Water body phytoplankton biomass scale monitoring and early warning system and method

Abstract

The present invention discloses a system and method for monitoring and warning the scale of phytoplankton biomass in water bodies. The system is an algae biomass scale measurement scheme based on an ecological mathematical model, including a sample collection module 100, a water quality type identification module 200, an initial model matching module 300, a target model calibration module 400, an algae biomass scale prediction module 500, and an algal bloom information processing module 600. Through the hierarchical classification technology logic of water quality type trigger response and matching model, the system has a phased leap response to high organic matter water quality, which has emergency management significance. By constructing a prediction model around population dynamics, placing the importance of organic matter content before nitrogen and phosphorus content, constructing a mathematical function for environmental factors classification, and using surface runoff variables to characterize the hydrodynamic characteristics of water bodies, the present invention saves data costs and computing overhead, and reduces the adaptability of the technical solution for promotion. The present invention also provides a system-based method for monitoring and warning the scale of phytoplankton biomass in water bodies.

Description

Technical Field

[0001] The present invention relates to a water environment monitoring technology, specifically monitoring the biomass of phytoplankton in water bodies and implementing early warnings based on the changing trends of biomass scale. This technology belongs to the field of measuring the biomass scale characteristics of phytoplankton in water bodies, measuring the characteristics of water bloom trends, and water environment monitoring technology. Background Art

[0002] Measuring the biomass of phytoplankton in water bodies, particularly monitoring its changing trends, is a core component of monitoring algal blooms and a crucial task in aquatic environmental management. When signs of an algal bloom emerge, predicting its changing trends—and, in other words, the growth of phytoplankton in the water—becomes a crucial component of water monitoring. With the integration of feedforward management into environmental crisis response mechanisms, scientifically predicting the occurrence of algal blooms has become an equally crucial task in water management.

[0003] Currently, the key issue in monitoring and early warning systems for phytoplankton growth in water bodies is constructing an algae biomass prediction model that combines environmental factors with algae content. This approach uses a dynamic growth and death perspective to establish an algae growth model, and a water-borne phytoplankton movement model to simulate the activity of phytoplankton in water bodies. This mathematical model reflects changes in phytoplankton size in water bodies through the mathematical process of biological population dynamics, thereby predicting the occurrence or trend of algal blooms. This is an ecological mathematical model approach to algal bloom monitoring research. Once the ecological mathematical model is established, the algae growth and death rates, as well as the algae movement rate with water, can be calculated based on nutrients, nutrient absorption, and other limiting factors, and the current algal biomass (or concentration) in the water body can be measured.

[0004] Existing technology ZL2022107809933 discloses an intelligent water bloom warning system based on water body nutrient status prediction. The system comprises a multi-scale information collection module, a nutrient status identification module, a water bloom warning module, and a digital visualization module. Specifically, it collects multi-scale water body information from a target water body, predicts nutrient concentrations in the target water body based on this multi-scale water body information, determines the nutrient status of the target water body based on the nutrient concentrations, predicts the probability of water bloom occurrence based on the water body nutrient status, and generates corresponding warning feedback notification information based on the bloom probability; and then visually displays the water body nutrient status, bloom probability, and warning feedback notification information. This technology has the following drawbacks: First, it uses multi-scale water body information as input and employs a machine learning algorithm to construct an algae density prediction model to predict algae density in the target water body. The raw data items are too complex, making it difficult to meet sample data requirements in actual production. Furthermore, the data collection task is heavy and computationally intensive, resulting in high technology implementation costs. Second, algal blooms are a typical r-strategy, insensitive to subtle changes in environmental factors. Predicting their biomass does not require overly complex and sophisticated models, resulting in a significant waste of data collection and computational effort. Third, the TLI (Trophy Status Index), derived through extensive computation, uses only a single eutrophication metric to categorize water characteristics. Essentially, it uses a unified model to predict algal growth and reproduction without distinguishing between nutrient components, a technically constrained approach. Furthermore, given the limitations of unified model prediction, even if the optimal solution for model parameters could be identified using abundant sample data, the remaining issues remain: increased sample data collection costs, the resulting target model's "optimality" may not be optimal, and the resulting computational costs contradict the original intention of feedforward management for prediction. Summary of the Invention

[0005] The purpose of the present invention is to provide a water body phytoplankton biomass scale monitoring and early warning system to address the deficiencies of the existing technology.

[0006] The present invention first provides a water body phytoplankton biomass scale monitoring and early warning system, and its technical solution is as follows.

[0007] A water body phytoplankton biomass scale monitoring and early warning system is characterized by comprising: a sample collection module 100, a water quality type identification module 200, an initial model matching module 300, a target model calibration module 400, and an algae biomass scale prediction module 500;

[0008] The sample collection module 100 obtains water quality monitoring data RD, meteorological data, and hydrological data of the monitored water body to form monitoring samples MS. Sample MS1 is used to generate a prediction target model, and sample MS2 is used to predict the biomass of phytoplankton.

[0009] The water quality type identification module 200 reads the sample MS1 of the module 100, identifies the water quality type of the monitored water body and adds a water quality model label. The water quality type rich in organic matter is added with a water quality label E;

[0010] The initial model matching module 300 matches the G prediction initial model according to the water quality model tag; if the water quality tag E is read, the G prediction initial model I is matched. The G prediction initial model I is expressed as formula 1:

[0011] G=A×g max ×G(T)×G(I)×G(COD)×G(Q) Formula 1

[0012] Where, G is the growth rate of phytoplankton biomass, dimensionless, G(T) is the water temperature function, G(I) is the sunshine function, G(COD) is the COD Cr Function, G(Q) - hydrodynamic function

[0013]

[0014]

[0015] Where, T is water temperature, unit is ℃, I is sunshine hours, unit is h, q is hydrodynamic variable, unit is determined by the selected variable, COD is COD Cr Chemical oxygen demand, unit mg / L; both are model independent variables;

[0016] A - overgrowth coefficient, dimensionless, g max - Maximum growth rate of phytoplankton, dimensionless, T opi - Optimal water temperature, unit ℃, I opi - Optimal sunshine hours, units: h, k COD - Semi-saturated COD Cr Concentration, unit mg / L, k q - hydrodynamic variable influence coefficient, dimensionless, q0 - hydrodynamic variable critical value, unit is determined by the selected variable; both are model parameters;

[0017] The target model calibration module 400 reads the sample MS1 of the module 100 and the G prediction initial model of the module 300, inputs the data RD into the G prediction initial model, determines the optimal solution set of the model parameters through parameter calibration, and generates the corresponding G prediction target model;

[0018] The algae scale prediction module 500 reads the sample MS2 and uses the G prediction target model to calculate the growth rate G of the phytoplankton biomass on the predicted day i. i .

[0019] The above-mentioned water body phytoplankton biomass scale monitoring and early warning system collects monitoring water body and environmental monitoring samples MS through the sample collection module 100. After the water quality type is identified by the water quality type identification module 200, the corresponding G prediction initial model is matched in the initial model matching module 300. The parameters of the G prediction initial model are calibrated using sample MS1 in the target model calibration module 400 to obtain the optimal solution set of model parameters, thereby generating the G prediction target model for algae biomass prediction. In the algae scale prediction module 500, sample MS2 (generally real-time monitoring data on the i-th day of the prediction period) is input into the G prediction target model, and the growth rate G of the phytoplankton biomass on the i-th day can be measured. i . Measurement results Growth rate G i is the percentage value of the phytoplankton biomass scale on the i-th day of the monitoring water body prediction period. The specific meaning is that the phytoplankton biomass scale in the monitoring water body will be expressed as G i Value growth and expansion.

[0020] The key concepts of the above-mentioned water body phytoplankton biomass scale monitoring and early warning system of the present invention mainly include three aspects.

[0021] First, the organic-rich water quality type is used as the primary prediction model matching factor. Once module 200 identifies the organic-rich water quality type, it adds the water quality tag E, and module 300 reads it and begins matching G to the initial prediction model. Previous research of this invention found that although various water quality types are related to algal blooms, water with high organic content is more closely associated with algal blooms. Maximum growth rate g max In this water environment, the algae reproduction capacity is characterized. In this water environment, since various biological and non-biological environmental factors are in a dynamic and balanced state of comprehensive health, the algae reproduction capacity is mainly limited by the mineral nutrition factors represented by nitrogen and phosphorus in the water. Therefore, g max The substantive meaning is the maximum growth rate under the condition of nutrient factors. However, when the water body is rich in organic matter, various environmental factors in the water body break through the comprehensive dynamic balance state, the algae reproduction restriction factors become invalid, and the organic matter-rich water body provides a more favorable ecological niche space for algae growth than the healthy water environment. The algae are actually in a state of being more than g max The most rapid overgrowth state. Therefore, the present invention puts the identification of water quality types rich in organic matter in a higher level concept than the identification of other water quality types, so that the prediction method can make the fastest feedback to the most urgent situation. For the identification of water quality types rich in organic matter, COD Cr The identification threshold is a concentration of 30 mg / L or higher. The combination of modules 200 and 300 can create a "first-level alert" for water bodies rich in organic matter, triggering the measurement and calculation of algae biomass growth rates in a timely manner.

[0022] Second, various environmental factor functions G(T), G(I), G(COD), and G(Q) are rationally constructed. The previous research of this invention found that for climate factors (water temperature T, sunshine I), the range and fluctuation law of the cosine function can fit the optimal value X. opi (X represents climate factors) dynamics, so the mapping relationship between algae growth and such environmental factors can be accurately captured. For nutritional factors (chemical oxygen demand COD Cr ), through the half-saturation concentration variable k Y (Y represents a nutrient factor). The sine function accurately characterizes the range, fluctuation patterns, and infinite convergence characteristics of the relationship between algal growth and these environmental factors. Regarding hydrodynamic factors, their impact on algal growth lies in the "positive and negative" interaction between low hydrodynamic forces promoting growth and high hydrodynamic forces hindering growth, as well as the slowing down of these interactions. Using the natural logarithm function supplemented with the critical value q0 effectively characterizes this influence.

[0023] Third, design effective hydrodynamic variables. In the G prediction model, the hydrodynamic variable q is a variable that characterizes the flow velocity, flow rate, or pressure of the monitored water body, and can also be a variable that characterizes the flow velocity, flow rate, or pressure of the surface runoff flowing into the monitored water body. In the prior art, variables that characterize the flow velocity, flow rate, or pressure of the monitored water body are usually used, but since such monitoring indicators are not routine monitoring items of hydrological monitoring stations. In particular, most natural small watersheds, river sections, and lakes do not have corresponding hydrological stations, and long-term continuous hydrodynamic observation data cannot be obtained. Preliminary studies of the present invention found that since the water areas where algal blooms occur are usually close to the water bank, the dynamic characteristics of the water body are mainly affected by the influx of surface runoff. By utilizing the relationship between the two, the hydrodynamic variable q of the G prediction model that can characterize the flow velocity, flow rate, or pressure of the surface runoff flowing into the monitored water body can be used as an effective model independent variable. Runoff data can be continuously obtained through meteorological data, ensuring the continuity and stability of the data required to build the target model, greatly reducing the cost of data acquisition, and improving the applicability of the prediction method.

[0024] Based on the above-mentioned water body phytoplankton biomass scale monitoring and early warning system, the present invention provides its optimization scheme. The optimization scheme can be implemented simultaneously or separately without affecting the system logic.

[0025] Optimization 1: Enrich the prediction of water quality types with low organic matter content. In the water quality type recognition module 200, for water quality types with low organic matter content, water quality labels are added based on the water quality N / P ratio. Specifically, if N / P ≤ 10, add the water quality label NP-; if N / P ≥ 22.6, add the water quality label NP+; if 10 < N / P < 22.6, add the water quality label NP±. In the initial model matching module 300, if the water quality label NP- is read, match the G prediction initial model II (Equation 2); if the water quality label NP+ is read, match the G prediction initial model III (Equation 3); if the water quality label NP± is read, match the G prediction initial model IV (Equation 4).

[0026] G = g max ×G(T)×G(I)×G(N)×G(Q) Equation 2

[0027] G = g max ×G(T)×G(I)×G(P)×G(Q) Equation 3

[0028]

[0029] In the formula, G(N) - total nitrogen function, G(P) - total phosphorus function, G(N / P) - N / P function;

[0030]

[0031] In the formula, N - total nitrogen, unit mg / L, P - total phosphorus, unit mg / L, N / P - nitrogen-phosphorus ratio, dimensionless; all are independent variables of the model; k N - half-saturation total nitrogen concentration, unit mg / L, k P - half-saturation total phosphorus concentration, unit mg / L, k N / P - half-saturation nitrogen-phosphorus ratio concentration, dimensionless; all are model parameters.

[0032] Optimization 2: The system includes a water bloom information processing module 600. The module 600 evaluates the occurrence / change trend of water blooms according to G i and the preset water bloom identification condition G (Equation 12). If the water bloom identification condition G is satisfied, a water bloom outbreak warning message is issued.

[0033] G i >> T G Equation 12

[0034] In the formula, T G - growth rate threshold for water bloom occurrence, dimensionless quantity.

[0035] By introducing the water bloom identification condition G and the corresponding judgment steps, it is possible to identify G iIn extremely critical situations, there is no need to consider the impact of natural death of algae biomass on total biomass, and algal bloom forecast and warning can be directly carried out.

[0036] Optimization 3: If the algal bloom identification condition G is not met in module 600, the effect of the natural death of algae biomass on the total biomass can be further considered, and the change in algae biomass under the action of life and death can be measured.

[0037] First, the target model calibration module 400 reads the monitoring sample MS of the module 100 and the G prediction initial model of the module 300, combines the G prediction initial model with the D prediction model (Formula 14), and constructs the c expressed in Formula 13. a Predict the initial model and input data RD into c a Predict the initial model, determine the optimal solution set of model parameters through parameter calibration, and generate the corresponding c a Prediction target model. Secondly, the algae scale prediction module 500 obtains the monitoring data MD of the water quality of the monitored water body on day i i , using c a The prediction target model estimates the scale c of phytoplankton biomass on day i a,i Finally, the water bloom information processing module 600 according to c a,i Evaluate the occurrence / change trend of algal bloom with the preset algal bloom identification conditions CA.

[0038] c a (t+1)=c a (t)+c a (t)·(GD) Equation 13

[0039]

[0040] Where c a (t), c a (t+1) - the chlorophyll content of phytoplankton in the monitored water body on day t and day t+1, in μg / L; D - the mortality rate of phytoplankton biomass, dimensionless, d max - Maximum mortality rate of phytoplankton, dimensionless, a model parameter.

[0041] In this optimization scheme, the phytoplankton mortality rate D model uses water temperature T as the basic factor of the model independent variable, and introduces the optimal water temperature T opi Compared with the existing technology, the expression of algae mortality rate D is more accurate.

[0042] By solving the problem of predicting the daily growth of phytoplankton in the monitored water body, the daily change trend of the scale of phytoplankton in the monitored water body can be predicted. By introducing the algae growth threshold T that induces algal bloom in the monitored water body, the algae growth threshold T ca Indicator, constructed by T caExpression of c a The judgment condition function can realize the early warning of the occurrence of algae bloom in the monitored water body. The threshold T corresponding to the change of algae bloom stage is used. ca , then the stage warning can be further completed. Threshold T ca It can be the daily growth or the cumulative growth in the early stage. Therefore, further optimization is that the algal bloom identification condition CA can be expressed by formula 15 to evaluate the different stages of the algal bloom process.

[0043] c a,i ≤f(T ca,j ,j=1,2,3,…n) Formula 15

[0044] Where, T ca,j ,j=1,2,3,…n-the phytoplankton biomass threshold T for monitoring the jth stage or jth degree of algal bloom in the water body ca .

[0045] The present invention also provides a method for monitoring and warning the biomass of phytoplankton in water bodies, which is specifically as follows.

[0046] A method for monitoring and warning the scale of phytoplankton biomass in a water body is implemented using the above-mentioned monitoring and warning method, and is characterized by:

[0047] First, conduct on-site surveys to obtain background data on monitored water bodies;

[0048] Secondly, the monitoring sample MS1 is obtained through the sample collection module 100;

[0049] Thirdly, a prediction target model is constructed through the water quality type identification module 200, the initial model matching module 300, and the target model calibration module 400;

[0050] Next, the monitoring sample MS2 is obtained through the sample collection module 100;

[0051] Thirdly, the monitoring sample MS2 is input into the prediction target model to obtain the predicted data of phytoplankton biomass scale in the monitored water body;

[0052] Finally, the risk of algal bloom in the monitored water bodies is evaluated by monitoring the predicted data of phytoplankton biomass.

[0053] The field survey referred to in this technology includes various surveys, reconnaissances, mapping, and measurements within the small watershed and along its banks where the monitoring and early warning scheme is located, as well as existing simulation experiments, testing experiments, observation experiments, and analytical experiments in the field, as well as the acquisition of historical water quality and hydrological records, relevant technical specifications, and empirical methods and data acquisition for reference. The data obtained from the field survey is collectively referred to as the baseline data for this technical scheme.

[0054] Compared with the existing technology, the beneficial effects of the present invention are as follows: First, the water body phytoplankton biomass scale monitoring and early warning system of the present invention is an algae biomass scale measurement scheme based on the algae ecological mathematical model. The core module of the system adopts the perspective of growth and death dynamics and the perspective of phytoplankton with water to establish a measurement model for the scale change of algae quantity, and reflects the scale change of phytoplankton in water bodies through the mathematical process of biological population dynamics. Compared with the technical scheme of the existing technology that uses multi-scale information as the nutrient status of water bodies and algal bloom early warning, the present invention strengthens the influence of environmental factors on algal biomass changes, greatly reduces the data requirements for system operation, saves data collection costs and system computing overhead, and reduces the promotion cost of technical solutions. Second, eutrophication of water bodies is the most critical factor inducing algal blooms, but the reproduction behavior of algae populations varies in different "nutrient type" environments. The present invention constructs different prediction models according to the nutrient characteristics of water quality. In fact, it adjusts the importance of nutrient factors to algae growth according to the nutrient physiological metabolic characteristics in different nutrient environments, which is more in line with the characteristics of biological behavior. At the same time, by constructing a classification model, the mathematical expression of the model is placed in a purer environment with more concentrated variable influences, the function mapping relationship is easier to identify, and the calculation is more economical. Third, on the basis of the water quality matching prediction model, the present invention further superimposes the technical logic of hierarchical execution based on previous research. Since it is found that the high concentration of organic matter in the water body promotes algae reproduction much more strongly than the NP nutrients, the present invention designs the technical logic of triggering the response according to the water quality type, so that the core module of the system is always in a stage-jump response state to the organic-rich water quality type, shortening the system monitoring response time. Since organic-rich water quality has a strong promoting effect on algae reproduction, the rapid measurement and evaluation of the algae scale in this type of water quality state has special significance for emergency management. This is a new concept that is different from the existing technology. Fourth, by respectively analyzing the range, change, rhythm and other characteristics of the mapping relationship between algae growth and different types of environmental factors, the present invention constructs factor functions with different mathematical characteristics based on the environmental factor type. Fifth, based on the analysis of the location of algal blooms in the water area and the hydrological characteristics on both sides of the natural water body, surface runoff variables that are easy to obtain stably are introduced to replace the hydrodynamic variables of the monitored water body that require special measurements to obtain. This reduces the difficulty of data collection for system operation and improves the applicability of the implementation of the technical solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a schematic diagram of the framework structure of the water body phytoplankton biomass scale monitoring and early warning system in Example 1.

[0056] Figure 2 This is a schematic diagram of the framework structure of the water body phytoplankton biomass scale monitoring and early warning system in Example 2.

[0057] Figure 3This is a schematic diagram of the framework structure of the water body phytoplankton biomass scale monitoring and early warning system in Example 3.

[0058] Figure 4 This is the comparison between the actual value and the predicted value of Example 4.

[0059] Figure 5 (a), (b), and (c) are comparisons of the actual value and the predicted value in Example 5.

[0060] The numbers in the accompanying drawings are:

[0061] Sample collection module 100; water quality type identification module 200; reading unit 210; labeling unit 220; initial model matching module 300; reading unit 310; model matching unit 320; matching output unit 330; target model calibration module 400; reading unit 410; D model storage unit 420; model combination unit 430; model calibration unit 440; target model unit 450; algae biomass scale prediction module 500; reading unit 510; prediction unit 520; result output unit 530; water bloom information processing module 600. DETAILED DESCRIPTION

[0062] The preferred embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0063] Example 1

[0064] In order to build an ecologically clean small watershed, the algal bloom risk and dynamic monitoring of the small watershed are implemented, and the water body phytoplankton biomass scale monitoring and early warning system of the present invention is established.

[0065] Designate small watersheds as monitoring water bodies, and conduct on-site surveys to obtain basic data on the monitoring water bodies.

[0066] Figure 1 This is a schematic diagram of the framework structure of the water body phytoplankton biomass scale monitoring and early warning system in Example 1.

[0067] The water body phytoplankton biomass scale monitoring and early warning system (hereinafter referred to as the system) includes a sample collection module 100, a water quality type identification module 200, an initial model matching module 300, a target model calibration module 400, and an algae biomass scale prediction module 500.

[0068] 1. Sample collection module 100

[0069] The sample collection module 100 collects monitoring samples MS required by the system. The monitoring samples MS include water quality data, meteorological data, and hydrological data.

[0070] The sample collection module 100 collects monitoring samples MS1 during monitoring period T1 for generating a prediction target model. The data items for each group of MS1 are shown in Table 1. Monitoring samples MS2 are collected during monitoring period T2 for predicting the biomass characteristics of phytoplankton; monitoring period T2 is set according to the monitoring project objectives. The data items of monitoring sample MS2 do not include the chlorophyll content c in the data items of monitoring sample MS1. a (t).

[0071] Table 1 Monitoring sample MS1 data items

[0072]

[0073] In Table 1, runoff depth is the hydrodynamic variable q specifically selected in this example.

[0074] 2. Water quality type identification module 200

[0075] The water quality type identification module 200 reads the monitoring sample MS1 of the module 100, identifies the water quality type of the monitored water body and adds a water quality model tag.

[0076] After the reading unit 210 reads the sample MS1 of the module 100 , the label adding unit 220 first identifies whether the water is rich in organic matter. If it is, the label adding unit 220 adds a water quality label E to the sample MS1 .

[0077] 3. Initial model matching module 300

[0078] The initial model matching module 300 predicts the initial model according to the water quality model label matching G.

[0079] The reading unit 310 detects the label filling status of the unit 220 in real time. Once the water quality label E is read, the G prediction initial model I is retrieved from the model matching unit 320 and sent to the matching output unit 330.

[0080] 4. Target model calibration module 400

[0081] The target model calibration module 400 reads the monitoring sample MS1 of module 100 and the G prediction initial model of module 300, inputs the sample MS1 into the G prediction initial model, determines the optimal solution set of model parameters through parameter calibration, and generates the corresponding G prediction target model.

[0082] The reading unit 410 of module 400 reads the monitoring sample MS1 of module 100 and the G prediction initial model in unit 330. The model calibration unit 440 inputs the monitoring sample MS into the G prediction initial model, determines the optimal solution set of model parameters through parameter calibration, generates the corresponding G prediction target model and sends it to the target model unit 450.

[0083] 5. Algae scale prediction module 500

[0084] The algae scale prediction module 500 obtains the monitoring sample MS2 of the monitored water body on the predicted day i, and calculates the growth rate G of the phytoplankton biomass on the day i using the G prediction target model. i .

[0085] The reading unit 510 of the module 500 reads the monitoring sample MS2 in the module 100 and the G prediction target model in the unit 450. The sample MS2 is the monitoring sample MS2 of the monitored water body on the predicted day i. i , and the prediction unit 520 inputs the monitoring sample MS2 into the G prediction target model to calculate the growth rate G of the phytoplankton biomass on the day i. i , and the result output unit 530 outputs the prediction result.

[0086] Embodiment 2

[0087] Improve the water body phytoplankton biomass scale monitoring and early warning system in Embodiment 1. The same parts will not be repeated. The differences lie in adding the processing function for water quality types that are not rich in organic matter and the water bloom information processing module 600.

[0088] Figure 2 is a schematic diagram of the framework structure of the water body phytoplankton biomass scale monitoring and early warning system in Embodiment 2.

[0089] 1. Water quality type identification module 200

[0090] After the reading unit 210 reads the sample MS1 of the module 100, the label加注 unit (it should be 'label adding unit' in English) 220 first identifies whether it is a water quality type rich in organic matter. If it is a water quality rich in organic matter, add the water quality label E to the sample MS1; otherwise, continue to identify the water quality type based on the water quality N / P ratio. Specifically: if N / P ≤ 10, add the water quality label NP-, if N / P ≥ 22.6, add the water quality label NP+, if 10 < N / P < 22.6, add the water quality label NP±.

[0091] By adopting a two-layer water quality type identification logic, the unit 220 first determines whether to add the label E. Only when there is no need to add the label E, will it add the NP type label (NP-, NP+, NP±). Therefore, there is always and only one water quality label in the unit 220, and the label E belongs to the optimization level.

[0092] 2. Initial model matching module 300

[0093] Reading unit 310 monitors the label loading status of unit 220 in real time. Once a newly generated water quality label is read, it retrieves the corresponding G prediction initial model from model matching unit 320 and sends it to matching output unit 330. Due to the label loading and storage characteristics within unit 220, initial model matching module 300 ensures that relevant calculations for organic-rich water quality are always prioritized in real time, thus ensuring the system's hierarchical classification modeling and prediction for different water quality types. The corresponding relationship between water quality type, label, and G prediction initial model is shown in Table 2.

[0094] Table 2 Correspondence between water quality type, label, and G prediction initial model

[0095] Serial number Water quality type Identification conditions Water quality label G prediction initial model 1 Rich in organic matter <![CDATA[COD Cr Concentration ≥30 mg / L]]> E Model I (Formula 1) 2 Low N / P N / P≤10 NP- Model II (Formula 2) 3 High N / P N / P≥22.6 NP+ Model III (Formula 3) 4 Medium N / P 10<N / P<22.6 NP± Model IV (Formula 4)

[0096] 3. Water bloom information processing module 600

[0097] Water bloom information processing module 600 according to G i The occurrence / change trend of algal bloom is evaluated with the preset algal bloom identification condition G. If the algal bloom identification condition G is met, an algal bloom outbreak forecast information is issued.

[0098] In this example, the water bloom identification condition G is expressed according to Formula 12.

[0099] Example 3

[0100] The system for monitoring and warning the biomass of phytoplankton in water bodies according to the second embodiment is improved. The similarities are not repeated here. The differences are that the natural death factor of algae biomass is introduced into the total biomass prediction, and module 600 is improved.

[0101] Figure 3 This is a schematic diagram of the framework structure of the water body phytoplankton biomass scale monitoring and early warning system in Example 3.

[0102] 1. Target model calibration module 400

[0103] The reading unit 410 of the target model calibration module 400 reads the monitoring sample MS of the module 100 and the G prediction initial model of the module 300, and reads the D prediction model (Formula 14) in the D model storage unit 420. The model combination unit 430 constructs c according to Formula 13. a Predict the initial model, the model calibration unit 440 inputs the monitoring sample MS1 into c a Predict the initial model, determine the optimal solution set of model parameters through parameter calibration, and generate the corresponding c a The predicted target model is sent to the target model unit 450.

[0104] 3. Algae scale prediction module 500

[0105] The algae scale prediction module 500 obtains the monitoring sample MS2 of the monitored water body on the prediction day i i , using c a The prediction target model estimates the scale c of phytoplankton biomass on day i a,i .

[0106] 3. Water bloom information processing module 600

[0107] Water bloom information processing module 600 according to c a,i The occurrence / change trend of algal bloom is evaluated with the preset algal bloom identification conditions CA. If the algal bloom identification conditions CA are met, an algal bloom outbreak forecast information is issued.

[0108] In this example, the algal bloom identification condition CA is expressed according to Formula 15, thereby realizing the forecast and early warning of different stages of algal bloom changes.

[0109] Example 4

[0110] The water phytoplankton biomass scale monitoring and early warning system of Example 3 was applied to an ecological clean construction project in a small watershed of the JQ River in a certain place to predict the changing trend of phytoplankton biomass in the water body and, when necessary, to forecast water blooms.

[0111] 1. Collect monitoring sample MS1

[0112] The sample collection module 100 collects monitoring samples MS1 during the monitoring period T1, obtaining a total of 97 sets of samples (each sample represents data for a monitoring day, the same below). Table 3 shows partial data of the 97 sets of samples MS1.

[0113] Table 3 Partial data of MS1 of 97 samples in JQ River small watershed

[0114] Serial number <![CDATA[COD Cr ]]> N P N / P T I q <![CDATA[c a (t)]]> 1 23.00 0.90 0.55 1.65 10.90 0.58 0.13 20.28 2 26.00 0.84 0.42 1.99 10.80 0.58 0.13 54.25 3 26.00 0.84 0.42 1.99 11.10 0.58 0.13 35.21 4 23.00 0.95 0.47 2.01 23.30 0.53 0.09 31.22 5 29.00 0.37 0.18 2.04 12.50 3.98 0.04 203.90 6 22.00 0.30 0.14 2.14 12.50 3.98 0.04 79.63 7 18.00 0.25 0.11 2.27 12.50 3.98 0.04 94.79 8 25.00 0.33 0.15 2.28 12.50 3.98 0.04 296.20 9 17.00 0.39 0.17 2.28 13.00 1.75 0.06 165.60 10 24.00 0.34 0.14 2.43 12.50 3.98 0.04 174.90 11 22.00 0.47 0.19 2.53 12.50 3.98 0.04 39.32 12 22.00 0.36 0.14 2.57 12.50 3.98 0.04 122.60 13 21.00 0.29 0.11 2.59 12.50 3.98 0.04 234.90 14 24.00 0.30 0.11 2.65 22.70 4.94 0.08 34.47 15 25.00 0.42 0.16 2.66 12.50 3.98 0.04 234.60 16 18.00 0.60 0.22 2.74 12.20 1.85 0.18 104.27 17 22.00 0.49 0.18 2.74 12.30 2.70 0.04 80.55 18 21.00 0.49 0.17 2.83 12.50 3.98 0.04 28.31 19 24.00 0.59 0.20 2.96 11.90 1.89 0.04 107.80 20 20.00 0.52 0.17 3.00 11.10 0.58 0.13 34.77 …… …… …… …… …… …… …… …… ……

[0115] 2. Water quality type identification

[0116] According to the sample MS1 water quality classification index (COD Cr ≤30mg / L, N / P≤10,) water quality type identification module 200 identifies the water quality type of sample MS1 according to Table 2 and adds a water quality label NP-.

[0117] 3. Initial model matching

[0118] The initial model matching module 300 reads the water quality label NP- and calls G to predict the initial model II.

[0119] 4. Target model rate

[0120] The target model calibration module 400 reads the sample MS1 of the module 100, the G prediction initial model II output by the module 300, and the D prediction model stored in itself, and constructs ca Predict the initial model (Formula 13-2), and input the sample MS1 into Formula 13-2. Through parameter calibration (the optimization algorithm in this example specifically uses genetic algorithm) to determine the optimal solution set of model parameters (Table 4), generate the corresponding c a Prediction target model (Formula 13-2-1).

[0121]

[0122] Table 4 Optimal solution of model parameters in Equation 13-2

[0123] Model parameters <![CDATA[g max ]]> <![CDATA[T opi ]]> <![CDATA[I opi ]]> <![CDATA[k N ]]> <![CDATA[d max ]]> <![CDATA[k q ]]> <![CDATA[q0]]> Optimal solution 0.24 29.23 7.24 0.10 0.78 0.14 0.34

[0124]

[0125] 5. Algae scale prediction

[0126] The sample collection module 100 collects 19 groups of monitoring samples MS2 during the monitoring period T2, each group being a monitoring sample for the predicted day i. The algae scale prediction module 500 reads the samples MS2 and the c generated by the module 400. a Predict the target model and input sample MS2 into c a Prediction target model, calculate the scale c of phytoplankton biomass on day i a,i .

[0127] In order to test the prediction accuracy of the system, 19 groups of samples MS2 c a The actual detection value verification module 500 calculates the predicted value. The results show that the determination coefficient R of the degree of fit between the predicted value of the test model and the true value is 2 =0.77, which meets the prediction accuracy requirements. Figure 4 This is the comparison between the actual value and the predicted value of Example 4.

[0128] 6. Water bloom information processing

[0129] In this example, the threshold value of phytoplankton biomass for the jth degree of algal bloom, T, is determined by referring to the Technical Specifications for Algal Bloom Degree Classification and Monitoring (DB44 / T 2261-2020). ca,j ,j. The prediction results were compared with the T ca , which can determine the evolution of the degree of algal bloom.

[0130] Example 5

[0131] The water phytoplankton biomass scale monitoring and early warning system of Example 3 was applied to an ecological clean construction project in a small watershed of DL Creek in a certain place to predict the changing trend of phytoplankton biomass in the water body and, when necessary, to forecast water blooms.

[0132] Using the system of the present invention, three prediction projects were designed and implemented to analyze the biomass trends of phytoplankton in the DL Creek watershed. Each project targeted a different study period for the DL Creek watershed. The description of the system application process, which is identical or highly similar to that in Example 4, as well as any duplicate descriptions of the three prediction projects, is omitted here. Only the key data charts related to biomass measurement for each prediction project are presented. The work of module 600 is omitted for all three prediction projects.

[0133] Table 5 Description of the main contents of the three prediction projects in the DL Creek watershed

[0134]

[0135] Table 6 Partial data of 139 samples MS1 in the DL Creek small watershed prediction project

[0136]

[0137]

[0138] Table 7 Partial data of 60 groups of samples MS1 in the DL Creek small watershed prediction project 2

[0139] Serial number P N <![CDATA[COD Cr ]]> T N / P I q <![CDATA[c a (t)]]> 1 0.08 1.55 17 8.1 19.32 2.52 0.075 21.11 2 0.07 1.06 16 11.5 15.53 4.37 0.067 45.49 3 0.05 1.16 17 12.1 22.47 0.91 0.075 34.12 4 0.06 1.03 15 13.7 17.79 0.91 0.074 43.03 5 0.06 1.00 17 11.1 16.04 1.95 0.061 45.98 6 0.07 1.08 15 11.8 14.76 1.15 0.097 39.80 7 0.10 1.08 17 15.3 10.79 2.11 0.065 148.00 8 0.06 0.96 17 19.5 17.05 1.87 0.059 32.53 9 0.06 0.95 18 19.1 16.40 3.12 0.12 35.08 10 0.06 1.00 19 18.4 17.31 1.47 0.059 37.89 11 0.06 1.35 17 15.6 21.30 2.17 0.074 29.88 12 0.05 1.13 20 21.7 21.32 5.75 0.061 37.11 13 0.10 1.22 20 23.4 12.20 4.08 0.119 35.58 14 0.06 1.10 22 28.2 18.33 6.76 0.047 43.96 15 0.05 1.08 21 25.2 21.60 3.01 0.116 30.34 16 0.07 1.02 19 24.1 14.57 2.52 0.166 54.09 17 0.08 1.08 21 25.1 13.50 3.44 0.053 38.11 18 0.06 1.00 20 28.9 16.67 5.72 0.122 46.56 19 0.13 1.38 16 29.5 10.62 7.43 0.034 43.10 20 0.07 1.03 19 27.9 14.71 4.15 6.228 30.29 …… …… …… …… …… …… …… …… ……

[0140] Table 8 Partial data of 330 samples MS1 of the DL Creek small watershed prediction project

[0141]

[0142]

[0143] Table 9 Optimal solutions for model parameters of three prediction projects in the DL Creek watershed

[0144]

[0145]

[0146]

[0147]

Claims

1. The water body phytoplankton biomass scale monitoring and early warning system is characterized by: It includes a sample collection module 100, a water quality type identification module 200, an initial model matching module 300, a target model calibration module 400, and an algal biomass scale prediction module 500; The sample collection module 100 obtains water quality monitoring data RD, meteorological data, and hydrological data of the monitored water body to form a monitoring sample MS. The sample MS1 is used to generate a prediction target model, and the sample MS2 is used for predicting the phytoplankton biomass; The water quality type identification module 200 reads the sample MS1 of the module 100, identifies the water quality type of the monitored water body and adds a water quality model label. The water quality type rich in organic matter adds the water quality label E; The initial model matching module 300 matches and predicts an initial model G according to the water quality model label; if the water quality label E is read, it matches and predicts the initial model I of G. The G prediction initial model I is expressed as in Equation 1, G = A × g max × G(T) × G(I) × G(COD) × G(Q) Equation 1 Where, G is the growth rate of phytoplankton biomass, dimensionless, G(T) is the water temperature function, G(I) is the sunshine function, G(COD) is the COD Cr Function, G(Q) - hydrodynamic function Where, T is water temperature, unit is ℃, I is sunshine hours, unit is h, q is hydrodynamic variable, unit is determined by the selected variable, COD is COD Cr Chemical oxygen demand, unit mg / L; both are model independent variables; A - overgrowth coefficient, dimensionless, g max - Maximum growth rate of phytoplankton, dimensionless, T opi - Optimal water temperature, unit ℃, I opi - Optimal sunshine hours, units: h, k COD - Semi-saturated COD Cr Concentration, unit mg / L, k q - hydrodynamic variable influence coefficient, dimensionless, q0 - hydrodynamic variable critical value, unit is determined by the selected variable; both are model parameters; The target model calibration module 400 reads the sample MS1 of the module 100 and the G prediction initial model of the module 300, inputs the data RD into the G prediction initial model, determines the optimal solution set of the model parameters through parameter calibration, and generates the corresponding G prediction target model; The algae scale prediction module 500 reads the sample MS2 and uses the G prediction target model to calculate the growth rate G of the phytoplankton biomass on the predicted day i. i .

2. The water body phytoplankton biomass scale monitoring and early warning system according to claim 1, characterized in that: In the water quality type identification module 200, for the water quality type that is not rich in organic matter, water quality labels are added according to the water quality N / P ratio. If N / P ≤ 10, the water quality label NP- is added. If N / P ≥ 22.6, the water quality label NP+ is added. If 10 < N / P < 22.6, the water quality label NP± is added; In the initial model matching module 300, if the water quality label NP- is read, it matches and predicts the initial model II of G. If the water quality label NP+ is read, it matches and predicts the initial model III of G. If the water quality label NP± is read, it matches and predicts the initial model IV of G; The G prediction initial model II, the G prediction initial model III, and the G prediction initial model IV are respectively expressed as in Equations 2, 3, and 4, G = g max ×G(T)×G(I)×G(N)×G(Q) Equation 2 G = g max × G(T) × G(I) × G(P) × G(Q) Equation 3 In the formula, G(N) - total nitrogen function, G(P) - total phosphorus function, G(N / P) - N / P function; In the formula, N - total nitrogen, unit mg / L, P - total phosphorus, unit mg / L, N / P - nitrogen-phosphorus ratio, dimensionless; all are model independent variables; k N - Half-saturated total nitrogen concentration, unit mg / L, k P - Half-saturated total phosphorus concentration, unit mg / L, k N / P - Half-saturated nitrogen-phosphorus ratio concentration, dimensionless; both are model parameters.

3. The system for monitoring and warning the biomass of phytoplankton in water bodies according to claim 2, characterized in that: The reading unit 310 of the initial model matching module 300 reads the water quality label added by the module 200. If the water quality label E is read, it retrieves the G prediction initial model I from the model matching unit 320 and sends it to the matching output unit 330, and sends a command to read the matching output unit 330 to the module 400; if the water quality label E is not read, it continues to read other water quality labels, and then retrieves the G prediction initial model II / III / IV that matches the label from the model matching unit 320 and sends it to the matching output unit 330, and sends a command to read the matching output unit 330 to the module 400.

4. The system for monitoring and warning the biomass of phytoplankton in water bodies according to claim 3, characterized in that: It also includes a water bloom information processing module 600, which processes water bloom information according to G i The occurrence / change trend of algal bloom is evaluated with the preset algal bloom identification condition G. If the algal bloom identification condition G is met, an algal bloom outbreak forecast information is issued; the algal bloom identification condition G is expressed according to formula 12; G i >>T G Formula 12 Where, T G - The growth rate threshold for algal bloom occurrence, dimensionless.

5. The system for monitoring and warning the biomass of phytoplankton in water bodies according to claim 4, characterized in that: If the algal bloom identification condition G does not hold, the target model calibration module 400 reads the monitoring sample MS of the module 100 and the G prediction initial model of the module 300, combines the G prediction initial model with the D prediction model, and constructs the c expressed in formula 13. a Predict the initial model and input data RD into c a Predict the initial model, determine the optimal solution set of model parameters through parameter calibration, and generate the corresponding c a Prediction target model; the D prediction model is expressed as formula 14; c a (t + 1)= c a (t)+ c a (t)·(G - D) Formula 13 Where c a (t), c a (t+1) - the chlorophyll content of phytoplankton in the monitored water body on day t and day t+1, in μg / L; D - the mortality rate of phytoplankton biomass, dimensionless, d max - Maximum mortality rate of phytoplankton, dimensionless, a model parameter; The algae scale prediction module 500 obtains the monitoring data MD of the water quality of the monitored water body on day i i , using c a The prediction target model estimates the scale c of phytoplankton biomass on day i a,i ; The water bloom information processing module 600 is based on c a,i Evaluate the occurrence / change trend of algal bloom with the preset algal bloom identification conditions CA.

6. The system for monitoring and warning the biomass of phytoplankton in water bodies according to claim 5, characterized in that: The target model calibration module 400 reads the monitoring sample MS of the module 100, the G prediction initial model of the module 300, and the D prediction model in the D model storage unit 420, and the model combination unit 430 reads the G prediction initial model and the D prediction model in the unit 420 to construct a D prediction model. a Predict the initial model, the model calibration unit 440 will monitor the sample MS input c a Predict the initial model, determine the optimal solution set of model parameters through parameter calibration, and generate the corresponding c a The predicted target model is sent to the target model unit 450.

7. The system for monitoring and warning the biomass of phytoplankton in water bodies according to claim 6, characterized in that: The water bloom identification condition CA in the water bloom information processing module 600 is expressed as in Equation 15, c a,i ≤f(T ca,j ,j=1,2,3,…n) Formula 15 Where, T ca,j ,j=1,2,3,…n-the phytoplankton biomass threshold T for monitoring the jth stage or jth degree of algal bloom in the water body ca .

8. The system for monitoring and warning the biomass of phytoplankton in water bodies according to any one of claims 1 to 7, characterized in that: The hydrodynamic variable q is a variable representing the flow velocity or flow rate or pressure of the monitored water body, or a variable representing the flow velocity or flow rate or pressure of the surface runoff flowing into the monitored water body.

9. The system for monitoring and warning the biomass of phytoplankton in water bodies according to any one of claims 1 to 7, characterized in that: The organic matter-rich water quality type is COD Cr The threshold value is concentration ≥30 mg / L.

10. A method for monitoring and warning the biomass scale of phytoplankton in a water body, implemented using the system for monitoring and warning the biomass scale of phytoplankton in a water body according to any one of claims 1 to 7, characterized in that: First, conduct on-site surveys to obtain background data on monitored water bodies; Secondly, the monitoring sample MS1 is obtained through the sample collection module 100; Thirdly, a prediction target model is constructed through the water quality type identification module 200, the initial model matching module 300, and the target model calibration module 400; Next, the monitoring sample MS2 is obtained through the sample collection module 100; Thirdly, the monitoring sample MS2 is input into the prediction target model to obtain the predicted data of phytoplankton biomass scale in the monitored water body; Finally, the risk of algal bloom in the monitored water bodies is evaluated by monitoring the predicted data of phytoplankton biomass.

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