A method and device for spatial downscaling of three-dimensional sea surface temperature and electronic equipment

Abstract

The application discloses a three-dimensional sea temperature spatial downscaling method and device and electronic equipment, and relates to the field of image processing. The method comprises the following steps: obtaining a target data set, wherein the target data set comprises wind field data, sea current data, first sea temperature data and second sea temperature data; inputting the wind field data, the sea current data, the first sea temperature data and the second sea temperature data in the target data set into a three-dimensional sea temperature generation model for spatial downscaling processing; and outputting three-dimensional sea temperature data. The wind field data, the sea current data and the second sea temperature data are introduced into the target data set in addition to the first sea temperature data. Since the wind field data and the sea current data contain information capable of representing the sea temperature data, the first sea temperature data, the wind field data, the sea current data and the second sea temperature data are input into the three-dimensional sea temperature generation model for downscaling processing, so that the finally output sea temperature data is more accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image recognition, and in particular to a three-dimensional sea temperature spatial downscaling method and device and electronic equipment. BACKGROUND

[0002] In recent years, deep learning (CNN, GAN, Transformer) has performed excellently in two-dimensional sea temperature super-resolution, but three-dimensional expansion still needs to be trained independently layer by layer, and the parameter quantity and parameter adjustment workload are multiplied by depth; while the existing models are many, but most of their inputs are low-resolution sea temperature data, which cannot couple key dynamic factors such as sea surface wind stress, Ekman pumping and geostrophic flow transport, resulting in insufficient physical consistency of the reconstruction results and inaccurate sea temperature data. Therefore, there is an urgent need for a method that can accurately obtain sea temperature data. SUMMARY

[0003] Therefore, the embodiments of the present application provide a three-dimensional sea temperature spatial downscaling method and device and electronic equipment to solve the problem of how to obtain more accurate sea temperature data.

[0004] According to a first aspect, the embodiments of the present application provide a three-dimensional sea temperature spatial downscaling method, comprising:

[0005] Obtaining a target data set, the target data set comprising: wind field data, sea current data, first sea temperature data and second sea temperature data, the resolutions of the wind field data, sea current data and first sea temperature data being smaller than the resolution of the second sea temperature data;

[0006] Inputting the wind field data, sea current data, first sea temperature data and second sea temperature data in the target data set into a three-dimensional sea temperature generation model for spatial downscaling processing, and outputting three-dimensional sea temperature data, the resolution of the three-dimensional sea temperature data being equal to the resolution of the second sea temperature data in the target data set.

[0007] The three-dimensional sea temperature spatial downscaling method provided in the present application obtains a target data set, and in addition to the first sea temperature data, wind field data, sea current data and second sea temperature data are introduced, since the wind field data and sea current data also contain information capable of representing sea temperature data, the first sea temperature data, wind field data, sea current data and second sea temperature data are input into a three-dimensional sea temperature generation model for downscaling processing, which makes the finally output sea temperature data more accurate.

[0008] According to a second aspect, the embodiments of the present application provide a three-dimensional sea temperature spatial downscaling device, which comprises:

[0009] The acquisition module is configured to acquire a target data set, wherein the target data set comprises wind field data, ocean current data, first sea temperature data and second sea temperature data, and resolutions of the wind field data, the ocean current data and the first sea temperature data are all less than a resolution of the second sea temperature data.

[0010] The processing module is configured to input the wind field data, the ocean current data, the first sea temperature data and the second sea temperature data in the target data set into a three-dimensional sea temperature generation model to perform spatial downscaling processing, and output three-dimensional sea temperature data, wherein a resolution of the three-dimensional sea temperature data is equal to the resolution of the second sea temperature data in the target data set.

[0011] The three-dimensional sea temperature spatial downscaling device provided by the embodiment of the present application acquires the target data set through the acquisition module, inputs the target data set into the processing module, performs spatial downscaling processing by using the three-dimensional sea temperature generation model in the processing module, uses the diversity data to represent more sea temperature details, and converts the low-resolution data into high-resolution data by using the spatial downscaling processing, thereby highlighting more sea temperature details and making the finally output sea temperature data more accurate.

[0012] According to a third aspect, an electronic device is provided, which comprises a memory and a processor, the memory and the processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for three-dimensional sea temperature spatial downscaling according to the first aspect or any one of the implementation manners of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0013] The features and advantages of the present application will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, which are shown by way of illustration and not by way of limitation, in which:

[0014] Figure 1 A flowchart of the method for three-dimensional sea temperature spatial downscaling provided by the embodiment of the present application is shown.

[0015] Figure 2 A comparison result diagram of root mean square errors in depth of the method for three-dimensional sea temperature spatial downscaling provided by the embodiment of the present application and other methods is shown.

[0016] Figure 3 A comparison result diagram of peak signal-to-noise ratios in depth of the method for three-dimensional sea temperature spatial downscaling provided by the embodiment of the present application and other methods is shown.

[0017] Figure 4 A comparison result diagram of average absolute errors in depth of the method for three-dimensional sea temperature spatial downscaling provided by the embodiment of the present application and other methods is shown.

[0018] Figure 5A structural schematic diagram of a device for three-dimensional sea temperature spatial downscaling provided by an embodiment of the present application.

[0019] Figure 6 A structural schematic diagram of an electronic device provided by an embodiment of the present application.

[0020] Reference numerals

[0021] 10 - acquisition module; 11 - processing module; 21 - processor; 20 - memory. DETAILED DESCRIPTION

[0022] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0023] The present application provides a method, device and electronic equipment for three-dimensional sea temperature spatial downscaling. As shown in the figure, a flowchart of a method for three-dimensional sea temperature spatial downscaling provided by an embodiment of the present application. The method for three-dimensional sea temperature spatial downscaling provided by the present application is applied to a smart terminal or a control system. The method uses wind field, sea current and sea temperature data as model input, can effectively extract information of atmospheric elements (wind field) and ocean elements (sea current) to guide three-dimensional sea temperature spatial downscaling, and the obtained high-resolution sea temperature is more accurate compared with a downscaling model using only low-resolution sea temperature as input. Figure 1

[0024] Continuing to refer to Figure 1 The method for three-dimensional sea temperature spatial downscaling provided by the present application includes the following steps:

[0025] S10, acquiring a target data set, the target data set including wind field data, sea current data, first sea temperature data and second sea temperature data, the resolutions of the wind field data, the sea current data and the first sea temperature data being smaller than the resolution of the second sea temperature data.

[0026] S11, inputting the wind field data, the sea current data, the first sea temperature data and the second sea temperature data in the target data set into a three-dimensional sea temperature generation model for spatial downscaling processing, and outputting three-dimensional sea temperature data, the resolution of the three-dimensional sea temperature data being equal to the resolution of the second sea temperature data in the target data set.

[0027] ​In the embodiment, the wind field data contains local or remote large-scale or mesoscale signals, and the sea current data contains smaller scale signals such as mesoscale eddies or submesoscale processes, which can highlight the details of the sea surface temperature data and help to form three-dimensional sea surface temperature data that is accurate and close to the real environment.

[0028] Since the wind field data, the sea current data, and the first sea surface temperature data are different dimensions of data, the second sea surface temperature data is introduced as a training label to ensure the consistency of the data format of the wind field data, the sea current data, and the first sea surface temperature data during data processing, thereby facilitating or benefiting the data processing capability in the subsequent steps. The first sea surface temperature data is low-resolution sea surface temperature data; the resolution of the second sea surface temperature data is high-resolution sea surface temperature data or super-resolution sea surface temperature data, and the output three-dimensional sea surface temperature data can be high-resolution sea surface temperature data or super-resolution sea surface temperature data.

[0029] In addition, spatial downscaling can convert low-resolution data into high-resolution data, which can obtain more accurate sea surface temperature data.

[0030] The method for three-dimensional sea surface temperature spatial downscaling provided in the embodiment can obtain a target data set, remove the first sea surface temperature data from the target data set, introduce wind field data and sea current data, and introduce second sea surface temperature data. Since the wind field data and the sea current data also contain information that can represent the sea surface temperature data, the first sea surface temperature data, the wind field data, the sea current data, and the second sea surface temperature data are input into a three-dimensional sea surface temperature generation model for downscaling processing, which makes the finally output sea surface temperature data more accurate.

[0031] The method for three-dimensional sea surface temperature spatial downscaling provided in the embodiment further includes, before obtaining the target data set:

[0032] S01, obtaining wind field original data, sea current original data, first sea surface temperature original data, and second sea surface temperature original data in a target sea area.

[0033] S02, performing maximum and minimum normalization on the wind field original data, the sea current original data, the first sea surface temperature original data, and the second sea surface temperature original data, so that the wind field original data, the sea current original data, the first sea surface temperature original data, and the second sea surface temperature original data tend to a target range, and output wind field data, sea current data, first sea surface temperature data, and second sea surface temperature data.

[0034] In the embodiment, the wind field original data, the sea current original data, the first sea surface temperature original data, and the second sea surface temperature original data can be image or digital data obtained by a corresponding sensor to represent wind field, sea current, and sea surface temperature information, for example, image data can be obtained by a shooting device.

[0035] In the embodiment, the maximum-minimum normalization is performed on the wind field original data, the sea current original data, the first sea temperature original data and the second sea temperature original data, so as to reduce the scale difference between different types of features, and facilitate subsequent model iteration processing, so that the data can converge faster.

[0036] Preferably, the target range of the maximum-minimum normalization can be normalized to [0, 1].

[0037] In the embodiment, the maximum-minimum normalization is performed, which can be specifically:

[0038] First, the parameters or target data set are determined, including: wind field Wind, sea current SC, low-resolution sea temperature data set and high-resolution sea temperature data set .

[0039] Among the various parameters, n data are contained, and the data format can be: the sea temperature and the sea current data are in three-dimensional format, that is, the data format can be [depth level, height, width]; the wind field is in two-dimensional format, that is, the data format can be [1, height, width], wherein

[0040] .

[0041] Among them, is the low-resolution sea temperature data (first sea temperature data); is the high-resolution sea temperature data (second sea temperature data); is the wind field data; is the sea current data.

[0042] Specifically, the maximum value in the current data is , the minimum value is , and the data is normalized, and the normalized result is represented as: .

[0043] The three-dimensional sea temperature space downscaling method provided in the application also needs to splice and align the wind field data and the sea current data before step S11, and output the spliced and aligned spliced data.

[0044] In the embodiment, when the depth levels of the wind field data and the sea current data are different, the wind field data and the sea current data need to be unified, for example, the format of the sea current and the wind field can be (B, C, D, H, W), B represents Batchsize, C represents the number of channels, D represents the number of depth levels, H represents the longitudinal resolution, and W represents the transverse resolution. Since the wind field data has only one layer, D = 1, for the wind field and the sea current, C = 1, and after data splicing and alignment, the format of the sea current and the wind field is (B, C, D+1, H, W). It can be understood that in the present application, the wind field data and the sea current data are spliced and aligned to ensure the consistency of subsequent data processing and reduce the complexity of data analysis.

[0045] The method for three-dimensional sea temperature spatial downscaling provided in the present application can be as follows when step S11 is performed:

[0046] S111, obtaining a preset noise value.

[0047] In the embodiment, the preset noise value can be sampled from a normal distribution , or can be obtained according to past experience, for example, a random noise specified by a user.

[0048] S112, sending the wind field data, the sea current data, the first sea temperature data and the second sea temperature data into a feature extraction network for iterative processing, and outputting a target condition vector.

[0049] Optionally, in step S112, the spliced data, the first sea temperature data and the second sea temperature data are sent into the feature extraction network for iterative processing, and a target condition vector is output.

[0050] In the embodiment, the feature extraction network can be a ResBlock, a HybridTransformerblock, a ResBlock, a HybridTransformerblock and a Fusion Layer architecture in sequence, specifically, the feature extraction network can be sequentially through 4 continuous ResBlocks, 4 continuous HybridTransformerblocks, 4 continuous ResBlocks, 4 continuous HybridTransformerblocks and one Fusion Layer, and finally the output is a target condition vector (B, 4, 1024).

[0051] S113, performing nearest neighbor interpolation processing on the first sea temperature data, and outputting processed first sea temperature data.

[0052] ​S114, input the preset noise value, target condition vector and processed first sea temperature data into the noise reduction network for iterative calculation, and output three-dimensional sea temperature data.

[0053] Optionally, the mean square error of the preset noise value and the noise value corresponding to the three-dimensional sea temperature data is calculated.

[0054] Specifically, taking generating a high-resolution three-dimensional sea temperature image as an example, in combination with the above steps, the implementation process can be:

[0055] First, a sequence is set , is a pre-set sequence, and Let , be the trained feature extraction network and be the 3DUnet network (noise reduction network).

[0056] Let wind field Wind, sea current SC, low-resolution sea temperature data , the sea temperature and sea current data are in three-dimensional format, i.e. [depth level, height, width]; the wind field is in two-dimensional format, i.e. [1, height, width].

[0057] First, the user can sample a noise from a normal distribution .

[0058] Second, in the order of , for each value in the sequence, the following steps are performed:

[0059] (1) first sample a noise of the same size as the second sea temperature data from a normal distribution .

[0060] (2) input the wind field Wind and sea current SC data into the feature extraction network FEN to obtain the feature information vector .

[0061] (3) use nearest neighbor interpolation to process the low-resolution sea temperature data to improve its resolution to the same size as the second sea temperature data, and obtain the processed first sea temperature data .

[0062] (4) calculate the three-dimensional sea temperature data , and the specific calculation formula is as follows: .

[0063] where the three-dimensional sea temperature data The diffusion processing can be noise-removed.

[0064] In the embodiment, in order to reduce data interference, the first sea temperature data after the nearest neighbor interpolation processing is sent into the 3DUnet network for noise reduction processing. The 3DUnet network is mainly used to perform noise reduction processing of the generated diffusion model under the guidance of the feature information vector.

[0065] In order to further understand the feature extraction network and the 3DUnet network, the training process can be:

[0066] First, the wind field Wind, the sea current SC, the first sea temperature data set and the second sea temperature data set each contain n data, and the sea temperature and sea current data are in three-dimensional format, that is, [depth level, height, width], and the wind field is in two-dimensional format, that is, [1, height, width]. Among them, there are .

[0067] Let , be a pre-set sequence, and . Let .

[0068] For each and the corresponding , the following steps are performed:

[0069] (1) sample a value from .

[0070] (2) sample a noise from the normal distribution .

[0071] (3) calculate , denoted as .

[0072] (4) input into the trained feature extraction network to obtain the feature information vector ; use the nearest neighbor interpolation processing to raise it to the same resolution as , to obtain the processed first sea temperature data .

[0073] (5) calculate the mean square error of and .

[0074] ​(6) Optimize the error and repeat the above process until convergence, thus obtaining the trained feature extraction network. With 3DUnet network .

[0075] The advantages of the three-dimensional sea surface temperature spatial downscaling method provided in this application embodiment are:

[0076] The proposed three-dimensional sea surface temperature (SST) spatial downscaling method is driven by wind and ocean currents, and can simultaneously take into account the relationship between atmospheric and oceanic elements and SST. This information is then used to drive the three-dimensional SST spatial downscaling, resulting in more accurate and high-resolution SST data.

[0077] By introducing wind field, ocean current, and sea surface temperature (SST) data as model inputs, information on atmospheric elements (wind field) and ocean elements (ocean current) can be effectively extracted to guide the downscaling of three-dimensional SST space. Compared with three-dimensional SST generation models that only use low-resolution SST as input, using wind field and ocean current data can obtain more data details, which is conducive to obtaining more accurate and high-resolution SST data (images).

[0078] Compared to two-dimensional sea surface temperature spatial downscaling models, the method provided in this application does not require separate training models for sea surface temperature at each depth level, which can reduce additional model training time and cumbersome parameter tuning processes.

[0079] like Figures 2 to 4 As shown, Figure 2 The figure shows a comparison of the root mean square error in depth between the three-dimensional sea surface temperature spatial downscaling method provided in this application embodiment and other methods. Figure 3 The figure shows a comparison of the peak signal-to-noise ratio at depth between the three-dimensional sea surface temperature spatial downscaling method provided in this application embodiment and other methods. Figure 4 A comparison of the mean absolute error in depth between the three-dimensional sea surface temperature spatial downscaling method provided in this application embodiment and other methods. Figures 2 to 4 Correspondingly, DSSR3 represents the root mean square error, peak signal-to-noise ratio, and mean absolute error output by the model at different depths using the three-dimensional sea surface temperature spatial downscaling method provided in this application; while Bicubic, MGRO, VSRT, DIFFDS, and EDSR3D represent the root mean square error, peak signal-to-noise ratio, and mean absolute error output by the model using other methods at different depths. It should be noted that in... Figures 2 to 4 In the graph, the vertical axis represents depth, and the horizontal axis represents the root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and mean absolute error (MAO). Smaller RMSE and MAO values ​​indicate better output results; a higher PNR value indicates better image reconstruction. Figures 2 to 4The comparison results show that the method / model provided in this application is superior to other methods in terms of root mean square error, peak signal-to-noise ratio, and mean absolute error at the depth level (comparison methods: Bicubic, MGRO, VSRT, DIFFDS, EDSR3D).

[0080] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0081] Accordingly, please refer to Figure 5 This invention provides a device for three-dimensional sea surface temperature spatial downscaling, the device comprising:

[0082] The acquisition module 10 is used to acquire a target dataset, which includes: wind field data, ocean current data, first sea surface temperature data and second sea surface temperature data. The resolution of the wind field data, ocean current data and first sea surface temperature data is smaller than the resolution of the second sea surface temperature data. For details, please refer to step S10.

[0083] Processing module 11 is used to input the wind field data, ocean current data, first sea surface temperature data and second sea surface temperature data in the target dataset into the three-dimensional sea surface temperature generation model for spatial downscaling processing, and output three-dimensional sea surface temperature data. The resolution of the three-dimensional sea surface temperature data is equal to the resolution of the second sea surface temperature data in the target dataset. For details, please refer to step S11.

[0084] The three-dimensional sea surface temperature spatial downscaling device provided in this application acquires a target dataset through an acquisition module, sends the target dataset to a processing module, and performs spatial downscaling processing using a three-dimensional sea surface temperature generation model in the processing module. This process utilizes diverse data to represent more sea surface temperature details, while using spatial downscaling processing to convert low-resolution data into high-resolution data highlights more sea surface temperature details, making the final output sea surface temperature data more accurate.

[0085] This invention also provides an electronic device, such as... Figure 6 As shown, the electronic device may include a processor 21 and a memory 20, wherein the processor 21 and the memory 20 may be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0086] Processor 21 can be a central processing unit (CPU). Processor 21 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0087] Memory 20, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the three-dimensional sea surface temperature spatial downscaling method in this embodiment of the invention (e.g., Figure 5 (As shown in the acquisition module 10 and processing module 11). The processor 21 executes various functional applications and data processing by running non-transitory software programs, instructions and modules stored in the memory 20, that is, to implement the three-dimensional sea surface temperature spatial downscaling method in the above method embodiment.

[0088] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor 21, etc. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 20 may optionally include memory remotely located relative to the processor 21, and these remote memories may be connected to the processor 21 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0089] The one or more modules are stored in the memory 20, and when executed by the processor 21, they perform actions such as... Figure 1 The method for spatial downscaling of three-dimensional sea surface temperature in the illustrated embodiment.

[0090] For specific details regarding the aforementioned electronic devices, please refer to the relevant documentation. Figure 1 The relevant descriptions and effects in the illustrated embodiments are for understanding purposes only and will not be repeated here.

[0091] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by computer program instructions related to hardware, and the program can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiment methods. Among them, the storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid state drive (SSD), etc. The storage medium can also include a combination of the above-mentioned types of memories.

[0092] Although the embodiments of the present application are described in conjunction with the drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A method for three-dimensional sea surface temperature spatial downscaling, characterized in that, The method comprises the following steps: obtaining a target data set, wherein the target data set comprises wind field data, sea current data, first sea temperature data and second sea temperature data, the resolutions of the wind field data, the sea current data and the first sea temperature data are all smaller than the resolution of the second sea temperature data, the second sea temperature data is a training label, and the training label is used as a reference to ensure the consistency of the data format of the wind field data, the sea current data and the first sea temperature data during data processing, the data format of the wind field data is [1, height, width], and the data format of the sea temperature data and the sea current data is [depth level, height, width]; splicing and aligning the wind field data and the sea current data to output spliced and aligned spliced data; inputting the wind field data, the sea current data, the first sea temperature data and the second sea temperature data in the target data set into a three-dimensional sea temperature generation model to perform spatial downscaling processing, and outputting three-dimensional sea temperature data, wherein the resolution of the three-dimensional sea temperature data is equal to the resolution of the second sea temperature data in the target data set, and the inputting the wind field data, the sea current data, the first sea temperature data and the second sea temperature data in the target data set into the three-dimensional sea temperature generation model to perform spatial downscaling processing and outputting the three-dimensional sea temperature data comprises the following steps: obtaining a preset noise value; sending the spliced data, the first sea temperature data and the second sea temperature data into a feature extraction network for iterative processing to output a target condition vector, wherein the feature extraction network comprises a ResBlock, a HybridTransformerblock, a ResBlock, a HybridTransformerblock and a Fusion Layer architecture which are sequentially connected; performing nearest neighbor interpolation processing on the first sea temperature data to output processed first sea temperature data; sending the preset noise value, the target condition vector and the processed first sea temperature data into a noise reduction network for iterative calculation to output three-dimensional sea temperature data.

2. The method of claim 1, wherein, Before the step of obtaining the target data set, the method comprises the following steps: obtaining wind field original data, sea current original data, first sea temperature original data and second sea temperature original data in a target sea area; performing maximum and minimum normalization on the wind field original data, the sea current original data, the first sea temperature original data and the second sea temperature original data to make the wind field original data, the sea current original data, the first sea temperature original data and the second sea temperature original data tend to a target range, and outputting the wind field data, the sea current data, the first sea temperature data and the second sea temperature data, wherein the target range is [0, 1].

3. The method of claim 1, wherein, The step of sending the preset noise value, the target condition vector and the processed first sea temperature data into the noise reduction network for iterative calculation to output the three-dimensional sea temperature data further comprises the following step: calculating the mean square error of the preset noise value and a noise value corresponding to the three-dimensional sea temperature data.

4. A device for three-dimensional sea surface temperature spatial downscaling, characterized in that, The method comprises the following steps: The acquisition module is used to acquire a target data set, the target data set comprising wind field data, sea current data, first sea temperature data and second sea temperature data, resolutions of the wind field data, the sea current data and the first sea temperature data being less than a resolution of the second sea temperature data, the second sea temperature data being a training label, the training label serving as a reference to ensure consistency of data formats of the wind field data, the sea current data and the first sea temperature data during data processing, a data format of the wind field data being [1, height, width], data formats of the sea temperature data and the sea current data being [depth level, height, width], the wind field data and the sea current data being spliced and aligned to output spliced and aligned spliced data; The processing module is used to input the wind field data, the sea current data, the first sea temperature data and the second sea temperature data in the target data set into a three-dimensional sea temperature generation model for spatial downscaling processing, and output three-dimensional sea temperature data, a resolution of the three-dimensional sea temperature data being equal to a resolution of the second sea temperature data in the target data set, wherein the inputting the wind field data, the sea current data, the first sea temperature data and the second sea temperature data in the target data set into the three-dimensional sea temperature generation model for spatial downscaling processing and outputting the three-dimensional sea temperature data comprises: acquiring a preset noise value; sending the spliced data, the first sea temperature data and the second sea temperature data into a feature extraction network for iterative processing to output a target condition vector, the feature extraction network comprising ResBlock, HybridTransformerblock, ResBlock, HybridTransformerblock and a Fusion Layer architecture which are sequentially cascaded; performing nearest neighbor interpolation processing on the first sea temperature data to output processed first sea temperature data; sending the preset noise value, the target condition vector and the processed first sea temperature data into a noise reduction network for iterative calculation to output three-dimensional sea temperature data.

5. An electronic device, comprising: comprise: a memory and a processor, which are in communication connection with each other, the memory has computer instructions stored therein, and the processor executes the computer instructions to perform the method for spatial downscaling of three-dimensional sea temperature according to any one of claims 1-3.

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