Method, device and equipment for predicting carbon emission and carbon sink after disaster and storage medium

Abstract

The invention relates to the technical field of data measurement and calculation, in particular to a post-disaster carbon emission and carbon sink prediction method and device, equipment and a storage medium, and the method comprises the steps: obtaining catastrophe data, and the carbon emission and carbon sink of a target area in unit time before a disaster; the carbon emission of the target area in a preset time period is predicted through a first prediction model corresponding to the catastrophe type, the carbon sink amount of the target area in the preset time period is predicted through a second prediction model corresponding to the catastrophe type, and therefore prediction results of the carbon emission and the carbon sink amount are obtained; according to the method, the carbon emission and the carbon sink after the disaster are predicted, and the influence of different catastrophe types on the carbon emission and the carbon sink is considered in the process, so that the finally obtained prediction result is closer to the actual situation, and the accuracy of the prediction result is ensured.

Description

Technical Field

[0001] The present disclosure relates to the field of data measurement technology, and in particular to a method, device, equipment and storage medium for predicting post-disaster carbon emissions and carbon sinks. Background Art

[0002] Since the late 20th century, greenhouse gases, especially carbon emissions, have become one of the main causes of global warming, posing a serious threat to human survival.

[0003] However, with global warming, various natural disasters have occurred frequently, causing the original ecosystem to be destroyed, directly affecting the carbon emissions and carbon sinks in the disaster area. Therefore, how to predict carbon emissions and carbon sinks after disasters is particularly important. Summary of the invention

[0004] In order to solve the above technical problems, the present disclosure provides a method, device, equipment and storage medium for predicting post-disaster carbon emissions and carbon sinks.

[0005] In a first aspect, the present invention provides a method for predicting carbon emissions and carbon sinks after a disaster, comprising:

[0006] Obtain disaster data, carbon emissions of the target area per unit time before the disaster, and carbon sinks of the target area per unit time before the disaster, where the disaster data is data corresponding to an indicator for measuring the degree of the disaster; select a first prediction model corresponding to the disaster type, and predict the carbon emissions of the target area after the disaster within a preset time period based on the disaster data and carbon emissions to obtain a first prediction result; select a second prediction model corresponding to the disaster type, and predict the carbon sinks of the target area after the disaster within a preset time period based on the disaster data and carbon sinks to obtain a second prediction result.

[0007] The method for predicting post-disaster carbon emissions and carbon sinks provided in this embodiment, after obtaining disaster data, carbon emissions and carbon sinks of the target area per unit time before the disaster, completes the prediction of carbon emissions of the target area within a preset time period through a first prediction model corresponding to the disaster type, and completes the prediction of carbon sinks of the target area within a preset time period through a second prediction model corresponding to the disaster type, thereby obtaining prediction results of carbon emissions and carbon sinks; the prediction of post-disaster carbon emissions and carbon sinks is completed in the above manner, and the above process takes into account the impact of different disaster types on carbon emissions and carbon sinks, so that the final prediction results are closer to the actual situation, thereby ensuring the accuracy of the prediction results.

[0008] In an optional implementation manner, before obtaining disaster data, carbon emissions per unit time in the target area before the disaster, and carbon sinks per unit time in the target area before the disaster, the method further includes:

[0009] The disaster-affected area is divided into grids to obtain multiple target areas.

[0010] In an optional implementation, the disaster types include wildfire disasters and rainstorm disasters.

[0011] In an optional implementation, when the disaster type is a wildfire disaster, the disaster data is the ratio of the burned area to the vegetation coverage area in the target area; when the disaster type is a rainstorm disaster, the disaster data is the rainfall.

[0012] In an optional implementation, when the disaster type is a wildfire disaster, the first prediction model is embodied in the following manner:

[0013]

[0014] in, It represents the carbon emission of the target area within the preset time period, and n represents the number of unit time periods contained in the preset time period. represents the carbon emission per unit time in the target area before the disaster, β represents a constant corresponding to the vegetation type in the target area, Indicates the disaster data corresponding to the wildfire disaster;

[0015] The second prediction model is embodied in the following way:

[0016]

[0017] in, It represents the carbon sink amount of the target area within the preset time period, and n represents the number of unit time periods contained in the preset time period. It represents the carbon sink in the target area per unit time before the disaster. α represents a constant corresponding to the vegetation type in the target area. Indicates the disaster data corresponding to the wildfire disaster.

[0018] In an optional implementation, when the disaster type is a rainstorm disaster, the first prediction model is embodied in the following manner:

[0019]

[0020] in, It represents the carbon emission of the target area within the preset time period, and n represents the number of unit time periods contained in the preset time period. represents the carbon emission per unit time in the target area before the disaster, θ represents a constant corresponding to the vegetation type in the target area, Indicates the disaster data corresponding to the rainstorm disaster;

[0021] The second prediction model is embodied in the following way:

[0022]

[0023] in, It represents the carbon sink amount of the target area within the preset time period, and n represents the number of unit time periods contained in the preset time period. represents the carbon sink in the target area per unit time before the disaster, γ represents a constant corresponding to the vegetation type in the target area, Represents the disaster data corresponding to the rainstorm disaster.

[0024] In a second aspect, the present invention provides a device for predicting carbon emissions and carbon sinks after a disaster, comprising:

[0025] An acquisition module is used to obtain disaster data, carbon emissions of the target area per unit time before the disaster, and carbon sinks of the target area per unit time before the disaster, wherein the disaster data is data corresponding to an indicator for measuring the degree of disaster; a first prediction module is used to select a first prediction model corresponding to the disaster type, and predict the carbon emissions of the target area after the disaster within a preset time period based on the disaster data and carbon emissions to obtain a first prediction result; a second prediction module is used to select a second prediction model corresponding to the disaster type, and predict the carbon sinks of the target area after the disaster within a preset time period based on the disaster data and carbon sinks to obtain a second prediction result.

[0026] In an optional implementation manner, before obtaining the module, the method further includes:

[0027] The partitioning module is used to divide the disaster-stricken area into grids to obtain multiple target areas.

[0028] In a third aspect, the present invention provides a computer device, comprising:

[0029] The memory and the processor are communicatively connected with each other, the memory stores computer instructions, and the processor executes the method for predicting post-disaster carbon emissions and carbon sinks of the first aspect and any of its implementation modes by executing the computer instructions.

[0030] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for predicting post-disaster carbon emissions and carbon sinks of the first aspect and any of its embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0033] Figure 1 is a flow chart of a method for predicting post-disaster carbon emissions and carbon sinks provided in accordance with an embodiment of the present invention;

[0034] Figure 2 is a schematic diagram of the structural connection of a device for predicting post-disaster carbon emissions and carbon sinks provided in accordance with an embodiment of the present invention;

[0035] Figure 3 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0037] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0038] According to an embodiment of the present invention, an embodiment of a method for predicting post-disaster carbon emissions and carbon sinks is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0039] In this embodiment, a method for predicting carbon emissions and carbon sinks after a disaster is provided. Figure 1 is a flow chart of a method for predicting carbon emissions and carbon sinks after a disaster according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0040] Step S101, obtaining disaster data, carbon emissions per unit time in the target area before the disaster, and carbon sinks per unit time in the target area before the disaster.

[0041] Specifically, the disaster data is data corresponding to indicators for measuring the extent of the disaster, and each disaster type corresponds to a type of disaster data. For example, when the disaster type is a flood disaster, the disaster data is the flood frequency; when the disaster type is an earthquake, the disaster data is the magnitude; when the disaster type is a rainstorm disaster, the disaster data is the rainfall; when the disaster type is a wildfire disaster, the disaster data is the proportion of the burned area to the vegetation coverage area in the target area, etc. The disaster type referred to in this embodiment can be any natural disaster that affects carbon sinks and / or carbon emissions, and will not be enumerated here.

[0042] Specifically, the target area and unit time are related to the vegetation type, soil information and accuracy requirements of the disaster area. When the prediction accuracy requirements are high, the target area is a sub-area of ​​the disaster area, and the unit time can be a small unit such as day or hour; when the prediction accuracy requirements are low, the target area is the disaster area, and the unit time can be a large unit such as month.

[0043] In an optional embodiment, when the prediction accuracy requirement is high, before step S101, the process further includes: dividing the disaster-stricken area into grids to obtain multiple target areas.

[0044] For example, when the disaster-affected area is gridded, the soil information and vegetation type at each location in the disaster-affected area need to be considered as reference factors, so that the soil information at each location in the target area is the same, and the vegetation in the target area belongs to the same vegetation type. The gridding method is not described here in detail, and those skilled in the art can select it according to the actual situation.

[0045] Step S102, selecting a first prediction model corresponding to the disaster type, predicting the carbon emissions of the target area after the disaster within a preset period of time according to the disaster data and the carbon emissions, and obtaining a first prediction result.

[0046] Specifically, the first prediction model can be a neural network model trained using a large amount of historical data, or it can be a function model with carbon emission prediction function fitted by a large amount of historical data.

[0047] Specifically, the disaster data and carbon emissions are input into the first prediction model, and the carbon emissions corresponding to each unit time within the preset time period are first predicted; then the carbon emissions of the target area within the preset time period are determined based on the corresponding carbon emissions in each unit time (i.e., the first prediction result).

[0048] It should be noted that when the target area is a sub-area of ​​the disaster-stricken area, the carbon emissions of each target area within a preset time period can be added together to obtain the carbon emissions of the disaster-stricken area within the preset time period.

[0049] In an optional embodiment, when the disaster type is a wildfire disaster, the first prediction model is embodied in the following manner:

[0050]

[0051] in, It represents the carbon emission of the target area within the preset time period, and n represents the number of unit time periods contained in the preset time period. represents the carbon emission per unit time in the target area before the disaster, β represents a constant corresponding to the vegetation type in the target area, Indicates the disaster data corresponding to the wildfire disaster.

[0052] In another optional embodiment, when the disaster type is a rainstorm disaster, the first prediction model is embodied in the following manner:

[0053]

[0054] in, It represents the carbon emission of the target area within the preset time period, and n represents the number of unit time periods contained in the preset time period. represents the carbon emission per unit time in the target area before the disaster, θ represents a constant corresponding to the vegetation type in the target area, Represents the disaster data corresponding to the rainstorm disaster.

[0055] Step S103, selecting a second prediction model corresponding to the disaster type, predicting the carbon sink amount of the target area after the disaster within a preset time period according to the disaster data and the carbon sink amount, and obtaining a second prediction result.

[0056] Specifically, the second prediction model is similar to the first prediction model and will not be described in detail here.

[0057] Specifically, similar to step S102, the disaster data and carbon sink amount are input into the second prediction model, and the carbon sink amount corresponding to each unit time within the preset time length is first predicted; then the carbon sink amount of the target area within the preset time length is determined based on the corresponding carbon sink amount in each unit time (i.e., the second prediction result).

[0058] It should be noted that when the target area is a sub-area of ​​the disaster-stricken area, the carbon sink amounts of each target area within a preset time period may be added together to obtain the carbon sink amount of the disaster-stricken area within the preset time period.

[0059] In an optional embodiment, when the disaster type is a wildfire disaster, the second prediction model is embodied in the following manner:

[0060]

[0061] in, It represents the carbon sink amount of the target area within the preset time period, and n represents the number of unit time periods contained in the preset time period. It represents the carbon sink in the target area per unit time before the disaster. α represents a constant corresponding to the vegetation type in the target area. Indicates the disaster data corresponding to the wildfire disaster.

[0062] In another optional embodiment, when the disaster type is a rainstorm disaster, the second prediction model is embodied in the following manner:

[0063]

[0064] in, It represents the carbon sink amount of the target area within the preset time period, and n represents the number of unit time periods contained in the preset time period. represents the carbon sink in the target area per unit time before the disaster, γ represents a constant corresponding to the vegetation type in the target area, Represents the disaster data corresponding to the rainstorm disaster.

[0065] In another optional embodiment, after step S102 and step S103, the first prediction result and the second prediction result may be summed to obtain a calculation result. Professionals and technicians in this field may provide corresponding corrective measures based on the first prediction result, the second prediction result and the calculation result.

[0066] The method for predicting post-disaster carbon emissions and carbon sinks provided in this embodiment, after obtaining disaster data, carbon emissions and carbon sinks of the target area per unit time before the disaster, completes the prediction of carbon emissions of the target area within a preset time period through a first prediction model corresponding to the disaster type, and completes the prediction of carbon sinks of the target area within a preset time period through a second prediction model corresponding to the disaster type, thereby obtaining prediction results of carbon emissions and carbon sinks; the prediction of post-disaster carbon emissions and carbon sinks is completed in the above manner, and the above process takes into account the impact of different disaster types on carbon emissions and carbon sinks, so that the final prediction results are closer to the actual situation, thereby ensuring the accuracy of the prediction results.

[0067] In this embodiment, a device for predicting carbon emissions and carbon sinks after a disaster is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0068] This embodiment provides a device for predicting carbon emissions and carbon sinks after a disaster. Figure 2 As shown, including:

[0069] The acquisition module 201 is used to acquire disaster data, carbon emissions of the target area per unit time before the disaster, and carbon sinks of the target area per unit time before the disaster. The disaster data is data corresponding to indicators for measuring the degree of disaster.

[0070] The first prediction module 202 is used to select a first prediction model corresponding to the disaster type, and predict the carbon emissions of the target area after the disaster within a preset period of time according to the disaster data and the carbon emissions to obtain a first prediction result.

[0071] The second prediction module 203 is used to select a second prediction model corresponding to the disaster type, and predict the carbon sink amount of the target area after the disaster within a preset time period according to the disaster data and the carbon sink amount to obtain a second prediction result.

[0072] In some optional implementations, in an optional implementation, before obtaining module 201, it also includes:

[0073] The partitioning module is used to divide the disaster-stricken area into grids to obtain multiple target areas.

[0074] In some optional implementations, in the first prediction module 202, the disaster types include wildfire disasters and rainstorm disasters.

[0075] In some optional embodiments, when the disaster type is a wildfire disaster, the disaster data in the acquisition module 201 is the ratio of the burned area to the vegetation coverage area in the target area; when the disaster type is a rainstorm disaster, the disaster data in the acquisition module 201 is the rainfall.

[0076] In some optional implementations, when the disaster type is a wildfire disaster, the first prediction model in the first prediction module 202 is embodied in the following manner:

[0077]

[0078] Among them, E T represents the carbon emissions of the target area within the preset time, n represents the number of unit time contained in the preset time, E 0 represents the carbon emission per unit time in the target area before the disaster, β represents a constant corresponding to the vegetation type in the target area, and F 0 Indicates the disaster data corresponding to the wildfire disaster;

[0079] The second prediction model in the second prediction module 203 is embodied in the following manner:

[0080]

[0081] Among them, C Trepresents the carbon sink in the target area within the preset time period, n represents the number of unit time periods contained in the preset time period, C 0 represents the carbon sink in the target area per unit time before the disaster, α represents a constant corresponding to the vegetation type in the target area, and F 0 Indicates the disaster data corresponding to the wildfire disaster.

[0082] In some optional implementations, when the disaster type is a rainstorm disaster, the first prediction model in the first prediction module 202 is embodied in the following manner:

[0083]

[0084] Among them, E T represents the carbon emissions of the target area within the preset time, n represents the number of unit time contained in the preset time, E 0 represents the carbon emission per unit time in the target area before the disaster, θ represents a constant corresponding to the vegetation type in the target area, and p 0 Indicates the disaster data corresponding to the rainstorm disaster;

[0085] The second prediction model in the second prediction module 203 is embodied in the following manner:

[0086]

[0087] Among them, C T represents the carbon sink in the target area within the preset time period, n represents the number of unit time periods contained in the preset time period, C 0 represents the carbon sink in the target area per unit time before the disaster, γ represents a constant corresponding to the vegetation type in the target area, and p 0 Represents the disaster data corresponding to the rainstorm disaster.

[0088] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0089] The post-disaster carbon emissions and carbon sinks prediction device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0090] The embodiment of the present invention also provides a computer device having the above Figure 2 The device for predicting carbon emissions and carbon sinks after a disaster is shown.

[0091] See also Figure 3 , Figure 3is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 3 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0092] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0093] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0094] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0095] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0096] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0097] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0098] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0099] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting carbon emissions and carbon sinks after a disaster, characterized in that: include: Obtain disaster data, carbon emissions per unit time in the target area before the disaster, and carbon sinks per unit time in the target area before the disaster, wherein the disaster data is data corresponding to an indicator for measuring the degree of the disaster; Selecting a first prediction model corresponding to the disaster type, predicting the carbon emissions of the target area within a preset period of time after the disaster based on the disaster data and the carbon emissions, and obtaining a first prediction result; A second prediction model corresponding to the disaster type is selected, and the carbon sink amount of the target area after the disaster within the preset time period is predicted based on the disaster data and the carbon sink amount to obtain a second prediction result.

2. The prediction method according to claim 1, characterized in that: Before obtaining the disaster data, the carbon emission per unit time of the target area before the disaster, and the carbon sink per unit time of the target area before the disaster, it also includes: The disaster-stricken area is divided into grids to obtain multiple target areas.

3. The prediction method according to claim 1 or 2, characterized in that: The disaster types include wildfire disasters and rainstorm disasters.

4. The prediction method according to claim 3, characterized in that: When the disaster type is a wildfire disaster, the disaster data is the ratio of the burned area to the vegetation coverage area in the target area; When the disaster type is a rainstorm disaster, the disaster data is rainfall.

5. The prediction method according to claim 3, characterized in that: When the disaster type is a wildfire disaster, the first prediction model is embodied in the following manner: in, It represents the carbon emission of the target area within the preset time period, and n represents the number of unit time periods contained in the preset time period. represents the carbon emission per unit time in the target area before the disaster, β represents a constant corresponding to the vegetation type in the target area, Indicates the disaster data corresponding to the wildfire disaster; The second prediction model is embodied in the following manner: in, It represents the carbon sink amount of the target area within the preset time period, and n represents the number of unit time periods contained in the preset time period. It represents the carbon sink in the target area per unit time before the disaster. α represents a constant corresponding to the vegetation type in the target area. Indicates the disaster data corresponding to the wildfire disaster.

6. The prediction method according to claim 3, characterized in that: When the disaster type is a rainstorm disaster, the first prediction model is embodied in the following manner: in, It represents the carbon emission of the target area within the preset time period, and n represents the number of unit time periods contained in the preset time period. represents the carbon emission per unit time in the target area before the disaster, θ represents a constant corresponding to the vegetation type in the target area, Indicates the disaster data corresponding to the rainstorm disaster; The second prediction model is embodied in the following manner: in, It represents the carbon sink amount of the target area within the preset time period, and n represents the number of unit time periods contained in the preset time period. represents the carbon sink in the target area per unit time before the disaster, γ represents a constant corresponding to the vegetation type in the target area, Represents the disaster data corresponding to the rainstorm disaster.

7. A device for predicting carbon emissions and carbon sinks after a disaster, characterized in that: include: An acquisition module is used to acquire disaster data, carbon emissions per unit time in the target area before the disaster, and carbon sinks per unit time in the target area before the disaster, wherein the disaster data is data corresponding to an indicator for measuring the degree of disaster; A first prediction module is used to select a first prediction model corresponding to the disaster type, and predict the carbon emissions of the target area within a preset time period after the disaster according to the disaster data and the carbon emissions to obtain a first prediction result; The second prediction module is used to select a second prediction model corresponding to the disaster type, and predict the carbon sink amount of the target area within the preset time period after the disaster based on the disaster data and the carbon sink amount to obtain a second prediction result.

8. The prediction device according to claim 7, characterized in that Before the acquisition module, it also includes: The partitioning module is used to divide the disaster-stricken area into grids to obtain multiple target areas.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for predicting post-disaster carbon emissions and carbon sinks according to any one of claims 1 to 6 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for predicting post-disaster carbon emissions and carbon sinks according to any one of claims 1 to 6.

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