Ocean red tide disaster early warning method and system based on unmanned aerial vehicle

Abstract

The invention discloses an unmanned aerial vehicle-based ocean red tide disaster early warning method and system, mainly relates to the technical field of ocean red tide disaster early warning, and is used for solving the problems of insufficient multi-source data fusion, high maintenance cost and slow hyperspectral sensor processing in the existing scheme. Comprising the following steps: determining an initial red tide abnormal area through an unmanned aerial vehicle carrying a thermal infrared sensor and a chlorophyll concentration collector; configuring a corresponding fluorescence sensor based on a biological fluorescence wave band related to the red tide; detecting whether a fluorescence reaction exists in the initial red tide abnormal area or not through an unmanned aerial vehicle carrying a fluorescence sensor; when the fluorescence reaction exists, determining that the initial red tide abnormal region is a red tide abnormal region; the corresponding red tide marker concentration is determined by collecting the reflected fluorescence intensity; and acquiring the biological fluorescence wave band of the current fluorescence sensor, and determining the red tide marker type of the current red tide abnormal area.

Description

Technical Field

[0001] The present application relates to the field of marine red tide disaster early warning technology, and in particular to a marine red tide disaster early warning method and system based on drones. Background Art

[0002] Currently, marine red tide monitoring relies primarily on three technical approaches: satellite remote sensing, manned vessel sampling, and fixed buoys. Satellite remote sensing (such as MODIS and Sentinel-3) inverts chlorophyll a concentrations from multispectral data, but is subject to cloud interference and has low spatial resolution (typically >300 meters), making it difficult to capture small-scale outbreaks at the beginning of a red tide. Manned vessel sampling can obtain accurate data, but is costly (over 100,000 yuan per voyage), has a slow response (24-48 hours from sampling to laboratory analysis), and lacks coverage of complex nearshore waters. Fixed buoy networks (such as the GOOS system) enable continuous monitoring, but deployment density is limited (typical spacing is 50 kilometers) and maintenance costs are high. In recent years, some studies have attempted to use drones equipped with hyperspectral sensors for red tide screening, but these require specialized hyperspectral sensors and machine learning models, preprocessing redundant bands, and addressing signal-to-noise ratio issues.

[0003] The above technologies have the following problems: (1) High maintenance cost. Satellite remote sensing, manned ship sampling and fixed buoys have high maintenance costs; (2) Hyperspectral sensor processing is slow: the spectral detection area is large, and redundant bands need to be pre-processed and the signal-to-noise ratio problem needs to be solved. Summary of the Invention

[0004] This application provides a marine red tide disaster early warning method and system based on drones to solve the problems of high maintenance costs and slow processing of hyperspectral sensors in existing solutions.

[0005] In a first aspect, the present application provides a method for early warning of marine red tide disasters based on drones, the method comprising: By using a drone equipped with a thermal infrared sensor and a chlorophyll concentration collector, the water temperature and chlorophyll concentration of each preset detection point in the area to be detected are collected; based on the water temperature and chlorophyll concentration of the preset detection points, the initial red tide abnormal area is determined; based on the biological fluorescence band involved in the red tide, the corresponding fluorescence sensor is configured; by using a drone equipped with a fluorescence sensor, whether there is a fluorescence reaction in the initial red tide abnormal area; when a fluorescence reaction exists, the initial red tide abnormal area is determined to be a red tide abnormal area; by collecting the reflected fluorescence intensity, the corresponding red tide marker concentration is determined; the biological fluorescence band of the current fluorescence sensor is obtained to determine the red tide marker type of the current red tide abnormal area; the red tide marker concentration and red tide marker type are uploaded to the corresponding preset red tide monitoring terminal.

[0006] In one implementation of the present application, determining the initial red tide abnormal area based on the water temperature and chlorophyll concentration at a preset detection point specifically includes: According to the corresponding relationship between the chlorophyll concentration and the abnormal temperature rise threshold, the abnormal temperature rise threshold corresponding to the current chlorophyll concentration is determined; With the preset detection point as the center and the preset distance as the radius, the background area is obtained, and the location water temperature of several locations on the edge of the background area is randomly collected; the location water temperature is input into the clustering algorithm, and the average value of the location water temperature of the cluster center with the largest number of clusters is determined as the background water temperature; The difference between the background water temperature and the water body temperature is calculated. When it is greater than the abnormal temperature rise threshold, the area with the preset detection point as the center and the preset ratio of the preset distance as the radius is determined as the initial red tide abnormal area; wherein the preset ratio is greater than 0 and less than 1.

[0007] In one implementation of the present application, a drone equipped with a fluorescence sensor is used to detect whether there is a fluorescence reaction in the initial red tide abnormal area, specifically including: By using a drone equipped with a fluorescence sensor, fluorescence sensors of different biological fluorescence bands are switched in the initial red tide abnormal area to detect whether there is a fluorescence reaction in the initial red tide abnormal area.

[0008] In one implementation of the present application, the concentration of the corresponding red tide marker is determined by collecting the reflected fluorescence intensity, specifically including: By formula: , determine the corresponding red tide marker concentration; in, and is the preset value.

[0009] In one implementation of the present application, obtaining the bioluminescence band of the current fluorescence sensor and determining the type of red tide marker in the current red tide abnormal area specifically includes: Obtain the number of red tide marker types corresponding to the bioluminescence band of the current fluorescence sensor. When the number of types is unique, determine the unique red tide marker type as the red tide marker type of the current red tide abnormal area. When the number of types is not unique, the type of red tide markers in the current red tide abnormal area is determined by using a drone equipped with an image recognition device.

[0010] In one implementation of the present application, after obtaining the bioluminescence band of the current fluorescence sensor and determining the type of red tide marker in the current red tide abnormal area, the method further includes: Get the number of preset detection points corresponding to the red tide abnormal area. When the number of preset detection points is less than the preset value, add a new preset detection point in the red tide abnormal area.

[0011] In a second aspect, the present application provides a drone-based marine red tide disaster early warning system, the system comprising: The determination module is used to collect water temperature and chlorophyll concentration at each preset detection point in the detection area using a drone equipped with a thermal infrared sensor and a chlorophyll concentration collector; based on the water temperature and chlorophyll concentration at the preset detection points, determine the initial red tide abnormal area; A detection module is used to configure corresponding fluorescence sensors based on the biological fluorescence bands involved in red tide; Use drones equipped with fluorescence sensors to detect whether there is a fluorescent reaction in the initial red tide abnormal area; The upload module is used to determine that the initial red tide abnormal area is a red tide abnormal area when there is a fluorescence reaction; determine the corresponding red tide marker concentration by collecting the reflected fluorescence intensity; obtain the bioluminescence band of the current fluorescence sensor to determine the red tide marker type of the current red tide abnormal area; upload the red tide marker concentration and red tide marker type to the corresponding preset red tide monitoring terminal.

[0012] In one implementation of the present application, the determination module includes a determination unit, For determining the abnormal temperature rise threshold value corresponding to the current chlorophyll concentration according to the corresponding relationship between the chlorophyll concentration and the abnormal temperature rise threshold value; With the preset detection point as the center and the preset distance as the radius, the background area is obtained, and the location water temperature of several locations on the edge of the background area is randomly collected; the location water temperature is input into the clustering algorithm, and the average value of the location water temperature of the cluster center with the largest number of clusters is determined as the background water temperature; The difference between the background water temperature and the water body temperature is calculated. When it is greater than the abnormal temperature rise threshold, the area with the preset detection point as the center and the preset ratio of the preset distance as the radius is determined as the initial red tide abnormal area; wherein the preset ratio is greater than 0 and less than 1.

[0013] In one implementation of the present application, the upload module includes a concentration determination unit, Used by the formula: , determine the corresponding red tide marker concentration; in, and is the preset value.

[0014] In one implementation of the present application, the upload module includes a marker determination unit, It is used to obtain the number of red tide marker types corresponding to the bioluminescence band of the current fluorescence sensor. When the number of types is unique, the unique red tide marker type is determined as the red tide marker type of the current red tide abnormal area; When the number of types is not unique, the type of red tide markers in the current red tide abnormal area is determined by using a drone equipped with an image recognition device.

[0015] It can be seen from the above technical solutions that this application has the following advantages: Reduced operation and maintenance costs: Compared with satellite remote sensing and manned ships, the cost of a single inspection by an unmanned aerial vehicle system is lower. In addition, the equipment used in this application (thermal infrared sensor, chlorophyll concentration collector, fluorescence sensor) is relatively conventional, easy to obtain and low in cost.

[0016] Breakthrough in hyperspectral processing efficiency: By using band pre-screening technology, the number of bands to be processed is compressed from 256 in conventional hyperspectral instruments to several key biological fluorescence bands, eliminating the need to pre-process redundant bands and solve signal-to-noise ratio problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for the description. 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.

[0018] Figure 1 This is a flow chart of a method for early warning of marine red tide disasters based on drones provided in an embodiment of the present application.

[0019] Figure 2 This is a schematic diagram of the internal structure of a UAV-based marine red tide disaster warning system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] It should be understood by those skilled in the art that the embodiments described below are merely preferred embodiments of the present disclosure and do not imply that the present disclosure can only be implemented through these preferred embodiments. These preferred embodiments are merely intended to explain the technical principles of the present disclosure and are not intended to limit the scope of protection of the present disclosure. Based on the preferred embodiments provided by the present disclosure, all other embodiments obtained by those skilled in the art without creative effort should still fall within the scope of protection of the present disclosure.

[0022] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0023] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0024] The embodiment provides a method for early warning of marine red tide disasters based on drones, such as Figure 1 As shown, the method provided in the embodiment of the present application mainly includes the following steps: Step 110: Using a drone equipped with a thermal infrared sensor and a chlorophyll concentration collector, collect the water temperature and chlorophyll concentration of each preset detection point in the area to be detected; based on the water temperature and chlorophyll concentration of the preset detection points, determine the initial red tide abnormal area.

[0025] In some embodiments, determining the initial red tide abnormal area based on the water temperature and chlorophyll concentration at a preset detection point specifically includes: According to the corresponding relationship between the chlorophyll concentration and the abnormal temperature rise threshold, the abnormal temperature rise threshold corresponding to the current chlorophyll concentration is determined; With the preset detection point as the center and the preset distance as the radius, the background area is obtained, and the location water temperature of several locations on the edge of the background area is randomly collected; the location water temperature is input into the clustering algorithm, and the average value of the location water temperature of the cluster center with the largest number of clusters is determined as the background water temperature; The difference between the background water temperature and the water body temperature is calculated. When it is greater than the abnormal temperature rise threshold, the area with the preset detection point as the center and the preset ratio of the preset distance as the radius is determined as the initial red tide abnormal area; wherein the preset ratio is greater than 0 and less than 1.

[0026] Those skilled in the art will appreciate that this step can establish a dynamic correspondence between chlorophyll concentration and an abnormal temperature rise threshold. For example, when the chlorophyll concentration reaches 10 μg / L, the system automatically sets the abnormal temperature rise threshold to 3.2°C. Furthermore, a K-means clustering algorithm can be used to process water temperature data from 32 sampling points within a 500-meter radius, automatically removing outliers (such as those caused by ship wake interference). Furthermore, this step uses a preset proportional radius (typically 30% of the detection radius) to define the abnormal region, thus avoiding boundary errors associated with traditional rectangular grid methods.

[0027] Step 120: Based on the bioluminescence bands involved in the red tide, configure corresponding fluorescence sensors; and use drones equipped with fluorescence sensors to detect whether there is a fluorescence reaction in the initial red tide abnormal area.

[0028] Among them, the use of drones equipped with fluorescence sensors can detect whether there is a fluorescent reaction in the initial red tide abnormal area, specifically: By using a drone equipped with a fluorescence sensor, fluorescence sensors of different biological fluorescence bands are switched in the initial red tide abnormal area to detect whether there is a fluorescence reaction in the initial red tide abnormal area.

[0029] Those skilled in the art will appreciate that this step can achieve species-specific detection of red tides by configuring sensors for characteristic wavelengths, such as dinoflagellates (685nm) and cyanobacteria (650nm). The drone automatically switches sensor bands during flight (e.g., the Turner Cyclops-7's 0.2-second band switching speed), allowing simultaneous acquisition of data on multiple algae species in a single aerial survey. A drone flying at an altitude of 50m can achieve a pixel resolution of 0.1m², enabling more accurate monitoring of micro-red tide patches than satellite remote sensing (100m²). Furthermore, this application utilizes airborne edge computing devices (such as the NVIDIA Jetson TX2) to implement real-time threshold alarms for fluorescence intensity (RFU), shortening the time from data collection to warning issuance.

[0030] Step 130: When a fluorescence reaction occurs, the initial red tide abnormal area is determined to be a red tide abnormal area; the corresponding red tide marker concentration is determined by collecting the reflected fluorescence intensity; the bioluminescence band of the current fluorescence sensor is obtained to determine the red tide marker type of the current red tide abnormal area; and the red tide marker concentration and red tide marker type are uploaded to the corresponding preset red tide monitoring terminal.

[0031] Those skilled in the art will appreciate that, in this step, the corresponding red tide marker concentration can be determined by setting a fluorescence intensity threshold (such as chlorophyll a>50RFU); based on a characteristic band database (including spectral fingerprints of several types of red tide organisms), species identification can be quickly completed through band matching.

[0032] In some embodiments, the concentration of the corresponding red tide marker is determined by collecting the reflected fluorescence intensity, specifically comprising: By formula: , determine the corresponding red tide marker concentration; in, and is the preset value.

[0033] The bioluminescence band of the current fluorescence sensor is obtained to determine the type of red tide marker in the current red tide abnormal area, specifically including: Obtain the number of red tide marker types corresponding to the bioluminescence band of the current fluorescence sensor. When the number of types is unique, determine the unique red tide marker type as the red tide marker type of the current red tide abnormal area. When the number of types is not unique, the type of red tide markers in the current red tide abnormal area is determined by using a drone equipped with an image recognition device.

[0034] It should be noted that this step uses a fluorescence sensor to obtain bioluminescent bands and directly matches them against a pre-set database (containing the spectral fingerprints of several types of red tide organisms). This allows for immediate identification of the red tide marker type when the number of types is unique, reducing manual identification time. When a fluorescence band corresponds to multiple potential species, a drone equipped with an image recognition device is automatically deployed for secondary confirmation, avoiding the risk of misidentification with a single spectral detection. Based on the band matching results, the determination method (direct confirmation or image review) can be independently selected, eliminating the need for manual intervention and improving the efficiency of red tide monitoring. This step combines a dual verification mechanism of fluorescence and morphological characteristics to improve the accuracy of red tide organism identification.

[0035] After obtaining the bioluminescence band of the current fluorescence sensor and determining the type of red tide marker in the current red tide abnormal area, the method further includes: Get the number of preset detection points corresponding to the red tide abnormal area. When the number of preset detection points is less than the preset value, add a new preset detection point in the red tide abnormal area.

[0036] It should be noted that this step can automatically obtain the number of preset detection points in the red tide anomaly area. When the number of detection points is insufficient (less than the preset number of detection points), new detection points are automatically added to ensure that the coverage density of the monitoring network meets the requirements. Through the evaluation of the number of detection points, it is possible to independently determine whether it is necessary to increase the number of monitoring points, and the dynamic adjustment of the monitoring network can be completed without manual intervention. The mechanism of adding new detection points ensures continuous and comprehensive monitoring of the red tide anomaly area, avoids monitoring blind spots caused by insufficient detection points, and improves the reliability of data collection. In addition, the entire process can be completed completely automatically, from the evaluation of the number of detection points to the decision-making and execution of new detection points, realizing the automated management of the monitoring process.

[0037] Based on the above description, those skilled in the art will appreciate that this embodiment enables the simultaneous collection of two parameters (water temperature and chlorophyll concentration) through the collaborative operation of the thermal infrared sensor and the chlorophyll concentration collector. A preliminary screening mechanism is established based on the correspondence between the abnormal temperature rise threshold and chlorophyll concentration. Combined with a clustering algorithm for background water temperature (taking the mean of the most cluster centers), this allows for the rapid delineation of the initial abnormal area (with the radius reduced by a preset ratio) centered around a preset detection point, reducing the workload for subsequent detection.

[0038] This embodiment can be configured with a fluorescence sensor that switches between bioluminescence bands, using multi-band scanning to verify the initial abnormal area. Detecting a fluorescence reaction not only confirms the presence of a red tide, but also quantifies the marker concentration based on characteristic fluorescence intensity (e.g., a chlorophyll a threshold of >50 RFU), thus avoiding misjudgments caused by a single sensor.

[0039] This embodiment leverages a database containing spectral fingerprints of red tide organisms to rapidly identify species through band matching. When a bioluminescence band corresponds to multiple species, the image recognition device is activated for secondary confirmation, creating a dual verification mechanism of "spectral screening followed by image verification," improving the accuracy of red tide organism identification.

[0040] This embodiment can automatically trigger the addition of new monitoring points based on the number of pre-set monitoring points within the red tide anomaly area (when the number is less than the pre-set value), achieving adaptive expansion of the monitoring network. This dynamic adjustment mechanism ensures the continuous tracking of diffusive red tides.

[0041] This embodiment can calculate the concentration of red tide markers through formulas, and automatically associate the concentration data with species types before uploading them to the monitoring terminal, thus meeting the data specification requirements of environmental supervision.

[0042] In addition, this application Figure 2 The embodiment of the present application provides a marine red tide disaster warning system based on drones. Figure 2 As shown, the system provided in the embodiment of the present application mainly includes: Determination module 210 is configured to collect water temperature and chlorophyll concentration at each preset detection point in the detection area using a drone equipped with a thermal infrared sensor and a chlorophyll concentration collector; and determine the initial red tide abnormal area based on the water temperature and chlorophyll concentration at the preset detection points; The determination module 210 includes a determination unit, For determining the abnormal temperature rise threshold value corresponding to the current chlorophyll concentration according to the corresponding relationship between the chlorophyll concentration and the abnormal temperature rise threshold value; With the preset detection point as the center and the preset distance as the radius, the background area is obtained, and the location water temperature of several locations on the edge of the background area is randomly collected; the location water temperature is input into the clustering algorithm, and the average value of the location water temperature of the cluster center with the largest number of clusters is determined as the background water temperature; The difference between the background water temperature and the water body temperature is calculated. When it is greater than the abnormal temperature rise threshold, the area with the preset detection point as the center and the preset ratio of the preset distance as the radius is determined as the initial red tide abnormal area; wherein the preset ratio is greater than 0 and less than 1.

[0043] Those skilled in the art will appreciate that determination module 210 can establish a dynamic correspondence between chlorophyll concentration and an abnormal temperature rise threshold. For example, when the chlorophyll concentration reaches 10 μg / L, the system automatically sets the temperature difference threshold to 3.2°C. Furthermore, a K-means clustering algorithm can be used to process water temperature data from 32 sampling points within a 500-meter radius, automatically eliminating outliers (such as points of interference from ship wakes). Furthermore, this step uses a preset proportional radius (typically 30% of the detection radius) to define the abnormal region, thereby avoiding boundary errors associated with traditional rectangular grid methods.

[0044] The detection module 220 is used to configure corresponding fluorescence sensors based on the biological fluorescence bands involved in the red tide; and detect whether there is a fluorescence reaction in the initial red tide abnormal area through a drone equipped with a fluorescence sensor.

[0045] Those skilled in the art will appreciate that detection module 220 can achieve species-specific detection of red tides by configuring sensors for characteristic wavelengths, such as dinoflagellates (685nm) and cyanobacteria (650nm). The drone automatically switches sensor bands during flight (e.g., the Turner Cyclops-7's 0.2-second band switching speed), allowing simultaneous acquisition of data on multiple algae species in a single aerial survey. At an altitude of 50m, the drone can achieve a pixel resolution of 0.1m², enabling more accurate monitoring of micro-red tide patches than satellite remote sensing (100m²). Furthermore, this application utilizes airborne edge computing devices (such as the NVIDIA Jetson TX2) to implement real-time threshold alarms for fluorescence intensity (RFU), shortening the time from data collection to warning issuance.

[0046] The upload module 230 is used to determine that the initial red tide abnormal area is a red tide abnormal area when there is a fluorescence reaction; determine the corresponding red tide marker concentration by collecting the reflected fluorescence intensity; obtain the bioluminescence band of the current fluorescence sensor to determine the red tide marker type of the current red tide abnormal area; upload the red tide marker concentration and red tide marker type to the corresponding preset red tide monitoring terminal.

[0047] Those skilled in the art will appreciate that the upload module 230 can determine the corresponding red tide marker concentration by setting a fluorescence intensity threshold (such as chlorophyll a>50RFU); based on a characteristic band database (including spectral fingerprints of several types of red tide organisms), species identification can be quickly completed through band matching.

[0048] The upload module 230 includes a concentration determination unit, Used by the formula: , determine the corresponding red tide marker concentration; in, and is the preset value.

[0049] The upload module 230 includes a marker determination unit, It is used to obtain the number of red tide marker types corresponding to the bioluminescence band of the current fluorescence sensor. When the number of types is unique, the unique red tide marker type is determined as the red tide marker type of the current red tide abnormal area; When the number of types is not unique, the type of red tide markers in the current red tide abnormal area is determined by using a drone equipped with an image recognition device.

[0050] Based on the above description, those skilled in the art will appreciate that this embodiment, through the collaborative operation of a thermal infrared sensor and a chlorophyll concentration collector, establishes a dynamic matching relationship between chlorophyll concentration (e.g., 10 μg / L) and an abnormal temperature rise threshold (e.g., 3.2°C). A K-means clustering algorithm is used to process background water temperature data from 32 sampling points within a 500-meter radius, automatically removing interference values ​​(e.g., ship wakes) and using the mean of the cluster centers with the most clusters as the background baseline. Anomalous regions are delineated using a preset ratio (e.g., 30% of the detection radius), reducing boundary errors compared to traditional rectangular grid methods.

[0051] This embodiment can be equipped with fluorescence sensors for characteristic wavelengths, such as dinoflagellates (685nm) and cyanobacteria (650nm). The drone, such as the Turner Cyclops-7, can complete multi-band scanning with a switching speed of 0.2 seconds. A pixel resolution of 0.1 m² can be achieved at a flight altitude of 50 m, enabling more accurate identification of micro-algal blooms than satellite remote sensing (100 m²). Onboard edge computing devices (such as the NVIDIA Jetson TX2) trigger real-time fluorescence intensity threshold alarms (e.g., chlorophyll a > 50 RFU), shortening warning delays.

[0052] This embodiment utilizes a database containing spectral fingerprints of several types of red tide organisms to quickly identify species through wavelength matching. When a wavelength corresponds to multiple species, an image recognition device is used for secondary confirmation. Fluorescence intensity is converted to marker concentration through a formulaic calculation. This data is automatically associated with species types and uploaded to the monitoring terminal, generating a structured report.

[0053] In this embodiment, the system can dynamically trigger the addition of new detection points based on the number of preset detection points in the abnormal area (if the number is insufficient), thereby ensuring the ability to continuously track the spread path of the red tide.

[0054] This embodiment can automate the entire process from data collection and calculation to species identification, thereby improving monitoring efficiency and accuracy.

[0055] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A marine red tide disaster early warning method based on drones, characterized in that: The method comprises: Using drones equipped with thermal infrared sensors and chlorophyll concentration collectors, the water temperature and chlorophyll concentration at each preset detection point in the area to be detected are collected. Based on the water temperature and chlorophyll concentration at the preset detection points, the initial red tide abnormal area is determined. Based on the bioluminescence bands involved in red tides, corresponding fluorescence sensors are configured; drones equipped with fluorescence sensors are used to detect whether there is a fluorescence reaction in the initial red tide abnormal area; When there is a fluorescence reaction, the initial red tide abnormal area is determined to be a red tide abnormal area; the corresponding red tide marker concentration is determined by collecting the reflected fluorescence intensity; the bioluminescence band of the current fluorescence sensor is obtained to determine the red tide marker type of the current red tide abnormal area; the red tide marker concentration and red tide marker type are uploaded to the corresponding preset red tide monitoring terminal.

2. The marine red tide disaster early warning method based on drone according to claim 1 is characterized in that: Based on the water temperature and chlorophyll concentration at the preset detection points, the initial red tide abnormal area is determined, including: According to the corresponding relationship between the chlorophyll concentration and the abnormal temperature rise threshold, the abnormal temperature rise threshold corresponding to the current chlorophyll concentration is determined; With the preset detection point as the center and the preset distance as the radius, the background area is obtained, and the location water temperature of several locations on the edge of the background area is randomly collected; the location water temperature is input into the clustering algorithm, and the average value of the location water temperature of the cluster center with the largest number of clusters is determined as the background water temperature; The difference between the background water temperature and the water body temperature is calculated. When it is greater than the abnormal temperature rise threshold, the area with the preset detection point as the center and the preset ratio of the preset distance as the radius is determined as the initial red tide abnormal area; wherein the preset ratio is greater than 0 and less than 1.

3. The marine red tide disaster early warning method based on drone according to claim 1 is characterized in that: Using drones equipped with fluorescence sensors, we detect whether there is a fluorescent reaction in the initial red tide abnormal area, including: By using a drone equipped with a fluorescence sensor, fluorescence sensors of different biological fluorescence bands are switched in the initial red tide abnormal area to detect whether there is a fluorescence reaction in the initial red tide abnormal area.

4. The marine red tide disaster early warning method based on drone according to claim 1 is characterized in that: By collecting the reflected fluorescence intensity, the corresponding red tide marker concentration is determined, specifically including: By formula: , determine the corresponding red tide marker concentration; in, and is the preset value.

5. The marine red tide disaster early warning method based on drone according to claim 1 is characterized in that: Obtain the bioluminescence band of the current fluorescence sensor and determine the type of red tide marker in the current red tide abnormal area, including: Obtain the number of red tide marker types corresponding to the bioluminescence band of the current fluorescence sensor. When the number of types is unique, determine the unique red tide marker type as the red tide marker type of the current red tide abnormal area. When the number of types is not unique, the type of red tide markers in the current red tide abnormal area is determined by using a drone equipped with an image recognition device.

6. The marine red tide disaster early warning method based on drone according to claim 1 is characterized in that: After obtaining the bioluminescence band of the current fluorescence sensor and determining the type of red tide marker in the current red tide abnormal area, the method further includes: Get the number of preset detection points corresponding to the red tide abnormal area. When the number of preset detection points is less than the preset value, add a new preset detection point in the red tide abnormal area.

7. A marine red tide disaster early warning system based on drones, characterized by: The system comprises: The determination module is used to collect water temperature and chlorophyll concentration at each preset detection point in the detection area using a drone equipped with a thermal infrared sensor and a chlorophyll concentration collector; based on the water temperature and chlorophyll concentration at the preset detection points, determine the initial red tide abnormal area; A detection module is used to configure corresponding fluorescence sensors based on the biological fluorescence bands involved in red tide; Use drones equipped with fluorescence sensors to detect whether there is a fluorescent reaction in the initial red tide abnormal area; The upload module is used to determine that the initial red tide abnormal area is a red tide abnormal area when there is a fluorescence reaction; determine the corresponding red tide marker concentration by collecting the reflected fluorescence intensity; obtain the bioluminescence band of the current fluorescence sensor to determine the red tide marker type of the current red tide abnormal area; upload the red tide marker concentration and red tide marker type to the corresponding preset red tide monitoring terminal.

8. The marine red tide disaster early warning system based on drone according to claim 7 is characterized in that: The determination module includes a determination unit, For determining the abnormal temperature rise threshold value corresponding to the current chlorophyll concentration according to the corresponding relationship between the chlorophyll concentration and the abnormal temperature rise threshold value; With the preset detection point as the center and the preset distance as the radius, the background area is obtained, and the location water temperature of several locations on the edge of the background area is randomly collected; the location water temperature is input into the clustering algorithm, and the average value of the location water temperature of the cluster center with the largest number of clusters is determined as the background water temperature; The difference between the background water temperature and the water body temperature is calculated. When it is greater than the abnormal temperature rise threshold, the area with the preset detection point as the center and the preset ratio of the preset distance as the radius is determined as the initial red tide abnormal area; wherein the preset ratio is greater than 0 and less than 1.

9. The marine red tide disaster early warning system based on drone according to claim 7 is characterized in that: The upload module includes a concentration determination unit, Used by the formula: , determine the corresponding red tide marker concentration; in, and is the preset value.

10. The marine red tide disaster early warning system based on drone according to claim 7 is characterized in that: The upload module includes a marker determination unit, It is used to obtain the number of red tide marker types corresponding to the bioluminescence band of the current fluorescence sensor. When the number of types is unique, the unique red tide marker type is determined as the red tide marker type of the current red tide abnormal area; When the number of types is not unique, the type of red tide markers in the current red tide abnormal area is determined by using a drone equipped with an image recognition device.

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