Method and device for monitoring duration of field flooding, electronic equipment and storage medium

Abstract

The application relates to a field submersion duration monitoring method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining quantitative precipitation estimation data and an optical remote sensing satellite image of a target area; obtaining daily water body distribution data of the target area according to the quantitative precipitation estimation data; simulating a daily submersion area of the target area by using a hydrological model to obtain daily submersion area data of the target area; identifying a field in the optical remote sensing satellite image to obtain field distribution data; and obtaining the duration of field submersion according to the daily water body distribution data, the daily submersion area data and the field distribution data. The application obtains daily water body distribution data based on quantitative precipitation estimation data. In combination with daily submersion area data simulated by a hydrological model, the duration of field submersion can be determined.

Description

Technical Field

[0001] This application relates to the field of geographic monitoring technology, and in particular to a method, apparatus, electronic device, and storage medium for monitoring the duration of field flooding. Background Technology

[0002] In recent years, with the intensification of climate change, extreme weather events have become increasingly frequent. Taking the mountainous areas of southern China as an example, crops are affected by disasters such as typhoons, extreme rainfall, and flash floods. When crops are submerged and flooded for a certain number of days, large-scale yield reductions are likely to occur. By using satellite remote sensing technology to monitor the duration of farmland flooding, timely information on the distribution and severity of crop damage can be provided. This information can be used to help relevant government departments formulate disaster prevention and mitigation strategies, which is of great significance.

[0003] However, the existing remote sensing data replay cycle is as long as 5-12 days, and the weather is often cloudy and rainy when disasters occur, making it difficult to obtain effective observation data. This makes it difficult to monitor the duration of farmland flooding, which brings great difficulties to crop remote sensing monitoring and disaster assessment. Summary of the Invention

[0004] Based on this, the purpose of this application is to provide a method, apparatus, electronic device and storage medium for monitoring the duration of field flooding, which can effectively monitor the duration of field flooding.

[0005] According to a first aspect of the embodiments of this application, a method for monitoring the duration of field flooding is provided, comprising the following steps:

[0006] Acquire quantitative precipitation estimation data and optical remote sensing satellite images of the target area;

[0007] Based on quantitative precipitation estimation data, daily water body distribution data for the target area are obtained;

[0008] The daily flooding area of ​​the target area is simulated using a hydrological model to obtain daily flooding area data for the target area.

[0009] Field identification is performed on optical remote sensing satellite images to obtain field distribution data;

[0010] Based on daily water distribution data, daily flooded area data, and field distribution data, the duration of field flooding is determined.

[0011] According to a second aspect of the embodiments of this application, a field flooding duration monitoring device is provided, comprising:

[0012] The data acquisition module is used to acquire quantitative precipitation estimation data and optical remote sensing satellite images of the target area;

[0013] The daily water body distribution data acquisition module is used to obtain daily water body distribution data for the target area based on quantitative precipitation estimation data;

[0014] The daily flooding area data acquisition module is used to simulate the daily flooding area of ​​the target area using a hydrological model, and obtain the daily flooding area data of the target area.

[0015] The field distribution data acquisition module is used to identify fields in optical remote sensing satellite images and obtain field distribution data.

[0016] The duration date acquisition module is used to obtain the duration date of field flooding based on daily water body distribution data, daily flooded area data, and field distribution data.

[0017] According to a third aspect of the embodiments of this application, an electronic device is provided, including: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to execute the steps of the method of the first aspect.

[0018] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the method of the first aspect.

[0019] This application's embodiments acquire quantitative precipitation estimation data and optical remote sensing satellite images of the target area; based on the quantitative precipitation estimation data, obtain daily water distribution data for the target area; use a hydrological model to simulate daily flooding areas in the target area, obtaining daily flooding area data for the target area; identify fields in the optical remote sensing satellite images, obtaining field distribution data; and based on the daily water distribution data, daily flooding area data, and field distribution data, determine the duration of field flooding. This application obtains daily water distribution data based on quantitative precipitation estimation data. Combined with the daily flooding area data simulated by the hydrological model, the duration of flooding for each field can be determined.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application.

[0021] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0022] Figure 1 A flowchart illustrating a method for monitoring the duration of flooding in fields according to an embodiment of this application;

[0023] Figure 2 A structural block diagram of a field flooding duration monitoring device provided in one embodiment of this application;

[0024] Figure 3 This is a schematic block diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0026] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0027] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0028] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0029] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0030] Please see Figure 1 This is a flowchart illustrating a method for monitoring the duration of flooding in farmland according to an embodiment of this application. The method for monitoring the duration of flooding in farmland according to this embodiment includes the following steps:

[0031] S10: Acquire quantitative precipitation estimation data and optical remote sensing satellite images of the target area.

[0032] The target area is the region where the duration of field flooding is monitored. Quantitative Precipitation Estimation (QPE) data is precipitation data acquired through radar, satellite, and other observation methods. It is used to understand precipitation conditions, monitor precipitation intensity, extent, area, and trend, and provides important initial field data for flood monitoring. Optical remote sensing satellite images are images of the Earth's surface captured by optical remote sensing satellites orbiting in Earth orbit.

[0033] In this embodiment, daily QPE data for the target area can be obtained from the National Meteorological Administration, the Hydrological Management Center, or a meteorological data platform. Optical remote sensing satellite images of the target area can be obtained from the National Space Administration or a remote sensing satellite platform.

[0034] S20: Obtain daily water distribution data for the target area based on quantitative precipitation estimation data.

[0035] The daily water body distribution data includes, but is not limited to, the location data of daily water bodies and the water body type. The water body type includes permanent water bodies, seasonal water bodies and temporary water bodies. Permanent water bodies include, but are not limited to, rivers, lakes, seas and fish ponds. Seasonal water bodies include paddy fields. Temporary water bodies include areas flooded by rainstorms.

[0036] In this embodiment of the application, quantitative precipitation estimation data can be converted into synthetic aperture radar data or Gaofen-3 data, and daily water distribution data of the target area can be extracted from the synthetic aperture radar data or Gaofen-3 data.

[0037] S30: Use a hydrological model to simulate the daily flooding area of ​​the target area and obtain daily flooding area data for the target area.

[0038] Hydrological models are approximate scientific models derived from simulation methods that generalize complex hydrological phenomena and processes. Daily flooding area data includes, but is not limited to, the location data of daily flooded areas and the water depth of those areas.

[0039] In this embodiment of the application, input data of the hydrological model is collected, the input data is preprocessed, and the preprocessed input data is input into the hydrological model to simulate the daily flooding area of ​​the target area and obtain the daily flooding area data of the target area.

[0040] S40: Identify fields in optical remote sensing satellite images to obtain field distribution data.

[0041] The field distribution data includes the location data of the fields.

[0042] In this embodiment, a field identification model is used to identify fields in optical remote sensing satellite images, thereby obtaining the location data of each field in the target area. The field identification model is trained based on a deep learning neural network.

[0043] S50: Based on daily water distribution data, daily flooded area data, and field distribution data, obtain the duration of field flooding.

[0044] In this embodiment, it can be determined whether a field is flooded based on daily water distribution data and field distribution data. It can also be determined whether a field is flooded based on daily flooded area data and field distribution data. After a field is flooded, the number of days the field is flooded is determined, thus obtaining the duration of the flooding.

[0045] By applying the embodiments of this application, quantitative precipitation estimation data and optical remote sensing satellite images of the target area are acquired; daily water distribution data of the target area is obtained based on the quantitative precipitation estimation data; daily flooding area simulation is performed on the target area using a hydrological model to obtain daily flooding area data of the target area; field identification is performed on the optical remote sensing satellite images to obtain field distribution data; and the duration of field flooding is determined based on the daily water distribution data, daily flooding area data, and field distribution data. This application obtains daily water distribution data based on quantitative precipitation estimation data. Combined with the daily flooding area data simulated by the hydrological model, the duration of flooding for each field can be determined.

[0046] In one embodiment, before step S20, step S201 is included, as follows:

[0047] S201: Preprocessing quantitative precipitation estimation data; preprocessing includes, but is not limited to, radiation correction, atmospheric correction and geometric correction.

[0048] Among them, radiation correction refers to the correction of systematic and random radiation distortion or aberration caused by external factors, data acquisition and transmission systems, in order to eliminate or correct image distortion caused by radiation errors.

[0049] Atmospheric correction refers to the fact that the total radiance of a ground target measured by a sensor does not reflect the true reflectance of the surface, as it includes errors in radiance caused by atmospheric absorption, especially scattering. Atmospheric correction is the process of eliminating these radiance errors caused by atmospheric influences to retrieve the true surface reflectance of the ground object.

[0050] Geometric correction refers to the process of eliminating or correcting geometric errors in remote sensing images.

[0051] In the embodiments of this application, radiation correction, atmospheric correction and geometric correction are performed on the quantitative precipitation estimation data, which can improve the accuracy and reliability of the quantitative precipitation estimation data.

[0052] In one embodiment, step S20 includes steps S21 to S22, as follows:

[0053] S21: Input quantitative precipitation estimation data into a trained image generation model to obtain daily synthetic aperture radar observation images.

[0054] Considering that the Sentinel-1 satellite has a replay cycle of approximately one week, it is impossible to obtain daily synthetic aperture radar (SAR) observation images. Therefore, an image generation model is used to generate daily SAR observation images.

[0055] Among them, the trained image generation model is the Stable Diffusion image generation model. The Stable Diffusion image generation model can greatly reduce memory usage and computational complexity by performing forward diffusion and backward generation processes in a low-dimensional latent space.

[0056] In this embodiment, quantitative precipitation estimation data is used as input to a trained image generation model, which outputs daily synthetic aperture radar (SAR) images. The spatial resolution of the SAR images is 10 meters.

[0057] S22: Polarize the daily synthetic aperture radar observation images to obtain daily water distribution data for the target area.

[0058] The polarization treatment includes, but is not limited to, HV cross-polarization and VH cross-polarization.

[0059] In the embodiments of this application, daily water distribution data of the target area can be obtained by performing HV cross-polarization or VH cross-polarization on the daily synthetic aperture radar observation images.

[0060] In one embodiment, before step S21, steps S211 to S212 are included, as follows:

[0061] S211: Acquire sample quantitative precipitation estimation data and sample synthetic aperture radar observation images;

[0062] S212: The sample quantitative precipitation estimation data is used as input, and the sample synthetic aperture radar observation image is used as output. These are then input into the image generation model for training and learning to obtain the trained image generation model.

[0063] In this embodiment, sample quantitative precipitation estimation data and sample synthetic aperture radar (SAR) observation images are collected in advance. The sample quantitative precipitation estimation data is input into the image generation model to obtain prediction results. The prediction results and sample SAR observation images are input into a preset loss function to obtain the loss function value. Based on the loss function value, the image generation model is iteratively trained until the loss function value is less than a preset threshold, thus obtaining a trained image generation model.

[0064] In one embodiment, the daily flooded area data includes the daily flood depth and the daily flooded range. Step S30 includes steps S31 to S32, as follows:

[0065] S31: Acquire surface meteorological data, topographic data, river data, soil characteristic data, land use type data, and field parameters for the target area;

[0066] In this embodiment, surface meteorological data includes precipitation, temperature, humidity, and wind speed data, which can be obtained through meteorological station observations or reanalysis. Topographic data is digital elevation model data, specifically SRTM topographic data. River channel data includes river width, depth, and slope data, which can be obtained through GIS technology. Soil characteristic data includes soil permeability and water-holding capacity data, which can be obtained from the Resource and Environmental Science and Data Center. Land use type data can be obtained from ESA WorldCover data. Field parameters include crop type, growth stage, and planting density, which can be obtained through analysis of remote sensing data.

[0067] S32: Input surface meteorological data, topographic data, river data, soil characteristic data, land use type data, and field parameters into the hydrological model to obtain the daily flooding depth and daily flooding range.

[0068] In this embodiment of the application, surface meteorological data, topographic data, river data, soil characteristic data, land use type data, and field parameters are used as inputs to the hydrological model. The hydrological model is run to simulate the daily flooded area. The output of the hydrological model includes the daily flood depth and the daily flood range.

[0069] In one embodiment, step S40 includes step S41, which is as follows:

[0070] S41: Input the optical remote sensing satellite image into the trained field identification model to obtain field distribution data;

[0071] The training field identification model includes:

[0072] Field digitization and sample enhancement processing were performed on optical remote sensing satellite sample images to obtain processed optical remote sensing satellite sample images;

[0073] The boundaries of the fields are delineated from the processed optical remote sensing satellite sample images to obtain the field sample distribution data.

[0074] The processed optical remote sensing satellite sample images are used as input, and the field sample distribution data are used as output. These are then input into the Transformer model for training and learning to obtain a trained field recognition model.

[0075] The Transformer model consists of an encoder and a decoder, with the encoder incorporating a self-attention mechanism.

[0076] In this embodiment, a large number of optical remote sensing satellite sample images are collected in advance. Then, for each optical remote sensing satellite sample image, the boundaries of the fields in the sample image are manually delineated one by one to obtain the field sample distribution data. The optical remote sensing satellite sample images are used as input and the field sample distribution data are used as output to be input into the Transformer model for training and learning, thereby obtaining a trained field recognition model.

[0077] In one embodiment, step S50 includes steps S51 to S53, as follows:

[0078] S51: Match the locations of each field in the field distribution data with the locations of each water body in the daily water body distribution data. When a field and a water body overlap, determine the field as the target field; or...

[0079] In this embodiment of the application, when the field overlaps with the water body, it means that the field is submerged.

[0080] S52: Match the locations of each field in the field distribution data with each flooded area in the daily flooded area data. When a field overlaps with a flooded area, the field is identified as the target field. The target field is the flooded field.

[0081] In this embodiment of the application, when the field overlaps with the flooded area, it means that the field is flooded.

[0082] S53: Calculate the number of consecutive days that each field in the target area is the target field within a preset time period, and obtain the duration of flooding for each field.

[0083] The preset time period can be manually set according to actual needs. For example, 7 days or 1 month.

[0084] In this embodiment of the application, the duration of flooding for each field can be obtained by counting the number of consecutive days each field was flooded.

[0085] The following are embodiments of the apparatus described in this application, which can be used to execute the methods described in the embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the methods described in the embodiments of this application.

[0086] Please see Figure 2 This document illustrates a schematic diagram of the field flooding duration monitoring device provided in an embodiment of this application. The field flooding duration monitoring device 6 provided in this embodiment includes:

[0087] Data acquisition module 61 is used to acquire quantitative precipitation estimation data and optical remote sensing satellite images of the target area;

[0088] The daily water distribution data acquisition module 62 is used to obtain daily water distribution data of the target area based on quantitative precipitation estimation data;

[0089] The daily flooding area data acquisition module 63 is used to simulate the daily flooding area of ​​the target area using a hydrological model, and obtain the daily flooding area data of the target area.

[0090] The field distribution data acquisition module 64 is used to identify fields in optical remote sensing satellite images and obtain field distribution data.

[0091] The duration date acquisition module 65 is used to obtain the duration date of field flooding based on daily water body distribution data, daily flooded area data, and field distribution data.

[0092] It should be noted that the field flooding duration monitoring device provided in the above embodiments is only illustrated by the division of the above functional modules when performing the field flooding duration monitoring method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the field flooding duration monitoring device and the field flooding duration monitoring method provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0093] The following are embodiments of the device described in this application, which can be used to execute the methods described in the embodiments of this application. For details not disclosed in the embodiments of the device described in this application, please refer to the methods described in the embodiments of this application.

[0094] Please see Figure 3This application also provides an electronic device 300, which may specifically be a computer, mobile phone, tablet computer, etc. In an exemplary embodiment of this application, the electronic device 300 is a computer, which may include: at least one processor 301, at least one memory 302, at least one display, at least one network interface 303, user interface 304, and at least one communication bus 305.

[0095] The user interface 304 is primarily used to provide an input interface for the user and to acquire user input data. Optionally, the user interface may also include a standard wired interface or a wireless interface.

[0096] The network interface 303 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0097] The communication bus 305 is used to enable communication between these components.

[0098] The processor 301 may include one or more processing cores. The processor connects to various parts of the electronic device using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory, and by calling data stored in memory. Optionally, the processor may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor.

[0099] The memory 302 may include random access memory (RAM) or read-only memory. Optionally, the memory may include a non-transitory computer-readable storage medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor. Figure 3 As shown, a memory, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and operating applications.

[0100] The processor can be used to call the application program storing the field flooding duration monitoring method in the memory, and specifically execute the method steps of the above-described embodiment. For the specific execution process, please refer to the detailed description shown in the embodiment, which will not be repeated here.

[0101] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0102] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for monitoring the duration of flooding in farmland, characterized in that, Includes the following steps: Acquire quantitative precipitation estimation data and optical remote sensing satellite images of the target area; Based on the quantitative precipitation estimation data, daily water distribution data for the target area is obtained, including: acquiring sample quantitative precipitation estimation data and sample synthetic aperture radar (SAR) images; using the sample quantitative precipitation estimation data as input and the sample SAR images as output, inputting them into an image generation model for training and learning to obtain a trained image generation model; inputting the quantitative precipitation estimation data into the trained image generation model to obtain daily SAR images; and performing polarization processing on the daily SAR images to obtain daily water distribution data for the target area. The target area is simulated daily using a hydrological model to obtain daily flooding data for the target area. Field identification is performed on the optical remote sensing satellite images to obtain field distribution data; Based on the daily water distribution data, the daily flooded area data, and the field distribution data, the duration of field flooding is determined.

2. The method for monitoring the duration of field flooding according to claim 1, characterized in that: The daily flooding area data includes the daily flooding depth and the daily flooding range; The step of simulating daily flooding areas of the target area using a hydrological model to obtain daily flooding area data for the target area includes: Acquire surface meteorological data, topographic data, river data, soil characteristic data, land use type data, and field parameters for the target area; The surface meteorological data, topographic data, river data, soil characteristic data, land use type data, and field parameters are input into the hydrological model to obtain the daily flooding depth and the daily flooding range.

3. The method for monitoring the duration of field flooding according to claim 1, characterized in that: The step of identifying field plots in the optical remote sensing satellite image to obtain field distribution data includes: The optical remote sensing satellite images are input into the trained field identification model to obtain field distribution data; Training the field identification model includes: Field digitization and sample enhancement processing were performed on optical remote sensing satellite sample images to obtain processed optical remote sensing satellite sample images; The processed optical remote sensing satellite sample images are used to delineate the field boundaries and obtain field sample distribution data; The processed optical remote sensing satellite sample image is used as input, and the field sample distribution data is used as output. The data is then input into the Transformer model for training and learning to obtain a trained field recognition model.

4. The method for monitoring the duration of field flooding according to any one of claims 1 to 3, characterized in that: The step of obtaining the duration of field flooding based on the daily water body distribution data, the daily flooded area data, and the field distribution data includes: The locations of each field in the field distribution data are matched with those of each water body in the daily water body distribution data. When a field and a water body overlap, the field is identified as the target field; or... The locations of each field in the field distribution data are matched with the locations of each flooded area in the daily flooded area data. When the locations of the field and the flooded area overlap, the field is determined as the target field; wherein, the target field is the flooded field. The number of consecutive days that each field in the target area is the target field within a preset time period is counted to obtain the duration of flooding for each field.

5. The method for monitoring the duration of field flooding according to any one of claims 1 to 3, characterized in that: Before the step of obtaining the daily water distribution data of the target area based on the quantitative precipitation estimation data, the following steps are included: The quantitative precipitation estimation data are preprocessed; the preprocessing includes, but is not limited to, radiation correction, atmospheric correction and geometric correction.

6. A device for monitoring the duration of flooding in farmland, characterized in that, include: The data acquisition module is used to acquire quantitative precipitation estimation data and optical remote sensing satellite images of the target area; The daily water distribution data acquisition module is used to obtain daily water distribution data of the target area based on the quantitative precipitation estimation data, including: acquiring sample quantitative precipitation estimation data and sample synthetic aperture radar (SAR) observation images; using the sample quantitative precipitation estimation data as input and the sample SAR observation images as output, inputting them into an image generation model for training and learning to obtain a trained image generation model; inputting the quantitative precipitation estimation data into the trained image generation model to obtain daily SAR observation images; and performing polarization processing on the daily SAR observation images to obtain daily water distribution data of the target area. The daily flooding area data acquisition module is used to simulate the daily flooding area of ​​the target area using a hydrological model, and obtain the daily flooding area data of the target area. The field distribution data acquisition module is used to identify fields in the optical remote sensing satellite image and obtain field distribution data. The duration date acquisition module is used to obtain the duration date of field flooding based on the daily water body distribution data, the daily flooded area data, and the field distribution data.

7. An electronic device, characterized in that, include: A processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to execute the steps of the field flooding duration monitoring method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the field flooding duration monitoring method as described in any one of claims 1 to 5.

Images