CN112687079A - Disaster early warning method, device, equipment and storage medium - Google Patents
Disaster early warning method, device, equipment and storage medium Download PDFInfo
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Abstract
The application provides a disaster early warning method, a disaster early warning device, equipment and a storage medium, wherein the disaster early warning method comprises the following steps: acquiring real-time meteorological data, and taking the real-time meteorological data as first meteorological data; generating second meteorological data related to the meteorological disaster according to the first meteorological data and the meteorological disaster knowledge map; acquiring a preset disaster early warning threshold value; performing multi-mode identification according to the second meteorological data and the disaster early warning threshold value to obtain a multi-mode identification result; and generating disaster early warning information according to the multi-mode recognition result. The method and the device can fuse various meteorological disaster early warning modes to carry out early warning on the meteorological disasters, and improve early warning accuracy and applicability of the meteorological disasters.
Description
Technical Field
The application relates to the field, in particular to a disaster early warning method, a disaster early warning device, disaster early warning equipment and a storage medium.
Background
In the prior art, various modes exist for carrying out early warning on disasters, but each mode only can carry out disaster early warning under specific scenes, so that the method has the defects of small application range and low accuracy.
Disclosure of Invention
An object of the embodiments of the present application is to provide a disaster early warning method, apparatus, device and storage medium, which are used to perform early warning on a meteorological disaster by fusing multiple meteorological disaster early warning modes, and improve early warning accuracy and applicability of the meteorological disaster.
Therefore, the embodiment of the application discloses a disaster early warning method, which comprises the following steps:
acquiring real-time meteorological data, and taking the real-time meteorological data as first meteorological data;
generating second meteorological data related to the meteorological disaster according to the first meteorological data and the meteorological disaster knowledge map;
acquiring a preset disaster early warning threshold value;
performing multi-mode identification according to the second meteorological data and the disaster early warning threshold value to obtain a multi-mode identification result;
and generating disaster early warning information according to the multi-mode recognition result.
In the first aspect of the application, at least a disaster early warning mode based on the knowledge graph and an early warning mode based on multi-mode recognition are integrated, so that the accuracy and the applicability of an early warning result can be further improved while early warning is performed on meteorological disasters.
In the first aspect of the present application, as an optional implementation manner, after the generating disaster early warning information according to the multi-modal recognition result, the method includes:
and adjusting the disaster early warning threshold value according to the disaster early warning information generated in the preset time period and the first weather data in the preset time period.
In this optional embodiment, the disaster early warning threshold value can be adjusted according to the disaster early warning information generated within the preset time period and the first weather data within the preset time period, so that a disaster early warning mode based on a self-adaptive system is further integrated, and the accuracy and the applicability of disaster early warning are further improved.
In the first aspect of the present application, as an optional implementation manner, the second meteorological data includes at least one meteorological disaster keyword;
and performing multi-mode identification according to the second meteorological data and the disaster early warning threshold value to obtain a multi-mode identification result, wherein the multi-mode identification result comprises the following steps:
determining a modal identification algorithm according to the meteorological disaster key words;
and calculating to obtain the multi-mode identification result according to the second meteorological data, the mode identification algorithm and the disaster early warning threshold value.
In this optional embodiment, a modality identification algorithm can be determined according to the weather disaster keyword, so that a weather data processing method can be determined according to different weather disasters, and the multi-modality identification result is obtained by calculation according to the second weather data, the modality identification algorithm and the disaster early warning threshold value, thereby finally realizing multi-modality identification.
In the first aspect of the present application, as an optional implementation manner, after the acquiring real-time meteorological data and using the real-time meteorological data as first meteorological data, before the generating second meteorological data related to a meteorological disaster according to the first meteorological data and a meteorological disaster knowledgegraph, the method further includes:
constructing a meteorological disaster expert keyword library;
acquiring a preset knowledge graph structure;
and constructing the meteorological disaster knowledge graph according to the preset knowledge graph structure and the meteorological disaster expert keyword library.
In the optional embodiment, the meteorological disaster intellectual property map can be constructed by constructing a meteorological disaster expert keyword library and acquiring a preset knowledge map structure.
In the first aspect of the present application, as an optional implementation manner, after the generating disaster early warning information according to the multi-modal recognition result, the method further includes:
when the meteorological disaster is continuous rainy, determining the maximum rainfall, the continuous rainfall days and the total rainfall in the continuous rainy period according to the first meteorological data;
and calculating the continuous overcast and rainy disaster index according to the maximum rainfall, the continuous rainfall days and the total rainfall.
In this optional embodiment, the maximum rainfall, the number of continuous rainfall days and the total rainfall amount in the continuous rainy period are determined according to the first weather data, so that the continuous rainy disaster index can be calculated, and the continuous rainy disaster degree can be displayed through the continuous rainy disaster index, so as to further improve the accuracy of disaster early warning.
In the first aspect of the present application, as an optional implementation manner, a calculation formula for calculating the continuous rainy disaster index according to the maximum rainfall, the number of consecutive rainfall days, and the total rainfall amount is as follows:
LYRI＝0.45X1+0.75X2+1.80X3；
wherein, LYRI represents the index of continuous rainy disasters, and X1, X2 and X3 are the index values of the maximum rainfall, the number of continuous rainfall days and the total rainfall respectively.
In this alternative embodiment, by calculating the formula: the LYRI is 0.45X1+0.75X2+1.80X3, and the index of continuous rainy disasters can be accurately calculated.
In the first aspect of the present application, as an optional implementation manner, before the acquiring real-time meteorological data, the method further includes:
acquiring an nc file;
and acquiring real-time meteorological data from the nc file.
In this optional embodiment, the real-time weather data can be acquired through the nc file.
The second aspect of the present application discloses a disaster early warning device, the device includes:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring real-time meteorological data and taking the real-time meteorological data as first meteorological data;
the first generation module is used for generating second meteorological data related to the meteorological disaster according to the first meteorological data and the meteorological disaster knowledge map;
the second acquisition module is used for acquiring a preset disaster early warning threshold value;
the multi-mode identification module is used for performing multi-mode identification according to the second meteorological data and the disaster early warning threshold value to obtain a multi-mode identification result;
and the second generation module is used for generating disaster early warning information according to the multi-mode recognition result.
The disaster early warning mode based on the knowledge graph and the early warning mode based on the multi-mode recognition are at least integrated, so that the accuracy and the applicability of an early warning result can be further improved while early warning is carried out on meteorological disasters.
A third aspect of the present application discloses a disaster early warning device, the device comprising:
a processor; and
a memory configured to store machine readable instructions which, when executed by the processor, cause the processor to perform the disaster warning method of the first aspect of the application.
The disaster early warning mode based on the knowledge graph and the early warning mode based on the multi-mode recognition are at least integrated, so that the accuracy and the applicability of an early warning result can be further improved while early warning is carried out on the meteorological disaster.
A fourth aspect of the present application discloses a storage medium storing a computer program, which is executed by a processor to perform the disaster warning method of the first aspect of the present application.
The storage medium of the third aspect of the application can further improve the accuracy and the applicability of the early warning result while early warning the meteorological disaster by at least fusing the disaster early warning mode based on the knowledge graph and the early warning mode based on the multi-mode recognition.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flowchart of a disaster warning method disclosed in an embodiment of the present application;
fig. 2 is a schematic structural diagram of a disaster early warning device disclosed in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a disaster early warning device disclosed in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a disaster warning method disclosed in an embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application includes the steps of:
101. acquiring real-time meteorological data, and taking the real-time meteorological data as first meteorological data;
102. generating second meteorological data related to the meteorological disaster according to the first meteorological data and the meteorological disaster knowledge map;
103. acquiring a preset disaster early warning threshold value and a disaster early warning threshold value;
104. performing multi-mode identification according to the second meteorological data and the disaster early warning threshold value to obtain a multi-mode identification result;
105. and generating disaster early warning information according to the multi-mode recognition result.
In the embodiment of the application, the disaster early warning mode based on the knowledge graph and the early warning mode based on the multi-mode recognition are at least integrated, so that the accuracy and the applicability of the early warning result can be further improved while the early warning is carried out on the meteorological disaster.
In the embodiment of the present application, as an example, the real-time meteorological data of a large area is obtained, where the real-time meteorological data of the large area includes data of temperature, humidity, precipitation and the like of at least one sub-area in the large area, for example, the temperature of the sub-area a is 25 degrees celsius, and the humidity is 60% RH.
In the embodiment of the present application, specifically, the real-time weather Data is pre-recorded in an nc (network Common Data form) file, and further, in order to obtain the real-time weather Data, the nc file may be obtained first, and then the real-time weather Data is extracted from the nc file.
In the embodiment of the present application, as an example, when the real-time meteorological data of the subregion a is extracted, since the real-time meteorological data of the subregion a is not correlated with the meteorological disaster at this time, it is necessary to correlate the real-time meteorological data, that is, the first meteorological data with the meteorological disaster by the meteorological disaster knowledgegraph to obtain the second meteorological data. For example, after the temperature and the humidity of the A sub-area are extracted, the following data can be obtained through a meteorological disaster knowledge graph, namely, { temperature, seedling rot, autumn low temperature and rainstorm }, namely, the data show that the temperature and the humidity of one area are related to the meteorological disaster phenomena such as seedling rot, autumn low temperature and rainstorm.
In the embodiment of the application, when the second meteorological data is obtained, it is indicated that the first meteorological data is related to one or more meteorological disaster phenomena, and in order to determine the relevance of the first meteorological data to the one or more meteorological disaster phenomena, multi-modal identification needs to be performed on the first meteorological data to further improve the accuracy of prediction of the meteorological disaster phenomena, so that multi-modal identification needs to be performed on the second meteorological data to obtain a multi-modal identification result, and finally disaster early warning information is generated based on the multi-modal identification result.
Further, as an example, assume that the second meteorological data is { temperature, sunshine, rainfall, continuous overcast and rainstorm }, wherein the values of temperature, sunshine and rainfall can be sequentially acquired according to the nc file, and then different modal identification algorithms are adopted for the rainstorm and continuous overcast and rainstorm based on the values of temperature, sunshine and rainfall to calculate the relationship between the values of temperature, sunshine and rainfall and the values of rainstorm, temperature, sunshine and rainfall and continuous overcast and rainstorm. For example, the relationship between the values of temperature, insolation, precipitation and rainstorm is calculated according to an empirical mode decomposition algorithm. For another example, a weather process in which the continuous cloudy days and the continuous rainfall days are 5d or more, the sunshine hours are 3h/d or less, and the process rainfall is 30mm in autumn and 20mm or more in spring is taken as a continuous cloudy rain process. It should be noted that "3 h/d", "30 mm", and "20 mm" as the disaster warning threshold may be read from the preset disaster threshold.
In the embodiment of the application, the disaster early warning information comprises a disaster index, and then can be displayed to a user.
In the embodiment of the present application, as an optional implementation manner, in step 105: after disaster early warning information is generated according to a multi-modal recognition result, the embodiment of the application comprises the following steps:
and adjusting the disaster early warning threshold value according to the disaster early warning information generated in the preset time period and the first meteorological data in the preset time period.
In the optional embodiment, the disaster early warning threshold value can be adjusted according to the disaster early warning information generated in the preset time period and the first weather data in the preset time period, so that a disaster early warning mode based on a self-adaptive system is further fused, and the accuracy and the applicability of disaster early warning are further improved.
In the embodiment of the present application, as an optional implementation manner, the second meteorological data includes at least one meteorological disaster keyword;
and, step 104: and performing multi-mode identification according to the second meteorological data and the disaster early warning threshold value to obtain a multi-mode identification result, wherein the method comprises the following steps:
determining a modal identification algorithm according to the keywords of the meteorological disaster;
and calculating according to the second meteorological data, the modal identification algorithm and the disaster early warning threshold value to obtain a multi-modal identification result.
In the optional embodiment, the mode identification algorithm can be determined according to the meteorological disaster keyword, so that the meteorological data processing method can be determined according to different meteorological disasters, a multi-mode identification result is obtained according to the second meteorological data, the mode identification algorithm and the disaster early warning threshold value, and multi-mode identification is finally achieved.
In the embodiment of the present application, as an optional implementation manner, in step 101: after acquiring the real-time meteorological data and using the real-time meteorological data as the first meteorological data, step 102: before generating the second meteorological data related to the meteorological disaster according to the first meteorological data and the meteorological disaster knowledge graph, the method of the embodiment of the application further comprises the following steps:
constructing a meteorological disaster expert keyword library;
acquiring a preset knowledge graph structure;
and constructing the meteorological disaster knowledge graph according to a preset knowledge graph structure and a meteorological disaster expert keyword library.
In this alternative embodiment, the knowledge-graph structure includes entity types, relationship types, and relationship attributes. Further optionally, the expert keyword library includes three types of keywords, namely a weather phenomenon keyword, a weather forecast keyword, and a weather disaster early warning keyword, wherein the weather phenomenon keyword, the weather forecast keyword, and the weather disaster early warning keyword are all used as weather disaster phenomena.
It should be noted that the weather phenomenon keyword, the weather forecast keyword, and the weather disaster warning keyword may be used as entities in the knowledge graph structure, and the corresponding entity type is a disaster phenomenon. On the other hand, the relationship type may include a correlation type, for example, assuming that the temperature is an entity, there is a connection with the entity of seedling rot, and the relationship attribute is strong.
In the optional embodiment, the meteorological disaster intellectual property map can be constructed by constructing the meteorological disaster expert keyword library and acquiring the preset knowledge map structure.
In the embodiment of the present application, as an optional implementation manner, in order to accurately reflect the degree of the meteorological disasters, each meteorological disaster is associated with a disaster index, for example, a consecutive rainy disaster index is associated with a consecutive rainy disaster index. Therefore, after generating the disaster warning information according to the multi-modal recognition result, the method of the embodiment of the present application further includes the steps of:
when the meteorological disaster is continuous rainy, determining the maximum rainfall, the continuous rainfall days and the total rainfall in the continuous rainy period according to the first meteorological data;
and calculating the continuous overcast and rainy disaster index according to the maximum rainfall, the continuous rainfall days and the total rainfall.
In the optional implementation mode, the maximum rainfall, the continuous rainfall days and the total rainfall amount in the continuous rainy period are determined according to the first weather data, the continuous rainy disaster index can be calculated, and the continuous rainy disaster degree can be displayed through the continuous rainy disaster index, so that the accuracy of disaster early warning is further improved.
In the embodiment of the present application, as an optional implementation manner, a calculation formula for calculating the continuous rainy disaster index according to the maximum rainfall, the number of continuous raining days, and the total rainfall amount is as follows:
LYRI＝0.45X1+0.75X2+1.80X3；
where, LYRI represents the index of continuous rainy disaster, and X1, X2 and X3 are index values of maximum rainfall, continuous rainfall days and total rainfall amount, respectively.
In this alternative embodiment, by calculating the formula: the LYRI is 0.45X1+0.75X2+1.80X3, and the index of continuous rainy disasters can be accurately calculated.
In the embodiment of the present application, as an optional implementation manner, in step 101: before acquiring real-time meteorological data, the method of the embodiment of the application further comprises the following steps:
acquiring an nc file;
and acquiring real-time meteorological data from the nc file.
In this optional embodiment, the real-time weather data can be acquired through the nc file.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a disaster warning device disclosed in the embodiment of the present application. As shown in fig. 2, the apparatus of the embodiment of the present application includes:
the first acquiring module 201 is configured to acquire real-time meteorological data, and use the real-time meteorological data as first meteorological data;
the first generation module 202 is used for generating second meteorological data related to the meteorological disaster according to the first meteorological data and the meteorological disaster knowledge map;
a second obtaining module 203, configured to obtain a preset disaster early warning threshold value;
the multi-mode identification module 204 is used for performing multi-mode identification according to the second meteorological data and the disaster early warning threshold value to obtain a multi-mode identification result;
and the second generating module 205 is configured to generate disaster early warning information according to the multi-modal recognition result.
The disaster early warning method based on the knowledge graph and the early warning method based on the multi-mode recognition are at least integrated, so that the accuracy and the applicability of an early warning result can be further improved while early warning is carried out on meteorological disasters.
In the embodiment of the present application, as an example, the real-time meteorological data of a large area is obtained, where the real-time meteorological data of the large area includes data of temperature, humidity, precipitation and the like of at least one sub-area in the large area, for example, the temperature of the sub-area a is 25 degrees celsius, and the humidity is 60% RH.
In the embodiment of the present application, specifically, the real-time weather Data is pre-recorded in an nc (network Common Data form) file, and further, in order to obtain the real-time weather Data, the nc file may be obtained first, and then the real-time weather Data is extracted from the nc file.
In the embodiment of the present application, as an example, when the real-time meteorological data of the subregion a is extracted, since the real-time meteorological data of the subregion a is not correlated with the meteorological disaster at this time, it is necessary to correlate the real-time meteorological data, that is, the first meteorological data with the meteorological disaster by the meteorological disaster knowledgegraph to obtain the second meteorological data. For example, after the temperature and the humidity of the A sub-area are extracted, the following data can be obtained through a meteorological disaster knowledge graph, namely, { temperature, seedling rot, autumn low temperature and rainstorm }, namely, the data show that the temperature and the humidity of one area are related to the meteorological disaster phenomena such as seedling rot, autumn low temperature and rainstorm.
In the embodiment of the application, when the second meteorological data is obtained, it is indicated that the first meteorological data is related to one or more meteorological disaster phenomena, and in order to determine the relevance of the first meteorological data to the one or more meteorological disaster phenomena, multi-modal identification needs to be performed on the first meteorological data to further improve the accuracy of prediction of the meteorological disaster phenomena, so that multi-modal identification needs to be performed on the second meteorological data to obtain a multi-modal identification result, and finally disaster early warning information is generated based on the multi-modal identification result.
Further, as an example, assume that the second meteorological data is { temperature, sunshine, rainfall, continuous overcast and rainstorm }, wherein the values of temperature, sunshine and rainfall can be sequentially acquired according to the nc file, and then different modal identification algorithms are adopted for the rainstorm and continuous overcast and rainstorm based on the values of temperature, sunshine and rainfall to calculate the relationship between the values of temperature, sunshine and rainfall and the values of rainstorm, temperature, sunshine and rainfall and continuous overcast and rainstorm. For example, the relationship between the values of temperature, insolation, precipitation and rainstorm is calculated according to an empirical mode decomposition algorithm. For another example, a weather process in which the continuous cloudy days and the continuous rainfall days are 5d or more, the sunshine hours are 3h/d or less, and the process rainfall is 30mm in autumn and 20mm or more in spring is taken as a continuous cloudy rain process. It should be noted that "3 h/d", "30 mm", and "20 mm" as the disaster warning threshold may be read from the preset disaster threshold.
In the embodiment of the application, the disaster early warning information comprises a disaster index, and then can be displayed to a user.
In this embodiment, the apparatus in this embodiment further includes:
and adjusting the disaster early warning threshold value according to the disaster early warning information generated in the preset time period and the first meteorological data in the preset time period.
In the optional embodiment, the disaster early warning threshold value can be adjusted according to the disaster early warning information generated in the preset time period and the first weather data in the preset time period, so that a disaster early warning mode based on a self-adaptive system is further fused, and the accuracy and the applicability of disaster early warning are further improved.
In the embodiment of the present application, as an optional implementation manner, the second meteorological data includes at least one meteorological disaster keyword;
and the specific way of performing the multi-modal identification by the multi-modal identification module 204 according to the second meteorological data and the disaster early warning threshold is as follows:
determining a modal identification algorithm according to the keywords of the meteorological disaster;
and calculating according to the second meteorological data, the modal identification algorithm and the disaster early warning threshold value to obtain a multi-modal identification result.
In the optional embodiment, the mode identification algorithm can be determined according to the meteorological disaster keyword, so that the meteorological data processing method can be determined according to different meteorological disasters, a multi-mode identification result is obtained according to the second meteorological data, the mode identification algorithm and the disaster early warning threshold value, and multi-mode identification is finally achieved.
In this embodiment of the present application, as an optional implementation manner, the apparatus of this embodiment of the present application further includes:
constructing a meteorological disaster expert keyword library;
acquiring a preset knowledge graph structure;
and constructing the meteorological disaster knowledge graph according to a preset knowledge graph structure and a meteorological disaster expert keyword library.
In this alternative embodiment, the knowledge-graph structure includes entity types, relationship types, and relationship attributes. Further optionally, the expert keyword library includes three types of keywords, namely a weather phenomenon keyword, a weather forecast keyword, and a weather disaster early warning keyword, wherein the weather phenomenon keyword, the weather forecast keyword, and the weather disaster early warning keyword are all used as weather disaster phenomena.
It should be noted that the weather phenomenon keyword, the weather forecast keyword, and the weather disaster warning keyword may be used as entities in the knowledge graph structure, and the corresponding entity type is a disaster phenomenon. On the other hand, the relationship type may include a correlation type, for example, assuming that the temperature is an entity, there is a connection with the entity of seedling rot, and the relationship attribute is strong.
In the optional embodiment, the meteorological disaster intellectual property map can be constructed by constructing the meteorological disaster expert keyword library and acquiring the preset knowledge map structure.
In the embodiment of the present application, as an optional implementation manner, in order to accurately reflect the degree of the meteorological disasters, each meteorological disaster is associated with a disaster index, for example, a consecutive rainy disaster index is associated with consecutive rainy disasters. Therefore, after generating the disaster warning information according to the multi-modal recognition result, the apparatus of the embodiment of the present application further includes:
the determining module is used for determining the maximum rainfall, the continuous rainfall days and the total rainfall in the continuous rainy period according to the first meteorological data when the meteorological disaster is continuous rainy;
and the calculation module is used for calculating the continuous rainy disaster index according to the maximum rainfall, the number of continuous rainfall days and the total rainfall amount.
In the optional implementation mode, the maximum rainfall, the continuous rainfall days and the total rainfall amount in the continuous rainy period are determined according to the first weather data, the continuous rainy disaster index can be calculated, and the continuous rainy disaster degree can be displayed through the continuous rainy disaster index, so that the accuracy of disaster early warning is further improved.
In the embodiment of the present application, as an optional implementation manner, a calculation formula for calculating the continuous rainy disaster index according to the maximum rainfall, the number of continuous raining days, and the total rainfall amount is as follows:
LYRI＝0.45X1+0.75X2+1.80X3；
where, LYRI represents the index of continuous rainy disaster, and X1, X2 and X3 are index values of maximum rainfall, continuous rainfall days and total rainfall amount, respectively.
In this alternative embodiment, by calculating the formula: the LYRI is 0.45X1+0.75X2+1.80X3, and the index of continuous rainy disasters can be accurately calculated.
In this embodiment, as an optional implementation manner, the apparatus in this embodiment further includes:
acquiring an nc file;
and acquiring real-time meteorological data from the nc file.
In this optional embodiment, the real-time weather data can be acquired through the nc file.
EXAMPLE III
Referring to fig. 3, fig. 3 is a schematic structural diagram of a disaster warning device disclosed in the embodiment of the present application. As shown in fig. 3, the apparatus of the embodiment of the present application includes:
a processor 301; and
a memory 302 configured to store machine readable instructions, which when executed by the processor 301, cause the processor 301 to perform the disaster warning method of the embodiments of the present application.
The disaster early warning mode based on the knowledge graph and the early warning mode based on the multi-mode recognition are at least integrated, so that the accuracy and the applicability of an early warning result can be further improved while early warning is carried out on meteorological disasters.
Example four
The embodiment of the application discloses a storage medium, wherein a computer program is stored in the storage medium, and the computer program is executed by a processor to execute the disaster early warning method in the embodiment of the application.
The storage medium of the embodiment of the application can further improve the accuracy and the applicability of the early warning result while early warning the meteorological disaster by at least fusing the disaster early warning mode based on the knowledge graph and the early warning mode based on the multi-mode recognition.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units 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 units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
It should be noted that the functions, if implemented in the form of software functional modules and sold or used as independent products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A disaster warning method, the method comprising:
acquiring real-time meteorological data, and taking the real-time meteorological data as first meteorological data;
generating second meteorological data related to the meteorological disaster according to the first meteorological data and the meteorological disaster knowledge map;
acquiring a preset disaster early warning threshold value;
performing multi-mode identification according to the second meteorological data and the disaster early warning threshold value to obtain a multi-mode identification result;
and generating disaster early warning information according to the multi-mode recognition result.
2. The method of claim 1, wherein after the generating disaster warning information from the multi-modal recognition results, the method comprises:
and adjusting the disaster early warning threshold value according to the disaster early warning information generated in the preset time period and the first weather data in the preset time period.
3. The method of claim 1, wherein the second meteorological data includes at least one meteorological disaster keyword;
and performing multi-mode identification according to the second meteorological data and the disaster early warning threshold value to obtain a multi-mode identification result, wherein the multi-mode identification result comprises the following steps:
determining a modal identification algorithm according to the meteorological disaster key words;
and calculating to obtain the multi-mode identification result according to the second meteorological data, the mode identification algorithm and the disaster early warning threshold value.
4. The method of claim 1, wherein after said obtaining real-time weather data and using said real-time weather data as first weather data, and before said generating second weather data associated with a weather hazard based on said first weather data and a weather hazard map, said method further comprises:
constructing a meteorological disaster expert keyword library;
acquiring a preset knowledge graph structure;
and constructing the meteorological disaster knowledge graph according to the preset knowledge graph structure and the meteorological disaster expert keyword library.
5. The method of claim 1, wherein after the generating disaster warning information from the multi-modal recognition results, the method further comprises:
when the meteorological disaster is continuous rainy, determining the maximum rainfall, the continuous rainfall days and the total rainfall in the continuous rainy period according to the first meteorological data;
and calculating the continuous overcast and rainy disaster index according to the maximum rainfall, the continuous rainfall days and the total rainfall.
6. The method of claim 5, wherein the calculation formula for calculating the continuous rainy disaster index based on the maximum rainfall, the number of consecutive days of rainfall and the total amount of rainfall is:
LYRI＝0.45X1+0.75X2+1.80X3；
wherein, LYRI represents the index of continuous rainy disasters, and X1, X2 and X3 are the index values of the maximum rainfall, the number of continuous rainfall days and the total rainfall respectively.
7. The method of claim 1, wherein prior to acquiring real-time meteorological data, the method further comprises:
acquiring an nc file;
and acquiring real-time meteorological data from the nc file.
8. A disaster early warning device, the device comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring real-time meteorological data and taking the real-time meteorological data as first meteorological data;
the first generation module is used for generating second meteorological data related to the meteorological disaster according to the first meteorological data and the meteorological disaster knowledge map;
the second acquisition module is used for acquiring a preset disaster early warning threshold value;
the multi-mode identification module is used for performing multi-mode identification according to the second meteorological data and the disaster early warning threshold value to obtain a multi-mode identification result;
and the second generation module is used for generating disaster early warning information according to the multi-mode recognition result.
9. A disaster early warning device, the device comprising:
a processor; and
a memory configured to store machine readable instructions which, when executed by the processor, cause the processor to perform the disaster warning method as claimed in any one of claims 1 to 7.
10. A storage medium storing a computer program for executing the disaster warning method according to any one of claims 1 to 7 by a processor.
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