A water environment blue-green algae bloom early warning method and system

Abstract

The application discloses a water environment blue-green algae bloom early warning method and system, relates to the technical field of environmental monitoring, and comprises the following steps: collecting multi-source monitoring data through an "space-ground" integrated network composed of a liftable layered sensor, a satellite, an unmanned aerial vehicle and a weather station, fusing the data to generate a blue algae space-time distribution thermal map, introducing a blue algae vertical migration factor and a plankton feeding factor, constructing a self-adaptive prediction model, accurately calculating blue algae cell density and bloom coverage area, clustering regional characteristic libraries based on climate zones and water body types, analyzing a ROC curve to dynamically match optimal early warning threshold values, and finally calculating early warning coefficients according to the prediction results and the self-adaptive threshold values, outputting graded early warning signals, and significantly improving the accuracy, reliability and practicability of blue-green algae bloom early warning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental monitoring, in particular to a water environment blue-green algae bloom early warning method and system. BACKGROUND

[0002] Blue-green algae bloom is a visible algae aggregate formed by overgrowth of blue-green algae in freshwater bodies. Its outbreak can destroy the ecological balance of water bodies, produce algal toxins, cause the death of aquatic organisms, and threaten human drinking water safety and health, such as causing liver damage and skin allergy.

[0003] The existing technologies are usually artificial monitoring, in-situ online monitoring, satellite remote sensing monitoring, etc.

[0004] Among them, artificial monitoring collects water samples on site by artificial means, analyzes blue-green algae cell density, chlorophyll a and other core indicators in the laboratory, and combines on-site determination of water temperature, pH and other environmental factors as the core, relying on artificial microscope observation to complete algae classification and data statistics.

[0005] In-situ online monitoring collects real-time data such as chlorophyll a, blue-green algae fluorescence, and nutrient salts in water bodies by deploying buoys or underwater sensors, and automatically triggers an early warning when the data reaches a preset threshold, realizing automation and real-time of the monitoring process.

[0006] The existing technologies have the following technical defects, which are specifically embodied in:

[0007] 1. Single monitoring means, lack of three-dimensional data: traditional methods mainly rely on fixed points or single platforms (such as only ground sensors or only satellite remote sensing), which cannot synchronously obtain detailed data and multi-dimensional spatial information of the vertical section of the water body, resulting in data blind spots and difficulty in fully reflecting the real distribution and migration state of blue-green algae.

[0008] 2. Simple prediction model, ignoring key ecological processes: existing models are mostly based on surface data or average level for prediction, without quantifying the vertical migration behavior of blue-green algae and key biological and physical regulation factors such as zooplankton feeding, resulting in insufficient mechanism of growth prediction model and large deviation of prediction results from actual situation.

[0009] 3. Rigid early warning threshold, poor universality: usually uses fixed and unified threshold standard, without considering the huge differences of different climate zones and water body types, resulting in false negatives in high-risk water bodies and false positives in low-risk water bodies, low accuracy and practicability. SUMMARY

[0010] In view of the above technical deficiencies, the purpose of the present application is to provide a water environment blue-green algae bloom early warning method and system.

[0011] To solve the above technical problems, the present application adopts the following technical solutions: The present application provides a water environment blue-green algae bloom early warning method, comprising: S1, multi-source data acquisition: through the "space-ground" integrated monitoring network composed of a liftable layered sensor array, a satellite, a UAV and a weather station, full profile hydrochemical data, remote sensing image data and weather data of the target water body are collected at a preset period.

[0012] S2, intelligent fusion of multi-source data: based on the full profile hydrochemical data, remote sensing image data and weather data of the target water body, data cleaning, noise reduction and space-time registration processing are performed to obtain a blue algae space-time distribution thermal map.

[0013] S3, adaptive prediction: based on the blue algae space-time distribution thermal map and historical data, by introducing a blue algae vertical migration factor and a plankton feeding factor, a weighted fusion is performed to calculate the prediction results of the blue algae cell density and the bloom coverage area.

[0014] S4, regional dynamic threshold judgment: based on the climate zone and water body type clustering to form a regional feature library, the true positive rate and the false positive rate are calculated through ROC curve analysis to select the optimal early warning threshold, and the target water body threshold is automatically matched.

[0015] S5, early warning output: based on the prediction results and the target water body threshold, the early warning coefficient of the target water body is calculated to further judge the early warning level of the target water body.

[0016] Preferably, the blue algae vertical migration factor is specifically analyzed as follows: the measured water temperature of each depth of the target water body is extracted from the full profile hydrochemical data of the target water body, the reference water temperature of each depth of the target water body is obtained from the database, the measured water temperature of each depth of the target water body is subtracted from the reference water temperature of each depth of the target water body to obtain the water temperature difference value of each depth of the target water body, the depth division threshold of the target water body is obtained from the database, and the water temperature difference value of each depth of the target water body is divided by the depth division threshold of the target water body to obtain the water temperature gradient coefficient of each depth of the target water body.

[0017] The dissolved oxygen concentration of each depth of the target water body is extracted from the full profile hydrochemical data of the target water body, the reference dissolved oxygen concentration of each depth of the target water body is obtained from the database, the dissolved oxygen concentration of each depth of the target water body is subtracted from the reference dissolved oxygen concentration of each depth of the target water body to obtain the dissolved oxygen concentration difference value of each depth of the target water body, and the dissolved oxygen concentration difference value of each depth of the target water body is divided by the depth division threshold of the target water body to obtain the dissolved oxygen concentration gradient coefficient of each depth of the target water body.

[0018] Based on the water temperature gradient coefficient of each depth of the target water body and the dissolved oxygen concentration gradient coefficient of each depth of the target water body, a weighted calculation is performed to obtain the blue algae vertical migration factor of each depth of the target water body.

[0019] Preferably, the zooplankton feeding factor is calculated by extracting the light intensity and the concentration of key nutrients at each depth of the target water body from the full-profile hydro-biochemical data of the target water body, and inputting the light intensity and the concentration of key nutrients at each depth of the target water body into a blue algae self-growth rate calculation formula to obtain the blue algae self-growth rate at each depth of the target water body.

[0020] The time variation rate of the chlorophyll a concentration at each depth of the target water body is extracted from the full-profile hydro-biochemical data of the target water body, and the zooplankton feeding factor at each depth of the target water body is calculated based on the blue algae self-growth rate at each depth of the target water body and the blue algae vertical migration factor at each depth of the target water body and the zooplankton feeding factor calculation formula.

[0021] Preferably, the prediction result of the blue algae cell density is obtained by obtaining the basic blue algae growth rate at each depth of the target water body from the database, calculating the actual blue algae growth rate at each depth of the target water body based on the blue algae vertical migration factor at each depth of the target water body and the zooplankton feeding factor at each depth of the target water body, extracting the initial cell density at each depth of the target water body from the blue algae spatio-temporal distribution thermal map, and inputting the actual blue algae growth rate at each depth of the target water body and the initial cell density at each depth of the target water body into a blue algae cell density prediction model to obtain the predicted value of the blue algae cell density at each depth of the target water body.

[0022] Preferably, the prediction result of the water bloom coverage area is obtained by extracting the water bloom coverage at each depth of the target water body from the full-profile hydro-biochemical data of the target water body, extracting the total area of the target water body from the remote sensing image data, and inputting the water bloom coverage at each depth of the target water body and the total area of the target water body into a water bloom coverage area prediction model to obtain the predicted value of the water bloom coverage area of the target water body.

[0023] Preferably, the optimal early warning threshold is obtained by extracting the sample labeled by historical data from the regional characteristic library formed based on the climate zone and the water body type, counting the number of samples corresponding to the true positive, the false positive, the true negative, and the false negative corresponding to each candidate threshold, dividing the number of samples corresponding to the true positive by the sum of the number of samples corresponding to the true positive and the false negative corresponding to each candidate threshold to obtain the true positive rate corresponding to each candidate threshold, dividing the number of samples corresponding to the false positive by the sum of the number of samples corresponding to the false positive and the true negative corresponding to each candidate threshold to obtain the false positive rate corresponding to each candidate threshold, and drawing a ROC curve based on the true positive rate corresponding to each candidate threshold and the false positive rate corresponding to each candidate threshold to obtain the optimal early warning threshold.

[0024] Preferably, the automatic matching target water body threshold value, the specific analysis method is: obtaining the climate zone characteristics and water body type characteristics of the target water body from the database to form a target water body characteristic vector, obtaining the characteristic vectors of each sub-class water body from the database, calculating the Euclidean distance between the target water body characteristic vector and the characteristic vectors of each sub-class water body through the Euclidean distance calculation formula, screening to obtain the sub-class water body corresponding to the minimum Euclidean distance, and extracting the optimal early warning threshold value of the sub-class water body as the optimal early warning threshold value of the target water body.

[0025] Preferably, the early warning coefficient of the target water body, the specific analysis method is: extracting the cyanobacterial cell density prediction value of each depth of the target water body, summing to obtain the cyanobacterial cell density prediction value of the target water body, extracting the water bloom coverage area prediction value of the target water body, extracting the risk factor corresponding to the optimal early warning threshold value of the target water body from the database, and inputting the cyanobacterial cell density prediction value, the water bloom coverage area prediction value and the risk factor of the target water body into the target water body early warning coefficient calculation formula to obtain the early warning coefficient of the target water body.

[0026] Preferably, the early warning level of the target water body is judged, and the specific analysis method is: extracting the early warning coefficient of the target water body, inputting it into the early warning level judgment model, and outputting the judgment result of the early warning level of the target water body.

[0027] The judgment result of the early warning level of the target water body contains the numerical values of 0, 1 and 2, if the judgment result of the early warning level of the target water body is 0, it is determined that the early warning level of the target water body is low, if the judgment result of the early warning level of the target water body is 1, it is determined that the early warning level of the target water body is medium, and if the judgment result of the early warning level of the target water body is 2, it is determined that the early warning level of the target water body is high.

[0028] The second aspect of the present application provides a water environment cyanobacterial bloom early warning method system, characterized in that it comprises: a multi-source data acquisition module: for collecting full-profile hydrological and biochemical data, remote sensing image data and meteorological data of a target water body in a preset period through a "space-air-ground" integrated monitoring network composed of a liftable layered sensor array, a satellite, a unmanned aerial vehicle and a weather station.

[0029] A multi-source data intelligent fusion module: for performing data cleaning, noise reduction and space-time registration processing based on the full-profile hydrological and biochemical data, remote sensing image data and meteorological data of the target water body to obtain a cyanobacterial space-time distribution heat map.

[0030] An adaptive prediction module: for calculating the prediction results of cyanobacterial cell density and water bloom coverage area by introducing a cyanobacterial vertical migration factor and a plankton feeding factor and weighted fusion based on the cyanobacterial space-time distribution heat map and historical data.

[0031] Regional dynamic threshold judgment module: used for forming a regional characteristic library based on climate zones and water body type clustering, calculating true positive rate and false positive rate through ROC curve analysis, selecting the optimal early warning threshold, and automatically matching the target water body threshold.

[0032] Early warning output module: used for calculating the early warning coefficient of the target water body based on the prediction result and the target water body threshold, and then judging the early warning level of the target water body.

[0033] The beneficial effects of the present application are:

[0034] 1. The present application realizes full-range data acquisition of water bodies from the surface to the bottom layer and from point to plane by constructing an air-space-ground integrated three-dimensional monitoring network, completely eliminating the monitoring blind area and providing a comprehensive and accurate data basis for early warning.

[0035] 2. The present application innovatively introduces and quantifies the blue-green algae vertical migration factor and the plankton feeding factor, and integrates them into the prediction model, making the model more in line with the actual ecological dynamics process, and significantly improving the accuracy and mechanism of blue-green algae cell density and water bloom area prediction.

[0036] 3. The present application dynamically matches the optimal early warning threshold for different climate zones and water body types by establishing a regional characteristic library and using ROC curve analysis, realizes the site-specific threshold, greatly reduces false positives and false negatives, and improves the accuracy and practicality of early warning. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0038] Figure 1 The present application method implementation step flow chart;

[0039] Figure 2 The present application system structure connection diagram. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0041] According toFigure 1 As shown, the present application provides a water environment blue-green algae bloom early warning method, comprising: S1, multi-source data acquisition: through the "space-air-ground" integrated monitoring network composed of liftable layered sensor array, satellite, unmanned aerial vehicle and weather station, the full profile hydrochemical data of the target water body, remote sensing image data and meteorological data are collected according to the preset period.

[0042] It should be noted that the full profile hydrochemical data of the target water body includes but is not limited to water temperature, flow rate and flow direction, transparency, dissolved oxygen concentration, conductivity, chlorophyll a concentration, nutrient salt concentration and blue algae cell density, the remote sensing image data includes but is not limited to water body basic information, such as water body boundary and range, water bloom intuitive distribution image, blue algae and water body optical parameter, such as blue algae specific index: such as blue algae index, remote sensing inversion chlorophyll a concentration suspended matter concentration and colored soluble organic matter, the meteorological data includes but is not limited to photosynthetic active radiation, sunshine duration, air temperature, wind speed and direction, precipitation and evaporation.

[0043] S2, multi-source data intelligent fusion: based on the full profile hydrochemical data of the target water body, remote sensing image data and meteorological data, data cleaning, noise reduction and space-time registration processing are carried out to obtain a blue algae space-time distribution thermal map.

[0044] S3, adaptive prediction: based on the blue algae space-time distribution thermal map and historical data, by introducing blue algae vertical migration factor and plankton feeding factor, weighted fusion is carried out to calculate the prediction results of blue algae cell density and water bloom coverage area.

[0045] In one embodiment, the blue algae vertical migration factor is specifically analyzed as follows: the measured water temperature of each depth of the target water body is extracted from the full profile hydrochemical data of the target water body, the reference water temperature of each depth of the target water body is obtained from the database, the measured water temperature of each depth of the target water body is subtracted from the reference water temperature of each depth of the target water body to obtain the water temperature difference value of each depth of the target water body, the depth division threshold value of the target water body is obtained from the database, and the water temperature difference value of each depth of the target water body is divided by the depth division threshold value of the target water body to obtain the water temperature gradient coefficient of each depth of the target water body.

[0046] It should be noted that the reference water temperature of each depth of the target water body is set by a professional, for example, according to the climate zone and water body type (such as subtropical lake, temperate reservoir) to which the target water body belongs, combined with the historical same period average stratified water temperature of the same type of water body in the regional characteristic library (such as the historical same period average value of the surface layer 0.5m, the average value of the middle layer according to the water depth 1 / 2, and the average value of the bottom layer 0.5m), to determine the basic reference value; then, according to the real-time stratified water temperature monitoring data of the target water body (such as the surface layer, middle layer and bottom layer data collected by the liftable sensor), combined with the key water layer of blue-green algae vertical migration (such as the surface layer with active photosynthesis, the middle layer of thermocline), the basic reference value is dynamically calibrated; the depth division threshold of the target water body is set by a professional, for example, according to the water body type (such as shallow lake, deep reservoir, river) and the characteristics of the climate zone, and referring to the typical depth structure of the same type of water body in the regional characteristic library (such as shallow lake with 3m as the boundary to divide the surface layer / bottom layer, deep reservoir according to the position of thermocline to divide the surface layer / thermocline / bottom layer), to determine the basic framework; then, combined with the key functional layer of blue-green algae vertical migration (the surface layer with active photosynthesis, the thermocline layer inhibiting growth, and the bottom layer releasing nutrients), and according to the season (such as summer to divide the thermocline interval) and the real-time monitoring of the water body thermal stratification data, the division is dynamically fine-tuned to ensure that it accurately matches the depth correlation law of blue-green algae growth and migration.

[0047] The dissolved oxygen concentration of each depth of the target water body is extracted from the full profile hydrological and biochemical data of the target water body, the reference dissolved oxygen concentration of each depth of the target water body is obtained from the database, the dissolved oxygen concentration of each depth of the target water body is subtracted from the reference dissolved oxygen concentration of each depth of the target water body, and the dissolved oxygen concentration difference of each depth of the target water body is obtained. The dissolved oxygen concentration difference of each depth of the target water body is divided by the depth division threshold of the target water body to obtain the dissolved oxygen concentration gradient coefficient of each depth of the target water body.

[0048] It should be noted that the reference dissolved oxygen concentration of each depth of the target water body is set by a professional, and the setting method refers to the reference water temperature of each depth of the target water body, which will not be described in detail.

[0049] Based on the water temperature gradient coefficient of each depth of the target water body and the dissolved oxygen concentration gradient coefficient of each depth of the target water body, a weighted calculation is performed to obtain the blue-green algae vertical migration factor of each depth of the target water body.

[0050] In one specific embodiment, the specific analysis method of the zooplankton feeding factor is: the light intensity and key nutrient salt concentration of each depth of the target water body are extracted from the full profile hydrological and biochemical data of the target water body, and are brought into the blue-green algae self growth rate calculation formula to calculate the blue-green algae self growth rate of each depth of the target water body.

[0051] It should be noted that the blue-green algae self growth rate calculation formula is:

[0052] ;

[0053] wherein, represents the blue algae self-growth rate, represents the blue algae maximum specific growth rate, represents the light intensity, represents the half-saturation light intensity, represents the key nutrient salt concentration, represents the half-saturation key nutrient salt concentration, represents the temperature influence function.

[0054] The time variation rate of chlorophyll-a concentration of each depth of the target water body is extracted from the full-profile hydro-biochemical data of the target water body, the blue algae self-growth rate of each depth of the target water body is combined, and the zooplankton grazing factor is calculated based on the zooplankton grazing factor calculation formula.

[0055] It should be noted that the zooplankton grazing factor calculation formula is:

[0056] ;

[0057] wherein, represents the zooplankton grazing factor, represents the time variation rate of chlorophyll-a concentration of the target water body, represents the blue algae biomass-chlorophyll-a conversion coefficient.

[0058] In one specific embodiment, the prediction result of the blue algae cell density is specifically analyzed as follows: the basic blue algae growth rate of each depth of the target water body is obtained from the database, the actual blue algae growth rate of each depth of the target water body is calculated based on the blue algae vertical migration factor of each depth of the target water body and the zooplankton grazing factor of each depth of the target water body, the initial cell density of each depth of the target water body is extracted from the blue algae spatio-temporal distribution thermal map, and the actual blue algae growth rate of each depth of the target water body and the initial cell density of each depth of the target water body are brought into the blue algae cell density prediction model to obtain the predicted value of the blue algae cell density of each depth of the target water body.

[0059] It should be noted that the basic blue algae growth rate of each depth of the target water body is set by professionals, for example, first, the initial reference value is determined according to the measured water temperature, light intensity, nitrogen and phosphorus nutrient salt (such as total nitrogen and total phosphorus) concentration and other key environmental factors at each depth, combined with historical same-period and same-depth blue algae growth monitoring data; then, the influence coefficient of each environmental factor on growth is quantified by referring to classical blue algae growth models such as Monod model and Logistic model, and the influence coefficient is adjusted according to the type of water body (such as lake and reservoir) and seasonal dynamics to ensure that it conforms to the actual growth law;

[0060] The calculation obtains the actual blue-green algae growth rate of each depth of the target water body, and the calculation formula is:

[0061] ;

[0062] Among them, represents the actual blue-green algae growth rate at the depth z of the target water body, represents the basic blue-green algae growth rate at the depth z of the target water body, represents the vertical migration factor of blue-green algae at the depth z of the target water body, represents the plankton feeding factor at the depth z of the target water body.

[0063] The blue-green algae cell density prediction model is expressed as:

[0064] ;

[0065] Among them, represents the predicted value of the blue-green algae cell density at the depth z of the target water body, represents the initial cell density at the depth z of the target water body, represents the initial monitoring time, represents the prediction time interval.

[0066] In one specific embodiment, the prediction result of the bloom coverage area is specifically analyzed as follows: the bloom coverage degree of each depth of the target water body is extracted from the full profile hydro-biochemical data of the target water body, the total area of the target water body is extracted from the remote sensing image data, and the bloom coverage degree of each depth of the target water body and the total area of the target water body are brought into the prediction model of the bloom coverage area to obtain the predicted value of the bloom coverage area of the target water body.

[0067] It should be noted that the prediction model of the bloom coverage area is expressed as:

[0068] ;

[0069] Among them, represents the predicted value of the bloom coverage area of the target water body, represents the total area of the target water body, represents the total depth of the target water body, represents the bloom coverage degree at the depth z of the target water body, represents the depth division threshold of the target water body, represents the total number of division depths.

[0070] S4, regional dynamic threshold judgment: based on the climate zone and water body type clustering to form a regional characteristic library, the true positive rate and the false positive rate are calculated through ROC curve analysis, the optimal early warning threshold is selected, and the target water body threshold is automatically matched.

[0071] In one specific embodiment, the optimal early warning threshold is selected, and the specific analysis method is: extracting historical data labeled samples from the regional feature library formed based on climate zones and water body types, and statistically obtaining the number of samples corresponding to true positives, false positives, true negatives, and false negatives corresponding to each candidate threshold. The number of true positive samples corresponding to each candidate threshold is divided by the sum of the number of samples corresponding to true positives and false negatives, to obtain the true positive rate corresponding to each candidate threshold. The number of false positive samples corresponding to each candidate threshold is divided by the sum of the number of samples corresponding to false positives and true negatives, to obtain the false positive rate corresponding to each candidate threshold. Based on the true positive rate corresponding to each candidate threshold and the false positive rate corresponding to each candidate threshold, a ROC curve is drawn, and then the optimal early warning threshold is obtained.

[0072] It should be noted that the optimal early warning threshold is obtained by selecting the threshold closest to the upper left corner (0, 1) on the curve as the optimal early warning threshold. The true positive core logic: the actual cyanobacterial early warning event, and the judgment result based on the threshold is also "triggering early warning" (correct judgment, no omission); the false positive core logic: the actual cyanobacterial early warning event, but the judgment result based on the threshold is "triggering early warning" (wrong judgment, belongs to "false alarm"); the true negative core logic: the actual cyanobacterial early warning event, and the judgment result based on the threshold is also "not triggering early warning" (correct judgment, no interference); the false negative core logic: the actual cyanobacterial early warning event, but the judgment result based on the threshold is "not triggering early warning" (wrong judgment, belongs to "missed alarm").

[0073] The automatic matching of the optimal early warning threshold is essentially a process of "classification first, then association": first, the target water body is classified into a preset "climate zone + water body type" subclass in the regional feature library based on its regional characteristics (climate zone and water body type); then the optimal early warning threshold of the subclass determined by ROC analysis is directly associated with the threshold of the target water body. The core is to rely on the characteristics of "similar cyanobacterial growth patterns in similar water bodies" to solve the problem of insufficient historical data of individual target water bodies, while ensuring the regional adaptability of the threshold.

[0074] In one specific embodiment, the automatic matching of the target water body threshold is analyzed as follows: obtaining the climate zone characteristics and water body type characteristics of the target water body from the database to form a target water body feature vector, obtaining the feature vectors of each subclass water body from the database, calculating the Euclidean distance between the target water body feature vector and the feature vectors of each subclass water body by the Euclidean distance calculation formula, screening the subclass water body corresponding to the minimum Euclidean distance, and extracting the optimal early warning threshold of the subclass water body as the optimal early warning threshold of the target water body.

[0075] It should be noted that the Euclidean distance calculation formula is a prior art, and will not be described in detail here.

[0076] S5, early warning output: based on the prediction result and the target water body threshold value, the early warning coefficient of the target water body is calculated, and then the early warning level of the target water body is judged.

[0077] In one specific embodiment, the early warning coefficient of the target water body is specifically analyzed as follows: the cyanobacterial cell density prediction value of each depth of the target water body is extracted, summed to obtain the cyanobacterial cell density prediction value of the target water body, the water bloom coverage area prediction value of the target water body is extracted, the risk factor corresponding to the optimal early warning threshold value of the target water body is extracted from the database, and the cyanobacterial cell density prediction value, the water bloom coverage area prediction value and the risk factor of the target water body are brought into the target water body early warning coefficient calculation formula to obtain the early warning coefficient of the target water body.

[0078] It should be noted that the target water body early warning coefficient calculation formula is:

[0079] ;

[0080] The target water body early warning coefficient is represented by: The cyanobacterial cell density weight coefficient is represented by: The target water body cyanobacterial cell density prediction total value is represented by: The optimal early warning threshold value of the target water body cyanobacterial cell density is represented by: The water bloom coverage area weight coefficient is represented by: The optimal early warning threshold value of the target water body water bloom coverage area is represented by: The risk factor corresponding to the optimal early warning threshold value of the target water body is represented by:

[0081] The essence of the optimal early warning threshold value of the target water body, the optimal early warning threshold value of the target water body cyanobacterial cell density and the optimal early warning threshold value of the target water body water bloom coverage area is the subordinate relationship of "overall threshold-value-sub-item threshold-value". The optimal early warning threshold value of the target water body cyanobacterial cell density is the optimal judgment threshold value selected by the ROC curve for the single index of "target water body cyanobacterial cell density prediction total value". The optimal early warning threshold value of the target water body water bloom coverage area is the optimal judgment threshold value selected by the ROC curve for the single index of "target water body water bloom coverage area prediction value". The optimal early warning threshold value of the target water body is the "comprehensive risk threshold value" for measuring the overall risk level under the synergistic effect of "cell density + coverage area", which is finally determined by the ROC curve based on the optimal early warning threshold value of the target water body cyanobacterial cell density, the optimal early warning threshold value of the target water body water bloom coverage area, the geographical characteristics (climatic zone, water body type, from the geographical characteristic library) of the target water body and the historical risk cases (true positive / false positive samples).

[0082] In one specific embodiment, the method for determining the warning level of the target water body comprises: extracting the warning coefficient of the target water body, inputting the warning coefficient into a warning level determination model, and outputting the determination result of the warning level of the target water body.

[0083] It should be noted that in the warning level determination model, the model expression is:

[0084] ;

[0085] wherein, represents the determination result of the warning level, represents the warning coefficient of the target water body, represents the low-moderate warning coefficient threshold, represents the moderate-high warning coefficient threshold.

[0086] The determination result of the warning level of the target water body contains the values of 0, 1 and 2. If the determination result of the warning level of the target water body is 0, it is determined that the warning level of the target water body is low warning. If the determination result of the warning level of the target water body is 1, it is determined that the warning level of the target water body is moderate warning. If the determination result of the warning level of the target water body is 2, it is determined that the warning level of the target water body is high warning.

[0087] According to the system for the method for warning of water environment cyanobacterial bloom, the system comprises a multi-source data acquisition module, a multi-source data intelligent fusion module, an adaptive prediction module and a regionalized dynamic threshold determination module. Figure 2 The multi-source data acquisition module is used for acquiring full-profile hydro-biochemical data, remote sensing image data and meteorological data of a target water body in a preset period through an "air-space-ground" integrated monitoring network composed of a liftable layered sensor array, a satellite, a drone and a weather station.

[0088] The multi-source data intelligent fusion module is used for performing data cleaning, noise reduction and space-time registration processing based on the full-profile hydro-biochemical data, remote sensing image data and meteorological data of the target water body to obtain a cyanobacterial space-time distribution heat map.

[0089] The adaptive prediction module is used for calculating a prediction result of cyanobacterial cell density and a prediction result of bloom coverage area by introducing a cyanobacterial vertical migration factor and a zooplankton feeding factor and weighted fusion based on the cyanobacterial space-time distribution heat map and historical data.

[0090] The regionalized dynamic threshold determination module is used for forming a regional feature library based on climate zones and water body types, calculating a true positive rate and a false positive rate through ROC curve analysis, selecting an optimal warning threshold, and automatically matching a threshold value of the target water body.

[0091] The regionalized dynamic threshold determination module is used for forming a regional feature library based on climate zones and water body types, calculating a true positive rate and a false positive rate through ROC curve analysis, selecting an optimal warning threshold, and automatically matching a threshold value of the target water body.The early warning output module is used for calculating the early warning coefficient of the target water body based on the prediction result and the target water body threshold, and further judging the early warning level of the target water body.

[0092] It should be noted that the database is also included.

[0093] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application.

Claims

1. A method for early warning of cyanobacterial blooms in aquatic environments, characterized in that, include: S1. Multi-source data acquisition: Through an integrated "air-space-ground" monitoring network consisting of a liftable layered sensor array, satellites, drones and meteorological stations, full-profile hydrological and biochemical data, remote sensing image data and meteorological data of the target water body are collected according to a preset cycle. S2. Intelligent fusion of multi-source data: Based on the full profile hydrological and biochemical data, remote sensing image data and meteorological data of the target water body, data cleaning, noise reduction and spatiotemporal registration are performed to obtain a spatiotemporal distribution heat map of cyanobacteria. S3. Adaptive prediction: Based on the spatiotemporal distribution heat map of cyanobacteria and historical data, the prediction results of cyanobacteria cell density are calculated by introducing cyanobacteria vertical migration factor and zooplankton feeding factor and weighted fusion. Based on the full profile hydrological and biochemical data of the target water body and the total area of ​​the target water body, the prediction results of algal bloom coverage area are calculated. The specific analytical method for the cyanobacterial vertical migration factor is as follows: The measured water temperature at each depth of the target water body is extracted from the full profile hydrological and biochemical data of the target water body. The reference water temperature at each depth of the target water body is obtained from the database. The measured water temperature at each depth of the target water body is subtracted from the reference water temperature at each depth of the target water body to obtain the water temperature difference at each depth of the target water body. The depth division threshold of the target water body is obtained from the database. The water temperature difference at each depth of the target water body is divided by the depth division threshold of the target water body to obtain the water temperature gradient coefficient at each depth of the target water body. Dissolved oxygen concentrations at various depths of the target water body are extracted from the full profile hydrological and biochemical data of the target water body. Reference dissolved oxygen concentrations at various depths of the target water body are obtained from the database. The dissolved oxygen concentrations at various depths of the target water body are subtracted from the reference dissolved oxygen concentrations at various depths of the target water body to obtain the dissolved oxygen concentration difference at various depths of the target water body. The dissolved oxygen concentration difference at various depths of the target water body is divided by the depth division threshold of the target water body to obtain the dissolved oxygen concentration gradient coefficient at various depths of the target water body. Based on the water temperature gradient coefficient and dissolved oxygen concentration gradient coefficient at each depth of the target water body, the vertical migration factor of cyanobacteria at each depth of the target water body is obtained by weighted calculation. The formula for calculating the zooplankton feeding factor is as follows: ； in, Indicates zooplankton feeding factors, This represents the rate of change in chlorophyll a concentration over time in the target water body. This represents the conversion factor between cyanobacterial biomass and chlorophyll a. ； in, Indicates the growth rate of cyanobacteria themselves. This indicates the maximum specific growth rate of cyanobacteria. Indicates light intensity. Indicates the intensity of half-saturated light. Indicates the concentration of key nutrients. This indicates the concentration of semi-saturated key nutrients. Represents the temperature effect function; S4. Regional dynamic threshold judgment: Based on the clustering of climate zones and water body types to form a regional feature database, the true positive rate and false positive rate are calculated by ROC curve analysis, the optimal early warning threshold is selected, and the target water body threshold is automatically matched. A true positive is defined as an event that is actually a cyanobacteria warning, and the threshold-based judgment result is also "warning triggered"; a false positive is defined as an event that is not actually a cyanobacteria warning, but the threshold-based judgment result is "warning triggered". S5. Early warning output: Based on the predicted results of cyanobacterial cell density, algal bloom coverage area and target water body threshold, the early warning coefficient of the target water body is calculated, and then the early warning level of the target water body is determined. The formula for calculating the early warning coefficient of the target water body is: ； Indicates the warning coefficient for the target water body. This represents the weighting coefficient for cyanobacterial cell density. This represents the total predicted value of cyanobacterial cell density in the target water body. This indicates the optimal warning threshold for the density of cyanobacteria cells in the target water body. This represents the weighting coefficient for the area covered by algal blooms. This indicates the optimal early warning threshold for the algal bloom coverage area of ​​the target water body. This represents the risk factor corresponding to the optimal early warning threshold for the target water body.

2. The method for early warning of cyanobacterial blooms in aquatic environments according to claim 1, characterized in that, The specific analysis method for predicting the cyanobacterial cell density is as follows: The baseline cyanobacterial growth rate at each depth of the target water body is obtained from the database. Based on the vertical migration factor of cyanobacteria and the zooplankton feeding factor at each depth of the target water body, the actual cyanobacterial growth rate at each depth of the target water body is calculated. The initial cell density at each depth of the target water body is extracted from the cyanobacterial spatiotemporal distribution heat map. The actual cyanobacterial growth rate at each depth of the target water body and the initial cell density at each depth of the target water body are then input into the cyanobacterial cell density prediction model to obtain the predicted value of cyanobacterial cell density at each depth of the target water body. The cyanobacterial cell density prediction model is expressed as follows: ； in, This represents the predicted cyanobacterial cell density at depth z in the target water body. This represents the initial cell density at depth z in the target water body. Indicates the initial monitoring time. Indicates the prediction time interval; ； in, This represents the actual cyanobacteria growth rate at a target water depth z. This represents the baseline cyanobacteria growth rate at a target water depth z. This represents the vertical migration factor of cyanobacteria at a target water depth z. This represents the zooplankton feeding factor at a target water depth z.

3. The method for early warning of cyanobacterial blooms in aquatic environments according to claim 2, characterized in that, The specific analysis method for predicting the algal bloom coverage area is as follows: The algal bloom coverage at each depth of the target water body is extracted from the full profile hydrological and biochemical data of the target water body, and the total area of ​​the target water body is extracted from the remote sensing image data. The algal bloom coverage at each depth of the target water body and the total area of ​​the target water body are then input into the algal bloom coverage prediction model to obtain the predicted value of the algal bloom coverage of the target water body. The prediction model for the algal bloom coverage area is expressed as follows: ； in, This represents the predicted area of ​​algal bloom coverage in the target water body. Indicates the total area of ​​the target water body. Indicates the total depth of the target water body. This represents the algal bloom coverage at depth z in the target water body. This indicates the depth threshold for defining the target water body. This indicates the total number of division depths.

4. The method for early warning of cyanobacterial blooms in aquatic environments according to claim 3, characterized in that, The specific analysis method for selecting the optimal early warning threshold is as follows: Historical data-annotated samples are extracted from a regional feature database formed by clustering based on climate zones and water body types. The number of samples corresponding to true positives, false positives, true negatives, and false negatives for each candidate threshold is statistically obtained. The number of true positive samples corresponding to each candidate threshold is divided by the sum of the number of true positive and false negative samples to obtain the true positive rate for each candidate threshold. The number of false positive samples corresponding to each candidate threshold is divided by the sum of the number of false positive and true negative samples to obtain the false positive rate for each candidate threshold. Based on the true positive rate and the false positive rate for each candidate threshold, an ROC curve is plotted to obtain the optimal warning threshold. A true negative is defined as an event that is not actually a cyanobacteria warning event, and the threshold-based judgment result is "no warning triggered"; a false negative is defined as an event that is actually a cyanobacteria warning event, but the threshold-based judgment result is "no warning triggered".

5. The method for early warning of cyanobacterial blooms in aquatic environments according to claim 4, characterized in that, The specific analysis method for automatically matching the target water body threshold is as follows: The climate zone characteristics and water type characteristics of the target water body are obtained from the database to form the feature vector of the target water body. The feature vectors of each subclass of water body are obtained from the database. The Euclidean distance between the feature vector of the target water body and the feature vectors of each subclass of water body is calculated using the Euclidean distance calculation formula. The subclass of water body corresponding to the minimum Euclidean distance is selected, and the optimal warning threshold of the subclass of water body is extracted and used as the optimal warning threshold of the target water body.

6. The method for early warning of cyanobacterial blooms in aquatic environments according to claim 5, characterized in that, The specific analysis method for the early warning coefficient of the target water body is as follows: The predicted values ​​of cyanobacterial cell density at various depths of the target water body are extracted and summed to obtain the predicted value of cyanobacterial cell density of the target water body. The predicted value of algal bloom coverage area of ​​the target water body is also extracted. The risk factors corresponding to the optimal warning threshold of the target water body are extracted from the database. The predicted values ​​of cyanobacterial cell density, algal bloom coverage area, and risk factors of the target water body are then substituted into the warning coefficient calculation formula of the target water body to obtain the warning coefficient of the target water body.

7. The method for early warning of cyanobacterial blooms in aquatic environments according to claim 6, characterized in that, The specific analysis method for determining the warning level of the target water body is as follows: Extract the warning coefficient of the target water body, input it into the warning level judgment model, and output the judgment result of the warning level of the target water body; The judgment result of the warning level of the target water body includes the values ​​of 0, 1 and 2. If the judgment result of the warning level of the target water body is 0, the warning level of the target water body is determined to be a low warning. If the judgment result of the warning level of the target water body is 1, the warning level of the target water body is determined to be a medium warning. If the judgment result of the warning level of the target water body is 2, the warning level of the target water body is determined to be a high warning.

8. A system for performing the early warning method for cyanobacterial blooms in aquatic environments according to any one of claims 1 to 7, characterized in that, include: Multi-source data acquisition module: Used to collect full-profile hydrological and biochemical data, remote sensing image data and meteorological data of the target water body according to a preset cycle through an integrated "air-space-ground" monitoring network consisting of a liftable layered sensor array, satellites, drones and meteorological stations; Multi-source data intelligent fusion module: used to perform data cleaning, noise reduction and spatiotemporal registration based on full-profile hydrological and biochemical data, remote sensing image data and meteorological data of the target water body, to obtain a spatiotemporal distribution heat map of cyanobacteria. Adaptive prediction module: Based on the spatiotemporal distribution heat map of cyanobacteria and historical data, it calculates the predicted result of cyanobacteria cell density by introducing cyanobacteria vertical migration factor and zooplankton feeding factor and weighted fusion. Based on the full profile hydrological and biochemical data of the target water body and the total area of ​​the target water body, it calculates the predicted result of algal bloom coverage area. Regionalized dynamic threshold judgment module: It is used to form a regional feature library based on climate zone and water body type clustering, calculate the true positive rate and false positive rate through ROC curve analysis, select the optimal warning threshold, and automatically match the target water body threshold. Early warning output module: Based on the predicted results of cyanobacterial cell density, algal bloom coverage area, and target water body threshold, the early warning coefficient of the target water body is calculated, and then the early warning level of the target water body is determined.

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