Water ecosystem health evaluation and prediction system based on multiple models

Abstract

The invention discloses a multi-model-based water ecosystem health evaluation and prediction system, which relates to the technical field of water ecosystem health evaluation, and comprises a water quality health evaluation unit, an early warning unit, a decision support unit, a water quality monitoring unit and a prediction unit, the water quality monitoring unit is used for performing real-time water quality monitoring on a water body based on a sensor installed in the water body, acquiring water quality data, preprocessing the water quality data, extracting feature data in the water quality data and transmitting the feature data in the water quality data to the multi-model prediction unit; and the multi-model prediction unit is used for performing training and prediction based on the feature data in the water quality data and the water quality data of different water ecosystem health evaluation models to obtain a comprehensive prediction result of the water ecosystem health state. According to the method, through multi-model integration, the precision and robustness of water ecological health assessment are improved, and accurate data support is provided for real-time water quality monitoring and data processing.

Description

Technical Field

[0001] The present invention relates to the technical field of water ecosystem health assessment, and in particular to a water ecosystem health assessment and prediction system based on multiple models. Background Art

[0002] The health of aquatic ecosystems is an important indicator for assessing water environment quality, ensuring the sustainable use of water resources, and ensuring ecological balance. In recent years, with the intensification of global water pollution problems, traditional water quality monitoring methods are often unable to reflect the health status of aquatic ecosystems in real time, comprehensively, and accurately. Therefore, there is an urgent need for a comprehensive, multi-dimensional technical means to dynamically monitor and evaluate the health of aquatic ecosystems. At present, some common models have been developed in water ecosystem health assessment technology, such as regression analysis, machine learning models, fuzzy logic models, etc., which can evaluate water quality indicators to a certain extent, but most of these models cannot comprehensively evaluate multiple factors and lack the ability to provide real-time early warning and intelligent decision-making support.

[0003] Based on this, a multi-model-based water ecosystem health assessment and prediction system was proposed. This system combines a variety of modern technologies, including sensor technology, data processing technology, model prediction technology, early warning technology, and decision support systems. Based on water quality monitoring, it can assess and predict the health of water ecosystems in real time and provide corresponding restoration decisions. Summary of the Invention

[0004] In order to solve the above technical problems, a multi-model-based water ecosystem health assessment and prediction system is provided. This technical solution solves the above problems.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] The multi-model-based water ecosystem health assessment and prediction system includes a water quality health assessment unit, an early warning unit, and a decision support unit, as well as:

[0007] A water quality monitoring unit, the water quality monitoring unit being used to perform real-time water quality monitoring of the water body based on sensors installed in the water body, obtain water quality data, pre-process the water quality data, extract feature data from the water quality data, and transmit the feature data from the water quality data to the multi-model prediction unit;

[0008] A multi-model prediction unit is provided, wherein the multi-model prediction unit is trained and predicted based on the characteristic data in the water quality data and the water quality data of different water ecosystem health assessment models, obtains a comprehensive prediction result of the health status of the water ecosystem, and transmits the prediction result to the health assessment unit.

[0009] Preferably, the water quality health assessment unit is used to receive the prediction results transmitted by the multi-model prediction unit, and evaluate the health status of the water ecosystem based on a preset health assessment standard, generate a health assessment report of the water ecosystem, and transmit the health assessment report to the early warning unit;

[0010] The early warning sheet is used to receive the health assessment report transmitted by the water quality health assessment unit, and judge whether the early warning condition is met based on a preset threshold value. If the early warning condition is met, an early warning signal is generated and transmitted to the decision support unit;

[0011] The decision support unit is used to receive the warning signal transmitted by the warning unit, and provide corresponding decision support according to the health status of the water ecosystem and the warning information, and provide managers with suggestions and measures to improve the health of the water ecosystem.

[0012] Preferably, the water quality health assessment unit is also used to generate a trend forecast report on water health based on the historical change trend of water quality monitoring data in combination with a pre-set assessment model, and transmit the report to the decision support unit. The decision support unit is used to prioritize the restoration measures of the water ecosystem based on the trend forecast report and the health status of the water ecosystem, and adjust the restoration measures through real-time data feedback, propose strategies for the restoration and improvement of the water ecosystem, and propose intervention measures, and regularly generate water quality assessment and improvement reports.

[0013] Preferably, the training and prediction based on water quality data of different water ecosystem health assessment models to obtain the prediction results of the water ecosystem health status specifically includes:

[0014] Regression models, machine learning models, and fuzzy logic models were selected to train the water ecosystem health assessment model. The data set was divided into training and test sets, with 70% to 80% of the data used for training and the rest for validation and testing.

[0015] The different sub-models are integrated and the weighted average algorithm is used to average the prediction results of different models according to their performance to obtain the final prediction result.

[0016] Based on the prediction results of different models, a comprehensive predicted water quality health score is obtained, the threshold of the water quality health score is set, and the health status of the water ecosystem is divided into different levels based on the preset threshold.

[0017] Preferably, the expression for selecting the regression model, the machine learning model, and the fuzzy logic model to train the water ecosystem health assessment model is:

[0018]

[0019] Where, represents the predicted health score of the regression model for the i-th sample, β0 is the intercept term, x i1 is the pH water quality characteristic, β1 is the weight of the pH water quality characteristic, β2 is the weight of the temperature water quality characteristic, x i2 is the temperature and water quality characteristics, x ip is the pth water quality characteristic, β n is the weight of the p-th water quality feature, Represents the prediction result of the machine learning model for the i-th sample, N is the number of decision trees in the model, j is the index variable, and f j (x i ) The jth tree for sample x i The prediction results, represents the prediction result of the fuzzy logic model for the i-th sample, R represents the total number of basis functions used by the model, r represents the number from the 1st basis function to the R-th basis function, and w r Represents the weight coefficient of the rth basis function, μ r (x i ) is the rth basis function for input x i The value of .

[0020] Preferably, the different sub-models are fused and a weighted average algorithm is used to weight the prediction results of different models according to their performance to obtain the final prediction result expression as follows:

[0021]

[0022] Where H is the comprehensive predicted water quality health score, L1 is The allocation weight of L2 is The allocation weight of L3 is The allocation weight of .

[0023] Preferably, the health assessment criteria include basic water quality indicators, ecological diversity, species community structure and eutrophication degree of water bodies, wherein the basic water quality indicators of water bodies include pH value, dissolved oxygen, ammonia nitrogen and total phosphorus, the ecological diversity is measured by the Shannon-Wiener index, the species community structure is described by the species abundance and diversity index, the eutrophication degree of water bodies is evaluated by the nitrogen-phosphorus ratio index, and the health assessment criteria also include aquatic plant coverage rate, bottom sediment status, and habitat and reproduction conditions of aquatic animals in the water ecosystem, wherein the aquatic plant coverage rate is calculated by remote sensing images and underwater sensor data, the bottom sediment status is obtained by sampling data and sediment quality analysis, and the habitat and reproduction conditions of aquatic animals are evaluated by underwater acoustic monitoring and field survey data.

[0024] Preferably, the early warning unit adopts a fuzzy control algorithm to set a sensitivity threshold based on the changing trend of water quality monitoring data, health assessment reports and historical data to determine whether the early warning conditions are met. At the same time, the judgment result is dynamically adjusted to adapt to the impact of water quality fluctuations in different seasons, climates and geographical locations. The early warning signals include warning signals, serious warning signals and emergency signals, and each signal is assigned a priority based on different health assessment results. The signals are further transmitted through mobile terminals, PC terminals and automated monitoring platforms.

[0025] Preferably, the decision support unit combines health assessment reports, trend forecast reports and early warning signals, prioritizes restoration measures for the water ecosystem by integrating multi-dimensional information, and pre-evaluates the effects of different restoration measures through predictive models. The decision support unit also includes an optimization algorithm based on reinforcement learning, which adjusts the restoration strategy according to historical processing data and real-time feedback to form a closed-loop system that continuously improves.

[0026] Preferably, the optimization algorithm of the reinforcement learning is expressed as:

[0027]

[0028] Where, the model parameters at the current time t, θ t+1 is the updated policy parameter after time t+1, α is the learning rate, -t is the time difference, is the action value function Q(s t , a t ) with respect to the policy parameters θ.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] This invention proposes to improve the accuracy and robustness of water ecological health assessment through multi-model integration, provide accurate data support for real-time water quality monitoring and data processing, dynamically adjust the response of the intelligent early warning mechanism, generate health status assessment and prediction reports based on historical trends, optimize the restoration measures through the decision support system, adopt the reinforcement learning automatic adjustment strategy, comprehensively consider the ecological diversity and eutrophication level, provide a comprehensive health assessment, efficiently transmit early warning signals, improve the management response speed, and support the continuous improvement of the water ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a schematic diagram of the system architecture of the multi-model-based water ecosystem health assessment and prediction system proposed in the present invention.

[0032] Reference numerals: 1. Water quality monitoring unit; 2. Model prediction unit; 3. Health assessment unit; 4. Early warning unit; 5. Decision support unit. DETAILED DESCRIPTION

[0033] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0034] Reference Figure 1 As shown, the water ecosystem health assessment and prediction system based on multiple models includes a water quality health assessment unit 3, an early warning unit 4, and a decision support unit 5;

[0035] A water quality monitoring unit 1, which is used to monitor the water quality of the water body in real time based on sensors installed in the water body, obtain water quality data, pre-process the water quality data, extract feature data from the water quality data, and transmit the feature data from the water quality data to the multi-model prediction unit 2;

[0036] Multi-model prediction unit 2, multi-model prediction unit 2 is used to train and predict water quality data based on characteristic data in water quality data and different water ecosystem health assessment models, obtain comprehensive prediction results of the health status of the water ecosystem, and transmit the prediction results to the health assessment unit 3.

[0037] The water quality health assessment unit 3 is used to receive the prediction results transmitted by the multi-model prediction unit 2, and evaluate the health status of the water ecosystem based on the preset health assessment standard, generate a health assessment report of the water ecosystem, and transmit the health assessment report to the early warning unit 4;

[0038] The early warning unit 4 is used to receive the health assessment report transmitted by the water quality health assessment unit 3, and determine whether the early warning condition is met based on a preset threshold value. If the early warning condition is met, it generates an early warning signal and transmits the early warning signal to the decision support unit 5;

[0039] The decision support unit 5 is used to receive the warning signal transmitted by the warning unit 4, and provide corresponding decision support based on the health status of the water ecosystem and the warning information, and provide managers with suggestions and measures to improve the health of the water ecosystem;

[0040] By receiving multi-model prediction results and generating health reports based on preset standards, this process not only considers the current health status, but also includes the integration of historical change trends, which can achieve dynamic health assessment and prediction. In addition, the early warning mechanism is based on threshold judgment and can issue alarm signals in time to ensure timely intervention in water health.

[0041] The water quality health assessment unit 3 is further configured to generate a water health trend forecast report based on the historical change trend of the water quality monitoring data in combination with a pre-set assessment model, and transmit the report to the decision support unit 5. The decision support unit 5 is configured to prioritize water ecosystem restoration measures based on the trend forecast report and the health status of the water ecosystem, adjust the restoration measures through real-time data feedback, propose strategies for water ecosystem restoration and improvement, propose intervention measures, and regularly generate water quality assessment and improvement reports;

[0042] The system combines historical water quality data and trend forecasts, dynamically adjusts restoration measures, and proposes water ecosystem restoration plans based on real-time data feedback. Through continuous data feedback, the system can provide gradually improved water ecological restoration strategies, making restoration work more scientific and systematic.

[0043] Based on the water quality data of different water ecosystem health assessment models, training and prediction are carried out to obtain the prediction results of the water ecosystem health status, including:

[0044] Regression models, machine learning models, and fuzzy logic models were selected to train the water ecosystem health assessment model. The data set was divided into training and test sets, with 70% to 80% of the data used for training and the rest for validation and testing.

[0045] The different sub-models are integrated and the weighted average algorithm is used to average the prediction results of different models according to their performance to obtain the final prediction result.

[0046] Based on the prediction results of different models, a comprehensive predicted water quality health score is obtained, the threshold of the water quality health score is set, and the health status of the water ecosystem is divided into different levels based on the preset threshold;

[0047] The fusion of different models is carried out through a weighted average algorithm. This method can assign different weights according to the performance of each model, thereby improving the accuracy of the final prediction results and overcoming the limitations that may exist in a single model. In particular, the combination of regression, machine learning and fuzzy logic models provides health assessment from different angles.

[0048] The expression for selecting regression model, machine learning model, and fuzzy logic model to train the water ecosystem health assessment model is:

[0049]

[0050] Where, represents the predicted health score of the regression model for the i-th sample, β0 is the intercept term, x i1 is the pH water quality characteristic, β1 is the weight of the pH water quality characteristic, β2 is the weight of the temperature water quality characteristic, xi2 is the temperature and water quality characteristics, x ip is the pth water quality characteristic, β n is the weight of the p-th water quality feature, Represents the prediction result of the machine learning model for the i-th sample, N is the number of decision trees in the model, j is the index variable, and f j (x i ) The jth tree for sample x i The prediction results, represents the prediction result of the fuzzy logic model for the i-th sample, R represents the total number of basis functions used by the model, r represents the number from the 1st basis function to the R-th basis function, and w r Represents the weight coefficient of the rth basis function, μ r (x i ) is the rth basis function for input x i The value of

[0051] A combined training method of regression model, machine learning model and fuzzy logic model is adopted to propose a multi-level and multi-dimensional model expression for different water quality characteristics. This method enhances the adaptability and diversity of the system and can adjust the model parameters according to different environmental conditions, thereby more accurately predicting the water quality health score.

[0052] The different sub-models are integrated and the weighted average algorithm is used to weight the prediction results of different models according to their performance. The expression of the final prediction result is:

[0053]

[0054] Where H is the comprehensive predicted water quality health score, L1 is The allocation weight of L2 is The allocation weight of L3 is The allocation weight of

[0055] The results of different models are integrated through a weighted average algorithm, and the weights of different models are dynamically adjusted according to their performance. This mechanism can effectively utilize the advantages of multiple models to obtain a comprehensive score, thereby providing support for the multi-dimensional assessment of the health of water ecosystems.

[0056] Health assessment criteria include basic water quality indicators, ecological diversity, species community structure, and eutrophication level. Basic water quality indicators include pH, dissolved oxygen, ammonia nitrogen, and total phosphorus. Ecological diversity is measured by the Shannon-Wiener index, species community structure is described by species abundance and diversity indices, and eutrophication level is evaluated by the nitrogen-phosphorus ratio. Health assessment criteria also include aquatic plant coverage, bottom sediment status, and the habitat and reproduction status of aquatic animals in the aquatic ecosystem. Aquatic plant coverage is calculated using remote sensing images and underwater sensor data, bottom sediment status is obtained through sampling data and sediment quality analysis, and the habitat and reproduction status of aquatic animals is assessed through underwater acoustic monitoring and field survey data.

[0057] The health assessment criteria not only include conventional water quality indicators, but also include indicators such as water body ecological diversity, species community structure, and eutrophication. These comprehensive criteria allow for a comprehensive assessment of the ecological functions of water bodies. This multi-dimensional evaluation system makes water health assessments more scientific and comprehensive.

[0058] Early warning unit 4 uses a fuzzy control algorithm to set a sensitivity threshold based on the changing trend of water quality monitoring data, health assessment reports, and historical data to determine whether the early warning conditions have been met. At the same time, the judgment result is dynamically adjusted to adapt to the impact of water quality fluctuations in different seasons, climates, and geographical locations. Early warning signals include warning signals, serious warning signals, and critical signals. Each signal is assigned a priority based on different health assessment results. The signals are further transmitted through mobile terminals, PCs, and automated monitoring platforms.

[0059] By adjusting the warning sensitivity through fuzzy control algorithms, the system can dynamically adjust to water quality fluctuations according to seasonal, climate change and geographical location. The classification mechanism of warning signals makes the response to water quality problems more sensitive and can take graded measures according to different health assessment situations.

[0060] Decision support unit 5 combines health assessment reports, trend forecast reports, and early warning signals to prioritize restoration measures for the aquatic ecosystem by integrating multi-dimensional information. It also uses predictive models to pre-evaluate the effectiveness of different restoration measures. Decision support unit 5 also includes an optimization algorithm based on reinforcement learning to adjust restoration strategies based on historical processing data and real-time feedback, forming a closed-loop system for continuous improvement.

[0061] By integrating health assessment reports, trend forecast reports and early warning signals, the system can provide decision makers with prioritized repair measures and use reinforcement learning algorithms for dynamic optimization. This optimization mechanism based on historical data and real-time feedback can ensure continuous improvement of repair strategies and maximize their effectiveness.

[0062] The optimization algorithm of reinforcement learning is expressed as:

[0063]

[0064] Where, the model parameters at the current time t, θ t+1 is the updated policy parameter after time t+1, α is the learning rate, -t is the time difference, is the action value function Q(s t , a t ) with respect to the policy parameters θ;

[0065] A reinforcement learning optimization algorithm enables the system to adjust its strategy based on feedback after each restoration. This closed-loop system is self-learning and continuously optimizes its decision-making process to adapt to different water ecological restoration needs and maximize water restoration results.

[0066] In summary, the advantages of the present invention are:

[0067] A variety of water ecosystem health assessment models, including regression models, machine learning models, and fuzzy logic models, are used to integrate the prediction results of different models through a weighted average algorithm. This method can fully utilize the advantages of different models and enhance the system's prediction accuracy and robustness for water ecosystem health status.

[0068] The water quality monitoring unit uses sensors installed in the water body to monitor basic water quality indicators in real time, such as pH, dissolved oxygen, ammonia nitrogen, total phosphorus, etc. At the same time, it pre-processes and extracts features from the monitoring data to provide high-quality data support for subsequent health assessment and prediction;

[0069] The early warning unit combines historical data with real-time data trends and uses a fuzzy control algorithm to set sensitivity thresholds to determine whether water quality meets early warning conditions. The system can dynamically adjust early warning standards based on seasonal changes, climate conditions and other factors, thereby more accurately reflecting water quality trends.

[0070] The health assessment unit can not only conduct health assessments based on current water quality data, but also generate trend forecast reports based on historical trends, providing long-term water quality trend forecasts for the decision support unit. This function can identify potential water quality issues in advance and enable early intervention.

[0071] By integrating water quality health assessment reports, trend forecast reports, and early warning signals, the decision support unit can prioritize water ecosystem restoration measures and dynamically adjust restoration strategies through reinforcement learning optimization algorithms. This system can not only automatically adjust restoration measures but also form a closed-loop system of continuous improvement based on real-time feedback, thereby improving the efficiency of restoration effects.

[0072] In addition to considering basic water quality indicators, the system also introduces more comprehensive evaluation criteria such as ecological diversity, species community structure, and eutrophication level in water quality assessment. This innovation can more accurately assess the comprehensive health of the aquatic ecosystem, rather than just the health of the water quality.

[0073] Early warning signals are transmitted through multiple channels, which improves the system's real-time response speed and the efficiency of information transmission, allowing managers to obtain feedback on the health of water ecosystems more quickly and respond accordingly.

[0074] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A water ecosystem health assessment and prediction system based on multiple models, characterized by: The system comprises a water quality health assessment unit (3), an early warning unit (4) and a decision support unit (5), and is characterized by: A water quality monitoring unit (1), the water quality monitoring unit (1) is used to monitor the water quality of the water body in real time based on a sensor installed in the water body, obtain water quality data, pre-process the water quality data, extract feature data from the water quality data, and transmit the feature data from the water quality data to a multi-model prediction unit (2); A multi-model prediction unit (2) is used to train and predict water quality data based on characteristic data in water quality data and different water ecosystem health assessment models, obtain a comprehensive prediction result of the water ecosystem health state, and transmit the prediction result to the health assessment unit (3).

2. The multi-model-based water ecosystem health assessment and prediction system according to claim 1 is characterized in that: The water quality health assessment unit (3) is used to receive the prediction results transmitted by the multi-model prediction unit (2), and evaluate the health status of the water ecosystem based on a preset health assessment standard, generate a health assessment report of the water ecosystem, and transmit the health assessment report to the early warning unit (4); The early warning unit (4) is used to receive the health assessment report transmitted by the water quality health assessment unit (3), and judge whether the early warning condition is met based on a preset threshold value. If the early warning condition is met, an early warning signal is generated and transmitted to the decision support unit (5); The decision support unit (5) is used to receive the warning signal transmitted by the warning unit (4), and provide corresponding decision support based on the health status of the water ecosystem and the warning information, and provide managers with suggestions and measures to improve the health of the water ecosystem.

3. The multi-model-based water ecosystem health assessment and prediction system according to claim 2 is characterized in that: The water quality health assessment unit (3) is further used to generate a water health trend forecast report based on the historical change trend of the water quality monitoring data in combination with a pre-set assessment model, and transmit the report to the decision support unit (5). The decision support unit (5) is used to prioritize the restoration measures of the water ecosystem based on the trend forecast report and the health status of the water ecosystem, adjust the restoration measures through real-time data feedback, propose strategies for the restoration and improvement of the water ecosystem, propose intervention measures, and regularly generate water quality assessment and improvement reports.

4. The multi-model-based water ecosystem health assessment and prediction system according to claim 1 is characterized in that: The training and prediction based on water quality data of different water ecosystem health assessment models to obtain the prediction results of the water ecosystem health status specifically include: Regression models, machine learning models, and fuzzy logic models were selected to train the water ecosystem health assessment model. The data set was divided into training and test sets, with 70% to 80% of the data used for training and the rest for validation and testing. The different sub-models are integrated and the weighted average algorithm is used to average the prediction results of different models according to their performance to obtain the final prediction result. Based on the prediction results of different models, a comprehensive predicted water quality health score is obtained, the threshold of the water quality health score is set, and the health status of the water ecosystem is divided into different levels based on the preset threshold.

5. The multi-model-based water ecosystem health assessment and prediction system according to claim 4 is characterized in that: The expression for selecting the regression model, the machine learning model, and the fuzzy logic model to train the water ecosystem health assessment model is: Where, represents the predicted health score of the regression model for the i-th sample, β0 is the intercept term, x i1 is the pH water quality characteristic, β1 is the weight of the pH water quality characteristic, β2 is the weight of the temperature water quality characteristic, x i2 is the temperature and water quality characteristics, x ip is the pth water quality characteristic, β n is the weight of the p-th water quality feature, Represents the prediction result of the machine learning model for the i-th sample, N is the number of decision trees in the model, j is the index variable, and f j (x i ) The jth tree for sample x i The prediction results, represents the prediction result of the fuzzy logic model for the i-th sample, R represents the total number of basis functions used by the model, r represents the number from the 1st basis function to the R-th basis function, and w r Represents the weight coefficient of the rth basis function, μ r (x i ) is the rth basis function for input x i The value of .

6. The multi-model-based water ecosystem health assessment and prediction system according to claim 4 is characterized in that: The different sub-models are integrated and a weighted average algorithm is used to weight the prediction results of different models according to their performance. The expression for the final prediction result is: Where H is the comprehensive predicted water quality health score, L1 is The allocation weight of L2 is The allocation weight of L3 is The allocation weight of .

7. The multi-model-based water ecosystem health assessment and prediction system according to claim 2, characterized in that: The health assessment standards include basic water quality indicators, ecological diversity, species community structure and eutrophication degree of water bodies, among which the basic water quality indicators of water bodies include pH value, dissolved oxygen, ammonia nitrogen and total phosphorus, the ecological diversity is measured by the Shannon-Wiener index, the species community structure is described by the species abundance and diversity index, and the eutrophication degree of water bodies is evaluated by the nitrogen-phosphorus ratio index. The health assessment standards also include the aquatic plant coverage rate, bottom sediment status, and the habitat and reproduction conditions of aquatic animals in the water ecosystem, among which the aquatic plant coverage rate is calculated by remote sensing images and underwater sensor data, the bottom sediment status is obtained by sampling data and sediment quality analysis, and the habitat and reproduction conditions of aquatic animals are evaluated by underwater acoustic monitoring and field survey data.

8. The multi-model-based water ecosystem health assessment and prediction system according to claim 3 is characterized in that: The early warning unit (4) adopts a fuzzy control algorithm, sets a sensitivity threshold according to the change trend of water quality monitoring data, health assessment report and historical data, judges whether the early warning condition is met, and dynamically adjusts the judgment result to adapt to the influence of different seasons, climates and geographical locations on water quality fluctuations. The early warning signal includes a warning signal, a serious warning signal and an emergency signal, and a priority is assigned to each signal according to different health assessment results. The signal is further transmitted through a mobile terminal, a PC terminal and an automated monitoring platform.

9. The multi-model-based water ecosystem health assessment and prediction system according to claim 8, characterized in that: The decision support unit (5) combines health assessment reports, trend forecast reports and early warning signals, prioritizes restoration measures for the water ecosystem by integrating multi-dimensional information, and pre-evaluates the effects of different restoration measures through a prediction model. The decision support unit (5) also includes an optimization algorithm based on reinforcement learning, which adjusts the restoration strategy according to historical processing data and real-time feedback, forming a closed-loop system with continuous improvement.

10. The multi-model-based water ecosystem health assessment and prediction system according to claim 9, characterized in that: The optimization algorithm of the reinforcement learning is expressed as: Where, the model parameters at the current time t, θ t+1 is the updated policy parameter after time t+1, α is the learning rate, -t is the time difference, is the action value function Q(s t , a t ) with respect to the policy parameters θ.

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