Water eutrophication and ecological flow collaborative early warning method based on water quality monitoring

Abstract

This invention relates to the field of water environment monitoring technology, and in particular to a method for coordinated early warning of eutrophication and ecological flow based on water quality monitoring. It integrates multi-dimensional real-time monitoring data, dynamically quantifies eutrophication risk using hydrological impact correction factors, avoids the disconnect between static assessments and actual risks, and directly correlates dynamic eutrophication risk levels with ecological flow thresholds. Dynamic adjustment of ecological flow is achieved through a flow increase ratio coefficient, and a correlation is established between current operating conditions and historical optimal treatment conditions. The calculated final value (Q) is validated, and structured early warning information is constructed, outputting clear warning text and structured information. This achieves a closed-loop process encompassing risk identification, threshold calculation, validity verification, and tiered early warning, providing direct and operable technical support for water resource regulation and pollution control decisions, significantly improving the efficiency and effectiveness of eutrophication prevention and ecological protection.

Description

Technical Field

[0001] This invention relates to the field of water environment monitoring technology, and in particular to a method for early warning of eutrophication and ecological flow in water bodies based on water quality monitoring. Background Technology

[0002] Eutrophication is one of the major water environment problems facing the world today. The core technology is the excessive accumulation of nutrients such as nitrogen and phosphorus in water bodies, which leads to the abnormal proliferation of algae and other plankton, resulting in decreased water transparency and reduced dissolved oxygen. This not only disrupts the balance of aquatic ecosystems and affects the safety of aquatic organisms' habitats, but may also trigger ecological disasters such as cyanobacterial blooms, seriously threatening drinking water safety and the sustainable use of water resources.

[0003] Ecological flow, as a core element in maintaining the ecological function of aquatic bodies, directly affects the self-purification capacity of water bodies, the efficiency of nutrient migration and transformation, and the living environment of aquatic organisms. When the water flow is insufficient, the water flow speed slows down, and nutrients tend to accumulate in local water areas, further exacerbating the risk of eutrophication.

[0004] Currently, eutrophication assessments are mostly based on single water quality parameters (such as total nitrogen and total phosphorus concentrations) or static trophic state indices, failing to fully consider the impact of dynamic hydrological changes (such as flow rate and velocity) on the eutrophication process. This leads to discrepancies between assessment results and actual risks, making it difficult to accurately reflect the dynamic evolution characteristics of eutrophication. At the same time, the determination of ecological flow thresholds is often based solely on the ecological function requirements of water bodies (such as maintaining basic habitats), without dynamic adjustments based on eutrophication risk levels. This results in a "one-size-fits-all" limitation, making it impossible to achieve a precise match between ecological flow and eutrophication control.

[0005] Therefore, there is an urgent need to develop a collaborative early warning method that can integrate multi-dimensional parameters of water quality, hydrology, and meteorology, and dynamically quantify eutrophication risk and ecological flow demand, in order to solve the problems of static eutrophication assessment, rigid ecological flow allocation, and fragmented early warning system in traditional methods, and provide technical support for precise watershed water environment management and ecological security. Summary of the Invention

[0006] The purpose of this invention is to provide a method for coordinated early warning of eutrophication and ecological flow based on water quality monitoring. This method integrates multi-dimensional real-time monitoring data, dynamically quantifies eutrophication risk, accurately matches ecological flow demand, and verifies the effectiveness of thresholds using historical data. It constructs a full-process analysis process encompassing risk assessment, flow calculation, effectiveness verification, and coordinated early warning, achieving deep synergy between eutrophication prevention and control and ecological flow management. This provides scientific and efficient technical support for precise watershed water environment management and ecological security, while also solving the technical problems of static eutrophication assessment, rigid ecological flow allocation, and fragmented early warning systems in traditional water bodies.

[0007] The objective of this invention can be achieved through the following technical solution: a method for coordinated early warning of eutrophication and ecological flow in water bodies based on water quality monitoring, comprising the following steps: Step 1: Collect and preprocess water quality parameters, hydrological parameters and meteorological parameters of the target area water body, calculate the basic eutrophication state index TLi of the core indicators, and analyze and output the comprehensive trophic state index TLZ by combining the preset weight coefficients wi of each core indicator. Step 2: Retrieve historical hydrological parameters of water bodies within the target area, and calculate the hydrological impact correction factor Kh based on the historical hydrological parameters; Step 3: Calculate the combined trophic state index TLZ and hydrological impact correction factor Kh, output the dynamic eutrophication risk level R, perform discrimination processing on R, and output the current risk level. Step 4: Analyze the basic flow information of the water body in the target area through the water balance equation, and obtain the required basic outflow flow Qjc and the flow increase ratio coefficient corresponding to the current risk level. Calculate and output the final ecological flow threshold Q_final. Step 5: Select the optimal working condition samples to form a validation dataset. Analyze the similarity S based on the validation dataset and determine whether Q is valid. If valid, proceed to Step 6; otherwise, output the list of ecological flow thresholds. Step 6: Based on the final effective time of Q, classify the ecological flow gap rate ΔQ of the current target area water body into gap risk levels, and combine it with the risk level corresponding to the current dynamic eutrophication risk level R to form structured early warning information and display it.

[0008] Preferably, the analysis process for the Comprehensive Nutritional Status Index (TLZ) is as follows: The system acquires water quality, hydrological, and meteorological parameters of the target area in real time. It preprocesses the acquired water quality parameters to obtain the relevant core indicators of total phosphorus (TP), total nitrogen (TN), chlorophyll a (Chl-a), and transparency (SD). The core indicators in the water quality parameters are marked as i. The core indicators are input into the corresponding preset basic eutrophication state index calculation formula to obtain the basic eutrophication state index TLi of each core indicator. At the same time, the preset weight coefficient wi of each core indicator is obtained. Comprehensive Nutritional Status Index TLZ = ∑ (preset weighting coefficient wi × basic eutrophication status index TLi).

[0009] Preferably, the calculation and analysis process of the hydrological influence correction factor Kh is as follows: Historical hydrological parameters of water bodies within the target area are retrieved. These parameters include the historical average flow velocity v0 and the historical average flow rate Q0 of the target water body. Based on the real-time flow rate Q and flow velocity v in the hydrological parameters, and combined with the analysis of historical hydrological parameters, the hydrological influence correction factor Kh is calculated. The hydrological influence correction factor Kh = α × v / v0 + β × Q / Q0, where α and β are preset weighting coefficients, and α + β = 1.

[0010] Preferably, the analysis process for the current risk level is as follows: By combining the basic eutrophication state index TLZ and the hydrological influence correction factor Kh, the dynamic eutrophication risk level R is calculated, where R = basic eutrophication state index TLZ × hydrological influence correction factor Kh. The preset dynamic eutrophication risk level interval [Rmin, Rmax] is retrieved for comparison, and the risk level (low risk / medium risk / high risk) corresponding to the current target area water body's dynamic eutrophication risk level R is output and displayed immediately.

[0011] Preferably, the risk process for the final ecological flow threshold Q is as follows: S1: Real-time acquisition of basic flow information of water bodies in the target area, including total inflow Qr, total outflow Qc, evaporation E, and leakage S; S2: Based on the water balance equation, calculate the basic information of the water flow in the target area to obtain the water volume change ΔV. The water volume change ΔV = total inflow Qr - total outflow Qc - evaporation E - leakage S. S3: Based on the ecological function of the water body in the target area, determine the minimum ecological water demand Vx and calculate the basic outflow rate Qjc required to maintain the water volume Vx; S4: Obtain the pre-set flow increase ratio coefficient corresponding to the risk level R of dynamic eutrophication risk level; S5: Final ecological flow threshold: Q_final = basic outflow flow Qjc × (1 + flow increase ratio coefficient).

[0012] Preferably, the final validity discrimination analysis process of Q is as follows: Collect historical monitoring data of water bodies in the target area for the past 10 years, screen out the optimal working condition samples that meet the conditions, extract the actual ecological flow data corresponding to the optimal working condition samples, and form a validation dataset. Using dynamic eutrophication risk level, water temperature T, sunshine duration, flow velocity, and inflow nutrient concentration as feature parameters, the cosine similarity algorithm is used to calculate the similarity S between the current operating condition and the historical best operating condition. The similarity S is then compared with a preset similarity threshold. If the similarity S > the preset similarity threshold, the average of the 5 historical ecological flow thresholds with the highest similarity in the validation dataset is calculated to obtain the average historical ecological flow threshold. If |Q_final - average historical ecological flow threshold| ≤ the preset threshold, then Q_final is considered valid. If |Q_final - average historical ecological flow threshold| > the preset threshold, then Q_final is considered invalid. A list of ecological flow thresholds composed of Q_final and the average historical ecological flow threshold is output. If the similarity S ≤ the preset similarity threshold, then output the threshold confirmation command, immediately respond to the threshold confirmation command and display the preset warning text: "Pending verification".

[0013] Preferably, the analysis process for classifying the gap risk level is as follows: When Q_final is determined to be valid, the difference between the real-time water flow Q and Q_final in the current target area is obtained. Based on (real-time water flow Q_final) / Q_final × 100%, the ecological flow gap rate ΔQ is calculated. The ecological flow gap rate ΔQ is then processed and a regular management command is output or compared with the preset flow gap interval [Q_min, Q_max]. When compared with [QKmin, QKmax]: output the gap risk level (low gap risk / medium gap risk / high gap risk). Structured early warning information is composed of the risk level corresponding to the current dynamic eutrophication risk level R and the gap risk level (low gap risk / medium gap risk / high gap risk) corresponding to the ecological flow gap rate ΔQ.

[0014] The beneficial effects of this invention are as follows:

[0015] (1) This invention combines real-time data from multiple dimensions of water quality, hydrology, and meteorology, and integrates core water quality indicators such as total nitrogen and total phosphorus with dynamic hydrological parameters such as real-time flow and velocity. It dynamically adjusts the comprehensive trophic state index through the hydrological influence correction factor Kh, accurately quantifies the impact of hydrological processes on eutrophication, and enables R to reflect the dynamic evolution characteristics of eutrophication in real time, avoiding the problem of static evaluation being disconnected from actual risks. Furthermore, it directly links the dynamic eutrophication risk level with the ecological flow threshold, and achieves dynamic adjustment of ecological flow through the flow increase ratio coefficient. At the same time, it determines the basic outflow based on the water balance equation and the ecological function requirements of the water body, which not only ensures the maintenance of aquatic organism habitats and the guarantee of self-purification capacity, but also takes into account the special needs of eutrophication prevention and control through the dynamic adjustment mechanism, thus achieving the dual goals of ecological protection and pollution control.

[0016] (2) This invention also establishes the correlation between the current working condition and the historical best governance working condition by screening the optimal working condition sample, and verifies the validity of the calculated Q final, avoiding the possible deviation of single model calculation, ensuring the rationality and feasibility of the ecological flow threshold, and constructing structured early warning information, outputting clear early warning text and structured information, realizing the whole process closed loop of risk identification, threshold calculation, validity verification and hierarchical early warning, providing direct and operable technical support for water resource regulation, pollution control and other decision-making, and greatly improving the efficiency and effect of eutrophication prevention and control and ecological protection. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings; Fig. 1 This is a reference flowchart of the method of the present invention; Fig. 2 This is a reference chart for dynamic eutrophication risk level analysis; Fig. 3 This is a reference diagram for the final ecological flow threshold analysis. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments; Example 1: Please refer to Figs. 1 to 3 As shown, this invention is a method for coordinated early warning of eutrophication and ecological flow in water bodies based on water quality monitoring, comprising the following steps: Step 1: Collect and preprocess water quality parameters, hydrological parameters and meteorological parameters of the target area water body, calculate the basic eutrophication state index TLi of the core indicators, and analyze and output the comprehensive trophic state index TLZ by combining the preset weight coefficients wi of each core indicator. Step 2: Retrieve historical hydrological parameters of water bodies within the target area, and calculate the hydrological impact correction factor Kh based on the historical hydrological parameters; Step 3: Calculate the combined trophic state index TLZ and hydrological impact correction factor Kh, output the dynamic eutrophication risk level R, perform discrimination processing on R, and output the current risk level. Step 4: Analyze the basic flow information of the water body in the target area through the water balance equation, and obtain the required basic outflow flow Qjc and the flow increase ratio coefficient corresponding to the current risk level. Calculate and output the final ecological flow threshold Q_final. Step 5: Select the optimal working condition samples to form a validation dataset. Analyze the similarity S based on the validation dataset and determine whether Q is valid. If valid, proceed to Step 6; otherwise, output the list of ecological flow thresholds. Step 6: Based on the final effective time of Q, classify the ecological flow gap rate ΔQ of the current target area water body into gap risk levels, and combine it with the risk level corresponding to the current dynamic eutrophication risk level R to form structured early warning information and display it. The following further clarifies step one, specifically including: Real-time acquisition of water quality, hydrological, and meteorological parameters of the target area's water bodies; Water quality parameters include, but are not limited to, total nitrogen (TN) concentration, total phosphorus (TP) concentration, chlorophyll a (Chl-a) concentration, and transparency (SD); Hydrological parameters include, but are not limited to, real-time flow rate (Q), flow velocity (v), and water level (H) of the water body; Meteorological parameters include, but are not limited to, surface water temperature (T), sunshine duration (t), and rainfall (P); The obtained water quality parameters are preprocessed (e.g., washed) to obtain core indicators such as total phosphorus (TP), total nitrogen (TN), chlorophyll a (Chl-a), and transparency (SD). The core indicators are labeled as i, i = 1, 2, 3, ..., n, where n is a natural number greater than zero. The core indicators are input into the corresponding preset basic eutrophication state index calculation formula to obtain the basic eutrophication state index TLi for each core indicator. At the same time, the preset weight coefficient wi for each core indicator is obtained. The comprehensive nutrient status index TLZ is calculated based on the basic eutrophication state index TLi and the preset weighting coefficient wi. The comprehensive nutrient status index TLZ = ∑ (preset weighting coefficient wi × basic eutrophication state index TLi). For example: Total phosphorus (TP) nutritional status index: TL(TP)=10×[2.46+0.69ln(TP)], unit requirement: TP concentration unit is mg / L (as P); Total nitrogen (TN) nutritional status index: TL(TN) = 10 × [2.02 + 0.67ln(TN)], unit requirement: TN concentration unit is mg / L; Step 2: Retrieve historical hydrological parameters of water bodies within the target area, and calculate the hydrological impact correction factor Kh based on these parameters. The calculation and analysis process for the hydrological impact correction factor Kh is as follows: Retrieve historical hydrological parameters of water bodies within the target area. These historical hydrological parameters include the historical average flow velocity v0 and the historical average flow rate Q0 of the target water body. Based on the real-time flow rate Q and velocity v of the water body in the hydrological parameters, and combined with the analysis of historical hydrological parameters, the hydrological influence correction factor Kh is calculated. The hydrological influence correction factor Kh = α × v / v0 + β × Q / Q0, where α and β are preset weight coefficients, and α + β = 1. Step 3: Calculate the dynamic eutrophication risk level R by combining the comprehensive trophic state index TLZ with the hydrological impact correction factor Kh. Perform discriminant processing on R to output the current risk level. The specific analysis process for the current risk level is as follows: Combining the baseline eutrophication state index TLZ and the hydrological impact correction factor Kh, the dynamic eutrophication risk level R is calculated, where R = baseline eutrophication state index TLZ × hydrological impact correction factor Kh. A preset dynamic eutrophication risk level interval [Rmin, Rmax] is then retrieved for comparison. If the dynamic eutrophication risk level R < Rmin, it is judged as low risk; if the dynamic eutrophication risk level R ∈ [Rmin, Rmax], it is judged as medium risk; if the dynamic eutrophication risk level R > Rmax, it is judged as high risk. Output the risk level (low risk / medium risk / high risk) corresponding to the dynamic eutrophication risk level R of the current target area water body and display it immediately.

[0020] Example 2: Step 4: Analyze the basic flow information of the target area water body using the water balance equation, and simultaneously obtain the required basic outflow flow Qjc and the flow increase ratio coefficient corresponding to the current risk level. Calculate and output the final ecological flow threshold Q_final. The risk process for the final ecological flow threshold Q_final is as follows: S1: Real-time acquisition of basic flow information of water bodies in the target area, including total inflow Qr, total outflow Qc, evaporation E, and leakage S; S2: Based on the water balance equation, calculate the basic information of the water flow in the target area to obtain the water volume change ΔV. The water volume change ΔV = total inflow Qr - total outflow Qc - evaporation E - leakage S. S3: Based on the ecological function of the target area water body (such as maintaining aquatic organism habitats and ensuring the water body's self-purification capacity), determine the minimum ecological water demand Vx, and calculate the basic outflow rate Qjc required to maintain the water body volume Vx, that is, the basic outflow rate Qjc = minimum ecological water demand Vx / Δt, where Δt represents the target area water body's set renewal cycle. S4: Obtain the risk level corresponding to the dynamic eutrophication risk level R of the current target area water body, and obtain the pre-set flow increase ratio coefficient corresponding to the risk level R. That is, when R increases, it is necessary to increase the total outflow Qc to accelerate water body renewal and improve self-purification capacity. S5: Final ecological flow threshold: Q_final = Basic outflow flow Qjc × (1 + flow increase ratio coefficient); Step 5: Select the optimal operating condition samples to form a validation dataset. Analyze the similarity S based on the validation dataset to determine if Q is valid. If valid, proceed to Step 6; otherwise, output the ecological flow threshold list. The validity discrimination analysis process for Q is as follows: Collect historical monitoring data of the target area's water bodies over the past 10 years, and select the optimal operating condition sample that meets the following conditions: The deviation between the dynamic eutrophication risk level R and the current dynamic eutrophication risk level R is ≤ a preset threshold (e.g., 5%), the deviation between the water temperature T and the current T is ≤ a preset threshold deviation (e.g., 2%), and the final nutrient concentration in the water body is controlled within the critical threshold, and no algal bloom occurs. Extract the actual ecological flow data corresponding to the optimal operating condition sample to form a validation dataset; Using dynamic eutrophication risk level, water temperature T, sunshine duration, flow velocity, and inflow nutrient concentration as feature parameters, the cosine similarity algorithm is used to calculate the similarity S between the current operating condition and the historical best operating condition. The similarity S is then compared with a preset similarity threshold. If the similarity S > the preset similarity threshold, the average of the 5 historical ecological flow thresholds with the highest similarity in the validation dataset is calculated to obtain the average historical ecological flow threshold. If |Q_final - average historical ecological flow threshold| ≤ the preset threshold, then Q_final is considered valid. If |Q_final - average historical ecological flow threshold| > the preset threshold, then Q_final is considered invalid. The list of ecological flow thresholds composed of Q_final and the average historical ecological flow threshold is output and displayed immediately. If the similarity S ≤ the preset similarity threshold, then output the threshold confirmation command, immediately respond to the threshold confirmation command and display the preset warning text: "Pending verification".

[0021] Example 3: Step 6: Based on the final effective time of Q, classify the ecological flow deficit rate ΔQ of the current target area water body into deficit risk levels, and combine it with the risk level corresponding to the current dynamic eutrophication risk level R to form structured early warning information and display it. The structured early warning information analysis process is as follows: When Q_final is deemed valid, the difference between the real-time water flow rate Q and Q_final in the current target area is obtained. The ecological flow gap rate ΔQ is calculated based on (real-time water flow rate Q_final) / Q_final × 100%. The ecological flow gap rate ΔQ is then processed. If ΔQ < the preset ecological flow gap rate, a regular management instruction is generated, and the preset warning text "Water exchange capacity is sufficient, no flow replenishment required" is immediately displayed. If ΔQ ≥ the preset ecological flow gap rate, the preset flow gap interval [Q_Kmin, Q_Kmax] is retrieved for comparison. If the ecological flow deficit rate ΔQ∈[preset ecological flow deficit rate, QKmin], it is judged as low deficit risk; if the ecological flow deficit rate ΔQ∈[QKmin, QKmax], it is judged as medium deficit risk; if the ecological flow deficit rate ΔQ≥QKmax, it is judged as high deficit risk. Structured early warning information is composed of the risk level corresponding to the current dynamic eutrophication risk level R and the gap risk level (low gap risk / medium gap risk / high gap risk) corresponding to the ecological flow gap rate ΔQ. The structured early warning information represents the risk level corresponding to the dynamic eutrophication risk level R + the gap risk level corresponding to the ecological flow gap rate ΔQ. The structured early warning information is output and displayed immediately so as to intuitively understand the current eutrophication and ecological flow risk of the target area water body, so as to carry out rational management. In summary, by combining real-time data from multiple dimensions of water quality, hydrology, and meteorology, and integrating core water quality indicators such as total nitrogen and total phosphorus with dynamic hydrological parameters such as real-time flow and velocity, the comprehensive trophic state index is dynamically adjusted using the hydrological influence correction factor Kh. This accurately quantifies the impact of hydrological processes on eutrophication, enabling R to reflect the dynamic evolution characteristics of eutrophication in real time, avoiding the problem of static assessment being disconnected from actual risks. Furthermore, the dynamic eutrophication risk level is directly linked to the ecological flow threshold, and the dynamic adjustment of ecological flow is achieved through the flow increase ratio coefficient. Meanwhile, the basic outflow rate is determined based on the water balance equation and the ecological function requirements of the water body. This ensures core ecological functions such as the maintenance of aquatic organism habitats and the guarantee of self-purification capacity. At the same time, a dynamic adjustment mechanism takes into account the special needs of eutrophication control, achieving the dual goals of ecological protection and pollution control. Furthermore, by screening the optimal operating condition sample, the correlation between the current operating condition and the historical best treatment operating condition is established to verify the validity of the calculated Q value, avoiding the possible biases of single model calculations and ensuring the rationality and feasibility of the ecological flow threshold. Moreover, structured early warning information is constructed, outputting clear warning text and structured information, realizing a closed loop of the entire process of risk identification, threshold calculation, validity verification, and graded early warning. This provides direct and operable technical support for decision-making in water resource regulation and pollution control, and significantly improves the efficiency and effectiveness of eutrophication control and ecological protection.

[0022] The threshold is set for comparative analysis of results to determine whether they are good or bad. The value of the threshold is determined by a combination of large-scale model analysis of sample data and human experience. It can also be adjusted appropriately based on seasonal or common-sense influencing factors.

[0023] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for coordinated early warning of eutrophication and ecological flow in water bodies based on water quality monitoring, characterized in that, Includes the following steps: Step 1: Collect and preprocess water quality parameters, hydrological parameters and meteorological parameters of the target area water body, calculate the basic eutrophication state index TLi of the core indicators, and analyze and output the comprehensive trophic state index TLZ by combining the preset weight coefficients wi of each core indicator. Step 2: Retrieve historical hydrological parameters of water bodies within the target area, and calculate the hydrological impact correction factor Kh based on the historical hydrological parameters; Step 3: Calculate the combined trophic state index TLZ and hydrological impact correction factor Kh, output the dynamic eutrophication risk level R, perform discrimination processing on R, and output the current risk level. Step 4: Analyze the basic flow information of the water body in the target area through the water balance equation, and obtain the required basic outflow flow Qjc and the flow increase ratio coefficient corresponding to the current risk level. Calculate and output the final ecological flow threshold Q_final. Step 5: Select the optimal working condition samples to form a validation dataset. Analyze the similarity S based on the validation dataset and determine whether Q is valid. If valid, proceed to Step 6; otherwise, output the list of ecological flow thresholds. Step 6: Based on the final effective time of Q, classify the ecological flow gap rate ΔQ of the current target area water body into gap risk levels, and combine it with the risk level corresponding to the current dynamic eutrophication risk level R to form structured early warning information and display it.

2. The method for coordinated early warning of eutrophication and ecological flow based on water quality monitoring according to claim 1, characterized in that, The analysis process for the Comprehensive Nutritional Status Index (TLZ) is as follows: The system acquires water quality, hydrological, and meteorological parameters of the target area in real time. It preprocesses the acquired water quality parameters to obtain the relevant core indicators of total phosphorus (TP), total nitrogen (TN), chlorophyll a (Chl-a), and transparency (SD). The core indicators in the water quality parameters are marked as i. The core indicators are input into the corresponding preset basic eutrophication state index calculation formula to obtain the basic eutrophication state index TLi of each core indicator. At the same time, the preset weight coefficient wi of each core indicator is obtained. Comprehensive Nutritional Status Index TLZ = ∑ (preset weighting coefficient wi × basic eutrophication status index TLi).

3. The method for coordinated early warning of eutrophication and ecological flow based on water quality monitoring according to claim 1, characterized in that, The calculation and analysis process of the hydrological impact correction factor Kh is as follows: Historical hydrological parameters of water bodies within the target area are retrieved. These parameters include the historical average flow velocity v0 and the historical average flow rate Q0 of the target water body. Based on the real-time flow rate Q and flow velocity v in the hydrological parameters, and combined with the analysis of historical hydrological parameters, the hydrological influence correction factor Kh is calculated. The hydrological influence correction factor Kh = α × v / v0 + β × Q / Q0, where α and β are preset weighting coefficients, and α + β = 1.

4. The method for coordinated early warning of eutrophication and ecological flow in water bodies based on water quality monitoring according to claim 1, characterized in that, The analysis process for the current risk level is as follows: By combining the basic eutrophication state index TLZ and the hydrological influence correction factor Kh, the dynamic eutrophication risk level R is calculated, where R = basic eutrophication state index TLZ × hydrological influence correction factor Kh. The preset dynamic eutrophication risk level interval [Rmin, Rmax] is retrieved for comparison, and the risk level (low risk / medium risk / high risk) corresponding to the current target area water body's dynamic eutrophication risk level R is output and displayed immediately.

5. The method for coordinated early warning of eutrophication and ecological flow based on water quality monitoring according to claim 1, characterized in that, The risk process for the final ecological flow threshold Q is as follows: S1: Real-time acquisition of basic flow information of water bodies in the target area, including total inflow Qr, total outflow Qc, evaporation E, and leakage S; S2: Based on the water balance equation, calculate the basic information of the water flow in the target area to obtain the water volume change ΔV. The water volume change ΔV = total inflow Qr - total outflow Qc - evaporation E - leakage S. S3: Based on the ecological function of the water body in the target area, determine the minimum ecological water demand Vx and calculate the basic outflow rate Qjc required to maintain the water volume Vx; S4: Obtain the pre-set flow increase ratio coefficient corresponding to the risk level R of dynamic eutrophication risk level; S5: Final ecological flow threshold: Q_final = basic outflow flow Qjc × (1 + flow increase ratio coefficient).

6. The method for coordinated early warning of eutrophication and ecological flow in water bodies based on water quality monitoring according to claim 1, characterized in that, The validity discrimination analysis process of Q is as follows: Collect historical monitoring data of water bodies in the target area for the past 10 years, screen out the optimal working condition samples that meet the conditions, extract the actual ecological flow data corresponding to the optimal working condition samples, and form a validation dataset. Using dynamic eutrophication risk level, water temperature T, sunshine duration, flow velocity, and inflow nutrient concentration as feature parameters, the cosine similarity algorithm is used to calculate the similarity S between the current operating condition and the historical best operating condition. The similarity S is then compared with a preset similarity threshold. If the similarity S > the preset similarity threshold, the average of the 5 historical ecological flow thresholds with the highest similarity in the validation dataset is calculated to obtain the average historical ecological flow threshold. If |Q_final - average historical ecological flow threshold| ≤ the preset threshold, Q_final is deemed valid. If |Q_final - average historical ecological flow threshold| > the preset threshold, Q_final is deemed invalid. A list of ecological flow thresholds composed of Q_final and the average historical ecological flow threshold is output. If the similarity S ≤ the preset similarity threshold, then output the threshold confirmation command, immediately respond to the threshold confirmation command and display the preset warning text: "Pending verification".

7. The method for coordinated early warning of eutrophication and ecological flow in water bodies based on water quality monitoring according to claim 1, characterized in that, The analysis process for classifying the gap risk levels is as follows: When Q_final is determined to be valid, the difference between the real-time water flow Q and Q_final in the current target area is obtained. Based on (real-time water flow Q_final) / Q_final × 100%, the ecological flow gap rate ΔQ is calculated. The ecological flow gap rate ΔQ is then processed and a regular management command is output or compared with the preset flow gap interval [Q_min, Q_max]. When compared with [QKmin, QKmax]: output the gap risk level (low gap risk / medium gap risk / high gap risk). Structured early warning information is composed of the risk level corresponding to the current dynamic eutrophication risk level R and the gap risk level (low gap risk / medium gap risk / high gap risk) corresponding to the ecological flow gap rate ΔQ.

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