Method for determining chlorophyll concentration value of phytoplankton based on ocean heat waves

Abstract

The invention discloses a method for determining a chlorophyll concentration value of phytoplankton based on ocean heat waves. The method comprises the following steps: acquiring ocean green ecological remote sensing image data and ocean heat wave data of a target sea area; and inputting the ocean heat wave data and the ocean green ecological remote sensing image data of the target sea area into a chlorophyll concentration determination model, so that the chlorophyll concentration determination model determines the ocean heat wave grade of the target sea area according to the ocean heat wave data, inputting the ocean green ecological remote sensing image data into a chlorophyll concentration determination sub-model according to the ocean heat wave grade, enabling the chlorophyll concentration determination sub-model to extract corresponding multispectral remote sensing image features and ocean microwave remote sensing image features, and outputting a phytoplankton chlorophyll concentration value of the target sea area; and transmitting the phytoplankton chlorophyll concentration value of the target sea area to a marine ecological monitoring system to assist the marine ecological monitoring system in sea area monitoring. The method can improve the accuracy of determining the chlorophyll concentration value of the phytoplankton and the application reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of marine ecological monitoring, and in particular to a method for determining phytoplankton chlorophyll concentration values ​​based on marine heat waves. Background Art

[0002] Phytoplankton chlorophyll concentration is a key indicator of the state of marine ecosystems. Its changes are closely related to extreme climate events such as marine heatwaves. Different levels of marine heatwaves will affect the chlorophyll concentration of phytoplankton in the ocean. This is because different levels of marine heatwaves provide different amounts of nutrients to phytoplankton. Different levels of nutrients will change the community structure of phytoplankton, leading to changes in phytoplankton chlorophyll concentration. However, when analyzing the chlorophyll concentration of phytoplankton, existing technologies only use a single model to predict chlorophyll concentration. This method of using a single model to predict chlorophyll concentration of phytoplankton under different levels of marine heatwaves ignores the impact of marine heatwaves on chlorophyll concentration of phytoplankton, resulting in low accuracy of chlorophyll concentration prediction results, which cannot provide reliable data support for research on the relationship between marine heatwaves and marine ecosystems. Summary of the Invention

[0003] An embodiment of the present invention provides a method for determining phytoplankton chlorophyll concentration values ​​based on ocean heat waves, which can solve the problem that the existing technology ignores the impact of ocean heat waves on phytoplankton chlorophyll concentration, resulting in low accuracy of phytoplankton chlorophyll concentration prediction results. It improves the accuracy of determining phytoplankton chlorophyll concentration values, and further improves the data support reliability when applying phytoplankton chlorophyll concentration values ​​to marine ecological monitoring systems for sea area monitoring.

[0004] An embodiment of the present invention provides a method for determining phytoplankton chlorophyll concentration based on ocean heat waves, comprising: Acquire marine green ecological remote sensing image data and marine heat wave data of the target sea area; wherein the marine heat wave data includes: historical sea surface temperature data within a preset monitoring period, sea surface temperature data for the current monitoring period, and calendar days of the current monitoring period; the marine green ecological remote sensing image data includes: multispectral remote sensing images and marine microwave remote sensing images; Inputting the ocean heat wave data and the ocean green ecology remote sensing image data of the target sea area into a chlorophyll concentration determination model, so that the chlorophyll concentration determination model determines the ocean heat wave level of the target sea area according to the ocean heat wave data, and inputting the ocean green ecology remote sensing image data into a chlorophyll concentration determination sub-model according to the ocean heat wave level, so that the chlorophyll concentration determination sub-model extracts corresponding multispectral remote sensing image features and ocean microwave remote sensing image features, and outputs a phytoplankton chlorophyll concentration value of the target sea area according to the multispectral remote sensing image features and the ocean microwave remote sensing image features; The chlorophyll concentration value of the phytoplankton in the target sea area is transmitted to the marine ecological monitoring system to assist the marine ecological monitoring system in performing sea area monitoring.

[0005] Furthermore, the marine heat wave level includes: mild heat wave level, moderate heat wave level, severe heat wave level and extreme heat wave level; Determining the ocean heat wave level of the target sea area according to the ocean heat wave data includes: The historical sea surface temperature data within the preset monitoring period are grouped by calendar day, and the historical sea surface temperature data for each calendar day are obtained: For each calendar day, the average of the historical sea surface temperature data for the current calendar day is calculated to obtain the ocean heat wave threshold for the current calendar day; Construct the ocean heatwave threshold matrix for calendar days throughout the year based on the ocean heatwave threshold for each calendar day; Obtaining, from the ocean heat wave threshold matrix according to the calendar day of the current monitoring period, the ocean heat wave threshold for each calendar day of the current monitoring period; The ocean heat wave intensity and duration of the target sea area during the current monitoring period are determined based on the sea surface temperature data of the current monitoring period and the ocean heat wave thresholds of each calendar day during the current monitoring period, and the ocean heat wave level of the target sea area is determined based on the ocean heat wave intensity and duration of the target sea area.

[0006] Furthermore, the construction of the chlorophyll concentration determination model includes: Acquire a number of ocean heatwave data samples and a number of ocean green ecological remote sensing image data samples for each ocean heatwave level, and construct a training sample set based on each ocean heatwave data sample and each ocean green ecological remote sensing image data sample; wherein each ocean heatwave data sample includes: a historical sea surface temperature data sample within a preset monitoring period, a historical sea surface data sample within a selected monitoring period, and a calendar day of the selected monitoring period; and each ocean green ecological remote sensing image data sample includes: a multispectral remote sensing image sample and an ocean microwave remote sensing image sample; Constructing an initial chlorophyll concentration determination model; wherein the initial chlorophyll concentration determination model includes: a classifier, a first chlorophyll concentration determination sub-model, a second chlorophyll concentration determination sub-model, a third chlorophyll concentration determination sub-model, and a fourth chlorophyll concentration determination sub-model, wherein the first chlorophyll concentration determination sub-model, the second chlorophyll concentration determination sub-model, the third chlorophyll concentration determination sub-model, and the fourth chlorophyll concentration determination sub-model correspond to a mild heat wave level, a moderate heat wave level, a severe heat wave level, and an extreme heat wave level, respectively; The initial chlorophyll concentration determination model is trained using the training sample set until the initial chlorophyll concentration determination model converges, thereby generating the chlorophyll concentration determination model.

[0007] Furthermore, the initial chlorophyll concentration determination model is trained using the training sample set until the initial chlorophyll concentration determination model converges, thereby generating the chlorophyll concentration determination model, including: Freezing network parameters of each chlorophyll concentration determination sub-model, training a classifier of the initial chlorophyll concentration determination model with each ocean heat wave data sample until the classifier of the initial chlorophyll concentration determination model converges, freezing the network parameters of the classifier of the initial chlorophyll concentration determination model, and unfreezing the network parameters of each chlorophyll concentration determination sub-model; Training each chlorophyll concentration determination sub-model using each marine green ecological remote sensing image data sample until each chlorophyll concentration determination sub-model converges, and then unfreezing the network parameters of the classifier; The classifier and each chlorophyll concentration determination sub-model are trained using a training sample set until the initial chlorophyll concentration determination model is converged as a whole, thereby generating the chlorophyll concentration determination model.

[0008] Furthermore, the training of the classifier of the initial chlorophyll concentration determination model using each ocean heat wave data sample includes: Inputting the ocean heat wave data sample into the classifier, so that the classifier determines the ocean heat wave intensity and the ocean heat wave duration corresponding to the current ocean heat wave data sample; The predicted ocean heat wave level of the current ocean heat wave data sample is output according to the ocean heat wave intensity and ocean heat wave duration corresponding to the current ocean heat wave data sample.

[0009] Furthermore, the training of each chlorophyll concentration determination sub-model using each marine green ecological remote sensing image data sample includes: Inputting the marine ecological remote sensing image data sample into the first chlorophyll concentration determination sub-model, so that the first chlorophyll concentration determination sub-model extracts the spectral reflectance of the blue light band and the spectral reflectance of the green light band of the multispectral remote sensing image sample, and determines a first spectral reflectance ratio according to the spectral reflectance of the blue light band and the spectral reflectance of the green light band; extracting the first microwave radiation feature of the marine microwave remote sensing image sample, and determining the mixed layer depth according to the first microwave radiation feature; outputting the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample according to the mixed layer depth and the first spectral reflectance ratio; Inputting the marine ecological remote sensing image data sample into the second chlorophyll concentration determination sub-model, so that the second chlorophyll concentration determination sub-model extracts the spectral reflectance of the red light band, the spectral reflectance of the green light band and the chlorophyll fluorescence peak of the multispectral remote sensing image sample, and determines the second spectral reflectance ratio according to the spectral reflectance of the red light band and the spectral reflectance of the green light band; extracting the second microwave radiation characteristics of the marine microwave remote sensing image sample, and determining the sea surface height anomaly according to the second microwave radiation characteristics; outputting the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample according to the second spectral reflectance ratio, the sea surface height anomaly and the chlorophyll fluorescence peak; Inputting the marine ecological remote sensing image data sample into the third chlorophyll concentration determination sub-model, so that the third chlorophyll concentration determination sub-model extracts the spectral reflectance of the red light band, the spectral reflectance of the near infrared band, and the chlorophyll fluorescence signal of the multispectral remote sensing image sample, determines a third spectral reflectance ratio according to the spectral reflectance of the red light band and the spectral reflectance of the near infrared band, and determines the photosynthetic efficiency of the target sea area according to the chlorophyll fluorescence signal; outputting a predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample according to the third spectral reflectance ratio and the photosynthetic efficiency; The marine ecological remote sensing image data sample is input into the fourth chlorophyll concentration determination sub-model so that the fourth chlorophyll concentration determination sub-model extracts the spectral reflectance of the near-infrared band and the thermal infrared signal of the multispectral remote sensing image sample; the thermal infrared sea surface temperature peak of the target sea area is determined according to the thermal infrared signal, and the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample is output according to the thermal infrared sea surface temperature peak, the spectral reflectance of the near-infrared band and the preset chlorophyll background value.

[0010] Furthermore, the classifier and each chlorophyll concentration determination sub-model are trained with a training sample set until the initial chlorophyll concentration determination model is converged as a whole, thereby generating the chlorophyll concentration determination model, including: Grouping each ocean heat wave data sample and each ocean green ecological remote sensing image data sample in the training sample set based on the ocean heat wave level to obtain a training sample subset under each ocean heat wave level; wherein the training sample subset includes the ocean heat wave data samples and the ocean green ecological remote sensing image data samples under the current ocean heat wave level; The classifier and the chlorophyll concentration determination sub-model of the ocean heat wave level corresponding to each training sample subset are trained with each training sample subset until the initial chlorophyll concentration determination model is converged as a whole, thereby generating the chlorophyll concentration determination model.

[0011] Furthermore, the outputting of the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample according to the second spectral reflectance ratio, the sea level height anomaly value, and the chlorophyll fluorescence peak value includes: Constructing a first correlation between the second spectral reflectance ratio and the chlorophyll concentration, constructing a second correlation between the sea level height anomaly and the chlorophyll concentration, and constructing a third correlation between the chlorophyll fluorescence peak and the chlorophyll concentration; A spatial rectangular coordinate system is established with the first association relationship as the horizontal axis, the second association relationship as the vertical axis, and the third association relationship as the vertical axis; wherein the plane formed by the horizontal axis and the vertical axis is the first plane, the plane formed by the horizontal axis and the vertical axis is the second plane, and the plane formed by the vertical axis and the vertical axis is the third plane; constructing a first correlation vector between the second spectral reflectance ratio, the sea level height anomaly, and the chlorophyll concentration, constructing a second correlation vector between the second spectral reflectance ratio, the chlorophyll fluorescence peak, and the chlorophyll concentration, and constructing a third correlation vector between the sea level height anomaly, the chlorophyll fluorescence peak, and the chlorophyll concentration; Projecting the first correlation vector, the second correlation vector, and the third correlation vector on the first plane, the second plane, and the third plane, respectively, to obtain a first projection of the first correlation vector on the first plane, a second projection on the second plane, and a third projection on the third plane; a fourth projection of the second correlation vector on the first plane, a fifth projection on the second plane, and a sixth projection on the second plane; a seventh projection of the third correlation vector on the first plane, an eighth projection on the second plane, and a ninth projection on the third plane; The first, fourth and seventh projections are superimposed to obtain a first superimposed projection, the second, fifth and eighth projections are superimposed to obtain a second superimposed projection, and the third, sixth and ninth projections are superimposed to obtain a third superimposed projection. The first, second and third superimposed projections are converted into vector form to obtain a first superimposed vector, a second superimposed vector and a third superimposed vector. The predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample is output through the first superimposed vector, the second superimposed vector and the third superimposed vector.

[0012] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for determining the chlorophyll concentration value of phytoplankton based on ocean heat waves as described in the above-mentioned embodiment of the invention.

[0013] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method for determining the chlorophyll concentration value of phytoplankton based on ocean heat waves described in the above-mentioned embodiment of the invention.

[0014] The following beneficial effects are achieved by implementing the present invention: The present invention provides a method for determining the chlorophyll concentration value of phytoplankton based on ocean heat waves. When confirming the chlorophyll concentration value of phytoplankton in the target sea area, the marine green ecological remote sensing image data and the marine heat wave data of the target sea area are obtained, and then the marine heat wave data and the marine green ecological remote sensing image data of the target sea area are input into a chlorophyll concentration determination model, so that the chlorophyll concentration determination model determines the marine heat wave level of the target sea area according to the marine heat wave data, and then according to the determined marine heat wave level, the chlorophyll concentration determination sub-model of the corresponding level in the chlorophyll concentration determination model is selected, and the chlorophyll concentration determination sub-model extracts the corresponding multi-light wave level. The multispectral remote sensing image characteristics and the ocean microwave remote sensing image characteristics are used to output the phytoplankton chlorophyll concentration value of the target sea area according to the multispectral remote sensing image characteristics and the ocean microwave remote sensing image characteristics. By first determining the ocean heat wave level and then determining the chlorophyll concentration value using the chlorophyll concentration determination sub-model under the corresponding ocean heat wave level, the problem that the existing technology ignores the impact of ocean heat waves on phytoplankton chlorophyll concentration, resulting in low accuracy of the phytoplankton chlorophyll concentration prediction results, is solved. The accuracy of the determination of phytoplankton chlorophyll concentration value is improved, thereby improving the data support reliability when the phytoplankton chlorophyll concentration value is applied to the marine ecological monitoring system for sea area monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a method for determining phytoplankton chlorophyll concentration values ​​based on ocean heat waves provided by one embodiment of the present invention.

[0016] Figure 2 Schematic diagram of the structure of a chlorophyll concentration determination model provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] like Figure 1 FIG. 1 is a method for determining phytoplankton chlorophyll concentration based on ocean heat waves according to an embodiment of the present invention, comprising: Step S1: Acquire marine green ecological remote sensing image data and marine heat wave data of the target sea area; wherein the marine heat wave data includes: historical sea surface temperature data within a preset monitoring period, sea surface temperature data of the current monitoring period, and calendar days of the current monitoring period; the marine green ecological remote sensing image data includes: multispectral remote sensing images and marine microwave remote sensing images; Step S2: inputting the ocean heat wave data and the ocean green ecology remote sensing image data of the target sea area into a chlorophyll concentration determination model, so that the chlorophyll concentration determination model determines the ocean heat wave level of the target sea area according to the ocean heat wave data, and inputting the ocean green ecology remote sensing image data into a chlorophyll concentration determination sub-model according to the ocean heat wave level, so that the chlorophyll concentration determination sub-model extracts corresponding multispectral remote sensing image features and ocean microwave remote sensing image features, and outputs the phytoplankton chlorophyll concentration value of the target sea area according to the multispectral remote sensing image features and the ocean microwave remote sensing image features; Step S3: Transmitting the chlorophyll concentration value of the phytoplankton in the target sea area to the marine ecological monitoring system to assist the marine ecological monitoring system in performing sea area monitoring.

[0019] For step S1, obtain marine green ecological remote sensing image data and marine heat wave data of the target sea area, wherein the marine heat wave data includes historical sea surface temperature data within a preset monitoring period, sea surface temperature data of the current monitoring period, and the calendar day of the current monitoring period; the marine green ecological remote sensing image includes a multispectral remote sensing image and a marine microwave remote sensing image. Exemplarily, in the present invention, the end time point of the preset monitoring period is usually taken as the start time point of the current monitoring period, the length of the preset monitoring period is 30 years, the start time point of the current monitoring period is set to January 2, 2025, and the length of the current monitoring period is 7 days, then the current monitoring period is from January 2, 2025 to January 8, 2025, the calendar days of the current monitoring period include January 2, January 3, January 4, January 5, January 6, January 7, and January 8, and the preset monitoring period is from January 2, 1995 to January 2, 2025.

[0020] In step S2, the ocean heatwave data and marine green ecology remote sensing image data of the target sea area are input into the chlorophyll concentration determination model. The chlorophyll concentration determination model determines the ocean heatwave level of the target sea area based on the ocean heatwave data. Based on the ocean heatwave level, the marine green ecology remote sensing image data is input into the chlorophyll concentration determination sub-model. The chlorophyll concentration determination sub-model extracts corresponding multispectral remote sensing image features and marine microwave remote sensing image features, and outputs the phytoplankton chlorophyll concentration value of the target sea area based on the multispectral remote sensing image features and marine microwave remote sensing image features.

[0021] In a preferred embodiment, the ocean heat wave levels include: mild heat wave level, moderate heat wave level, severe heat wave level, and extreme heat wave level; determining the ocean heat wave level of the target sea area based on the ocean heat wave data includes: grouping historical sea surface temperature data within a preset monitoring period by calendar day to obtain historical sea surface temperature data for each calendar day; for each calendar day, calculating the average value of the historical sea surface temperature data for the current calendar day to obtain an ocean heat wave threshold for the current calendar day; constructing an ocean heat wave threshold matrix for calendar days throughout the year based on the ocean heat wave thresholds for each calendar day; obtaining the ocean heat wave threshold for each calendar day in the current monitoring period from the ocean heat wave threshold matrix based on the calendar day of the current monitoring period; determining the ocean heat wave intensity and ocean heat wave duration of the target sea area in the current monitoring period based on the sea surface temperature data of the current monitoring period and the ocean heat wave threshold for each calendar day in the current monitoring period, and determining the ocean heat wave level of the target sea area based on the ocean heat wave intensity and ocean heat wave duration of the target sea area.

[0022] Specifically, the classification of marine heatwave levels is based on a grading system generally recognized by the international academic community, formed by the dual indicators of the magnitude of the sea surface temperature anomaly (i.e., the intensity of the marine heatwave) and the duration of the sea surface temperature anomaly (i.e., the duration of the marine heatwave). This is an objective classification standard. When the intensity of the marine heatwave in the target sea area is between (1, 2] and the duration of the marine heatwave is between 5-10 days, the marine heatwave level is considered to be a mild heatwave level (Level I); when the intensity of the marine heatwave in the target sea area is between (2, 3] and the duration of the marine heatwave is between 11-30 days, the marine heatwave level is considered to be a moderate heatwave level (Level II); when the intensity of the marine heatwave in the target sea area is between (3, 4] and the duration of the marine heatwave is between 31-90 days, the marine heatwave level is considered to be a severe heatwave level (Level III); and when the intensity of the marine heatwave in the target sea area is greater than 4 and the duration is greater than 90 days, the marine heatwave level is considered to be an extreme heatwave level (Level IV).

[0023] It should be noted that during mild ocean heatwaves, the shallowing of the mixed layer leads to short-term nutrient replenishment and slight fluctuations in phytoplankton communities. During moderate ocean heatwaves, nutrient limitation becomes apparent, diatoms transform into dinoflagellates, and chlorophyll concentrations decrease by 20%-40%. During severe ocean heatwaves, photosynthetic efficiency decreases significantly, pheophytin increases, and ecosystem functions are impaired. During extreme ocean heatwaves, phytoplankton community structure collapses, microalgae account for more than 50%, and coral bleaching rates exceed 70%. The ecological response characteristics of phytoplankton to different ocean heatwave levels indicate that as the ocean heatwave level increases, the chlorophyll concentration of phytoplankton in the target sea area decreases, indicating that the ocean heatwave level will have an impact on the chlorophyll concentration value in the target sea area.

[0024] Based on the above division basis, when determining the ocean heat wave level of the target sea area, the historical sea surface temperature data within the preset monitoring period are first grouped in units of calendar days. In the present invention, the calendar day unit includes all calendar days in a complete year of non-leap years, that is, 365 calendar days corresponding to the period from January 1 to December 31. The historical sea surface temperature data for each calendar day is obtained through the above grouping. For example, when the preset monitoring period is 30 years, the obtained calendar day January 1 includes the historical sea surface temperature data for January 1 of each year within the preset monitoring period of 30 years. For each calendar day, the average value of the historical sea surface temperature data for the corresponding calendar day is calculated to obtain the ocean heat wave threshold for the current calendar day. The ocean heat wave threshold matrix for calendar days throughout the year is constructed based on the ocean heat wave threshold for each calendar day. According to the calendar days of the current monitoring period, the ocean heatwave threshold values ​​for each calendar day of the current monitoring period are obtained from the ocean heatwave threshold matrix. The sea surface temperature data for each calendar day of the current monitoring period are compared with the ocean heatwave threshold values ​​for the corresponding calendar days to determine the intensity of the ocean heatwave. The duration of the ocean heatwave in the current monitoring period can be determined based on the ocean heatwave intensity for each calendar day of the current monitoring period.

[0025] For example, assuming the current monitoring period is from January 2, 2025, to January 8, 2025, and the sea surface temperatures from January 2, 2025, to January 8, 2025, are a, b, c, d, e, f, g, and h, respectively. The ocean heatwave thresholds A, B, C, D, E, F, G, and H from January 2, 2025, to January 8, 2025, are obtained from the ocean heatwave threshold matrix. The difference between a and A is calculated to obtain the ocean heatwave intensity σ1 for January 2, 2025. Similarly, the ocean heatwave intensities from January 3, 2025, to January 8, 2025, are σ2, σ3, σ4, σ5, σ6, σ7, and σ8, respectively. The duration of the ocean heatwave intensity is determined based on the continuity of the values ​​between σ1-σ8. The current monitoring period is used here as an example only. In actual applications, the current monitoring period is typically 365 days to more accurately determine the intensity and duration of the ocean heatwave. After determining the ocean heat wave intensity and duration, the corresponding ocean heat wave level can be obtained by comparing them with the ranges of ocean heat wave intensity and duration of each ocean heat wave level in the above description.

[0026] The above steps are mainly processed in the classifier of the chlorophyll concentration determination model. After the classifier outputs the ocean heat wave level according to the above processing, it indexes the chlorophyll concentration determination sub-model corresponding to the ocean heat wave level, and inputs the ocean green ecological remote sensing image data into the chlorophyll concentration determination sub-model, so that the chlorophyll concentration determination sub-model extracts the corresponding multispectral remote sensing image features and ocean microwave remote sensing image features, and outputs the phytoplankton chlorophyll concentration value of the target sea area based on the multispectral remote sensing image features and the ocean microwave remote sensing image features.

[0027] In a preferred embodiment, the construction of the chlorophyll concentration determination model includes: Acquire a number of ocean heatwave data samples and a number of ocean green ecological remote sensing image data samples for each ocean heatwave level, and construct a training sample set based on each ocean heatwave data sample and each ocean green ecological remote sensing image data sample; wherein each ocean heatwave data sample includes: a historical sea surface temperature data sample within a preset monitoring period, a historical sea surface data sample within a selected monitoring period, and a calendar day of the selected monitoring period; and each ocean green ecological remote sensing image data sample includes: a multispectral remote sensing image sample and an ocean microwave remote sensing image sample; Constructing an initial chlorophyll concentration determination model; wherein the initial chlorophyll concentration determination model includes: a classifier, a first chlorophyll concentration determination sub-model, a second chlorophyll concentration determination sub-model, a third chlorophyll concentration determination sub-model, and a fourth chlorophyll concentration determination sub-model, wherein the first chlorophyll concentration determination sub-model, the second chlorophyll concentration determination sub-model, the third chlorophyll concentration determination sub-model, and the fourth chlorophyll concentration determination sub-model correspond to a mild heat wave level, a moderate heat wave level, a severe heat wave level, and an extreme heat wave level, respectively; The initial chlorophyll concentration determination model is trained using the training sample set until the initial chlorophyll concentration determination model converges, thereby generating the chlorophyll concentration determination model.

[0028] Specifically, after obtaining the training sample set, construct Figure 2 The initial chlorophyll concentration determination model shown in the figure includes a classifier and four chlorophyll concentration determination sub-models, wherein the first chlorophyll concentration determination sub-model corresponds to the mild heat wave level, and is used to specifically determine the chlorophyll concentration of phytoplankton in the target sea area under the mild heat wave level; the second chlorophyll concentration determination sub-model corresponds to the moderate heat wave level, and is used to specifically determine the chlorophyll concentration of phytoplankton in the target sea area under the moderate heat wave level; the third chlorophyll concentration determination sub-model corresponds to the severe heat wave level, and is used to specifically determine the chlorophyll concentration of phytoplankton in the target sea area under the severe heat wave level; the fourth chlorophyll concentration determination sub-model corresponds to the extreme heat wave level, and is used to specifically determine the chlorophyll concentration of phytoplankton in the target sea area under the extreme heat wave level; the role of the classifier is to determine the ocean heat wave level according to the ocean heat wave data, and then index the ocean green ecological remote sensing image into the chlorophyll concentration determination sub-model corresponding to the ocean heat wave level for processing. After completing the construction of the initial chlorophyll concentration determination model, the initial chlorophyll concentration determination model is trained using the constructed training sample set until the loss function of the initial chlorophyll concentration determination model is minimized or the maximum number of iterations is reached, that is, when the initial chlorophyll concentration determination model converges, the above-mentioned chlorophyll concentration determination model is generated.

[0029] In a preferred embodiment, the initial chlorophyll concentration determination model is trained using the training sample set until the initial chlorophyll concentration determination model converges, thereby generating the chlorophyll concentration determination model, including: Freezing network parameters of each chlorophyll concentration determination sub-model, training a classifier of the initial chlorophyll concentration determination model with each ocean heat wave data sample until the classifier of the initial chlorophyll concentration determination model converges, freezing the network parameters of the classifier of the initial chlorophyll concentration determination model, and unfreezing the network parameters of each chlorophyll concentration determination sub-model; Training each chlorophyll concentration determination sub-model using each marine green ecological remote sensing image data sample until each chlorophyll concentration determination sub-model converges, and then unfreezing the network parameters of the classifier; The classifier and each chlorophyll concentration determination sub-model are trained using a training sample set until the initial chlorophyll concentration determination model is converged as a whole, thereby generating the chlorophyll concentration determination model.

[0030] Specifically, the training of the initial chlorophyll concentration determination model is divided into preliminary training of the classifier, preliminary training of each chlorophyll concentration determination sub-model and overall fine-tuning of the initial chlorophyll concentration determination model. This training method determines the network parameters through step-by-step training and then fine-tunes them, which can reduce the dependence of the initial chlorophyll concentration determination model training on the number of training samples, reduce the amount of training data, and improve training efficiency.

[0031] In a preferred embodiment, the training of the classifier of the initial chlorophyll concentration determination model using each ocean heat wave data sample includes: inputting the ocean heat wave data sample into the classifier so that the classifier determines the ocean heat wave intensity and ocean heat wave duration corresponding to the current ocean heat wave data sample; and outputting a predicted ocean heat wave level for the current ocean heat wave data sample based on the ocean heat wave intensity and ocean heat wave duration corresponding to the current ocean heat wave data sample.

[0032] Specifically, during the initial training of the classifier, the network parameters of each chlorophyll concentration determination sub-model must first be frozen, and only the network parameters of the classifier must be trained. Secondly, during training, the marine heatwave data samples are used as the input of the classifier of the initial chlorophyll concentration determination model, and the predicted marine heatwave level corresponding to the marine heatwave data samples is used as the output of the classifier of the initial chlorophyll concentration determination model. The classifier of the initial chlorophyll concentration determination model is iteratively trained until the classifier of the initial chlorophyll concentration determination model converges. At this point, the network parameters of the classifier of the initial chlorophyll concentration determination model are frozen. During each iterative training process, the classifier of the initial chlorophyll concentration determination model determines the ocean heatwave intensity and ocean heatwave duration corresponding to the current ocean heatwave data sample based on the ocean heatwave data sample and the above-mentioned method for determining the ocean heatwave intensity and ocean heatwave duration, and then outputs a predicted ocean heatwave level based on the ocean heatwave intensity and ocean heatwave duration corresponding to the current ocean heatwave data sample and the above-mentioned ocean heatwave level determination rule. The classification loss of the current iterative training is determined based on the predicted ocean heatwave level and the actual predicted ocean heatwave level of the current ocean heatwave data sample, and the network parameters of the classifier are adjusted based on the classification loss to improve the prediction accuracy of the next iteration; wherein, the actual predicted ocean heatwave level of the current ocean heatwave data sample is determined based on historical monitoring data and annotated on the current ocean heatwave data sample. The above-mentioned training process of the classifier is a supervised learning process.

[0033] In a preferred embodiment, the training of each chlorophyll concentration determination sub-model using each marine green ecological remote sensing image data sample includes: Inputting the marine ecological remote sensing image data sample into the first chlorophyll concentration determination sub-model, so that the first chlorophyll concentration determination sub-model extracts the spectral reflectance of the blue light band and the spectral reflectance of the green light band of the multispectral remote sensing image sample, and determines a first spectral reflectance ratio according to the spectral reflectance of the blue light band and the spectral reflectance of the green light band; extracting the first microwave radiation feature of the marine microwave remote sensing image sample, and determining the mixed layer depth according to the first microwave radiation feature; outputting the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample according to the mixed layer depth and the first spectral reflectance ratio; Inputting the marine ecological remote sensing image data sample into the second chlorophyll concentration determination sub-model, so that the second chlorophyll concentration determination sub-model extracts the spectral reflectance of the red light band, the spectral reflectance of the green light band and the chlorophyll fluorescence peak of the multispectral remote sensing image sample, and determines the second spectral reflectance ratio according to the spectral reflectance of the red light band and the spectral reflectance of the green light band; extracting the second microwave radiation characteristics of the marine microwave remote sensing image sample, and determining the sea surface height anomaly according to the second microwave radiation characteristics; outputting the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample according to the second spectral reflectance ratio, the sea surface height anomaly and the chlorophyll fluorescence peak; Inputting the marine ecological remote sensing image data sample into the third chlorophyll concentration determination sub-model, so that the third chlorophyll concentration determination sub-model extracts the spectral reflectance of the red light band, the spectral reflectance of the near infrared band, and the chlorophyll fluorescence signal of the multispectral remote sensing image sample, determines a third spectral reflectance ratio according to the spectral reflectance of the red light band and the spectral reflectance of the near infrared band, and determines the photosynthetic efficiency of the target sea area according to the chlorophyll fluorescence signal; outputting a predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample according to the third spectral reflectance ratio and the photosynthetic efficiency; The marine ecological remote sensing image data sample is input into the fourth chlorophyll concentration determination sub-model so that the fourth chlorophyll concentration determination sub-model extracts the spectral reflectance of the near-infrared band and the thermal infrared signal of the multispectral remote sensing image sample; the thermal infrared sea surface temperature peak of the target sea area is determined according to the thermal infrared signal, and the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample is output according to the thermal infrared sea surface temperature peak, the spectral reflectance of the near-infrared band and the preset chlorophyll background value.

[0034] Specifically, after completing preliminary training of the classifier for the initial chlorophyll concentration determination model, the classifier's network parameters are frozen, and the network parameters of each chlorophyll concentration determination sub-model are unfrozen. Because each chlorophyll concentration determination sub-model is independent, they can be trained in parallel.

[0035] When training each chlorophyll concentration determination sub-model, the marine ecological remote sensing image data samples are used as the input of the chlorophyll concentration determination sub-model, and the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data samples is used as the output of the chlorophyll concentration determination sub-model, and only the extracted features are differentiated for training.

[0036] When training the first chlorophyll concentration determination sub-model, in each iterative training process, the marine ecological remote sensing image data sample is used as the input of the first chlorophyll concentration determination sub-model, so that the first chlorophyll concentration determination sub-model extracts the spectral reflectance of the blue light band and the spectral reflectance of the green light band of the multispectral remote sensing image sample, and calculates the ratio between the spectral reflectance of the blue light band and the spectral reflectance of the green light band according to the spectral reflectance of the blue light band and the spectral reflectance of the green light band (that is, the above-mentioned first spectral reflectance ratio), and this ratio is the initial chlorophyll concentration value; in addition, the first chlorophyll concentration determination sub-model The model also extracts the first microwave radiation characteristics of the ocean microwave remote sensing image sample, and then determines the mixed layer depth based on the first microwave radiation characteristics, corrects the initial chlorophyll concentration value by the mixed layer depth, and outputs the predicted phytoplankton chlorophyll concentration value corresponding to the ocean ecological remote sensing image data sample; determines the first prediction loss of the first chlorophyll concentration determination submodel based on the predicted phytoplankton chlorophyll concentration value and the actual phytoplankton chlorophyll concentration value of the ocean ecological remote sensing image data sample, and adjusts the network parameters of the first chlorophyll concentration determination submodel based on the first prediction loss to optimize the prediction accuracy of the next iterative training.

[0037] When training the second chlorophyll concentration determination sub-model, in each iterative training process, the marine ecological remote sensing image data sample is used as the input of the second chlorophyll concentration determination sub-model, so that the second chlorophyll concentration determination sub-model extracts the spectral reflectance of the red light band, the spectral reflectance of the green light band, and the chlorophyll fluorescence peak of the multispectral remote sensing image sample, and determines a second spectral reflectance ratio (i.e., the initial chlorophyll concentration under the spectrum) based on the spectral reflectance of the red light band and the spectral reflectance of the green light band; extracts a second microwave radiation feature of the marine microwave remote sensing image sample, and determines a sea surface height anomaly based on the second microwave radiation feature; outputs a predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample based on the second spectral reflectance ratio, the sea surface height anomaly, and the chlorophyll fluorescence peak; determines a second prediction loss of the second chlorophyll concentration determination sub-model based on the predicted phytoplankton chlorophyll concentration value and the actual phytoplankton chlorophyll concentration value of the marine ecological remote sensing image data sample, and adjusts the network parameters of the second chlorophyll concentration determination sub-model based on the second prediction loss to optimize the prediction accuracy of the next iterative training.

[0038] In a preferred embodiment, the outputting of the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample according to the second spectral reflectance ratio, the sea level height anomaly value and the chlorophyll fluorescence peak value comprises: Constructing a first correlation between the second spectral reflectance ratio and the chlorophyll concentration, constructing a second correlation between the sea level height anomaly and the chlorophyll concentration, and constructing a third correlation between the chlorophyll fluorescence peak and the chlorophyll concentration; A spatial rectangular coordinate system is established with the first association relationship as the horizontal axis, the second association relationship as the vertical axis, and the third association relationship as the vertical axis; wherein the plane formed by the horizontal axis and the vertical axis is the first plane, the plane formed by the horizontal axis and the vertical axis is the second plane, and the plane formed by the vertical axis and the vertical axis is the third plane; constructing a first correlation vector between the second spectral reflectance ratio, the sea level height anomaly, and the chlorophyll concentration, constructing a second correlation vector between the second spectral reflectance ratio, the chlorophyll fluorescence peak, and the chlorophyll concentration, and constructing a third correlation vector between the sea level height anomaly, the chlorophyll fluorescence peak, and the chlorophyll concentration; Projecting the first correlation vector, the second correlation vector, and the third correlation vector on the first plane, the second plane, and the third plane, respectively, to obtain a first projection of the first correlation vector on the first plane, a second projection on the second plane, and a third projection on the third plane; a fourth projection of the second correlation vector on the first plane, a fifth projection on the second plane, and a sixth projection on the second plane; a seventh projection of the third correlation vector on the first plane, an eighth projection on the second plane, and a ninth projection on the third plane; The first, fourth and seventh projections are superimposed to obtain a first superimposed projection, the second, fifth and eighth projections are superimposed to obtain a second superimposed projection, and the third, sixth and ninth projections are superimposed to obtain a third superimposed projection. The first, second and third superimposed projections are converted into vector form to obtain a first superimposed vector, a second superimposed vector and a third superimposed vector. The predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample is output through the first superimposed vector, the second superimposed vector and the third superimposed vector.

[0039] Specifically, by constructing the correlation between the second spectral reflectance ratio, sea level anomaly, chlorophyll fluorescence peak and chlorophyll concentration respectively, the core influence of each parameter is extracted separately to avoid the one-sided reflection of complex ecological relationships by a single parameter; the three-dimensional correlation relationship is then mapped to the spatial rectangular coordinate system and plane projection to intuitively present the interaction between parameters (such as the synergistic influence of sea level anomaly and chlorophyll fluorescence peak), breaking through the limitations of traditional linear models in expressing nonlinear relationships; by projecting and superimposing vector information of different dimensions, the complementarity of each parameter (second spectral reflectance ratio, sea level anomaly, chlorophyll fluorescence peak and chlorophyll concentration) is enhanced while the measurement error of a single parameter is weakened. Finally, by superimposing vectors to synthesize the comprehensive results, the predicted value simultaneously carries the multi-dimensional characteristics of the spectral reflectance ratio (second spectral reflectance ratio), physical environment (sea level anomaly), and physiological activity (chlorophyll fluorescence peak), which is more in line with the actual ecological mechanism by which phytoplankton chlorophyll concentration is affected by the synergistic influence of multiple factors, thereby improving the prediction accuracy.

[0040] When training the third chlorophyll concentration determination sub-model, in each iterative training process, the marine ecological remote sensing image data sample is used as the input of the third chlorophyll concentration determination sub-model, so that the third chlorophyll concentration determination sub-model extracts the spectral reflectance of the red light band, the spectral reflectance of the near-infrared band, and the chlorophyll fluorescence signal of the multispectral remote sensing image sample, determines a third spectral reflectance ratio (i.e., the initial chlorophyll concentration under the spectrum) based on the spectral reflectance of the red light band and the spectral reflectance of the near-infrared band, and determines the photosynthetic efficiency of the target sea area based on the chlorophyll fluorescence signal; outputs the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample based on the third spectral reflectance ratio and the photosynthetic efficiency; determines a third prediction loss of the third chlorophyll concentration determination sub-model based on the predicted phytoplankton chlorophyll concentration value and the actual phytoplankton chlorophyll concentration value of the marine ecological remote sensing image data sample, and adjusts the network parameters of the third chlorophyll concentration determination sub-model based on the third prediction loss to optimize the prediction accuracy of the next iterative training.

[0041] When training the fourth chlorophyll concentration determination sub-model, in each iterative training process, the marine ecological remote sensing image data sample is used as the input of the fourth chlorophyll concentration determination sub-model, so that the fourth chlorophyll concentration determination sub-model extracts the spectral reflectance of the near-infrared band of the multispectral remote sensing image sample (that is, the initial chlorophyll concentration under the spectrum) and the thermal infrared signal; the thermal infrared sea surface temperature peak of the target sea area is determined according to the thermal infrared signal, and the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample is output according to the thermal infrared sea surface temperature peak, the spectral reflectance of the near-infrared band and the preset chlorophyll background value; the fourth prediction loss of the fourth chlorophyll concentration determination sub-model is determined according to the predicted phytoplankton chlorophyll concentration value and the actual phytoplankton chlorophyll concentration value of the marine ecological remote sensing image data sample, and the network parameters of the fourth chlorophyll concentration determination sub-model are adjusted according to the fourth prediction loss to optimize the prediction accuracy of the next iterative training.

[0042] After completing the training of each chlorophyll concentration determination sub-model, the network parameters of the classifier are unfrozen, and the initial chlorophyll concentration determination sub-model is fine-tuned as a whole using the training sample set.

[0043] In a preferred embodiment, the training of the classifier and each chlorophyll concentration determination sub-model using a training sample set until the initial chlorophyll concentration determination model converges as a whole, thereby generating the chlorophyll concentration determination model, comprises: Grouping each ocean heat wave data sample and each ocean green ecological remote sensing image data sample in the training sample set based on the ocean heat wave level to obtain a training sample subset under each ocean heat wave level; wherein the training sample subset includes the ocean heat wave data samples and the ocean green ecological remote sensing image data samples under the current ocean heat wave level; The classifier and the chlorophyll concentration determination sub-model of the ocean heat wave level corresponding to each training sample subset are trained with each training sample subset until the initial chlorophyll concentration determination model is converged as a whole, thereby generating the chlorophyll concentration determination model.

[0044] Specifically, the classifier and the chlorophyll concentration determination sub-model of the ocean heat wave level corresponding to each training sample subset are iteratively trained with each training sample subset, and the joint loss of the classifier and each chlorophyll concentration determination sub-model under joint training is determined. The loss function of the initial chlorophyll concentration determination model is constructed according to the joint loss, classification loss, first prediction loss, second prediction loss, third prediction loss and fourth prediction loss. When the sum of the loss functions of the initial chlorophyll concentration determination model is minimized, the initial chlorophyll concentration determination model converges and a chlorophyll concentration determination model is generated.

[0045] For step S3, after determining the phytoplankton chlorophyll concentration value of the target sea area, the phytoplankton chlorophyll concentration value of the target sea area is transmitted to the marine ecological monitoring system. Preferably, data such as the marine heat wave level, marine heat wave intensity and marine heat wave duration of the target sea area can be synchronously transmitted to the marine ecological monitoring system, so that the marine ecological monitoring system monitors the target sea area according to the received phytoplankton chlorophyll concentration value, marine heat wave level, marine heat wave intensity and marine heat wave duration, and when the phytoplankton chlorophyll concentration value is lower than the preset threshold value, corresponding measures are taken in time to maintain fishery farming in the target sea area.

[0046] Based on the above method embodiment, the present invention provides a corresponding terminal device embodiment.

[0047] An embodiment of the present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, a method for determining the chlorophyll concentration value of phytoplankton based on ocean heat waves as described in any one of the present inventions is implemented.

[0048] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0049] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the terminal device and connects various parts of the entire terminal device using various interfaces and lines.

[0050] The memory can be used to store the computer program. The processor implements the various functions of the terminal device by running or executing the computer program stored in the memory and accessing the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store an operating system, at least one application required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0051] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment.

[0052] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a method for determining the chlorophyll concentration value of phytoplankton based on ocean heat waves as described in any one of the present inventions.

[0053] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium.

[0054] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for determining phytoplankton chlorophyll concentration based on ocean heat waves, characterized in that: include: Acquire marine green ecological remote sensing image data and marine heat wave data of the target sea area; wherein the marine heat wave data includes: historical sea surface temperature data within a preset monitoring period, sea surface temperature data for the current monitoring period, and calendar days of the current monitoring period; the marine green ecological remote sensing image data includes: multispectral remote sensing images and marine microwave remote sensing images; Inputting the ocean heat wave data and the ocean green ecology remote sensing image data of the target sea area into a chlorophyll concentration determination model, so that the chlorophyll concentration determination model determines the ocean heat wave level of the target sea area according to the ocean heat wave data, and inputting the ocean green ecology remote sensing image data into a chlorophyll concentration determination sub-model according to the ocean heat wave level, so that the chlorophyll concentration determination sub-model extracts corresponding multispectral remote sensing image features and ocean microwave remote sensing image features, and outputs a phytoplankton chlorophyll concentration value of the target sea area according to the multispectral remote sensing image features and the ocean microwave remote sensing image features; The chlorophyll concentration value of the phytoplankton in the target sea area is transmitted to the marine ecological monitoring system to assist the marine ecological monitoring system in performing sea area monitoring.

2. The method for determining phytoplankton chlorophyll concentration based on ocean heat waves according to claim 1, wherein: The marine heat wave levels include: mild heat wave level, moderate heat wave level, severe heat wave level and extreme heat wave level; Determining the ocean heat wave level of the target sea area according to the ocean heat wave data includes: The historical sea surface temperature data within the preset monitoring period are grouped by calendar day, and the historical sea surface temperature data for each calendar day are obtained: For each calendar day, the average of the historical sea surface temperature data for the current calendar day is calculated to obtain the ocean heat wave threshold for the current calendar day; Construct the ocean heatwave threshold matrix for calendar days throughout the year based on the ocean heatwave threshold for each calendar day; Obtaining, from the ocean heat wave threshold matrix according to the calendar day of the current monitoring period, the ocean heat wave threshold for each calendar day of the current monitoring period; The ocean heat wave intensity and duration of the target sea area during the current monitoring period are determined based on the sea surface temperature data of the current monitoring period and the ocean heat wave thresholds of each calendar day during the current monitoring period, and the ocean heat wave level of the target sea area is determined based on the ocean heat wave intensity and duration of the target sea area.

3. The method for determining phytoplankton chlorophyll concentration based on ocean heat waves according to claim 2, wherein: The construction of the chlorophyll concentration determination model includes: Acquire a number of ocean heatwave data samples and a number of ocean green ecological remote sensing image data samples for each ocean heatwave level, and construct a training sample set based on each ocean heatwave data sample and each ocean green ecological remote sensing image data sample; wherein each ocean heatwave data sample includes: a historical sea surface temperature data sample within a preset monitoring period, a historical sea surface data sample within a selected monitoring period, and a calendar day of the selected monitoring period; and each ocean green ecological remote sensing image data sample includes: a multispectral remote sensing image sample and an ocean microwave remote sensing image sample; Constructing an initial chlorophyll concentration determination model; wherein the initial chlorophyll concentration determination model includes: a classifier, a first chlorophyll concentration determination sub-model, a second chlorophyll concentration determination sub-model, a third chlorophyll concentration determination sub-model, and a fourth chlorophyll concentration determination sub-model, wherein the first chlorophyll concentration determination sub-model, the second chlorophyll concentration determination sub-model, the third chlorophyll concentration determination sub-model, and the fourth chlorophyll concentration determination sub-model correspond to a mild heat wave level, a moderate heat wave level, a severe heat wave level, and an extreme heat wave level, respectively; The initial chlorophyll concentration determination model is trained using the training sample set until the initial chlorophyll concentration determination model converges, thereby generating the chlorophyll concentration determination model.

4. The method for determining phytoplankton chlorophyll concentration based on ocean heat waves according to claim 3, wherein: The initial chlorophyll concentration determination model is trained using the training sample set until the initial chlorophyll concentration determination model converges, thereby generating the chlorophyll concentration determination model, including: Freezing network parameters of each chlorophyll concentration determination sub-model, training a classifier of the initial chlorophyll concentration determination model with each ocean heat wave data sample until the classifier of the initial chlorophyll concentration determination model converges, freezing the network parameters of the classifier of the initial chlorophyll concentration determination model, and unfreezing the network parameters of each chlorophyll concentration determination sub-model; Training each chlorophyll concentration determination sub-model using each marine green ecological remote sensing image data sample until each chlorophyll concentration determination sub-model converges, and then unfreezing the network parameters of the classifier; The classifier and each chlorophyll concentration determination sub-model are trained using a training sample set until the initial chlorophyll concentration determination model is converged as a whole, thereby generating the chlorophyll concentration determination model.

5. The method for determining phytoplankton chlorophyll concentration based on ocean heat waves according to claim 4, wherein: The training of the classifier of the initial chlorophyll concentration determination model using each ocean heat wave data sample includes: Inputting the ocean heat wave data sample into the classifier, so that the classifier determines the ocean heat wave intensity and the ocean heat wave duration corresponding to the current ocean heat wave data sample; The predicted ocean heat wave level of the current ocean heat wave data sample is output according to the ocean heat wave intensity and ocean heat wave duration corresponding to the current ocean heat wave data sample.

6. The method for determining phytoplankton chlorophyll concentration based on ocean heat waves according to claim 5, wherein: The training of each chlorophyll concentration determination sub-model using each marine green ecological remote sensing image data sample includes: Inputting the marine ecological remote sensing image data sample into the first chlorophyll concentration determination sub-model, so that the first chlorophyll concentration determination sub-model extracts the spectral reflectance of the blue light band and the spectral reflectance of the green light band of the multispectral remote sensing image sample, and determines a first spectral reflectance ratio according to the spectral reflectance of the blue light band and the spectral reflectance of the green light band; extracting the first microwave radiation feature of the marine microwave remote sensing image sample, and determining the mixed layer depth according to the first microwave radiation feature; outputting the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample according to the mixed layer depth and the first spectral reflectance ratio; Inputting the marine ecological remote sensing image data sample into the second chlorophyll concentration determination sub-model, so that the second chlorophyll concentration determination sub-model extracts the spectral reflectance of the red light band, the spectral reflectance of the green light band and the chlorophyll fluorescence peak of the multispectral remote sensing image sample, and determines the second spectral reflectance ratio according to the spectral reflectance of the red light band and the spectral reflectance of the green light band; extracting the second microwave radiation characteristics of the marine microwave remote sensing image sample, and determining the sea surface height anomaly according to the second microwave radiation characteristics; outputting the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample according to the second spectral reflectance ratio, the sea surface height anomaly and the chlorophyll fluorescence peak; Inputting the marine ecological remote sensing image data sample into the third chlorophyll concentration determination sub-model, so that the third chlorophyll concentration determination sub-model extracts the spectral reflectance of the red light band, the spectral reflectance of the near infrared band, and the chlorophyll fluorescence signal of the multispectral remote sensing image sample, determines a third spectral reflectance ratio according to the spectral reflectance of the red light band and the spectral reflectance of the near infrared band, and determines the photosynthetic efficiency of the target sea area according to the chlorophyll fluorescence signal; outputting a predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample according to the third spectral reflectance ratio and the photosynthetic efficiency; The marine ecological remote sensing image data sample is input into the fourth chlorophyll concentration determination sub-model so that the fourth chlorophyll concentration determination sub-model extracts the spectral reflectance of the near-infrared band and the thermal infrared signal of the multispectral remote sensing image sample; the thermal infrared sea surface temperature peak of the target sea area is determined according to the thermal infrared signal, and the predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample is output according to the thermal infrared sea surface temperature peak, the spectral reflectance of the near-infrared band and the preset chlorophyll background value.

7. The method for determining phytoplankton chlorophyll concentration based on ocean heat waves according to claim 6, wherein: The classifier and each chlorophyll concentration determination sub-model are trained with a training sample set until the initial chlorophyll concentration determination model is converged as a whole, thereby generating the chlorophyll concentration determination model, including: Grouping each ocean heat wave data sample and each ocean green ecological remote sensing image data sample in the training sample set based on the ocean heat wave level to obtain a training sample subset under each ocean heat wave level; wherein the training sample subset includes the ocean heat wave data samples and the ocean green ecological remote sensing image data samples under the current ocean heat wave level; The classifier and the chlorophyll concentration determination sub-model of the ocean heat wave level corresponding to each training sample subset are trained with each training sample subset until the initial chlorophyll concentration determination model is converged as a whole, thereby generating the chlorophyll concentration determination model.

8. The method for determining phytoplankton chlorophyll concentration based on ocean heat waves according to claim 7, wherein: The method of outputting a predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample according to the second spectral reflectance ratio, the sea level height anomaly value, and the chlorophyll fluorescence peak value comprises: Constructing a first correlation between the second spectral reflectance ratio and the chlorophyll concentration, constructing a second correlation between the sea level height anomaly and the chlorophyll concentration, and constructing a third correlation between the chlorophyll fluorescence peak and the chlorophyll concentration; A spatial rectangular coordinate system is established with the first association relationship as the horizontal axis, the second association relationship as the vertical axis, and the third association relationship as the vertical axis; wherein the plane formed by the horizontal axis and the vertical axis is the first plane, the plane formed by the horizontal axis and the vertical axis is the second plane, and the plane formed by the vertical axis and the vertical axis is the third plane; constructing a first correlation vector between the second spectral reflectance ratio, the sea level height anomaly, and the chlorophyll concentration, constructing a second correlation vector between the second spectral reflectance ratio, the chlorophyll fluorescence peak, and the chlorophyll concentration, and constructing a third correlation vector between the sea level height anomaly, the chlorophyll fluorescence peak, and the chlorophyll concentration; Projecting the first correlation vector, the second correlation vector, and the third correlation vector on the first plane, the second plane, and the third plane, respectively, to obtain a first projection of the first correlation vector on the first plane, a second projection on the second plane, and a third projection on the third plane; a fourth projection of the second correlation vector on the first plane, a fifth projection on the second plane, and a sixth projection on the second plane; a seventh projection of the third correlation vector on the first plane, an eighth projection on the second plane, and a ninth projection on the third plane; The first, fourth and seventh projections are superimposed to obtain a first superimposed projection, the second, fifth and eighth projections are superimposed to obtain a second superimposed projection, and the third, sixth and ninth projections are superimposed to obtain a third superimposed projection. The first, second and third superimposed projections are converted into vector form to obtain a first superimposed vector, a second superimposed vector and a third superimposed vector. The predicted phytoplankton chlorophyll concentration value corresponding to the marine ecological remote sensing image data sample is output through the first superimposed vector, the second superimposed vector and the third superimposed vector.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for determining the chlorophyll concentration value of phytoplankton based on marine heat waves as described in any one of claims 1 to 8 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method for determining the chlorophyll concentration value of phytoplankton based on marine heat waves as described in any one of claims 1 to 8.

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