A method, device and system for monitoring and early warning of cyanobacteria blooms

Abstract

The present invention relates to the technical field of optical analysis, specifically to a method, device, and system for monitoring and warning of cyanobacterial blooms. The method comprises: obtaining a spectral reflectance curve for cyanobacteria and a spectral reflectance curve for each pixel in remote sensing spectral data; using the absolute value of the difference between the spectral reflectance curves of adjacent pixels as an intensity curve; obtaining a number of peak clusters based on the amplitude of the data points in the intensity curve, and further constructing a spectral reflectance-to-spectral reflectance conversion model based on the spectral reflectance curve of the cyanobacteria. The model also obtains the spectral reflectance of the corresponding end members of all peak clusters within their overall wavelength range, thereby monitoring and warning of cyanobacterial blooms. Based on the uniform distribution of various substances within a local area of ​​a lake, the present invention constructs a spectral reflectance-to-reflectance conversion model to accurately monitor the proportion of cyanobacterial components at each location, achieving accurate monitoring and timely warning of cyanobacterial blooms.

Description

Technical Field

[0001] The present invention relates to the technical field of optical analysis, and in particular to a cyanobacteria bloom monitoring and early warning method, equipment and system. Background Art

[0002] Cyanobacterial bloom refers to the phenomenon in which cyanobacteria in water bodies rapidly proliferate in a eutrophic environment to form a blue-green algae layer; when cyanobacteria proliferate rapidly, they will consume a large amount of dissolved oxygen in the water body, causing a large number of aquatic organisms such as fish and shrimp to die due to lack of oxygen, and the dead aquatic organisms will release toxic and harmful substances during the decomposition process, further damaging the water ecology and seriously endangering the health of humans and animals; therefore, cyanobacterial blooms need to be continuously and accurately monitored and warned of, so as to timely detect potential risks and ensure the safety of water resources.

[0003] When traditionally monitoring and warning of cyanobacterial blooms based on spectral data is carried out, it is difficult to construct a complete end-member spectral library due to the presence of various suspended particles and dissolved colored organic matter in the water body. This results in the inability to accurately obtain the proportion of cyanobacterial components in each pixel in the remote sensing spectral data, and thus the inability to effectively monitor and warn of cyanobacterial blooms. Summary of the Invention

[0004] The present invention provides a cyanobacteria bloom monitoring and early warning method, device and system to solve the existing problem that it is difficult to construct a complete end-member spectral library due to the presence of various suspended particles and dissolved colored organic matter in water bodies, and it is impossible to effectively monitor and early warning cyanobacteria blooms.

[0005] The present invention provides a method, device, and system for monitoring and early warning of cyanobacteria blooms using the following technical solutions:

[0006] One embodiment of the present invention provides a method for monitoring and early warning of cyanobacteria blooms, the method comprising the following steps:

[0007] Obtaining a spectral reflectance curve of cyanobacteria and a spectral reflectance curve of each pixel in remote sensing spectral data, wherein the pixel contains several end members;

[0008] The intensity curves of adjacent pixels are obtained based on the differences between the spectral reflectance curves of adjacent pixels. The reflectivity of each data point in the intensity curve and the peak segments of the intensity curve are obtained based on the data points within the local range of each data point in the intensity curve. All peak segments of all intensity curves are clustered to obtain several peak segment clusters.

[0009] The peak segments in all peak segment clusters are compared with the spectral reflectance curves of cyanobacteria to screen the cyanobacteria benchmark clusters and obtain the cyanobacteria benchmark curves; based on the amplitude of the data points within the peak segment in each peak segment cluster, the spectral reflectance of all wavelengths within the overall wavelength range of each peak segment cluster is obtained. Combined with the cyanobacteria benchmark curve, a spectral reflectance level-spectral reflectance conversion model is constructed to obtain the spectral reflectance of the end members corresponding to all peak segment clusters within their overall wavelength range;

[0010] Based on the spectral reflectance of all end members within their overall wavelength range, the proportion of cyanobacteria in each pixel in the remote sensing spectral data is obtained, and cyanobacterial blooms are monitored and warned.

[0011] Preferably, the method of obtaining the reflectivity of each data point in the intensity curve and a number of peak segments of the intensity curve based on the data points within the local range of each data point in the intensity curve includes the following specific methods:

[0012] For any intensity curve data points, the distance between the intensity curve and the intensity curve The most recent data point data point, as the first data point in the intensity curve The local data points of the data points, is the preset number of local data points;

[0013] According to the intensity curve The local data point of the data point is the same as the intensity curve The sum of the data points, and the The local data point of the data point is the same as the intensity curve The difference between the data points is the first in the intensity curve. The specific calculation method for the strength of the reflection ability of each data point is:

[0014]

[0015] Where, Indicates the intensity curve The strength of the reflective ability of each data point; Indicates the intensity curve data point and its The difference in amplitude between local data points; Indicates the intensity curve The amplitude of the data point; Indicates the intensity curve The data point The amplitude of a local data point; Indicates the intensity curve The mean of the difference in amplitude between a data point and each of its local data points; Indicates the intensity curve The number of local data points for each data point; represents the linear normalization function;

[0016] Get the reflectivity of all data points in each intensity curve and set a threshold for the reflectivity , any intensity curve with a strength greater than The data point is taken as the peak point in the intensity curve, and the curve segment composed of continuous peak segments in the intensity curve is taken as the peak segment of the intensity curve.

[0017] Preferably, clustering all peak segments of all intensity curves to obtain a number of peak segment clusters includes the following specific methods:

[0018] All peak segments of all intensity curves are grouped into a peak segment set; for the The peak segment and peak segments, get the peak segment set The wavelength range corresponding to the first peak segment is The intersection-and-combination ratio of the wavelength range corresponding to the peak segment is used as the negative correlation map of the intersection-and-combination ratio in the peak segment set. The peak segment and The degree of band difference between the peak segments;

[0019] The band difference degree between all the peak segments in the peak segment set is obtained, and the band difference degree between all the peak segments in the peak segment set is used as the measurement distance. All the peak segments in the peak segment set are clustered to obtain a number of peak segment clusters.

[0020] Preferably, the method of comparing the peak segments in all peak segment clusters with the spectral reflectance curve of cyanobacteria, screening the cyanobacteria benchmark clusters and obtaining the cyanobacteria benchmark curve includes the following specific methods:

[0021] For any peak segment cluster, the intersection of the wavelength ranges corresponding to all peak segments in the peak segment cluster is used as the overall wavelength range of the peak segment cluster, and the spectral reflectance curve of the cyanobacteria within the overall wavelength range of the peak segment cluster is used as the cyanobacteria reference curve of the peak segment cluster;

[0022] For any peak segment in the peak segment cluster, matching the peak segment in the peak segment cluster with a cyanobacteria reference curve of the peak segment cluster using a DTW algorithm to obtain a plurality of matching pairs;

[0023] According to the difference between the two elements in each matching pair, combined with the strength of the reflectivity of each data point in the peak segment in the peak segment cluster, the similarity between the peak segment in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster is obtained;

[0024] Obtaining the similarity between all peak segments in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster, and taking the average of the similarities between all peak segments in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster as the similarity between the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster;

[0025] The similarity between all peak segment clusters and the cyanobacteria reference curve of the peak segment cluster is obtained, and the peak segment cluster corresponding to the maximum similarity value and the cyanobacteria reference curve of the peak segment cluster are recorded as the cyanobacteria benchmark cluster and the cyanobacteria benchmark curve, respectively.

[0026] Preferably, the specific calculation formula for obtaining the similarity between the peak segment in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster is:

[0027]

[0028] Where, Indicates the similarity between the peak segment in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster; represents the number of matching pairs; Indicates the the strength of the reflectivity of the data points corresponding to the peak segments in the peak segment clusters within a matching pair; Indicates the The amplitude of the data point corresponding to the cyanobacteria reference curve of the peak segment cluster within the matching pair; Indicates the The amplitude of the data point corresponding to the peak segment in the peak segment cluster within the matching pair; Indicates the absolute value function; Represents an exponential function with a natural constant as its base.

[0029] Preferably, the method of obtaining the spectral reflectance of all wavelengths within the overall wavelength range of each peak segment cluster according to the amplitude of the data points within the peak segment in each peak segment cluster includes the following specific methods:

[0030] Any wavelength within the entire wavelength range of any peak segment cluster is recorded as the target wavelength; the product of the amplitude mean and the amplitude standard deviation of all peak segments in the peak segment cluster at the target wavelength is taken as the spectral reflectivity level of the peak segment cluster at the target wavelength.

[0031] Preferably, the method of constructing a spectral reflectance level-spectral reflectance conversion model to obtain the spectral reflectance of the end members corresponding to all peak segment clusters within their entire wavelength range includes the following specific methods:

[0032] The spectral reflectance levels and spectral reflectances at the same wavelengths in the cyanobacteria benchmark clusters and the cyanobacteria benchmark curves were grouped into the same data group to obtain several data groups. All data groups were linearly fitted using the least squares method to obtain a spectral reflectance-spectral reflectance conversion model.

[0033] The spectral reflectivity levels of all wavelengths within the overall wavelength range of each peak segment cluster are input into the spectral reflectivity level-spectral reflectivity conversion model to obtain the spectral reflectivity of the end member corresponding to each peak segment cluster within its overall wavelength range.

[0034] Preferably, the method of obtaining the proportion of cyanobacteria in each pixel in the remote sensing spectral data based on the spectral reflectance of all end members within their entire wavelength range and monitoring and early warning of cyanobacterial blooms includes the following specific methods:

[0035] Using semi-blind hyperspectral unmixing, the spectral reflectance of all end members in their entire wavelength range, the spectral reflectance of cyanobacteria, and the spectral reflectance curves of all pixels in the remote sensing spectral data are input into the semi-blind hyperspectral unmixing to obtain the proportion of cyanobacteria in all pixels in the remote sensing spectral data.

[0036] Preset two warning ranges respectively and For any pixel, when the proportion of cyanobacteria in the pixel is less than or equal to , it is judged that the cyanobacteria in the pixel is in a normal state; when the proportion of cyanobacteria in the pixel is greater than Less than or equal to , it is judged that the cyanobacteria in the pixel is in a mild cyanobacterial bloom state; when the proportion of cyanobacteria in the pixel is greater than , judge that the cyanobacteria in the pixel is in a state of severe cyanobacteria bloom; for normal locations in the lake, continue to monitor the proportion of cyanobacteria components; for locations in the lake with a mild cyanobacteria bloom, increase the frequency of monitoring the proportion of cyanobacteria components; for locations in the lake with a severe cyanobacteria bloom, immediately warn relevant technical personnel.

[0037] The present invention also proposes a cyanobacteria bloom monitoring and early warning system, which includes:

[0038] Data acquisition module: used to obtain the spectral reflectance curve of cyanobacteria and the spectral reflectance curve of each pixel in the remote sensing spectral data, wherein the pixel contains several end members;

[0039] Data analysis module: used to obtain the intensity curves of adjacent pixels based on the differences between the spectral reflectance curves of adjacent pixels; obtain the reflectivity strength of each data point in the intensity curve and the number of peak segments of the intensity curve based on the data points within the local range of each data point in the intensity curve; cluster all the peak segments of all the intensity curves to obtain several peak segment clusters;

[0040] Model construction module: used to compare the peak segments in all peak segment clusters with the spectral reflectance curves of cyanobacteria, screen the cyanobacteria benchmark clusters and obtain the cyanobacteria benchmark curve; based on the amplitude of the data points within the peak segment in each peak segment cluster, obtain the spectral reflectance level of all wavelengths within the overall wavelength range of each peak segment cluster; combined with the cyanobacteria benchmark curve, construct a spectral reflectance level-spectral reflectance conversion model, and then obtain the spectral reflectance of the end members corresponding to all peak segment clusters within their overall wavelength range;

[0041] Monitoring and early warning module: It is used to obtain the proportion of cyanobacteria in each pixel in the remote sensing spectral data based on the spectral reflectance of all end members within their overall wavelength range, and to monitor and warn of cyanobacterial blooms.

[0042] The present invention also proposes a cyanobacteria bloom monitoring and early warning device, including a spectral data analysis device, the spectral data analysis device including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the module of the above system is implemented.

[0043] The beneficial effect of the technical solution of the present invention is that, since the proportions of endmember components in adjacent pixels in remote sensing spectral data are similar but also have slight differences, when a certain endmember in adjacent pixels has a high spectral reflectance at a specific wavelength, even if the difference in the proportions of the endmember components is slight, it will lead to a significant difference in the spectral reflectance of the two pixels at that wavelength. Therefore, the present application uses the absolute value of the difference between the spectral reflectance curves of adjacent pixels as an intensity curve; and obtains several peak segment clusters based on the amplitudes of the data points in the intensity curve, and each peak segment cluster corresponds to an endmember, which includes a peak segment cluster corresponding to cyanobacteria;

[0044] Furthermore, by calculating the similarity between the peak segment clusters and the spectral curves of cyanobacteria, the cyanobacteria benchmark clusters are screened. The cyanobacteria benchmark clusters are the peak segment clusters corresponding to cyanobacteria. Since the larger the amplitude of each peak segment in the same peak segment cluster and the greater the amplitude difference between different peak segments, the stronger the spectral reflectivity of the end members corresponding to the peak segment cluster in each peak segment cluster. In this way, the spectral reflectivity of the end members corresponding to all peak segment clusters in their corresponding wavelength ranges is quantified. Then, according to the spectral reflectivity of the cyanobacteria benchmark clusters in their corresponding wavelength ranges and the spectral reflectivity curve of cyanobacteria, a spectral reflectivity curve of cyanobacteria can be constructed. The spectral reflectivity level-spectral reflectivity conversion model is used to obtain the spectral reflectivity of the end members corresponding to all peak clusters within their overall wavelength range through the spectral reflectivity levels of the end members corresponding to all peak clusters within their corresponding wavelength ranges, and obtain the spectral reflectivity of all end members, so as to obtain the proportion of cyanobacteria in all pixels in the remote sensing spectral data and monitor and warn of cyanobacterial blooms; this application is based on the characteristic of uniform distribution of various substances in the local area of ​​the lake, constructs a spectral reflectivity intensity-reflectivity conversion model, accurately inverts the proportion of cyanobacteria components at each location, and realizes accurate monitoring and timely warning of cyanobacterial blooms. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 This is a flowchart of the steps of a cyanobacteria bloom monitoring and early warning method of the present invention;

[0047] Figure 2 is the spectral reflectance curve of adjacent pixels;

[0048] Figure 3 Schematic diagram of the intensity curve corresponding to the spectral reflectance curve of adjacent pixels. DETAILED DESCRIPTION

[0049] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a cyanobacteria bloom monitoring and early warning method, device, and system proposed in accordance with the present invention. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0050] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0051] The following describes in detail a method, device and system for monitoring and early warning of cyanobacteria blooms provided by the present invention with reference to the accompanying drawings.

[0052] See also Figure 1 , which shows a flowchart of a method for monitoring and early warning of cyanobacteria blooms provided by one embodiment of the present invention, the method comprising the following steps:

[0053] Step S001: obtaining the spectral reflectance curve of cyanobacteria and the spectral reflectance curve of each pixel in the remote sensing spectral data.

[0054] It should be noted that cyanobacterial blooms can severely damage the ecological environment and endanger the health of humans and animals. Therefore, cyanobacterial blooms need to be monitored to prevent their rapid proliferation. Traditional cyanobacterial bloom monitoring methods mainly rely on manual water sampling or buoy monitoring. However, manual water sampling or buoy monitoring has problems such as limited spatial coverage, delayed response, and high operation and maintenance costs, making it difficult to meet the actual needs of large-scale, high-precision identification and early warning of cyanobacterial blooms. Monitoring cyanobacterial blooms using remote sensing spectral data has the advantages of multi-temporal and wide-band continuous coverage, enabling large-scale, high-precision, non-contact dynamic monitoring of cyanobacterial blooms. Therefore, this embodiment proposes a cyanobacterial bloom monitoring and early warning method. Specifically, remote sensing spectral data of lakes is collected through satellite remote sensing. Based on the remote sensing spectral data and the spectral reflectance of cyanobacteria, cyanobacterial blooms are monitored and warned.

[0055] Specifically, the spectral reflectance curve of cyanobacteria is obtained by a spectrometer, and the remote sensing spectral data of the lake is obtained by a hyperspectral observation satellite. The remote sensing spectral data is composed of the spectral reflectance curves of several pixels, and each pixel contains several end members, each of which has its own unique spectral reflectance curve.

[0056] Step S002: Obtain intensity curves of adjacent pixels based on the differences between the spectral reflectance curves of adjacent pixels; obtain the strength of the reflectivity of each data point in the intensity curve and several peak segments of the intensity curve based on the data points within the local range of each data point in the intensity curve; cluster all peak segments of all intensity curves to obtain several peak segment clusters.

[0057] It should be noted that due to the presence of endmembers corresponding to various suspended particulate matter and dissolved colored organic matter in lakes, it is difficult to construct a complete endmember spectral library. As a result, it is impossible to obtain the component ratios of the substances corresponding to the various endmembers in the pixel based on the spectral reflectance curve of the pixel. As a result, it is impossible to accurately obtain the component ratios of cyanobacteria in the pixel, and therefore it is impossible to effectively monitor and warn of cyanobacterial blooms. Therefore, this embodiment proposes a cyanobacterial bloom monitoring and early warning method, the ultimate goal of which is to obtain the cyanobacterial component ratios in each pixel in remote sensing spectral data. To this end, it is necessary to obtain the spectral reflectance curves of various endmembers in the lake.

[0058] It should be further explained that since the corresponding substances of various endmembers in the remote sensing spectral curve are evenly distributed in the local range of the lake, that is, the proportions of endmember components in adjacent pixels in the remote sensing spectral data are similar but there are also slight differences. When a certain endmember in adjacent pixels has a high spectral reflectance at a specific wavelength, even if the difference in the proportion of the endmember components is slight, it will lead to a significant difference in the spectral reflectance of the two pixels at that wavelength. Therefore, the difference between the spectral reflectance curves of adjacent pixels can be used to quantify the reflectivity of the endmembers in the adjacent pixels at each wavelength, and the spectral reflectivity intensity curves in the adjacent pixels can be obtained to represent the reflectivity of the endmembers in the adjacent pixels at each wavelength. Since the reflected energy of the endmember at each wavelength is an inherent property of the substance corresponding to the endmember, the number of endmembers with unknown spectral reflectivity can be obtained by traversing the reflectivity of the endmembers in all adjacent pixels at each wavelength.

[0059] Preferably, in a specific embodiment of the present invention, for any adjacent pixels, the absolute value of the difference between the spectral reflectance curves of the adjacent pixels is used as the end-member spectral reflectance intensity curve in the adjacent pixels;

[0060] Similarly, the endmember spectral reflectance intensity curves in all adjacent pixels in the remote sensing spectral data are obtained, and the endmember spectral reflectance intensity curves in all adjacent pixels are recorded as intensity curves.

[0061] It should be noted that the intensity curve represents the difference between the spectral reflectance curves of adjacent pixels. Therefore, the larger the amplitude of the intensity curve in a specific wavelength range, the greater the difference between the spectral reflectance curves of adjacent pixels. Since the substances corresponding to various end members in the remote sensing spectral curve are evenly distributed in the local range of the lake, that is, the proportion of end member components in adjacent pixels in the remote sensing spectral data is similar, when the amplitude of the intensity curve in a certain wavelength range is large, it means that there is a certain end member in the adjacent pixels corresponding to the intensity curve that has high reflectivity characteristics in the wavelength range, and the larger the amplitude, the higher its reflectivity characteristics. Since the spectral reflectance of the end member is an inherent property of the substance corresponding to the end member, that is, the wavelength range corresponding to the slight difference in the proportion of a certain end member component in adjacent pixels is the same, the number of unknown end members can be obtained by traversing the differences between the spectral reflectance curves of all adjacent pixels and combining the specific wavelength range corresponding to the high reflectivity.

[0062] Preferably, in a specific embodiment of the present invention, for any intensity curve data points, the distance between the intensity curve and the intensity curve The most recent data point data point, as the first data point in the intensity curve The local data points of the data points, is the preset number of local data points, The specific value of can be set according to the actual situation. This embodiment does not make a hard requirement. Take this as an example to describe;

[0063] According to the intensity curve The local data point of the data point is the same as the intensity curve The sum of the data points, and the The local data point of the data point is the same as the intensity curve The difference between the data points in the intensity curve is obtained. The specific calculation formula for the strength of the reflection ability of each data point is:

[0064]

[0065] Where, Indicates the intensity curve The strength of the reflective ability of each data point; Indicates the intensity curve data point and its The difference in amplitude between local data points; Indicates the intensity curve The amplitude of the data point; Indicates the intensity curve The data point The amplitude of a local data point; Indicates the intensity curve The mean of the difference in amplitude between a data point and each of its local data points; Indicates the intensity curve The number of local data points for each data point; Represents a linear normalization function, whose specific normalization range is all data points in all intensity curves .

[0066] Furthermore, the reflectivity of all data points in each intensity curve is obtained, and a threshold value of the reflectivity is preset. , The specific value of can be set according to the actual situation. This embodiment does not make a hard requirement. Take the example to describe, any intensity curve with a strength greater than The data point is taken as the peak point in the intensity curve, and the curve segment composed of continuous peak segments in the intensity curve is taken as the peak segment of the intensity curve.

[0067] It should be noted that when the spectral reflectance of a certain end member is high in a certain wavelength range, even if there is a slight difference in the component proportion of the end member in two pixels, the spectral reflectance curves of the two pixels will produce a large difference in the wavelength range. Since the intensity curve represents the difference between the spectral reflectance curves of adjacent pixels, and the component proportions of various end members in adjacent pixels only have a slight difference, The larger the value of , the more it means that there is a certain end member in the adjacent pixel, and the end member is the first in the intensity curve. The higher the spectral reflectivity of the data point at the corresponding wavelength. And because the spectral reflectivity of the end member at a certain wavelength is higher, It means the All local data points of the data point and the The fluctuation of the difference between the data points is due to the fact that when the spectral reflectance of the end member is higher, the All local data points of the data point and the The greater the volatility of the differences between data points, the The larger the value of The more data points there are, the stronger the reflection ability is. Figure 2 and Figure 3 As shown, Figure 2 is the spectral reflectance curve of adjacent pixels, Figure 3The intensity curves corresponding to the spectral reflectance curves of adjacent pixels are shown in Figure 2. Based on this, the wavelength range of the strong spectral reflectance of all end members can be obtained.

[0068] Preferably, in a specific embodiment of the present invention, all peak segments of all intensity curves are grouped into a peak segment set; The peak segment and peak segments, according to the first The wavelength range corresponding to the first peak segment is The intersection-and-combination ratio of the wavelength range corresponding to the peak segment is used as the negative correlation map of the intersection-and-combination ratio in the peak segment set. The peak segment and The degree of band difference between the peak bands.

[0069] As an example, the peak segment set The peak segment and The specific calculation formula for the degree of band difference between peak segments is:

[0070]

[0071] Where, Indicates the peak segment set The peak segment and The degree of band difference between the peak segments; Indicates the peak segment set The wavelength range corresponding to each peak segment; Indicates the peak segment set The wavelength range corresponding to each peak segment; represents the intersection operation; Represents a union operation.

[0072] Furthermore, the degree of band difference between all peak segments in the peak segment set is obtained, and the degree of band difference between all peak segments in the peak segment set is used as the distance measurement, and the optimal number of clusters for all peak segments in the peak segment set is obtained through the elbow method; all peak segments in the peak segment set are clustered through the k-means clustering algorithm, and the K value in the k-means clustering algorithm is set to the optimal number of clusters for all peak segments in the peak segment set, to obtain several peak segment clusters; since the elbow method and the k-means clustering algorithm are both well-known existing technologies, they will not be repeated in this embodiment.

[0073] It should be noted that the peak segment indicates that there is a certain end member with a strong spectral reflectivity feature within the wavelength range corresponding to the peak segment, and the degree of band difference between different peak segments in the peak segment set indicates the difference between different peak segments in the corresponding wavelength range. It represents the intersection-and-union ratio of different peak segments in the peak segment set in the corresponding wavelength range. The larger the value of the intersection-and-union ratio, the higher the overlap of different peak segments in the corresponding wavelength range. The larger the value of , the greater the band difference between different peak segments in the peak segment set; and since the spectral reflectance of the end member is the inherent property of the material corresponding to the end member, the peak segments can be clustered according to the wavelength range corresponding to the peak segment. The wavelength range corresponding to the peak segment in each peak segment cluster obtained represents the wavelength range corresponding to the strong spectral reflectance of each end member.

[0074] At this point, several peak clusters are obtained.

[0075] Step S003: Compare the peak segments in all peak segment clusters with the spectral reflectance curves of cyanobacteria, screen the cyanobacteria benchmark clusters and obtain the cyanobacteria benchmark curve; according to the amplitude of the data points in the peak segments in each peak segment cluster, obtain the spectral reflectance level of all wavelengths within the overall wavelength range of each peak segment cluster, and construct a spectral reflectance level-spectral reflectance conversion model in combination with the cyanobacteria benchmark curve to obtain the spectral reflectance of the end members corresponding to all peak segment clusters within their overall wavelength range.

[0076] It should be noted that the wavelength range corresponding to the peak segment in each peak segment cluster obtained in step S002 represents the wavelength range corresponding to the strong spectral reflectance of each end member, that is, each peak segment cluster corresponds to one end member; and the end members corresponding to all peak segment clusters obtained in step S002 include cyanobacteria; the peak segment cluster corresponding to cyanobacteria can be screened out from all peak segment clusters by analyzing the correlation between the spectral reflectance curve of cyanobacteria and the peak segments in the peak segment cluster; and when the amplitude of each peak segment in the same peak segment cluster is larger and the amplitude difference between different peak segments is larger, it means that the peak segment cluster has a strong spectral reflectance of cyanobacteria. The end members corresponding to the segment clusters have stronger spectral reflectivity in the wavelength range corresponding to the peak segment in each peak segment cluster, so as to quantify the degree of spectral reflectivity of the end members corresponding to all peak segment clusters in their corresponding wavelength ranges; further combined with the spectral reflectivity curve of cyanobacteria, a spectral reflectivity degree-spectral reflectivity conversion model is constructed to obtain the spectral reflectivity of the end members corresponding to all peak segment clusters in their overall wavelength range, so as to better obtain the proportion of cyanobacteria in all pixels in the future through the spectral reflectivity of the end members corresponding to all clusters.

[0077] Preferably, in a specific embodiment of the present invention, for any peak segment cluster, the intersection of the wavelength ranges corresponding to all peak segments in the peak segment cluster is used as the overall wavelength range of the peak segment cluster, and the spectral reflectance curve of the cyanobacteria within the overall wavelength range of the peak segment cluster is used as the cyanobacteria reference curve of the peak segment cluster;

[0078] Furthermore, for any peak segment in the peak segment cluster, the peak segment in the peak segment cluster is matched with the cyanobacteria reference curve of the peak segment cluster using the DTW algorithm to obtain a plurality of matching pairs. Since the DTW algorithm is a well-known prior art, it will not be described in detail in this embodiment.

[0079] Based on the difference between the two elements in each matching pair and the strength of the reflectivity of each data point in the peak segment in the peak segment cluster, the similarity between the peak segment in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster is obtained. The specific calculation formula is:

[0080]

[0081] Where, Indicates the similarity between the peak segment in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster; represents the number of matching pairs; Indicates the the strength of the reflectivity of the data points corresponding to the peak segments in the peak segment clusters within a matching pair; Indicates the The amplitude of the data point corresponding to the cyanobacteria reference curve of the peak segment cluster within the matching pair; Indicates the The amplitude of the data point corresponding to the peak segment in the peak segment cluster within the matching pair; Indicates the absolute value function; Represents an exponential function with a natural constant as the base. In this embodiment, Model to present inverse proportional relationship and normalization processing, As the input of the model, the implementer can set the inverse proportional function and normalization function according to the actual situation.

[0082] It should be noted that, the greater the reflection ability of the data points in the peak segment in the peak segment cluster, the higher the spectral reflectivity of the data points in the peak segment in the peak segment cluster at the corresponding wavelength, and the more it can show the spectral reflectivity characteristics of the peak segment cluster corresponding to the substance. Therefore, when quantifying the similarity between the peak segment in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster by matching the distance between the data points within the pair, the reflection ability of the data points corresponding to the peak segment in the peak segment cluster within the matching pair is used as the weight, so as to accurately obtain the similarity between the peak segment in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster.

[0083] Similarly, the similarity between all peak segments in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster is obtained, and the average similarity between all peak segments in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster is used as the similarity between the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster;

[0084] Furthermore, the similarity between all peak segment clusters and the cyanobacteria reference curve of the peak segment cluster is obtained, and the peak segment cluster corresponding to the maximum similarity value and the cyanobacteria reference curve of the peak segment cluster are recorded as the cyanobacteria benchmark cluster and the cyanobacteria benchmark curve, respectively.

[0085] It should be noted that the end member corresponding to the cyanobacteria benchmark cluster is cyanobacteria; and when the amplitude of the peak segment in the same peak segment cluster is larger and the amplitude difference between different peak segments is larger, it means that the end member corresponding to the peak segment cluster has a stronger spectral reflectivity in the wavelength range corresponding to the peak segment in each peak segment cluster. Therefore, this can be used to quantify the degree of spectral reflectivity of all wavelengths within the overall wavelength range of all peak segment clusters.

[0086] Preferably, in a specific embodiment of the present invention, any wavelength within the entire wavelength range of any peak segment cluster is recorded as the target wavelength; the product of the amplitude mean and the amplitude standard deviation of all peak segments in the peak segment cluster at the target wavelength is used as the spectral reflectance level of the peak segment cluster at the target wavelength;

[0087] Similarly, the spectral reflectance levels of all wavelengths within the overall wavelength range of all peak clusters are obtained.

[0088] It should be noted that, when the amplitude mean of all peak segments in the peak segment cluster at any wavelength is larger, the difference in spectral reflectance of all adjacent pixels at this wavelength is larger; when the amplitude standard deviation of all peak segments in the peak segment cluster at any wavelength is larger, the change in spectral reflectance of all adjacent pixels at this wavelength is larger; and because the proportion of end-member components between adjacent pixels is similar, when the amplitude mean and amplitude standard deviation of all peak segments in the peak segment cluster at any wavelength are larger, the more it indicates that the corresponding end-member of the peak segment cluster has a slight change, which can greatly affect the spectral reflectance of adjacent pixels in the overall wavelength range of the peak segment cluster, that is, the spectral reflectance intensity of all wavelengths in the overall wavelength range of the peak segment cluster is high. Furthermore, after obtaining the spectral reflectivity levels of all wavelengths within the overall wavelength range of all peak clusters, the spectral reflectivity levels of all wavelengths within the overall wavelength range can be used as a standard, combined with the cyanobacteria benchmark curve, to construct a spectral reflectivity level-spectral reflectivity conversion model to obtain the spectral reflectivity of all wavelengths in the overall wavelength range of all peak clusters.

[0089] Preferably, in a specific embodiment of the present invention, the spectral reflectance levels of all wavelengths within the overall wavelength range of the cyanobacteria benchmark cluster are obtained, and the spectral reflectance levels and spectral reflectances at the same wavelengths in the cyanobacteria benchmark cluster and the cyanobacteria benchmark curve are classified into the same data group to obtain several data groups. A linear fit is performed on all data groups using the least squares method to obtain a spectral reflectance level-spectral reflectance conversion model. Since the least squares method is a well-known prior art, it will not be described in detail in this embodiment.

[0090] Furthermore, the spectral reflectivity levels of all wavelengths within the overall wavelength range of each peak segment cluster are input into the spectral reflectivity level-spectral reflectivity conversion model to obtain the spectral reflectivity of the corresponding end member of each peak segment cluster within its overall wavelength range.

[0091] It should be noted that the spectral reflectivity level-spectral reflectivity conversion model is based on the spectral reflectivity of cyanobacteria and is obtained by fitting the linear relationship between the spectral reflectivity level of cyanobacteria and the spectral reflectivity. The spectral reflectivity level-spectral reflectivity conversion model can represent the relationship between the spectral reflectivity level and the spectral reflectivity of the end members corresponding to each peak segment cluster. Therefore, according to the spectral reflectivity level-spectral reflectivity conversion model and the spectral reflectivity level of all wavelengths in the overall wavelength range of the peak segment cluster, the spectral reflectivity of all wavelengths in the overall wavelength range of all peak segment clusters can be obtained, providing a theoretical basis for the subsequent acquisition of the proportion of cyanobacteria components in each pixel in the remote sensing spectral data.

[0092] At this point, the spectral reflectance of the end members corresponding to all peak clusters within their entire wavelength range is obtained.

[0093] Step S004: According to the spectral reflectance of all end members within their entire wavelength range, the proportion of cyanobacteria in each pixel in the remote sensing spectral data is obtained, and cyanobacteria blooms are monitored and warned.

[0094] It should be noted that after obtaining the spectral reflectance of the end members corresponding to all peak segment clusters within their overall wavelength range in step S003, the spectral reflectance of the end members corresponding to all peak segment clusters within their overall wavelength range can be used as a basis to analyze all pixels in the remote sensing spectral data, and accurately obtain the proportion of cyanobacteria in all pixels in the remote sensing spectral data, thereby achieving accurate monitoring of cyanobacterial blooms and timely early warning of cyanobacterial blooms.

[0095] Specifically, using semi-blind hyperspectral unmixing, the spectral reflectance of the end members corresponding to all peak clusters within their entire wavelength range, the spectral reflectance of cyanobacteria, and the spectral reflectance curves of all pixels in the remote sensing spectral data are input into the semi-blind hyperspectral unmixing to obtain the proportion of cyanobacteria in all pixels in the remote sensing spectral data;

[0096] Furthermore, two warning ranges are preset respectively and , and The specific value of can be set according to the actual situation. This embodiment does not make a hard requirement. 、 For example, for any pixel, when the proportion of cyanobacteria in the pixel is less than or equal to , it is judged that the cyanobacteria in the pixel is in a normal state; when the proportion of cyanobacteria in the pixel is greater than Less than or equal to , it is judged that the cyanobacteria in the pixel is in a mild cyanobacterial bloom state; when the proportion of cyanobacteria in the pixel is greater than , it is judged that the cyanobacteria in the pixel is in a severe cyanobacterial bloom state; for normal locations in the lake, continue to monitor the proportion of cyanobacteria components as normal; for locations in the lake with a mild cyanobacterial bloom state, increase the frequency of monitoring the proportion of cyanobacteria components; for locations in the lake with a severe cyanobacterial bloom state, immediately warn relevant technical personnel.

[0097] It should be noted that, since semi-blind hyperspectral unmixing is a well-known existing technology, it will not be described in detail in this embodiment. This embodiment is based on the characteristic that various substances are evenly distributed in the local area of ​​the lake. The wavelength range that can strongly reflect the spectrum in the spectral reflectance curve of various substances in the lake is analyzed and obtained, and the degree of spectral reflectance of each wavelength range is quantified. Combined with the spectral reflectance of cyanobacteria, a spectral reflectance degree-spectral reflectance conversion model is constructed to obtain the wavelength range that various substances in the lake can strongly reflect the spectrum, so as to accurately obtain the proportion of cyanobacteria in all pixels in the remote sensing spectral data; further, according to the proportion of cyanobacteria in all pixels in the remote sensing spectral data, timely warning of cyanobacteria blooms can be issued, thereby avoiding cyanobacteria water from damaging the ecological environment and endangering human and animal health.

[0098] Another embodiment of the present invention provides a cyanobacteria bloom monitoring and early warning system, the system comprising:

[0099] Data acquisition module: used to obtain the spectral reflectance curve of cyanobacteria and the spectral reflectance curve of each pixel in the remote sensing spectral data, wherein the pixel contains several end members;

[0100] Data analysis module: used to obtain the intensity curves of adjacent pixels based on the differences between the spectral reflectance curves of adjacent pixels; obtain the reflectivity strength of each data point in the intensity curve and the number of peak segments of the intensity curve based on the data points within the local range of each data point in the intensity curve; cluster all the peak segments of all the intensity curves to obtain several peak segment clusters;

[0101] Model construction module: used to compare the peak segments in all peak segment clusters with the spectral reflectance curves of cyanobacteria, screen the cyanobacteria benchmark clusters and obtain the cyanobacteria benchmark curve; based on the amplitude of the data points within the peak segment in each peak segment cluster, obtain the spectral reflectance level of all wavelengths within the overall wavelength range of each peak segment cluster; combined with the cyanobacteria benchmark curve, construct a spectral reflectance level-spectral reflectance conversion model, and then obtain the spectral reflectance of the end members corresponding to all peak segment clusters within their overall wavelength range;

[0102] Monitoring and early warning module: It is used to obtain the proportion of cyanobacteria in each pixel in the remote sensing spectral data based on the spectral reflectance of all end members within their overall wavelength range, and to monitor and warn of cyanobacterial blooms.

[0103] Another embodiment of the present invention provides a cyanobacteria bloom monitoring and early warning device, including a spectral data analysis device, the spectral data analysis device including a memory, a processor, and a computer program stored in the memory and running on the processor, when the processor executes the computer program, the above-mentioned system module is implemented.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for monitoring and early warning of cyanobacteria blooms, characterized in that: The method comprises the following steps: Obtaining a spectral reflectance curve of cyanobacteria and a spectral reflectance curve of each pixel in remote sensing spectral data, wherein the pixel contains several end members; The intensity curves of adjacent pixels are obtained based on the differences between the spectral reflectance curves of adjacent pixels. The reflectivity of each data point in the intensity curve and the peak segments of the intensity curve are obtained based on the data points within the local range of each data point in the intensity curve. All peak segments of all intensity curves are clustered to obtain several peak segment clusters. The peak segments in all peak segment clusters are compared with the spectral reflectance curves of cyanobacteria to screen the cyanobacteria benchmark clusters and obtain the cyanobacteria benchmark curves; based on the amplitude of the data points within the peak segment in each peak segment cluster, the spectral reflectance level of all wavelengths within the overall wavelength range of each peak segment cluster is obtained. Combined with the cyanobacteria benchmark curve, a spectral reflectance level-spectral reflectance conversion model is constructed to obtain the spectral reflectance of the end members corresponding to all peak segment clusters within their overall wavelength range; Based on the spectral reflectance of all end members within their entire wavelength range, the proportion of cyanobacteria in each pixel in the remote sensing spectral data is obtained, and cyanobacteria blooms are monitored and warned. The specific method of obtaining the strength of the reflectivity of each data point in the intensity curve and several peak segments of the intensity curve based on the data points within the local range of each data point in the intensity curve includes: For any intensity curve data points, the distance between the intensity curve and the intensity curve The most recent data point data point, as the first data point in the intensity curve The local data points of the data points, is the preset number of local data points; According to the intensity curve The local data point of the data point is the same as the The sum of the data points, and the The local data point of the data point is the same as the The difference between the data points in the intensity curve is obtained. The specific calculation method for the strength of the reflection ability of each data point is: Where, Indicates the intensity curve The strength of the reflective ability of each data point; Indicates the intensity curve data point and its The difference in amplitude between local data points; Indicates the intensity curve The amplitude of the data point; Indicates the intensity curve The data point The amplitude of a local data point; Indicates the intensity curve The mean of the difference in amplitude between a data point and each of its local data points; Indicates the intensity curve The number of local data points for each data point; represents the linear normalization function; Get the reflectivity of all data points in each intensity curve and set a threshold for the reflectivity , any intensity curve with a strength greater than The data point is taken as the peak point in the intensity curve, and the curve segment composed of continuous peak segments in the intensity curve is taken as the peak segment of the intensity curve.

2. The method for monitoring and early warning of cyanobacteria blooms according to claim 1, characterized in that: The method of clustering all peak segments of all intensity curves to obtain a number of peak segment clusters includes: All peak segments of all intensity curves are grouped into a peak segment set; for the The peak segment and peak segments, get the peak segment set The wavelength range corresponding to the first peak segment is The intersection-and-combination ratio of the wavelength range corresponding to the peak segment is used as the negative correlation map of the intersection-and-combination ratio in the peak segment set. The peak segment and The degree of band difference between the peak segments; The band difference degree between all the peak segments in the peak segment set is obtained, and the band difference degree between all the peak segments in the peak segment set is used as the measurement distance. All the peak segments in the peak segment set are clustered to obtain a number of peak segment clusters.

3. The method for monitoring and early warning of cyanobacteria blooms according to claim 1, characterized in that: The specific method of comparing the peak segments in all peak segment clusters with the spectral reflectance curves of cyanobacteria, screening the cyanobacteria benchmark clusters and obtaining the cyanobacteria benchmark curve includes: For any peak segment cluster, the intersection of the wavelength ranges corresponding to all peak segments in the peak segment cluster is used as the overall wavelength range of the peak segment cluster, and the spectral reflectance curve of the cyanobacteria within the overall wavelength range of the peak segment cluster is used as the cyanobacteria reference curve of the peak segment cluster; For any peak segment in the peak segment cluster, matching the peak segment in the peak segment cluster with a cyanobacteria reference curve of the peak segment cluster using a DTW algorithm to obtain a plurality of matching pairs; According to the difference between the two elements in each matching pair, combined with the strength of the reflectivity of each data point in the peak segment in the peak segment cluster, the similarity between the peak segment in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster is obtained; Obtaining the similarity between all peak segments in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster, and taking the average of the similarities between all peak segments in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster as the similarity between the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster; The similarity between all peak segment clusters and the cyanobacteria reference curve of the peak segment cluster is obtained, and the peak segment cluster corresponding to the maximum similarity value and the cyanobacteria reference curve of the peak segment cluster are recorded as the cyanobacteria benchmark cluster and the cyanobacteria benchmark curve, respectively.

4. A cyanobacteria bloom monitoring and early warning method according to claim 3, characterized in that: The specific calculation formula for obtaining the similarity between the peak segment in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster is: Where, Indicates the similarity between the peak segment in the peak segment cluster and the cyanobacteria reference curve of the peak segment cluster; represents the number of matching pairs; Indicates the the strength of the reflectivity of the data points corresponding to the peak segments in the peak segment clusters within a matching pair; Indicates the The amplitude of the data point corresponding to the cyanobacteria reference curve of the peak segment cluster within the matching pair; Indicates the The amplitude of the data point corresponding to the peak segment in the peak segment cluster within the matching pair; Indicates the absolute value function; Represents an exponential function with a natural constant as its base.

5. The method for monitoring and early warning of cyanobacteria blooms according to claim 1, characterized in that: The specific method for obtaining the spectral reflectance of all wavelengths within the overall wavelength range of each peak segment cluster based on the amplitude of the data points within the peak segment in each peak segment cluster is as follows: Any wavelength within the entire wavelength range of any peak segment cluster is recorded as the target wavelength; the product of the amplitude mean and the amplitude standard deviation of all peak segments in the peak segment cluster at the target wavelength is taken as the spectral reflectivity level of the peak segment cluster at the target wavelength.

6. The method for monitoring and early warning of cyanobacteria blooms according to claim 1, characterized in that: The specific method of constructing the spectral reflectivity high and low degree-spectral reflectivity conversion model and then obtaining the spectral reflectivity of the end members corresponding to all peak segment clusters within their entire wavelength range is as follows: The spectral reflectance levels and spectral reflectances at the same wavelengths in the cyanobacteria benchmark clusters and the cyanobacteria benchmark curves were grouped into the same data group to obtain several data groups. All data groups were linearly fitted using the least squares method to obtain a spectral reflectance-spectral reflectance conversion model. The spectral reflectivity levels of all wavelengths within the overall wavelength range of each peak segment cluster are input into the spectral reflectivity level-spectral reflectivity conversion model to obtain the spectral reflectivity of the end member corresponding to each peak segment cluster within its overall wavelength range.

7. The method for monitoring and early warning of cyanobacteria blooms according to claim 1, characterized in that: The method of obtaining the proportion of cyanobacteria in each pixel in the remote sensing spectral data based on the spectral reflectance of all end members within their entire wavelength range and monitoring and warning of cyanobacterial blooms includes the following specific methods: Using semi-blind hyperspectral unmixing, the spectral reflectance of all end members in their entire wavelength range, the spectral reflectance of cyanobacteria, and the spectral reflectance curves of all pixels in the remote sensing spectral data are input into the semi-blind hyperspectral unmixing to obtain the proportion of cyanobacteria in all pixels in the remote sensing spectral data. Preset two warning ranges respectively and For any pixel, when the proportion of cyanobacteria in the pixel is less than or equal to , it is judged that the cyanobacteria in the pixel is in a normal state; when the proportion of cyanobacteria in the pixel is greater than Less than or equal to , it is judged that the cyanobacteria in the pixel is in a mild cyanobacterial bloom state; when the proportion of cyanobacteria in the pixel is greater than , judge that the cyanobacteria in the pixel is in a state of severe cyanobacteria bloom; for normal locations in the lake, continue to monitor the proportion of cyanobacteria components; for locations in the lake with a mild cyanobacteria bloom, increase the frequency of monitoring the proportion of cyanobacteria components; for locations in the lake with a severe cyanobacteria bloom, immediately warn relevant technical personnel.

8. A cyanobacteria bloom monitoring and early warning system, characterized in that: The system includes: Data acquisition module: used to obtain the spectral reflectance curve of cyanobacteria and the spectral reflectance curve of each pixel in the remote sensing spectral data, wherein the pixel contains several end members; Data analysis module: used to obtain the intensity curves of adjacent pixels based on the differences between the spectral reflectance curves of adjacent pixels; obtain the reflectivity strength of each data point in the intensity curve and the number of peak segments of the intensity curve based on the data points within the local range of each data point in the intensity curve; cluster all the peak segments of all the intensity curves to obtain several peak segment clusters; Model construction module: used to compare the peak segments in all peak segment clusters with the spectral reflectance curves of cyanobacteria, screen the cyanobacteria benchmark clusters and obtain the cyanobacteria benchmark curve; based on the amplitude of the data points within the peak segment in each peak segment cluster, obtain the spectral reflectance level of all wavelengths within the overall wavelength range of each peak segment cluster; combined with the cyanobacteria benchmark curve, construct a spectral reflectance level-spectral reflectance conversion model, and then obtain the spectral reflectance of the end members corresponding to all peak segment clusters within their overall wavelength range; Monitoring and early warning module: used to obtain the proportion of cyanobacteria in each pixel in remote sensing spectral data based on the spectral reflectance of all end members within their overall wavelength range, and to monitor and warn of cyanobacterial blooms; The specific method of obtaining the strength of the reflectivity of each data point in the intensity curve and several peak segments of the intensity curve based on the data points within the local range of each data point in the intensity curve includes: For any intensity curve data points, the distance between the intensity curve and the intensity curve The most recent data point data point, as the first data point in the intensity curve The local data points of the data points, is the preset number of local data points; According to the intensity curve The local data point of the data point is the same as the The sum of the data points, and the The local data point of the data point is the same as the The difference between the data points in the intensity curve is obtained. The specific calculation method for the strength of the reflection ability of each data point is: Where, Indicates the intensity curve The strength of the reflective ability of each data point; Indicates the intensity curve data point and its The difference in amplitude between local data points; Indicates the intensity curve The amplitude of the data point; Indicates the intensity curve The data point The amplitude of a local data point; Indicates the intensity curve The mean of the difference in amplitude between a data point and each of its local data points; Indicates the intensity curve The number of local data points for each data point; represents the linear normalization function; Get the reflectivity of all data points in each intensity curve and set a threshold for the reflectivity , any intensity curve with a strength greater than The data point is taken as the peak point in the intensity curve, and the curve segment composed of continuous peak segments in the intensity curve is taken as the peak segment of the intensity curve.

9. A cyanobacteria bloom monitoring and early warning device, comprising a spectral data analysis device, characterized in that: The spectral data analysis device includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the cyanobacteria bloom monitoring and early warning method as described in any one of claims 1 to 7 are implemented.

Images