithm to generate a disaster prediction layer.

[0169] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0170] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0171] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0172] Obtaining original disaster reference data, and performing spatiotemporal data transformation processing on the original disaster reference data to obtain initial disaster reference data;

[0173] Calculating the vulnerability index and hazard index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and hazard index algorithm;

[0174] Inputting the vulnerability index and the hazard index into a preset disaster risk index calculation formula to obtain a disaster risk index;

[0175] The disaster risk index is visualized using a preset convolution kernel algorithm to generate a disaster prediction layer.

[0176] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0177] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0178] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0179] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0180] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0181] Blockchain, as used in this article, refers to a novel application model for computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Blockchain is essentially a decentralized database, a series of data blocks generated using cryptographic methods. Each block contains information about a batch of online transactions, used to verify the validity of this information (to prevent counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, the platform product service layer, and the application service layer.

[0182] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0183] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Terms such as "first" and "second" are used to indicate names and do not imply any particular order.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A disaster prediction layer generation method, characterized in that: The method comprises: Obtaining original disaster reference data, using a preset missing value detection statement to detect whether there are missing values ​​in the original disaster reference data, when there are missing values ​​in the original disaster reference data, completing the missing values ​​to obtain completed reference data, mapping the completed reference data to a preset projected coordinate system, and mirroring and rotating the completed reference data on the projected coordinate system to obtain initial disaster reference data; Extracting population reference data, economic reference data, and land reference data from the initial disaster reference data, performing dimensionless transformation on the population reference data and the economic reference data to obtain normalized population data and normalized economic data, and inputting the normalized population data, the normalized economic data, and the land reference data into a preset vulnerability index calculation formula to obtain a vulnerability index; Extracting environmental reference data and disaster induction coefficients from the initial disaster reference data, and inputting the environmental reference data and the disaster induction coefficients into a preset hazard index calculation formula to obtain a hazard index; Inputting the vulnerability index and the hazard index into a preset disaster risk index calculation formula to obtain a disaster risk index; A downscaling calculation is performed on the pre-acquired grid layer to obtain a downscaling layer, and a focus calculation is performed on the downscaling layer to obtain a focus grid layer. The disaster risk index is smoothly distributed on the focus grid layer using a preset convolution function to obtain a disaster prediction layer.

2. The disaster prediction layer generation method according to claim 1, wherein: The downscaling calculation is performed on the pre-acquired grid layer to obtain the downscaled layer, including: Obtaining preset resolution parameters and interpolation functions, and identifying the layer resolution of the grid layer; The layer resolution of the grid layer is converted into the resolution parameter based on the interpolation function to obtain a downscaled layer.

3. The disaster prediction layer generation method according to claim 1, wherein: The step of performing focus calculation on the downscaled layer to obtain a focus grid layer includes: Dividing the downscaled layer into multiple sublayers according to a preset partition size; Performing focus positioning on the multiple sub-layers using a preset focus positioning function to obtain multiple focus sub-layers; The plurality of focus sub-layers are merged to obtain a focus grid layer.

4. The disaster prediction layer generation method according to claim 1, wherein: The preset vulnerability index calculation formula is: in, is the vulnerability index, is the normalized population data, is the normalized economic data, is the land reference data.

5. A disaster prediction layer generation device, characterized in that: The device comprises: a data transformation module, configured to obtain original disaster reference data, detect whether there are missing values ​​in the original disaster reference data using a preset missing value detection statement, and when there are missing values ​​in the original disaster reference data, perform complement processing on the missing values ​​to obtain complement reference data, map the complement reference data to a preset projected coordinate system, and mirror and rotate the complement reference data on the projected coordinate system to obtain initial disaster reference data; a reference index generation module, configured to extract population reference data, economic reference data, and land reference data from the initial disaster reference data, perform dimensionless transformation on the population reference data and the economic reference data, respectively, to obtain normalized population data and normalized economic data, and input the normalized population data, the normalized economic data, and the land reference data into a preset vulnerability index calculation formula to obtain a vulnerability index; extract environmental reference data and a disaster inducing coefficient from the initial disaster reference data, and input the environmental reference data and the disaster inducing coefficient into a preset hazard index calculation formula to obtain a hazard index; a hazard index calculation module, configured to input the vulnerability index and the hazard index into a preset disaster hazard index calculation formula to obtain a disaster hazard index; The visualization module is used to perform downscaling calculation on the pre-acquired grid layer to obtain a downscaled layer, perform focus calculation on the downscaled layer to obtain a focus grid layer, and use a preset convolution function to smoothly distribute the disaster risk index on the focus grid layer to obtain a disaster prediction layer.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the disaster prediction layer generation method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the disaster prediction layer generation method according to any one of claims 1 to 4 is implemented.

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