An adaptive sky-ground-water integrated water bloom monitoring method and platform

Abstract

The application provides a self-adaptive sky-ground-water integrated water bloom monitoring method and platform, which is used for, on the basis of using unmanned aerial vehicles, satellite remote sensing, near-ground hyperspectrum and other stereoscopic monitoring means to establish a water bloom intelligent identification facility of dense time series sky-ground-water collaborative observation, combining a dynamic calibration mechanism of capturing a dynamic mapping relationship from inherent optical properties to apparent optical properties, to realize adaptive calibration of algal spectral feature positions, and then promote real-time, efficient and accurate intelligent water bloom identification processing, to present water bloom change trends in a multi-dimensional, multi-perspective and multi-scale manner in a space-time distribution mode, and to provide effective data support for water bloom early warning of major water sources.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of water quality monitoring, in particular to a self-adaptive sky-ground-water integrated water bloom monitoring method and platform. BACKGROUND

[0002] The existing water bloom monitoring system of large water sources mainly relies on manual monitoring, which is mainly monthly and quarterly. The monitoring frequency is low, the monitoring cost is high, the monitoring time is long, the time and space range of the collected samples is limited, and it is difficult to provide the spatial and temporal distribution pattern of water quality safety of the whole reservoir.

[0003] Therefore, an intelligent water bloom monitoring platform is of great significance.

[0004] However, the present inventors have found that some existing technologies use spectral index method and target recognition method to intelligently identify water bloom, but these methods have the following significant shortcomings:

[0005] 1) The threshold value required by the spectral index method cannot automatically adapt to different monitoring equipment, and is affected by atmospheric correction algorithm, weather, water depth, and sensor differences, and needs to be adjusted manually in real time.

[0006] 2) The target recognition method uses deep learning based on RGB images to identify water bloom, but some algae species such as pseudocohnia have very weak optical signals, and their color features in the RGB image are very weak, so it is easy to miss the mild and moderate levels of algal bloom. SUMMARY

[0007] The present application provides a self-adaptive sky-ground-water integrated water bloom monitoring method and platform, which is used to establish an intelligent water bloom identification facility based on the use of unmanned aerial vehicles, satellite remote sensing, near-ground hyperspectral and other stereoscopic monitoring means to establish a dense time series of sky-ground-water collaborative observation of water bloom, and combines a dynamic calibration mechanism that captures the dynamic mapping relationship between inherent optical properties and apparent optical properties to realize adaptive calibration of algal spectral feature positions, and further promotes real-time, efficient and accurate intelligent water bloom identification processing, and presents the water bloom trend in a multi-dimensional, multi-view and multi-scale spatial and temporal distribution manner, and provides effective data support for water bloom warning of major water sources.

[0008] In a first aspect, the present application provides a self-adaptive sky-ground-water integrated water bloom monitoring method, which comprises:

[0009] For a target water body area where a sky-ground-water integrated water bloom monitoring platform is deployed, an apparent optical quantity data to be processed by a current monitoring task is acquired by a data acquisition part including a shipborne monitoring facility, a vehicle-mounted monitoring facility, a shore-based monitoring facility and a satellite remote sensing monitoring facility;

[0010] The buffer zone is constructed according to the fluorescence peak band position of the phytoplankton particle absorption coefficient of the target water body region and the valley band position of the phytoplankton particle absorption coefficient of the inherent optical property;

[0011] On the basis of the apparent optical quantity data, the iterative search is performed in combination with the buffer zone to locate the off-water reflection valley band position and the off-water reflection peak band position of the phytoplankton signal in the apparent optical quantity data;

[0012] The water bloom quantitative index value of the target water body region is determined based on the off-water reflection valley band position, the off-water reflection peak band position and the reflectivity of the two.

[0013] In combination with the water bloom quantitative index value and the index threshold value, it is determined whether the target water body region has the water bloom phenomenon.

[0014] In combination with the first aspect of the present application, in a first possible implementation manner of the first aspect of the present application, the method further includes:

[0015] In the wave band range of 650nm-750nm, the index of the phytoplankton particle absorption coefficient is calculated to determine the index of the first candidate fluorescence peak band position greater than 0:

[0016] ,

[0017] wherein, is the phytoplankton particle absorption coefficient at the wave band position represented by the wavelength 1, the unit of is nm;

[0018] In the wave band range of 650nm-750nm, the index of the phytoplankton particle absorption coefficient is calculated to determine the index of the second candidate fluorescence peak band position less than 0:

[0019] ;

[0020] In the first candidate fluorescence peak band position and the second candidate fluorescence peak band position, the wave band position with the maximum absorption coefficient value is taken as the fluorescence peak band position and output, and the wave band position with the minimum absorption coefficient value is taken as the valley band position and output.

[0021] In conjunction with the first possible implementation of the first aspect of this application, in the second possible implementation of the first aspect of this application, based on the apparent phytoscopic data, an iterative search is performed using a buffer zone to locate the positions of the water-leaving reflection valley band and the water-leaving reflection front band of the phytoplankton signal in the apparent phytoscopic data, including:

[0022] For apparent optical quantity data falling within the first buffer zone of 15 nm before and after the fluorescence peak position, the indices for different wavelengths are calculated using the following formula. To obtain indicators Location of the first candidate water-out reflection valley band with a value greater than 0:

[0023] ,

[0024] in, For wavelength 2 represents the reciprocal of the water reflectivity at the wavelength location. The unit is nm. ;

[0025] For apparent optical quantity data falling within the first buffer zone, the indices for different bands are calculated using the following formula. To obtain indicators Location of the second candidate water-out reflection valley band with a value less than 0:

[0026] ;

[0027] The overlapping band positions of the first and second candidate water-leaving reflection valley positions are used as the water-leaving reflection valley positions of the phytoplankton signal and output, where the wavelength of the overlapping band position is... ;

[0028] For apparent optical quantity data falling within the second buffer zone (15 nm expansion before and after the trough band position), the indices for different bands are calculated using the following formula. To obtain indicators Location of the first candidate water-leaving reflection front with a value greater than 0:

[0029] ,

[0030] in, For wavelength The water reflectivity at the indicated wavelength position The unit is nm. ;

[0031] For apparent optical quantity data falling within the second buffer zone, the indices for different bands are calculated using the following formula. To obtain indicators Location of the second candidate water-leaving reflection front with a value less than 0:

[0032] ;

[0033] The overlapping band positions of the first and second candidate water-leaving reflection fronts are used as the water-leaving reflection front positions of the phytoplankton signal and output as such. The wavelength of the overlapping band position is... .

[0034] In conjunction with the second possible implementation of the first aspect of this application, in the third possible implementation of the first aspect of this application, the quantitative index value of algal bloom in the target water body area is determined based on the location of the water-leaving reflection valley band, the location of the water-leaving reflection front band, and the reflectivity of both, including:

[0035] Based on the location of the water-leaving reflection valley band, the location of the water-leaving reflection front band, and the reflectivity of both, the quantitative index value of algal bloom in the target water body area is determined by the following formula:

[0036] ,

[0037] Where k is the quantitative index value of algal bloom, This represents the reflectivity of the waveband at the location of the water-leaving reflection front, and the maximum value (Max) is taken when there are multiple locations of the water-leaving reflection front. Substitute the band position PkIndex into the code and use it. This represents the reflectivity of the water-out-of-water reflection valley location, and when there are multiple water-out-of-water reflection valley locations, the maximum value Max( Substitute the band position (GuIndex) into the input;

[0038] Correspondingly, step S105 combines the quantitative index value and the index threshold to determine whether algal blooms exist in the target water body area, including:

[0039] If k>0, then the target water body area is determined to have an algal bloom phenomenon;

[0040] If k≤0, then it is determined that there is no algal bloom phenomenon in the target water body area.

[0041] In conjunction with the first aspect of this application, in the fourth possible implementation of the first aspect of this application, the method is applied to an integrated air-ground-water algal bloom monitoring platform, which includes a data acquisition part and an information platform as a data processing part.

[0042] The shipborne monitoring facilities include a first hyperspectral water quality multi-parameter analyzer, a first multi-parameter water quality analyzer, and a first rotary-wing UAV, all configured with the water quality monitoring vessel as the core. The first rotary-wing UAV is equipped with a first hyperspectral imager.

[0043] The vehicle-mounted monitoring facilities include a fully automated unmanned boat with an emergency monitoring vehicle as its core configuration and a second rotary-wing drone. The fully automated unmanned boat is equipped with a second multi-parameter water quality analyzer, and the second rotary-wing drone is equipped with a second hyperspectral imager.

[0044] The shore-based monitoring facility includes a third hyperspectral water quality multi-parameter analyzer mounted on top of a vertical support and a gray-white panel, which is used for data correction of the third hyperspectral water quality multi-parameter analyzer.

[0045] In conjunction with the fourth possible implementation of the first aspect of this application, in the fifth possible implementation of the first aspect of this application, the shipborne monitoring facility stores the first monitoring data in the server database of the information platform through the industrial control computer on the ship;

[0046] The vehicle-mounted monitoring equipment stores the second monitoring data in a server database through a switch or router on the vehicle.

[0047] The shore-based monitoring facilities store third-party monitoring data in a server database via a wireless network card;

[0048] Satellite remote sensing monitoring facilities store fourth-order monitoring data in a server database through an interface that connects to the images.

[0049] In conjunction with the fourth possible implementation of the first aspect of this application, in the sixth possible implementation of the first aspect of this application, the first monitoring data includes the first water body hyperspectral data collected by the first hyperspectral water quality multi-parameter analyzer, the first water quality data collected by the first multi-parameter water quality analyzer, the first UAV hyperspectral image collected by the first hyperspectral imager, and the first UAV panchromatic image collected by the first hyperspectral imager.

[0050] The second monitoring data includes the second water quality data collected by the second multi-parameter water quality analyzer, the second UAV hyperspectral image collected by the second hyperspectral imager, and the second UAV panchromatic image collected by the second hyperspectral imager.

[0051] The third monitoring data includes hyperspectral data of the third water body collected by the third hyperspectral water quality multi-parameter analyzer;

[0052] The fourth monitoring data includes satellite hyperspectral images, satellite panchromatic images, and satellite multispectral images obtained from the satellite imagery data interface.

[0053] In conjunction with the fourth possible implementation of the first aspect of this application, in the seventh possible implementation of the first aspect of this application, the information platform is further deployed with a geographic information system visualization interface, which integrates application functions for statistical analysis and large-screen display of remote sensing integrated data, business data and meteorological data.

[0054] The geographic information system visualization interface, based on the underlying services of the map engine integrated with the digital twin system and the web-based 3D rendering, displays the monitoring points, real-time data, historical data change trends, source data in real time, historical source data change trends, and processing progress of the algal bloom monitoring and processing results, and also includes corresponding early warning reminders.

[0055] In a second aspect, this application provides a processing device, including a processor and a memory, wherein a computer program is stored in the memory, and the processor executes the method provided in the first aspect of this application when it invokes the computer program in the memory.

[0056] Thirdly, this application provides a computer-readable storage medium storing a plurality of instructions adapted for loading by a processor to execute the method provided in the first aspect of this application.

[0057] From the above, it can be concluded that this application has the following beneficial effects:

[0058] To address the goal of adaptive integrated air-ground-water algal bloom monitoring, this application establishes a dense, time-series, air-ground collaborative intelligent algal bloom identification facility using three-dimensional monitoring methods such as UAVs, satellite remote sensing, and near-ground hyperspectral imaging. It also incorporates a dynamic calibration mechanism that captures the dynamic mapping relationship between inherent optical properties and apparent optical properties to achieve adaptive calibration of algal spectral feature locations. This enables real-time, efficient, and accurate intelligent algal bloom identification and processing, presenting algal bloom change trends in a multi-dimensional, multi-perspective, and multi-scale manner, providing effective data support for algal bloom early warning in major water sources. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a schematic diagram of an adaptive integrated sky-ground-water algal bloom monitoring method of this application;

[0061] Figure 2This is a schematic diagram of an adaptive integrated sky-ground-water algal bloom monitoring platform of this application.

[0062] Figure 3 This is another schematic diagram of the adaptive integrated sky-ground-water algal bloom monitoring platform of this application. Detailed Implementation

[0063] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices. The naming or numbering of steps appearing in this application does not imply that the steps in the method flow must be performed in the chronological / logical order indicated by the naming or numbering. The execution order of named or numbered process steps can be changed according to the desired technical purpose, as long as the same or similar technical effect is achieved.

[0065] The module division described in this application is a logical division. In practical applications, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between modules shown or discussed may be through some interfaces, and the indirect coupling or communication connection between modules may be electrical or other similar forms, none of which are limited in this application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules. Some or all of the modules may be selected to achieve the purpose of the solution in this application according to actual needs.

[0066] First, refer to Figure 1 , Figure 1The diagram illustrates a flowchart of the adaptive integrated sky-ground-water algal bloom monitoring method of this application. The adaptive integrated sky-ground-water algal bloom monitoring method provided by this application may specifically include the following steps S101 to S105:

[0067] Step S101: For the target water body area where the integrated air-ground-water algal bloom monitoring platform has been deployed, the surface phytochemical data to be processed in the current monitoring task is obtained through the data acquisition part of the monitoring facilities including shipborne monitoring facilities, vehicle-mounted monitoring facilities, shore-based monitoring facilities and satellite remote sensing monitoring facilities.

[0068] As can be seen here, the integrated air-ground-water algal bloom monitoring platform that implements the adaptive integrated air-ground-water algal bloom monitoring method proposed in this application involves four main parts for the data acquisition part or data acquisition facilities specifically involved in algal bloom monitoring: shipborne monitoring facilities, vehicle-mounted monitoring facilities, shore-based monitoring facilities, and satellite remote sensing monitoring facilities. This application uses this three-dimensional monitoring method to establish a dense temporal-series air-ground collaborative observation algal bloom intelligent identification architecture. In addition, the integrated air-ground-water algal bloom monitoring platform can also be configured with a data processing center, that is, an information platform serving as the data processing part, to execute the main processing schemes involved in the integrated air-ground-water algal bloom monitoring method of this application.

[0069] It should be noted that this information platform can not only perform the algal bloom monitoring and treatment involved in the solution of this application, but also other types of algal bloom monitoring and treatment. Different algal bloom monitoring and treatment logics can correspond to different data inputs and data analyses.

[0070] In addition, it should be noted that, in practice, this application does not necessarily require the simultaneous use of data collected by the four major facilities—shipborne monitoring facilities, vehicle-mounted monitoring facilities, shore-based monitoring facilities, and satellite remote sensing monitoring facilities—every time the algal bloom monitoring and treatment involved in this application is implemented. It is possible that only data collected by one or more facilities will be used, which is also possible and can be adjusted as needed.

[0071] In the specific implementation of the plan, the plan is usually initiated along with the relevant monitoring tasks, and the required apparent physiology data are collected for the target water area (also known as key water area, major water area, etc.) that the task directly or indirectly points to.

[0072] In layman's terms, the optical measurement data in this table is a dataset of dynamic parameters characterizing the optical properties of water bodies. The data content may be directly collected by shipborne monitoring facilities, vehicle-mounted monitoring facilities, shore-based monitoring facilities, and satellite remote sensing monitoring facilities, or it may be processed / converted based on the data collected by these data collection facilities.

[0073] Understandably, considering that the apparent scientometric data itself is not the focus of this application and falls within the scope of prior art, it has not been elaborated on here.

[0074] Step S102: Construct buffer zones for the fluorescence peak band position and the valley band position of the absorption coefficient of phytoplankton particles, which are inherent optical properties of the target water body area.

[0075] Understandably, in algae remote sensing identification, different algae (such as cyanobacteria, green algae, and diatoms) have certain differences in the position of their spectral reflectance peaks due to differences in pigment composition and cell structure.

[0076] For example, the fluorescence peak of cyanobacteria around 685 nm is usually slightly lower than that of green algae, while diatoms tend to show a wider reflection peak around 700 nm.

[0077] Although the spectral feature positions of different algae exhibit statistical regularity in controlled experiments or typical water bodies, in practical applications, they are also affected by interference from non-pigmentary particulate matter and colored soluble organic matter (CDOM) in the water, differences in sensor bands, and changes in environmental conditions (such as light and water depth). The spectral feature positions of algae often shift, leading to a decrease in the accuracy of the reflection peak identification method based on fixed bands.

[0078] In response, this application establishes a dynamic mapping relationship from inherent optical properties to apparent optical properties to achieve adaptive calibration of the spectral feature positions of algae, and optimizes the spectral feature positions of algae through a dynamic calibration mechanism.

[0079] In this context, it is understandable that this application can obtain the position of the fluorescence front band when the absorption coefficient of phytoplankton particles reaches its maximum and the position of the trough band when the absorption coefficient of phytoplankton particles reaches its minimum in the inherent optical properties of the current target water body area by pre-determining, directly extracting existing data, or manually entering data.

[0080] Among them, the position of the (inherent) fluorescence front band when the absorption coefficient of phytoplankton particles reaches its maximum in the inherent optical properties can be denoted as: The position of the (inherent) trough wavelength where the absorption coefficient of phytoplankton particles reaches its minimum in the inherent optical properties can be denoted as: .

[0081] Furthermore, it can be seen that there is no specific order of execution between step S102 and the preceding step S101. Once the target water area for this treatment is determined, the corresponding treatment can be carried out.

[0082] Step S103: Based on the apparent phytometry data, perform iterative search in combination with the buffer to locate the position of the water-leaved reflection valley band and the water-leaved reflection front band of the phytometry signal in the apparent phytometry data.

[0083] It is easy to see that after obtaining the apparent optical data and the two inherent optical properties of the water-leaving reflection valley band position and the water-leaving reflection front band position through the previous two steps, the water-leaving reflection valley band position and the water-leaving reflection front band position that are suitable for the current actual situation can be determined through the iterative search designed in this application.

[0084] Specifically, the search range for the iterative search is a buffer zone constructed by the two inherent optical properties of the water-leaving reflection valley band position and the water-leaving reflection front band position.

[0085] As an example, the buffer can be constructed by expanding forward and backward (left and right) by 15nm from the original wavelength position as the center point. Specifically, the buffer can have: positions corresponding to the valley wavelength bands. and the corresponding fluorescence front band position The former refers to the location of the water-reflection valley band to be obtained through iterative searching. The iterative search range, where the latter is the position of the water-leaving reflection front waveband to be obtained through iterative search. The iterative search range.

[0086] Thus, by combining the search range planned by the buffer zone with the apparent optical data falling within that range, a specific iterative search is performed on the location of the water-leaving reflection valley band and the water-leaving reflection front band of the phytoplankton signal.

[0087] Step S104: Based on the location of the water-free reflection valley band, the location of the water-free reflection front band, and the magnitude of their reflectivity, determine the quantitative index value of algal bloom in the target water body area.

[0088] Understandably, this application designs a quantitative index for algal blooms, which can be denoted as k, and comprehensively considers the location of the water reflection valley band. Location of water reflection valley band Corresponding reflectivity, water-leaving reflection front position Location of water-reflecting front band The corresponding reflectance is used to specifically quantify the algal bloom situation in the current target water body area, and is reflected by specific algal bloom quantitative index values.

[0089] Step S105: Combine the quantitative index value and the index threshold to determine whether there is an algal bloom phenomenon in the target water body area.

[0090] As is easy to understand, in accordance with the algal bloom quantification index designed in this application, this application also configures an index threshold. After obtaining the algal bloom quantification index value of the current target water body area, the index threshold can be combined to perform a binary judgment process to determine whether algal bloom exists in the target water body area.

[0091] For example, when the quantitative index value of algal bloom is greater than the index threshold, it can be determined that algal bloom exists in the target water body area; conversely, when the quantitative index value of algal bloom is less than or equal to the index threshold, it can be determined that algal bloom does not exist in the target water body area.

[0092] At the same time, it's easy to understand that in practical situations, after determining whether algal blooms exist in the target water area, further processing of the output results is required. This can be done as follows:

[0093] The system can store the judgment results locally or remotely, display the results, push the results, output a notification indicating completion of monitoring, or perform further data processing and analysis.

[0094] Obviously, the specific use of the judgment results can be flexibly adjusted based on the pre-configured and real-time configuration results.

[0095] Thus, from the above Figure 1 As can be seen from the embodiments shown, for the adaptive integrated air-ground-water algal bloom monitoring target, this application, based on the establishment of a dense temporal air-ground collaborative observation intelligent algal bloom identification facility using three-dimensional monitoring methods such as UAVs, satellite remote sensing, and near-ground hyperspectral imaging, combines a dynamic calibration mechanism that captures the dynamic mapping relationship from inherent optical properties to apparent optical properties to achieve adaptive calibration of algal spectral feature positions. This promotes real-time, efficient, and accurate intelligent algal bloom identification and processing, presenting algal bloom change trends in a multi-dimensional, multi-perspective, and multi-scale manner in a spatiotemporal distribution, and providing effective data support for algal bloom early warning in major water sources.

[0096] Next, we will continue with the above. Figure 1 The steps of the illustrated embodiment and their possible implementation methods in practical applications are described in detail.

[0097] As an example implementation, this corresponds to the previously mentioned predetermined fluorescent front band position. Valley value band position In some cases, the method of this application may also include:

[0098] 1) Calculate the absorption coefficient of phytoplankton particles in the 650nm-750nm wavelength range. To determine the indicators Position of the first candidate fluorescence peak with a value greater than 0:

[0099] ,

[0100] in, For wavelength 1 represents the absorption coefficient of phytoplankton particles at the specified spectral position. The unit is nm. ;

[0101] Here we can see that the iterative search process involves a wavelength interval of 1 nm. .

[0102] Understandable, when When this occurs, it indicates that a local maximum may exist at that point. Candidate positions for fluorescence peaks in the absorption coefficient of phytoplankton particles were included.

[0103] 2) Calculate the absorption coefficient of phytoplankton particles in the 650nm-750nm wavelength range. To determine the indicators Position of the second candidate fluorescence peak with a value less than 0:

[0104] ;

[0105] Understandable, when When this occurs, it indicates that the spectral curve at that location exhibits a locally convex shape. It is also included as a candidate position for the fluorescence peak of the absorption coefficient of phytoplankton particulate matter.

[0106] 3) The band position where the maximum absorption coefficient value is obtained between the first candidate fluorescence peak position and the second candidate fluorescence peak position. The output will be based on the position of the fluorescence peak band, which will yield the band position with the minimum absorption coefficient value. The output is used as the location of the valley band.

[0107] Understandably, in real-world scenarios, data noise is often present, and multiple candidate fluorescence peaks are identified. Therefore, the maximum / minimum condition band / wavelength trade-off mechanism here can be used to select the final output fluorescence peak band position. Valley value band position .

[0108] Next, as illustrated in the previous example, the buffer setting for service iteration search can be constructed by expanding forward and backward (left and right) by 15nm from the original wavelength position as the center point. Of course, other expansion strategies with different spans can also be involved, but in this application's solution, a 15nm expansion span can be understood to lay a good foundation for accurate and concise search results.

[0109] Continuing with the example of a 15nm expansion span, we will introduce a specific adaptation scheme for iterative search proposed in this application.

[0110] Correspondingly, as an exemplary embodiment, step S103, based on the apparent phytoscopic data, performs an iterative search in conjunction with the buffer to locate the positions of the water-leaving reflection valley band and the water-leaving reflection front band of the phytoplankton signal in the apparent phytoscopic data. Specifically, this may include:

[0111] 1) Location of water reflection valley band

[0112] 1.1) For apparent optical quantity data falling within the first buffer zone of 15 nm expansion before and after the fluorescence peak position, the indices for different bands are calculated using the following formula. To obtain indicators Location of the first candidate water-out reflection valley band with a value greater than 0:

[0113] ,

[0114] in, For wavelength 2 represents the reciprocal of the water reflectivity at the wavelength location. The unit is nm. ;

[0115] The first buffer zone, as mentioned earlier, is... .

[0116] Understandable, when When this occurs, it indicates that a local minimum may exist at that location. Water-free reflection valleys incorporating phytoplankton signals Candidate positions.

[0117] 1.2) For apparent optical quantity data falling within the first buffer zone, the indices for different wavebands are calculated using the following formula. To obtain indicators Location of the second candidate water-out reflection valley band with a value less than 0:

[0118] ;

[0119] Understandable, when When this occurs, it indicates that the spectral curve at that location exhibits a locally convex shape. Also included is the water-free reflection valley of phytoplankton signals. Candidate positions.

[0120] 1.3) The overlapping band positions of the first and second candidate water-leaving reflection valley positions are output as the water-leaving reflection valley positions of the phytoplankton signal, wherein the wavelength of the overlapping band position is... .

[0121] In this context, it's easy to understand that in practical operations, for conditions that need to be met simultaneously... and conditional You can either filter the judgment conditions separately and then make a judgment based on simultaneous / overlapping conditions, or you can filter the next judgment condition based on the band results after filtering the previous judgment condition.

[0122] 2) Location of the water-reflecting front band

[0123] 2.1) For apparent optical quantity data falling within the second buffer zone (15 nm expansion before and after the trough band position), the indices for different bands are calculated using the following formula. To obtain indicators Location of the first candidate water-leaving reflection front with a value greater than 0:

[0124] ,

[0125] in, For wavelength The water reflectivity at the indicated wavelength position The unit is nm. ;

[0126] The second buffer, as mentioned earlier, .

[0127] Understandable, when When this occurs, it indicates that a local minimum may exist at that location. Water-leaving reflection front incorporating phytoplankton signals Candidate positions.

[0128] 2.2) For apparent optical quantity data falling within the second buffer zone, the indices for different wavebands are calculated using the following formula. To obtain indicators Location of the second candidate water-leaving reflection front with a value less than 0:

[0129] ;

[0130] Understandable, when When this occurs, it indicates that the spectral curve at that location exhibits a locally convex shape. It also incorporates the water-leaving reflection front of phytoplankton signals. Candidate positions.

[0131] 2.3) The overlapping band positions of the first and second candidate water-leaving reflection front positions are output as the water-leaving reflection front positions of the phytoplankton signal, where the wavelength of the overlapping band position is... .

[0132] In this context, it's easy to understand that in practical operations, for conditions that need to be met simultaneously... and conditional You can either filter the judgment conditions separately and then make a judgment based on simultaneous / overlapping conditions, or you can filter the next judgment condition based on the band results after filtering the previous judgment condition.

[0133] In addition, it is worth noting that for and As can be seen from the above introduction, there are cases where they are reciprocals of each other. In addition, the following conversion relationships also exist:

[0134] .

[0135] As can be seen from the above embodiments, compared with existing methods that often use fixed wavelengths or narrow bands, this application sets a 15nm buffer before and after the apparent optical quantity and constructs a two-layer processing mechanism to search for key feature points within the buffer. Under this setting, the feature position is allowed to fluctuate with sensor and data conditions, which effectively improves robustness. When the sensor band resolution is low or the data noise is high, it can continue to select the most significant feature from multiple feature positions output simultaneously by continuing to adopt the optimization selection of taking the maximum / minimum.

[0136] Next, we will introduce the quantitative indicators for algal blooms that were specifically designed for this application.

[0137] As an exemplary embodiment, step S104 determines the quantitative index value of algal bloom in the target water body area based on the location of the water-leaving reflection valley band, the location of the water-leaving reflection front band, and the reflectivity of both. Specifically, this may include:

[0138] Based on the location of the water-leaving reflection valley band, the location of the water-leaving reflection front band, and the reflectivity of both, the quantitative index value of algal bloom in the target water body area is determined by the following formula:

[0139] ,

[0140] Where k is the quantitative index value of algal bloom, This represents the reflectivity of the waveband at the location of the water-leaving reflection front, and the maximum value (Max) is taken when there are multiple locations of the water-leaving reflection front. Substitute the band position PkIndex into the code and use it. This represents the reflectivity of the water-out-of-water reflection valley location, and when there are multiple water-out-of-water reflection valley locations, the maximum value Max( The band position GuIndex is used as the input.

[0141] As can be seen, in the actual situation mentioned above, due to the large amount of data noise, there may be multiple water-leaving reflection valley band positions / water-leaving reflection front band positions. A maximum / minimum value selection mechanism can be adopted.

[0142] Of course, if there is only one water-reflecting valley / Water-reflecting front These can be directly used as key feature points of GuIndex / PkIndex and substituted into the above formula for use.

[0143] In the above situation, step S105 combines the quantitative index value and the index threshold to determine whether there is an algal bloom phenomenon in the target water area, which may include:

[0144] If k>0, then the target water body area is determined to have an algal bloom phenomenon;

[0145] If k≤0, then it is determined that there is no algal bloom phenomenon in the target water body area.

[0146] As can be seen, the specific threshold for the indicator is 0.

[0147] It is understood that the above-mentioned processing in this application is a complete algal bloom determination system constructed under the condition of following the corresponding physical laws. This can effectively filter out the interference of other water components (such as non-pigmented particulate matter and colored soluble organic matter) or noise on algal bloom identification in actual situations.

[0148] Next, we will introduce the adaptive integrated air-ground-water algal bloom monitoring platform that implements the method of this application.

[0149] As briefly introduced above, the method of this application is applied to the integrated air-ground-water algal bloom monitoring platform. On the one hand, the integrated air-ground-water algal bloom monitoring platform includes a data acquisition part, which specifically includes shipborne monitoring facilities, vehicle-mounted monitoring facilities, shore-based monitoring facilities and satellite remote sensing monitoring facilities. On the other hand, the integrated air-ground-water algal bloom monitoring platform also includes an information platform as the data processing part.

[0150] refer to Figure 2 The diagram shown illustrates an architecture of the integrated air-ground-water algal bloom monitoring platform of this application. Specifically, its structure includes:

[0151] (1) Shipborne monitoring facilities

[0152] Shipborne monitoring facilities or shipborne monitoring systems include a first hyperspectral water quality multi-parameter analyzer, a first multi-parameter water quality analyzer, and a first rotary-wing UAV, with the first rotary-wing UAV carrying a first hyperspectral imager.

[0153] It is easy to understand that hyperspectral water quality multi-parameter analyzers, multi-parameter water quality analyzers, and hyperspectral imagers are existing devices that can be involved in the monitoring scenario. Hyperspectral water quality multi-parameter analyzers can collect hyperspectral data of water bodies, multi-parameter water quality analyzers can collect water quality data, and hyperspectral imagers can collect panchromatic images from drones.

[0154] The hyperspectral water quality multi-parameter analyzer and the multi-parameter water quality analyzer are directly deployed and configured on the water quality monitoring vessel sailing on the water surface (specifically, it can be a fully automatic unmanned vessel or other types of hull), while the hyperspectral imager can be configured on a rotary-wing UAV and then deployed on the water quality monitoring vessel along with the rotary-wing UAV. The rotary-wing UAV can take off from the ship and collect the corresponding monitoring data in the air through the hyperspectral imager.

[0155] It is worth noting that, in order to better distinguish the same type of monitoring equipment among different monitoring facilities, this application specifically uses "first", "second" and "third" to correspond to different monitoring facilities.

[0156] In practical applications, for the same type of monitoring equipment involved in different monitoring facilities, such as hyperspectral water quality multi-parameter analyzers, the same specific equipment or substantially different specific equipment can be used. This can be flexibly adjusted according to the actual situation.

[0157] (2) Vehicle-mounted monitoring facilities

[0158] The vehicle-mounted monitoring facilities, or vehicle-mounted emergency monitoring systems, include fully automated unmanned boats and second-rotor drones with emergency monitoring vehicles as the core configuration. The fully automated unmanned boats are equipped with a second multi-parameter water quality analyzer, and the second-rotor drones are equipped with a second hyperspectral imager.

[0159] It can be noted that the vehicle-mounted monitoring facilities here can be equipped with rotary-wing drones like the water quality monitoring boats mentioned above, as well as fully automated unmanned boats. This allows for the rapid transport of fully automated unmanned boats by vehicle to specific locations for deployment, and the use of multi-parameter water quality analyzers deployed on board for data monitoring.

[0160] (3) Shore-based monitoring facilities

[0161] The shore-based monitoring facility, or shore-based remote sensing online monitoring system, includes a third hyperspectral water quality multi-parameter analyzer mounted on the top of a vertical support and a gray-white panel. The gray-white panel is used for data correction of the third hyperspectral water quality multi-parameter analyzer.

[0162] It is easy to see that a major difference between shore-based monitoring facilities and the ship / vehicle-mounted monitoring facilities mentioned earlier is that the monitoring points are fixed, which corresponds to the setting of vertical brackets. On the top of the vertical brackets, a hyperspectral water quality multi-parameter analyzer can be deployed, and a gray-white board is used to assist the analyzer in its work and to carry out its data correction work (which can be understood as a conventional auxiliary structure for cameras).

[0163] The specific support structure of the vertical support can be flexibly configured according to the actual situation, as long as it meets the working conditions involved in the hyperspectral water quality multi-parameter analyzer and the gray-white board.

[0164] In addition, it is understandable that each of the above three monitoring facilities can be equipped with corresponding cameras to conduct monitoring based on conventional images and videos, in order to assist in relevant on-site monitoring needs.

[0165] (4) Satellite remote sensing monitoring facilities

[0166] Satellite remote sensing monitoring facilities, or satellite remote sensing monitoring systems, include host equipment used for accessing satellite image data interfaces.

[0167] It is understandable that satellite remote sensing monitoring facilities mainly include equipment that accesses relevant satellite image data in order to collect satellite image data through relevant monitoring satellites.

[0168] In practical applications, satellite remote sensing monitoring facilities do not need to include monitoring satellites and / or corresponding ground satellite control systems. That is, readily available satellite observation data products can be used. Of course, in some cases, it is not ruled out that satellite remote sensing monitoring facilities may include monitoring satellites and / or corresponding ground satellite control systems in their equipment scope.

[0169] Thus, through the four major monitoring facilities mentioned above, monitoring of the sky, land, and water is carried out, forming a systematic and comprehensive three-dimensional monitoring architecture, which provides rich monitoring data support for the subsequent information platform.

[0170] (5) Information platform

[0171] The information platform updates the multi-source basic database based on the first monitoring data collected by shipborne monitoring facilities, the second monitoring data collected by vehicle-mounted monitoring facilities, the third monitoring data collected by shore-based monitoring facilities, and the fourth monitoring data collected by satellite remote sensing monitoring facilities. Based on the multi-source basic database, it carries out algal bloom monitoring and processing to obtain algal bloom monitoring and processing results.

[0172] Understandably, the monitoring equipment in the above four aspects serves the function of collecting source data. Corresponding to the platform function design of this application for accessing multi-source monitoring data, it can further facilitate the real-time collection of corresponding monitoring data as needed, thus meeting the solution objective of real-time access to monitoring data from different aspects.

[0173] Among them, satellite remote sensing monitoring facilities (space-based platforms) are responsible for conducting large-scale and regular macroscopic scans, providing basic information on the spatial distribution and trend changes of algal blooms in major reservoirs; vehicle-mounted monitoring facilities (air-based platforms) serve as a flexible intermediate layer, capable of quickly responding to the needs of key areas and conducting detailed inspections, overcoming the limitations of satellite revisit cycles and resolution; shore-based monitoring facilities (ground-based platforms) provide fixed-point, continuous high-frequency spectral and water quality data, supporting model calibration, sensor cross-validation, and long-term change analysis; and shipborne monitoring facilities (surface platforms) are in direct contact with the water body, conducting synchronous measurements of water surface spectra and in-situ water quality, providing reliable ground-based real values ​​for remote sensing inversion models.

[0174] In this context, satellite data outlines the macroscopic contours, drone data focuses on hotspot areas, and shore-based and shipborne data provide precise location verification. This multi-layered monitoring architecture helps to significantly improve the accuracy and reliability of algal bloom identification.

[0175] In this way, after receiving the monitoring data obtained by the four monitoring devices, the information platform responsible for centralized data processing in the background can update the corresponding multi-source basic database, laying a good foundation for the subsequent coupling of multi-source monitoring data to monitor algal blooms.

[0176] The updating of the multi-source basic database mentioned here may involve the initial construction and processing of the multi-source basic database, followed by the continuous storage and recording of newly acquired monitoring data. While recording the relevant monitoring data collected in the monitoring area, it is also possible to promote the systematic and comprehensive integrated sky-ground-water algal bloom inversion processing based on the pre-set correspondence between monitoring data and algal bloom conditions (specifically implemented through pre-configured algal bloom inversion logic) and the systematic and comprehensive monitoring data.

[0177] The results of the algal bloom inversion or algal bloom monitoring can be used to monitor the algal bloom situation at the current time point / real-time, the algal bloom situation at past time points, or the algal bloom situation over a past time period (the last time point can be the current time point). Furthermore, a future prediction mechanism can be introduced, that is, by combining the characteristics of time series, to predict the algal bloom situation at future time points or over future time periods.

[0178] Furthermore, regarding the data transmission link between the four monitoring facilities and the back-end information platform, taking monitoring data as an example, the shipborne monitoring facilities can specifically store the first monitoring data in the server database (database within the server) of the information platform through the industrial control computer on board.

[0179] The vehicle-mounted monitoring equipment can store the second monitoring data in a server database through a switch or router on the vehicle.

[0180] Specifically, shore-based monitoring facilities can use wireless network cards to store third-party monitoring data in a server database;

[0181] Satellite remote sensing monitoring facilities can store fourth-order monitoring data in a server database through an interface that connects to the imagery.

[0182] In the information platform, or more specifically, the server database here, the multi-source basic database can be further subdivided into:

[0183] 1. Basic database, used to store water area information, which may include water area information primary key, name, parent water area, nature, area, altitude, maximum width, maximum depth, total water storage, location description and pictures, etc.

[0184] 2. Intelligent sensor management database, used to store information about hyperspectral water quality multi-parameter analyzers and cameras, including primary keys, names, codes, types, models, parameters, and images;

[0185] 3. Spectral database, used to store hyperspectral data of water bodies collected by the hyperspectral water quality multi-parameter analyzer, including name, spectral information, collection time and collection environment, etc.;

[0186] 4. Water quality database, used to store water quality data collected by multi-parameter water quality analyzers;

[0187] 5. Remote sensing database, used to store information on rotary-wing UAV images and satellite images, including shooting time, specific images, etc.;

[0188] 6. Model information database, used to store algal bloom inversion models.

[0189] As an example, the hyperspectral imager carried on the drone can have a spectral range of 400nm-1000nm, and the satellite imagery can specifically use satellite observation data products such as Sentinel-2 satellite imagery products.

[0190] Furthermore, regarding the monitoring data obtained from the four major monitoring facilities, refer to Figure 3 The schematic diagram shown here illustrates another architecture of the integrated air-ground-water algal bloom monitoring platform of this application. As an exemplary embodiment, it may specifically include:

[0191] 1. The first monitoring data includes the first water body hyperspectral data collected by the first hyperspectral water quality multi-parameter analyzer, the first water quality data collected by the first multi-parameter water quality analyzer, the first UAV hyperspectral image collected by the first hyperspectral imager, and the first UAV panchromatic image collected by the first hyperspectral imager;

[0192] Among them, the first water body hyperspectral data and the first water quality data can specifically correspond to the quadrat scale, while the first UAV hyperspectral image and the first UAV panchromatic image can specifically correspond to the landscape scale.

[0193] 2. The second monitoring data includes the second water quality data collected by the second multi-parameter water quality analyzer, the second UAV hyperspectral image collected by the second hyperspectral imager, and the second UAV panchromatic image collected by the second hyperspectral imager;

[0194] Among them, the second water quality data can specifically correspond to the sample point scale, and the second UAV hyperspectral image and the second UAV panchromatic image can specifically correspond to the landscape scale.

[0195] 3. The third monitoring data includes the hyperspectral data of the third water body collected by the third hyperspectral water quality multi-parameter analyzer;

[0196] Among them, the hyperspectral data of the third water body can specifically correspond to the quadrat scale.

[0197] 4. The fourth monitoring data includes satellite hyperspectral images, satellite panchromatic images, and satellite multispectral images obtained from the satellite imagery data interface.

[0198] Among them, satellite hyperspectral imagery, satellite panchromatic imagery, and satellite multispectral imagery can specifically correspond to regional scales.

[0199] Furthermore, based on the multi-source monitoring data mentioned above, this application, considering to further improve or enhance the data integration effect, can also introduce corresponding data transformation processing to further process multi-source monitoring data that are originally in the same (or as close as possible) observation time / space range within the same data type / space, so as to promote a more efficient and accurate algal bloom monitoring effect.

[0200] Furthermore, regarding the display of results for algal bloom determination mentioned earlier, this application can also be configured with highly integrated and user-friendly result display functions based on a Geographic Information System (GIS).

[0201] Specifically, as an exemplary embodiment, the information platform also deploys a geographic information system visualization interface, which can integrate application functions such as statistical analysis and large-screen display of data such as remote sensing integrated data, business data and meteorological data.

[0202] Understandably, the visualization interface of this geographic information system can integrate not only the data involved in different processing stages that are the focus of this application, but also the relevant business data involved in the platform's operation, as well as meteorological data that can reflect relevant meteorological conditions (some meteorological conditions themselves can also affect algal blooms). In addition, it can continue to integrate data from other aspects as needed. The integrated data can be specifically reflected in the interface through application functions such as statistical analysis and large-screen display.

[0203] Specifically, the visualization interface of this geographic information system can, on the basis of underlying services (underlying data services and underlying technical services) such as the map engine for the integrated digital twin system construction and web-based 3D rendering, display the monitoring points (such as the point display of specific monitoring equipment such as shore-based hyperspectral multi-parameter analyzers), real-time data display, historical data change trend display, real-time source data display (i.e., involving monitoring data obtained by the four major monitoring facilities), historical source data change trend display, and processing process display (which can involve input / output / intermediate data of different processing processes, or the representation of relevant process status), and involve corresponding early warning reminders (involving early warning judgment processing based on thresholds).

[0204] Furthermore, in terms of details, the specific display items and display formats can be configured with real-time adjustment functions to meet the display needs of algal bloom monitoring under different circumstances or personalized needs.

[0205] In this way, through the visualization interface of the geographic information system, based on the spatiotemporal distribution, the situation of algal blooms can be presented in multiple dimensions, perspectives, and scales, so as to realize the early warning of algal blooms in major water sources.

[0206] Meanwhile, from a hardware perspective, this application also provides an adaptive integrated sky-ground-water algal bloom monitoring platform, which is used to execute the aforementioned adaptive integrated sky-ground-water algal bloom monitoring method. The specific platform structure involved in the hardware aspect has already been described above, and those skilled in the art can clearly understand it. For the sake of convenience and brevity, the specific working process of the adaptive integrated sky-ground-water algal bloom monitoring platform described above can be referred to as follows: Figure 1 The description of the adaptive integrated sky-ground-water algal bloom monitoring method in the corresponding embodiment will not be repeated here.

[0207] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0208] Therefore, this application provides a computer-readable storage medium storing a plurality of instructions that can be loaded by a processor to execute the present application. Figure 1 The steps of the adaptive integrated sky-ground-water algal bloom monitoring method in the corresponding embodiment can be referred to as follows for specific operations. Figure 1 The description of the adaptive integrated sky-ground-water algal bloom monitoring method in the corresponding embodiment will not be repeated here.

[0209] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0210] Because of the instructions stored in the computer-readable storage medium, the present application can be executed as described above. Figure 1 The steps of the adaptive integrated sky-ground-water algal bloom monitoring method in the corresponding embodiment can therefore achieve the results of this application. Figure 1 The beneficial effects that the adaptive integrated air-ground-water algal bloom monitoring method in the corresponding embodiment can achieve are detailed in the preceding description and will not be repeated here.

[0211] The adaptive integrated air-ground-water algal bloom monitoring method, platform, and computer-readable storage medium provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An adaptive integrated sky-ground-water algal bloom monitoring method, characterized in that, The method includes: For the target water body area where the integrated air-ground-water algal bloom monitoring platform has been deployed, the surface phytochemical data to be processed in the current monitoring task is obtained through the data acquisition part, which includes shipborne monitoring facilities, vehicle-mounted monitoring facilities, shore-based monitoring facilities and satellite remote sensing monitoring facilities. Buffer zones are constructed for the fluorescence peak band position and the valley band position of the absorption coefficient of phytoplankton particles, which are inherent optical properties of the target water body region, respectively. Based on the above-mentioned phytoscopic data, an iterative search is performed in conjunction with the buffer to locate the position of the water-leaved reflection valley band and the water-leaved reflection front band of the phytoplankton signal in the above-mentioned phytoscopic data. Based on the location of the water-free reflection valley band, the location of the water-free reflection front band, and the reflectivity of both, the water bloom quantitative index value of the target water body area is determined. By combining the quantitative index value and the index threshold, it is determined whether the target water body area has an algal bloom phenomenon.

2. The method according to claim 1, characterized in that, The method further includes: The index for calculating the absorption coefficient of phytoplankton particles in the 650nm-750nm wavelength range. To determine the indicator Position of the first candidate fluorescence peak with a value greater than 0: ， in, For wavelength 1 represents the absorption coefficient of the phytoplankton particles at the specified wavelength. The unit is nm. ; Within the wavelength range of 650nm-750nm, calculate the absorption coefficient of the phytoplankton particles. To determine the indicator Position of the second candidate fluorescence peak with a value less than 0: ； The wavelengths at which the maximum absorption coefficient value is obtained are located within the first candidate fluorescence peak wavelength range and the second candidate fluorescence peak wavelength range. The output is the position of the fluorescence peak band, which will yield the position of the band with the minimum absorption coefficient value. The valley band position is output as the valley value.

3. The method according to claim 2, characterized in that, The step of iteratively searching based on the surface phytometry data and the buffer zone to locate the positions of the water-leaving reflection valleys and water-leaving reflection fronts of the phytometry signal in the surface phytometry data includes: For the apparent optical quantity data falling within the first buffer zone of 15 nm expansion before and after the fluorescence peak position, the indices for different bands are calculated using the following formula. To obtain the aforementioned indicators Location of the first candidate water-out reflection valley band with a value greater than 0: ， in, For wavelength 2 represents the reciprocal of the water reflectivity at the wavelength location, where the wavelength is... The unit is nm. ; For the apparent optical quantity data that falls within the first buffer, the indices for different wavebands are calculated using the following formula. To obtain the aforementioned indicators Location of the second candidate water-out reflection valley band with a value less than 0: ； The repeated band positions of the first candidate water-leaving reflection valley position and the second candidate water-leaving reflection valley position are output as the water-leaving reflection valley position of the phytoplankton signal, wherein the wavelength of the repeated band position is [wavelength value missing]. ; For the apparent optical quantity data falling within the second buffer zone of 15 nm expansion before and after the valley band position, the index for different bands is calculated using the following formula. To obtain the aforementioned indicators Location of the first candidate water-leaving reflection front with a value greater than 0: ， in, For wavelength The water reflectivity at the indicated wavelength position, the wavelength The unit is nm. ; For the apparent optical quantity data that falls within the second buffer, the indices for different bands are calculated using the following formula. To obtain the aforementioned indicators Location of the second candidate water-leaving reflection front with a value less than 0: ； The repeated band positions of the first candidate water-leaving reflection front and the second candidate water-leaving reflection front are output as the water-leaving reflection front positions of the phytoplankton signal, wherein the wavelength of the repeated band positions is [wavelength value missing]. .

4. The method according to claim 3, characterized in that, The determination of the algal bloom quantification index value for the target water body area based on the location of the water-leaving reflection valley band, the location of the water-leaving reflection front band, and the reflectivity of both includes: Based on the location of the water-leaving reflection valley band, the location of the water-leaving reflection front band, and the reflectivity of both, the quantitative index value of algal bloom in the target water body area is determined by the following formula: ， Where k is the quantitative index value of algal bloom. The reflectivity is the magnitude of the waveband where the water-leaving reflection front is located, and when there are multiple water-leaving reflection front locations, the maximum value Max( is taken). Substitute the band position PkIndex into the code and use it. The reflectivity is the magnitude of the water-leaving reflection valley band location, and when there are multiple water-leaving reflection valley band locations, the maximum value Max( is taken). Substitute the band position (GuIndex) into the input; The determination of whether algal blooms exist in the target water body area by combining the algal bloom quantitative index value and the index threshold includes: If k>0, then it is determined that the algal bloom phenomenon exists in the target water body area; If k≤0, then it is determined that the algal bloom phenomenon does not exist in the target water body area.

5. The method according to claim 1, characterized in that, The method is applied to an integrated air-ground-water algal bloom monitoring platform, which includes the data acquisition part and an information platform as the data processing part. The shipborne monitoring facilities include a first hyperspectral water quality multi-parameter analyzer, a first multi-parameter water quality analyzer, and a first rotary-wing UAV, all configured with the water quality monitoring vessel as the core. The first rotary-wing UAV is equipped with a first hyperspectral imager. The vehicle-mounted monitoring facility includes a fully automated unmanned boat and a second rotary-wing drone, with the emergency monitoring vehicle as the core configuration. The fully automated unmanned boat is equipped with a second multi-parameter water quality analyzer, and the second rotary-wing drone is equipped with a second hyperspectral imager. The shore-based monitoring facility includes a third hyperspectral water quality multi-parameter analyzer mounted on top of a vertical support and a gray-white panel, the gray-white panel being used for data correction of the third hyperspectral water quality multi-parameter analyzer.

6. The method according to claim 5, characterized in that, The shipborne monitoring facility stores the first monitoring data in the server database of the information platform via the ship's industrial control computer; The vehicle-mounted monitoring facility stores the second monitoring data in the server database via a switch or router on the vehicle. The shore-based monitoring facility stores the third monitoring data in the server database via a wireless network card; The satellite remote sensing monitoring facility stores the fourth monitoring data in the server database through an interface that accesses the images.

7. The method according to claim 6, characterized in that, The first monitoring data includes the first water body hyperspectral data collected by the first hyperspectral water quality multi-parameter analyzer, the first water quality data collected by the first multi-parameter water quality analyzer, the first UAV hyperspectral image collected by the first hyperspectral imager, and the first UAV panchromatic image collected by the first hyperspectral imager. The second monitoring data includes second water quality data collected by the second multi-parameter water quality analyzer, second UAV hyperspectral images collected by the second hyperspectral imager, and second UAV panchromatic images collected by the second hyperspectral imager. The third monitoring data includes the hyperspectral data of the third water body collected by the third hyperspectral water quality multi-parameter analyzer; The fourth monitoring data includes satellite hyperspectral images, satellite panchromatic images, and satellite multispectral images obtained from the satellite imagery data interface.

8. The method according to claim 5, characterized in that, The information platform is also deployed with a geographic information system visualization interface, which integrates application functions for statistical analysis and large-screen display of remote sensing integrated data, business data and meteorological data. The geographic information system visualization interface, based on the underlying services of the integrated digital twin system map engine and web-based 3D rendering, displays the monitoring points, real-time data, historical data change trends, source data in real time, historical source data change trends, and processing progress of the algal bloom monitoring and processing results, and also includes corresponding early warning reminders.

9. An adaptive integrated air-ground-water algal bloom monitoring platform, characterized in that, The integrated air-ground-water algal bloom monitoring platform is used to perform the method described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted for loading by a processor to perform the method of any one of claims 1 to 8.

Images