CN114048364A - Disaster prediction layer generation method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to an artificial intelligence technology, and discloses a disaster prediction image layer generation method, which comprises the following steps: performing space-time data transformation processing on the original disaster reference data to obtain initial disaster reference data; calculating a vulnerability index and a risk index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and a risk index algorithm; inputting the vulnerability index and the risk index into a preset disaster risk index calculation formula to obtain a disaster risk index; and carrying out visual processing on the disaster risk index by using a preset convolution kernel algorithm to generate a disaster prediction layer. In addition, the invention also relates to a block chain technology, and the disaster risk index can be stored in the node of the block chain. The invention also provides a disaster prediction layer generation device, electronic equipment and a storage medium. The method and the device can improve the accuracy of the generation of the disaster prediction image layer.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a disaster prediction layer generation method and device, electronic equipment and a computer readable storage medium.
Background
At present, a plurality of common disasters such as geological disasters and flood disasters have the characteristics of high destructive power, strong burstiness and difficult prevention, and cause a great amount of casualties and huge property loss to China every year, so that the work of forecasting the geological disasters is urgent. In the conventional method for generating a disaster prediction map, disaster factors related to a disaster are generally acquired, and the acquired disaster factors are standardized to form a disaster prediction map. The method does not consider the influence of disaster factors related to disasters on disaster dangers, so that the generated disaster prediction image layer is not accurate enough.
Disclosure of Invention
The invention provides a method and a device for generating a disaster prediction layer and a computer readable storage medium, and mainly aims to improve the accuracy of generating the disaster prediction layer.
In order to achieve the above object, the method for generating a disaster prediction map layer provided by the present invention comprises:
acquiring original disaster reference data, and performing space-time data transformation processing on the original disaster reference data to obtain initial disaster reference data;
calculating a vulnerability index and a risk index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and a risk index algorithm;
inputting the vulnerability index and the risk index into a preset disaster risk index calculation formula to obtain a disaster risk index;
and carrying out visualization processing on the disaster risk index by using a preset convolution kernel algorithm to generate a disaster prediction image layer.
Optionally, the visualizing the disaster risk index by using a preset convolution kernel algorithm to generate a disaster prediction map layer includes:
carrying out downscaling calculation on a grid layer obtained in advance to obtain a downscaled layer;
performing focus calculation on the downscale image layer to obtain a focus grid image layer;
and smoothly distributing the disaster risk index on the focus grid image layer by using a preset convolution function to obtain a disaster prediction image layer.
Optionally, the performing downscaling calculation on the pre-obtained grid layer to obtain a downscaling layer includes:
acquiring preset resolution parameters and an interpolation function, and identifying the layer resolution of the grid layer;
and converting the layer resolution of the grid layer into the resolution parameter based on the interpolation function to obtain a downscaling layer.
Optionally, the performing focus calculation on the downscale image layer to obtain a focus grid image layer includes:
dividing the downscaled layer into a plurality of sub-layers according to a preset division size;
performing focus positioning on the plurality of sub-layers by using a preset focus positioning function to obtain a plurality of focus sub-layers;
and combining the plurality of focus sub-layers to obtain a focus grid layer.
Optionally, the calculating the vulnerability index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm includes:
extracting population reference data, economic reference data and land reference data in the initial disaster reference data;
carrying out dimensionless transformation on the population reference data and the economic reference data respectively 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.
Optionally, the preset vulnerability index calculation formula is as follows:
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.
Optionally, the performing spatio-temporal data transformation processing on the original disaster reference data to obtain initial disaster reference data includes:
detecting whether a missing value exists in the original disaster reference data by using a preset missing value detection statement;
when the missing value exists in the original disaster reference data, performing completion processing on the missing value to obtain completion reference data;
and mapping the complementing reference data to a preset projection coordinate system, and carrying out mirror image and rotation change on the complementing reference data on the projection coordinate system to obtain initial disaster reference data.
In order to solve the above problem, the present invention further provides a disaster prediction map layer generation apparatus, including:
the data transformation module is used for acquiring original disaster reference data and performing space-time data transformation processing on the original disaster reference data to obtain initial disaster reference data;
the reference index generation module is used for calculating the vulnerability index and the risk index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and a preset risk index algorithm;
the risk index calculation module is used for inputting the vulnerability index and the risk index into a preset disaster risk index calculation formula to obtain a disaster risk index;
and the visualization module is used for performing visualization processing on the disaster risk index by using a preset convolution kernel algorithm to generate a disaster prediction map layer.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the disaster prediction image layer generation method described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the disaster prediction image layer generation method described above.
According to the embodiment of the invention, the acquired original disaster reference data is subjected to space-time data transformation processing, so that the transformed original disaster reference data can be conveniently used as the basis of index calculation. And calculating the vulnerability index and the risk index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and a risk index algorithm, and obtaining the disaster risk index by combining a preset disaster risk index calculation formula. And performing visualization processing on the disaster risk index by using a preset convolution kernel algorithm to generate a disaster prediction layer, wherein the visualization processing realizes the fusion and display of data and the layer, and improves the accuracy of the generation method of the disaster prediction layer. Therefore, the disaster prediction layer generation method, the disaster prediction layer generation device, the electronic equipment and the computer readable storage medium provided by the invention can solve the problem that the disaster prediction layer generation accuracy is not high enough.
Drawings
Fig. 1 is a schematic flowchart of a method for generating a disaster prediction map layer according to an embodiment of the present invention;
fig. 2 is a functional block diagram of a disaster prediction image layer generation apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device implementing the method for generating a disaster prediction layer according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a disaster prediction layer generation method. The executing body of the disaster prediction image layer generating method includes, but is not limited to, at least one of electronic devices that can be configured to execute the method provided by the embodiment of the present application, such as a server and a terminal. In other words, the disaster prediction image layer generation method may be executed by software or hardware installed in a terminal device or a server device, where the software may 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, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flow chart of a method for generating a disaster prediction image layer according to an embodiment of the present invention. In this embodiment, the method for generating a disaster prediction map layer includes:
and S1, acquiring original disaster reference data, and performing space-time data transformation processing on the original disaster reference data to obtain the original disaster reference data.
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 office flow data, seismic excitation coefficient, landslide, debris flow coefficient, disaster environment data, and the like. The original disaster reference data is mainly derived from official public and open source data sets and the like.
Specifically, the performing spatio-temporal data transformation processing on the original disaster reference data to obtain the initial disaster reference data includes:
detecting whether a missing value exists in the original disaster reference data by using a preset missing value detection statement;
when the missing value exists in the original disaster reference data, performing completion processing on the missing value to obtain completion reference data;
and mapping the complementing reference data to a preset projection coordinate system, and carrying out mirror image and rotation change on the complementing reference data on the projection coordinate system to obtain initial disaster reference data.
In detail, the preset missing value detection statement may be a missing value detection java statement, and when the missing value detection statement detects that the original disaster reference data has a missing value, the original disaster reference data may be subjected to missing value completion processing by using an existing missing value filling method. And performing space-time data conversion on the completion reference data through GIS (Geographic Information System) software, wherein the mainly adopted space-time data conversion mode comprises mirroring and rotation change on a projection coordinate System, and the generated initial disaster reference data comprises a 3D rainfall data grid with the resolution of 3km x 3km and disaster inducing data.
The source data of the space-time data transformation are data with various resolutions, the precipitation data are 3km resolution grid data, and the parameter coefficient data are 5km grid data. Space-time data change is based on an ArcGis framework, and 3-dimensional grid data are preprocessed by using methods such as space overturning, space mirroring, space translation and the like to generate 1km resolution ratio rainfall grid data and induced dynamic index data.
In detail, existing missing value filling methods include, but are not limited to, filling default values, mean, mode, KNN filling, and the like.
And S2, calculating the vulnerability index and the risk index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and a preset risk index algorithm.
In an embodiment of the present invention, the calculating the vulnerability index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm includes:
extracting population reference data, economic reference data and land reference data in the initial disaster reference data;
carrying out dimensionless transformation on the population reference data and the economic reference data respectively 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.
In detail, the initial disaster reference data includes multiple types of reference data, and population reference data, economic reference data and land reference data in the initial disaster reference data are extracted, wherein 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 a land cover type. The dimensionless transformation refers to normalization.
Specifically, the step of 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 includes:
the preset vulnerability index calculation formula is as follows:
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.
Further, the calculating a risk index corresponding to the initial disaster reference data based on a preset risk index algorithm includes:
extracting environment reference data and disaster inducing coefficients in the initial disaster reference data;
and inputting the environmental reference data and the disaster inducing coefficient into a preset risk index calculation formula to obtain a risk index.
In detail, the environmental reference data refers to water disaster pregnancy environmental data and water disaster induced dynamic data, and the disaster induced coefficient includes a seismic excitation coefficient and a disturbance correction coefficient.
Specifically, the inputting the environmental reference data and the disaster inducing coefficient into a preset risk index calculation formula includes:
the preset risk index calculation formula is as follows:
D＝HFE×MHE×ECC×DC
wherein D is the risk index, HFE is water disaster pregnancy environment data in the environment reference data, MHE is water disaster induced dynamic data in the environment reference data, ECC is an earthquake excitation coefficient in the disaster induction coefficient, and DC is a disturbance correction coefficient in the disaster induction coefficient.
And S3, inputting the vulnerability index and the risk index into a preset disaster risk index calculation formula to obtain a disaster risk index.
In an embodiment of the present invention, the inputting the vulnerability index and the risk index into a preset disaster risk index calculation formula to obtain a disaster risk index includes:
the preset disaster risk index calculation formula is as follows:
R＝D×V
wherein R is the disaster risk index, D is the risk index, and V is the vulnerability index.
In detail, the area of the possible water disaster risk, the scale of the risk and the probability of the risk can be determined according to the risk index and the vulnerability index, and the calculated disaster risk index is more accurate and reliable.
And S4, performing visualization processing on the disaster risk index by using a preset convolution kernel algorithm to generate a disaster prediction map layer.
In the embodiment of the invention, the disaster risk index can reflect the potential influence of mountain disasters on the local, and in order to conveniently view mountain disaster indexes of various places, the disaster risk index can be visualized to generate a disaster prediction map layer.
Specifically, the generating a disaster prediction map layer by performing visualization processing on the disaster risk index by using a preset convolution kernel algorithm includes:
carrying out downscaling calculation on a grid layer obtained in advance to obtain a downscaled layer;
performing focus calculation on the downscale image layer to obtain a focus grid image layer;
and smoothly distributing the disaster risk index on the focus grid image layer by using a preset convolution function to obtain a disaster prediction image layer.
Further, the performing downscaling calculation on the pre-obtained grid map layer to obtain a downscaling map layer includes:
acquiring preset resolution parameters and an interpolation function, and identifying the layer resolution of the grid layer;
and converting the layer resolution of the grid layer into the resolution parameter based on the interpolation function to obtain a downscaling layer.
In detail, the resolution parameter may be 250m, and the interpolation function may be an interpolation function.
Specifically, the performing focus calculation on the downscale map layer to obtain a focus grid map layer includes:
dividing the downscaled layer into a plurality of sub-layers according to a preset division size;
performing focus positioning on the plurality of sub-layers by using a preset focus positioning function to obtain a plurality of focus sub-layers;
and combining the plurality of focus sub-layers to obtain a focus grid layer.
Wherein the focus positioning function may be a GIS function.
According to the embodiment of the invention, the acquired original disaster reference data is subjected to space-time data transformation processing, so that the transformed original disaster reference data can be conveniently used as the basis of index calculation. And calculating the vulnerability index and the risk index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and a risk index algorithm, and obtaining the disaster risk index by combining a preset disaster risk index calculation formula. And performing visualization processing on the disaster risk index by using a preset convolution kernel algorithm to generate a disaster prediction layer, wherein the visualization processing realizes the fusion and display of data and the layer, and improves the accuracy of the generation method of the disaster prediction layer. Therefore, the disaster prediction layer generation method provided by the invention can solve the problem that the accuracy of the disaster prediction layer generation is not high enough.
Fig. 2 is a functional block diagram of a disaster prediction image layer generation apparatus according to an embodiment of the present invention.
The disaster prediction image layer generation apparatus 100 according to the present invention may be installed in an electronic device. According to the implemented functions, the disaster prediction map layer generation apparatus 100 may include a data transformation module 101, a reference index generation module 102, a risk index calculation module 103, and a visualization module 104. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the data transformation module 101 is configured to obtain original disaster reference data, and perform spatio-temporal data transformation processing on the original disaster reference data to obtain initial disaster reference data;
the reference index generation module 102 is configured to calculate a vulnerability index and a risk index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and a preset risk index algorithm;
the risk index calculation module 103 is configured to input the vulnerability index and the risk index into a preset disaster risk index calculation formula to obtain a disaster risk index;
the visualization module 104 is configured to perform visualization processing on the disaster risk index by using a preset convolution kernel algorithm, and generate a disaster prediction map layer.
In detail, the disaster prediction map layer generation apparatus 100 includes the following modules:
the method comprises the steps of firstly, acquiring original disaster reference data, and carrying out space-time data transformation processing on the original disaster reference data to obtain the initial disaster reference data.
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 office flow data, seismic excitation coefficient, landslide, debris flow coefficient, disaster environment data, and the like. The original disaster reference data is mainly derived from official public and open source data sets and the like.
Specifically, the performing spatio-temporal data transformation processing on the original disaster reference data to obtain the initial disaster reference data includes:
detecting whether a missing value exists in the original disaster reference data by using a preset missing value detection statement;
when the missing value exists in the original disaster reference data, performing completion processing on the missing value to obtain completion reference data;
and mapping the complementing reference data to a preset projection coordinate system, and carrying out mirror image and rotation change on the complementing reference data on the projection coordinate system to obtain initial disaster reference data.
In detail, the preset missing value detection statement may be a missing value detection java statement, and when the missing value detection statement detects that the original disaster reference data has a missing value, the original disaster reference data may be subjected to missing value completion processing by using an existing missing value filling method. And performing space-time data conversion on the completion reference data through GIS (Geographic Information System) software, wherein the mainly adopted space-time data conversion mode comprises mirroring and rotation change on a projection coordinate System, and the generated initial disaster reference data comprises a 3D rainfall data grid with the resolution of 3km x 3km and disaster inducing data.
The source data of the space-time data transformation are data with various resolutions, the precipitation data are 3km resolution grid data, and the parameter coefficient data are 5km grid data. Space-time data change is based on an ArcGis framework, and 3-dimensional grid data are preprocessed by using methods such as space overturning, space mirroring, space translation and the like to generate 1km resolution ratio rainfall grid data and induced dynamic index data.
In detail, existing missing value filling methods include, but are not limited to, filling default values, mean, mode, KNN filling, and the like.
And secondly, calculating the vulnerability index and the risk index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and a preset risk index algorithm.
In an embodiment of the present invention, the calculating the vulnerability index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm includes:
extracting population reference data, economic reference data and land reference data in the initial disaster reference data;
carrying out dimensionless transformation on the population reference data and the economic reference data respectively 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.
In detail, the initial disaster reference data includes multiple types of reference data, and population reference data, economic reference data and land reference data in the initial disaster reference data are extracted, wherein 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 a land cover type. The dimensionless transformation refers to normalization.
Specifically, the step of 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 includes:
the preset vulnerability index calculation formula is as follows:
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.
Further, the calculating a risk index corresponding to the initial disaster reference data based on a preset risk index algorithm includes:
extracting environment reference data and disaster inducing coefficients in the initial disaster reference data;
and inputting the environmental reference data and the disaster inducing coefficient into a preset risk index calculation formula to obtain a risk index.
In detail, the environmental reference data refers to water disaster pregnancy environmental data and water disaster induced dynamic data, and the disaster induced coefficient includes a seismic excitation coefficient and a disturbance correction coefficient.
Specifically, the inputting the environmental reference data and the disaster inducing coefficient into a preset risk index calculation formula includes:
the preset risk index calculation formula is as follows:
D＝HFE×MHE×ECC×DC
wherein D is the risk index, HFE is water disaster pregnancy environment data in the environment reference data, MHE is water disaster induced dynamic data in the environment reference data, ECC is an earthquake excitation coefficient in the disaster induction coefficient, and DC is a disturbance correction coefficient in the disaster induction coefficient.
And step three, inputting the vulnerability index and the risk index into a preset disaster risk index calculation formula to obtain a disaster risk index.
In an embodiment of the present invention, the inputting the vulnerability index and the risk index into a preset disaster risk index calculation formula to obtain a disaster risk index includes:
the preset disaster risk index calculation formula is as follows:
R＝D×V
wherein R is the disaster risk index, D is the risk index, and V is the vulnerability index.
In detail, the area of the possible water disaster risk, the scale of the risk and the probability of the risk can be determined according to the risk index and the vulnerability index, and the calculated disaster risk index is more accurate and reliable.
And fourthly, performing visualization processing on the disaster risk index by using a preset convolution kernel algorithm to generate a disaster prediction map layer.
In the embodiment of the invention, the disaster risk index can reflect the potential influence of mountain disasters on the local, and in order to conveniently view mountain disaster indexes of various places, the disaster risk index can be visualized to generate a disaster prediction map layer.
Specifically, the generating a disaster prediction map layer by performing visualization processing on the disaster risk index by using a preset convolution kernel algorithm includes:
carrying out downscaling calculation on a grid layer obtained in advance to obtain a downscaled layer;
performing focus calculation on the downscale image layer to obtain a focus grid image layer;
and smoothly distributing the disaster risk index on the focus grid image layer by using a preset convolution function to obtain a disaster prediction image layer.
Further, the performing downscaling calculation on the pre-obtained grid map layer to obtain a downscaling map layer includes:
acquiring preset resolution parameters and an interpolation function, and identifying the layer resolution of the grid layer;
and converting the layer resolution of the grid layer into the resolution parameter based on the interpolation function to obtain a downscaling layer.
In detail, the resolution parameter may be 250m, and the interpolation function may be an interpolation function.
Specifically, the performing focus calculation on the downscale map layer to obtain a focus grid map layer includes:
dividing the downscaled layer into a plurality of sub-layers according to a preset division size;
performing focus positioning on the plurality of sub-layers by using a preset focus positioning function to obtain a plurality of focus sub-layers;
and combining the plurality of focus sub-layers to obtain a focus grid layer.
Wherein the focus positioning function may be a GIS function.
According to the embodiment of the invention, the acquired original disaster reference data is subjected to space-time data transformation processing, so that the transformed original disaster reference data can be conveniently used as the basis of index calculation. And calculating the vulnerability index and the risk index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and a risk index algorithm, and obtaining the disaster risk index by combining a preset disaster risk index calculation formula. And performing visualization processing on the disaster risk index by using a preset convolution kernel algorithm to generate a disaster prediction layer, wherein the visualization processing realizes the fusion and display of data and the layer, and improves the accuracy of the generation method of the disaster prediction layer. Therefore, the disaster prediction layer generation device provided by the invention can solve the problem that the accuracy of the disaster prediction layer generation is not high enough.
Fig. 3 is a schematic structural diagram of an electronic device implementing a disaster prediction layer generation method according to an embodiment of the present invention.
The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may further include a computer program, such as a disaster prediction image layer generation program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing a program or a module (for example, executing a disaster prediction layer generation program, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used to store not only application software installed in the electronic device and various types of data, such as codes of a disaster prediction image layer generation program, but also data that has been output or is to be output temporarily.
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 arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically 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), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The disaster prediction layer generation program stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when running in the processor 10, can implement:
acquiring original disaster reference data, and performing space-time data transformation processing on the original disaster reference data to obtain initial disaster reference data;
calculating a vulnerability index and a risk index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and a risk index algorithm;
inputting the vulnerability index and the risk index into a preset disaster risk index calculation formula to obtain a disaster risk index;
and carrying out visualization processing on the disaster risk index by using a preset convolution kernel algorithm to generate a disaster prediction image layer.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring original disaster reference data, and performing space-time data transformation processing on the original disaster reference data to obtain initial disaster reference data;
calculating a vulnerability index and a risk index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and a risk index algorithm;
inputting the vulnerability index and the risk index into a preset disaster risk index calculation formula to obtain a disaster risk index;
and carrying out visualization processing on the disaster risk index by using a preset convolution kernel algorithm to generate a disaster prediction image layer.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A disaster prediction map layer generation method is characterized by comprising the following steps:
acquiring original disaster reference data, and performing space-time data transformation processing on the original disaster reference data to obtain initial disaster reference data;
calculating a vulnerability index and a risk index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and a risk index algorithm;
inputting the vulnerability index and the risk index into a preset disaster risk index calculation formula to obtain a disaster risk index;
and carrying out visualization processing on the disaster risk index by using a preset convolution kernel algorithm to generate a disaster prediction image layer.
2. The method as claimed in claim 1, wherein the step of generating a disaster prediction map layer by visualizing the disaster risk index using a predetermined convolution kernel algorithm comprises:
carrying out downscaling calculation on a grid layer obtained in advance to obtain a downscaled layer;
performing focus calculation on the downscale image layer to obtain a focus grid image layer;
and smoothly distributing the disaster risk index on the focus grid image layer by using a preset convolution function to obtain a disaster prediction image layer.
3. The disaster prediction image layer generation method according to claim 2, wherein the down-scaling calculation on the pre-obtained grid image layer to obtain a down-scaling image layer includes:
acquiring preset resolution parameters and an interpolation function, and identifying the layer resolution of the grid layer;
and converting the layer resolution of the grid layer into the resolution parameter based on the interpolation function to obtain a downscaling layer.
4. The method according to claim 2, wherein the performing the focus calculation on the downscale image layer to obtain a focus grid image layer comprises:
dividing the downscaled layer into a plurality of sub-layers according to a preset division size;
performing focus positioning on the plurality of sub-layers by using a preset focus positioning function to obtain a plurality of focus sub-layers;
and combining the plurality of focus sub-layers to obtain a focus grid layer.
5. The method for generating a disaster prediction map layer according to claim 1, wherein the calculating the vulnerability index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm includes:
extracting population reference data, economic reference data and land reference data in the initial disaster reference data;
carrying out dimensionless transformation on the population reference data and the economic reference data respectively 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.
6. The disaster prediction map layer generation method as claimed in claim 5, wherein said predetermined vulnerability index calculation formula is:
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.
7. The disaster prediction map layer generation method as claimed in any one of claims 1 to 6, wherein said performing spatiotemporal data transformation on said original disaster reference data to obtain initial disaster reference data comprises:
detecting whether a missing value exists in the original disaster reference data by using a preset missing value detection statement;
when the missing value exists in the original disaster reference data, performing completion processing on the missing value to obtain completion reference data;
and mapping the complementing reference data to a preset projection coordinate system, and carrying out mirror image and rotation change on the complementing reference data on the projection coordinate system to obtain initial disaster reference data.
8. A disaster prediction map layer generation apparatus, comprising:
the data transformation module is used for acquiring original disaster reference data and performing space-time data transformation processing on the original disaster reference data to obtain initial disaster reference data;
the reference index generation module is used for calculating the vulnerability index and the risk index corresponding to the initial disaster reference data based on a preset vulnerability index algorithm and a preset risk index algorithm;
the risk index calculation module is used for inputting the vulnerability index and the risk index into a preset disaster risk index calculation formula to obtain a disaster risk index;
and the visualization module is used for performing visualization processing on the disaster risk index by using a preset convolution kernel algorithm to generate a disaster prediction map layer.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the disaster prediction layer generation method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the disaster prediction image layer generation method according to any one of claims 1 to 7.
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| WO2023205275A1 (en) * | 2022-04-19 | 2023-10-26 | Vizient, Inc. | Servers, systems, and methods for mapping attributes to a geographical location |
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