Water quality monitoring and early warning method, system, medium and equipment

Abstract

The invention relates to the technical field of water quality monitoring, and provides a water quality monitoring and early warning method, system, medium and equipment, and the method comprises the steps: predicting the water quality index of each moment in a future time period by adopting a deep learning model based on the water quality index of the current time period; on the basis of the water quality index of the future time period predicted by the deep learning model, optimizing by adopting a Kalman filtering algorithm embedded with a gating circulation unit, and updating the water quality index of each moment in the future time period; according to the Kalman filtering algorithm embedded with the gating circulation units, three gating circulation units are arranged to replace a state conversion matrix, a process noise matrix and a measurement noise matrix in Kalman filtering respectively; based on the updated water quality indexes in the future period, water quality single factor evaluation, comprehensive pollution index evaluation and water quality index change trend analysis are carried out, and early warning is carried out. The defect that a standard Kalman filtering algorithm is only suitable for a linear system can be overcome, and the precision of water quality prediction of a nonlinear system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of water quality monitoring, and in particular relates to a water quality monitoring and early warning method, system, medium and equipment. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Water quality directly reflects the health of a variety of aquatic ecosystems, such as rivers, lakes, estuaries, and oceans, and is crucial to environmental stability, human production, and drinking water resources. With climate change and the intensification of human activities, the deterioration of water quality has led to many disasters and adverse events, which has attracted widespread attention from the society. The United Nations 2030 Sustainable Development Goals emphasize the need to protect water bodies and maintain water quality. Therefore, water quality monitoring and prediction models can provide more timely and accurate insights into changes in aquatic ecosystems, especially in areas with weak monitoring capabilities.

[0004] Water quality monitoring covers a variety of indicators, including physical parameters (e.g., water temperature, turbidity), chemical parameters (e.g., hydrogen ion concentration index pH, dissolved oxygen, total nitrogen, total phosphorus, ammonia nitrogen, chemical oxygen demand), as well as biological indicators, heavy metals and petroleum residues. Moreover, water quality work is required in a variety of ecological and environmental scenarios such as rivers, lakes, estuaries, coastal waters, and oceans, and the monitoring indicators of concern in different scenarios will also be different. Given the diversity of these indicators and the complexity of various ecosystems, it is both expensive and challenging to achieve high-frequency and high-precision measurements. Therefore, water quality prediction can provide key support for water resources monitoring and management, namely pollution early warning.

[0005] In recent years, with the development of artificial intelligence technology, the use of machine learning or deep learning models for water quality prediction has become a popular method, such as convolutional neural network (CNN), long short-term memory neural network (LSTM) and other models. The Kalman filter algorithm is a method to improve prediction accuracy through data assimilation. Existing prediction work uses the prediction results of deep learning models and then optimizes them through the Kalman filter to improve accuracy.

[0006] However, this type of method has certain limitations. First, this type of method usually requires assumptions about several covariance matrices in the Kalman filter, which is a rough approximation of the actual situation, will cause certain errors and often requires a large number of experiments to obtain a relatively accurate assumption; at the same time, since the standard Kalman filter algorithm is a linear algorithm, it has certain limitations for nonlinear models. Summary of the invention

[0007] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a water quality monitoring and early warning method, system, medium and equipment. By embedding the GRU unit into the Kalman filter assimilation algorithm, the three covariance matrices in the previous Kalman filter algorithm are replaced, so that there is no need for rough approximate assumptions and continuous attempts and manual updates. In addition, GRU-KF can make up for the deficiency that the standard Kalman filter algorithm is only applicable to linear systems, thereby improving the accuracy of water quality prediction of nonlinear systems.

[0008] In order to achieve the above object, the present invention adopts the following technical solution: A first aspect of the present invention provides a water quality monitoring and early warning method, comprising: Obtain water quality indicators at several consecutive moments in the current period; Based on the water quality indicators of the current period, a deep learning model is used to predict the water quality indicators at each moment in the future period; Based on the water quality index of the future time period predicted by the deep learning model, the Kalman filter algorithm embedded with the gated loop unit is used for optimization to update the water quality index at each moment in the future time period; the Kalman filter algorithm embedded with the gated loop unit sets three gated loop units to replace the state transition matrix, process noise matrix and measurement noise matrix in the Kalman filter respectively; Based on the updated water quality indicators for future time periods, single factor evaluation of water quality, comprehensive pollution index evaluation and trend analysis of water quality indicator changes are carried out, and early warnings are issued.

[0009] Furthermore, the water quality indicators are hydrogen ion concentration index, total nitrogen, total phosphorus, dissolved oxygen, conductivity, ammonia nitrogen, turbidity, permanganate index and / or water temperature.

[0010] Furthermore, in the Kalman filter algorithm embedded with the gated loop unit, the input of the first gated loop unit is the predicted value of the water quality index at the previous moment, the first gated loop unit generates a current state as the input of the second gated loop unit, the second gated loop unit generates a process covariance, the water quality index at the current moment predicted by the deep learning model is input into the third gated loop unit to generate a measurement covariance, the current state, the water quality index at the current moment predicted by the deep learning model, the process covariance and the measurement covariance enter the Kalman filter algorithm to update the predicted value of the water quality index at the current moment.

[0011] Furthermore, the single factor evaluation of water quality is as follows: Calculate the excess multiple ,in, C i is the predicted value of the i-th water quality index, S i is the evaluation standard value of the i-th water quality index; when P iWhen >1, a water quality index exceeds the standard signal is issued and the excess multiple is output.

[0012] Furthermore, the comprehensive pollution index evaluation is as follows: calculating the comprehensive pollution index ,in, is the excess multiple of the i-th water quality index, m is the number of water quality indexes; when P <0.7, no warning signal is generated; when 0.7 ≤ P <1, a potential pollution signal is issued in combination with the single factor evaluation results of water quality; when 1 ≤ P <2, a water pollution warning signal is issued; when P When the air quality index is ≥ 2, a serious pollution warning signal will be issued.

[0013] Furthermore, the water quality index change trend analysis is as follows: if the water quality index sequence at each moment in the updated future period is X={ }, n is the number of moments in the future period, calculate the trend change of water quality concentration ; ; ; ;when Z MK >0 When Z MK = 0 , no warning signal is generated; when Z MK <0 , no warning signal is generated.

[0014] A second aspect of the present invention provides a water quality monitoring and early warning system, comprising: A water quality data acquisition module is configured to: acquire water quality indicators at a number of consecutive moments in a current period; The water quality prediction module based on deep learning is configured to: predict the water quality indicators at each moment in the future period based on the water quality indicators of the current period using a deep learning model; A data assimilation module is configured to: optimize the water quality index of the future time period based on the deep learning model, and update the water quality index at each moment in the future time period by using a Kalman filter algorithm embedded in a gated cyclic unit; the Kalman filter algorithm embedded in a gated cyclic unit sets three gated cyclic units, which respectively replace the state transition matrix, the process noise matrix and the measurement noise matrix in the Kalman filter; The water pollution early warning module is configured to: conduct single factor evaluation of water quality, comprehensive pollution index evaluation and trend analysis of water quality index changes based on updated water quality indicators for future time periods, and issue early warnings.

[0015] Furthermore, the water quality indicators are hydrogen ion concentration index, total nitrogen, total phosphorus, dissolved oxygen, conductivity, ammonia nitrogen, turbidity, permanganate index and / or water temperature.

[0016] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in a water quality monitoring and early warning method as described above.

[0017] The fourth aspect of the present invention provides a computer device, including a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, wherein when the processor executes the program, the steps in a water quality monitoring and early warning method as described above are implemented.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention replaces the three covariance matrices in the previous Kalman filter algorithm by embedding the GRU unit into the Kalman filter assimilation algorithm, so that it does not need to make rough approximate assumptions and continuous attempts and manual updates. In addition, GRU-KF can make up for the deficiency that the standard Kalman filter algorithm is only applicable to linear systems, and improves the accuracy of water quality prediction for nonlinear systems.

[0019] The present invention performs data assimilation between the deep learning model and the GRU-KF algorithm, effectively improving the water quality prediction accuracy of the deep learning model, obtaining high-precision water quality prediction results, and performing water quality evaluation and trend analysis on the prediction results to form a water pollution warning, which can be applied to a variety of water quality indicators and to different ecological environment scenarios such as rivers, lakes, estuaries, and oceans. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0021] Figure 1 Schematic diagram of a single GRU-KF structure of the first embodiment of the present invention; Figure 2 Schematic diagram of the GRU-KF algorithm of the first embodiment of the present invention; Figure 3 This is a flow chart of a water quality monitoring and early warning method according to the first embodiment of the present invention; Figure 4 It is a structural diagram of a computer device according to a fourth embodiment of the present invention. DETAILED DESCRIPTION

[0022] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0023] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0024] Embodiment 1 This embodiment provides a water quality monitoring and early warning method.

[0025] The present embodiment provides a water quality monitoring and early warning method, which embeds a gated recurrent unit (GRU) into a water quality monitoring and early warning method (GRU-KF) in a Kalman filter assimilation algorithm (Kalman Filter, KF), replaces three covariance matrices in the Kalman filter assimilation algorithm with GRU units, and replaces a large number of experiments to assume the mode of the covariance matrix by training the GRU units. In this way, the Kalman filter will be continuously updated with the passage of time (data input), so that it does not need to make rough approximate assumptions and continuous attempts and manual updates. The improved GRU-KF can make up for the deficiency that the standard Kalman filter algorithm is only applicable to linear systems due to the ability of GRU to process nonlinear data. Therefore, it can be used for water quality prediction of nonlinear systems.

[0026] This embodiment provides a water quality monitoring and early warning method, such as Figure 3 As shown, the following steps are included: Step 1: Collect daily monitoring data (including pH, total nitrogen, total phosphorus, dissolved oxygen, conductivity, ammonia nitrogen, turbidity, permanganate index, water temperature and other indicators) from multiple water quality monitoring sections in a study area (e.g., rivers, lakes, estuaries, oceans, etc.), and pre-process the historical monitoring data through interpolation, missing value filling, normalization and other methods.

[0027] Step 2: Based on historical monitoring data, use a deep learning model (such as convolutional neural network CNN, recurrent neural network RNN, long short-term memory neural network LSTM, etc.) to build a prediction model for water quality indicators and output the basic prediction data for a period of time ( Y base ).

[0028] Step 3: Divide the basic prediction data (time series prediction value) in step 2 and the corresponding observation data into training set and test set in a ratio of 7:3, and input them into GRU-KF for training and testing.

[0029] Step 4: Construct GRU-KF (GRU embedded in KF algorithm), train and test GRU-KF, and evaluate the output results.

[0030] The standard Kalman filter algorithm formula is as follows: Prediction stage: ; ; Update phase: ; ; .

[0031] Where A is the state transition matrix, Q is the process noise matrix, R is the measurement noise matrix, is the estimated noise covariance, is the Kalman gain; H is the mapping matrix, which is set to the unit matrix I in this embodiment, which can be understood as all the results predicted at time t enter the time t+1; is the noise covariance at time t after the update; represents the predicted water quality index at time t; represents the observed value at time t.

[0032] In this embodiment, three GRU units GRU are set in the GRU-KF algorithm. f , GRU Q and GRU R , respectively replace the state transition matrix (A), process noise matrix (Q) and measurement noise matrix (R) in the Kalman filter. The structural diagram of the three GRUs embedded in the Kalman filter is shown in Figure 1 shown.

[0033] The GRU-KF algorithm after embedding GRU into the Kalman filter algorithm is as follows: Figure 2 As shown, the first gated recurrent unit GRU f The input is the predicted value of the water quality index at the previous moment, and generates a current state As the second gated recurrent unit GRU Q Input, second gated recurrent unit GRU Q The process covariance is generated ; In addition, the water quality index observation value at the current moment (The water quality index at the current moment predicted by the deep learning model) is input to the third gated recurrent unit GRU R , yielding a measurement covariance ;final, , , and Enter the Kalman filter algorithm to obtain the final water quality index prediction value at time t The formula is as follows: During the prediction phase: ; ;in, Represents GRU f , yes The Jacobian form of It is GRU Q Output of GRU Q GRU f The output of is the input; During the update phase: ; ; .in, It is GRU R Input, is the observed value at time t; is the Kalman gain; GRU R Take observations as input.

[0034] Three GRU units GRU f , GRU Q and GRU R The structural parameter settings of GRU are different. f It consists of two GRU layers with 200 hidden units, a random dropout of 0.1, and a fully connected layer. Q and GRU R They are composed of a 50 hidden unit and a fully connected layer, and the activation function is the linear rectification function (ReLU).

[0035] The output of the deep learning model in step 2 ( Y base ) as the observation input Z={ }, the output of the GRU-KF algorithm is the predicted value Y={ }.

[0036] The first 70% of the data is used as the training set for training, and the loss function of GRU-KF is RMSE, and the learning rate is 0.01. Through training, each GRU module can replace the three matrices in the standard Kalman filter algorithm, avoiding the tedious process of setting up through rough estimation, continuous trial and error, and manual adjustment, and can learn the hidden rules in the monitoring data, which is conducive to promoting the improvement of accuracy.

[0037] After training, the last 30% of the data is used as a test set to evaluate the final prediction accuracy.

[0038] Step 5: Use the trained deep learning model and GRU-KF to predict water quality and issue warnings for water quality exceeding standards, pollution, and concentration increase trends (trend change warning analysis).

[0039] Step 401: Acquire monitoring data of a number of consecutive moments in the current period (eg, moments 1 to t on November 10).

[0040] The monitoring data can be water quality indicators such as pH, total nitrogen, total phosphorus, dissolved oxygen, conductivity, ammonia nitrogen, turbidity, permanganate index, water temperature, etc. of a water quality monitoring section in a certain study area.

[0041] Step 402: Based on the monitoring data of several consecutive moments in the current period, the trained deep learning model is used to predict the water quality index ( Y base ).

[0042] Step 403: Output of the deep learning model ( Y base ) as the observation input Z={ }, optimize the water quality index for the future period predicted by the deep learning model, and update the water quality index for the future period Y={ }.

[0043] Step 403: Output the water quality prediction at time t in the future period C t ( C (1)t , C (2)t …C (n)t ) for single factor evaluation and comprehensive pollution index evaluation, among which, C (i)t It represents the predicted value of the i-th water quality index at time t in the future period output by the GRU-KF algorithm.

[0044] Based on the single factor evaluation and comprehensive pollution index calculation method, the predicted value output at each moment is used to evaluate the water quality, and the exceeding threshold and alarm threshold are set according to the degree of pollution to warn of potential pollution.

[0045] The calculation method of single factor evaluation is as follows: ;in, C i is the predicted concentration of pollutants, that is, the predicted value of the i-th water quality indicator; S i is the evaluation standard value of the i-th water quality index. Here, several types of evaluation standard values ​​are set according to different scenarios and water quality management goals.P i >1, that is, when the water quality index exceeds the standard limit, an index exceeding standard signal will be issued and the exceeding multiple will be output ( P i The value is the excess multiple).

[0046] The evaluation method of the comprehensive pollution index is as follows: ,in, P is a comprehensive pollution index. P The value can determine the comprehensive level of the water body, that is, the degree of pollution of the water body. P <0.7, it will show that the overall water quality is good and no warning signal will be generated; when 0.7 ≤ P <1, the water quality is potentially polluted, and a potential pollution signal is issued in combination with the single factor evaluation results; when 1 ≤ P <2, the predicted values ​​of several water quality indicators exceed the standard, and a water pollution warning signal is issued; P When it is ≥ 2, that is, the predicted values ​​of a large number of indicators exceed the standard limit and some indicators exceed the verification, a serious pollution warning signal is issued.

[0047] Step 404: In addition to the pollution warning for the predicted concentration at time t, a warning for the change trend of the indicator concentration is also set for the prediction result in a future time period.

[0048] A trend analysis is performed on the predicted value for a period of time. If an increasing concentration trend is shown, a concentration increase warning signal will be generated accordingly.

[0049] Let the time series of a water quality index be X={ }, the water quality concentration trend change calculation formula is as follows: ; ; ; .

[0050] when Z MK >0 When , it means that the predicted concentration of water quality indicators in this period has an upward trend, and an early warning signal of increased concentration will be issued; when Z MK = 0 , which means that there is no obvious change trend in the concentration during this period of time and no warning signal is generated; when Z MK < 0 , which means that the predicted concentration has a downward trend during this time period and no warning signal is generated. The length of time X can be set according to the needs of the user.

[0051] In this embodiment, deep learning models such as LSTM are used to assimilate data with the GRU-KF algorithm, and the water quality prediction accuracy of LSTM is effectively improved to obtain high-precision water quality prediction results. The prediction results are then evaluated and trend analyzed to form a water pollution warning, which can be applied to a variety of water quality indicators and to different ecological and environmental scenarios such as rivers, lakes, estuaries, and oceans.

[0052] The present embodiment provides a water quality monitoring and early warning method, which replaces the three covariance matrices in the previous Kalman filter algorithm by embedding the GRU unit into the Kalman filter assimilation algorithm, so that it does not need to make rough approximate assumptions and continuous attempts and manual updates, and the GRU-KF can make up for the deficiency that the standard Kalman filter algorithm is only applicable to linear systems.

[0053] A water quality monitoring and early warning method provided in this embodiment can improve prediction accuracy by integrating deep learning algorithm with GRU-KF, and better provide data and technical support for water quality prediction and pollution early warning and other businesses of ecological environment management departments.

[0054] Embodiment 2 This embodiment provides a water quality monitoring and early warning system, which specifically includes: A water quality data acquisition module is configured to: acquire water quality indicators at a number of consecutive moments in a current period; The water quality prediction module based on deep learning is configured to: predict the water quality indicators at each moment in the future period based on the water quality indicators of the current period using a deep learning model; A data assimilation module is configured to: optimize the water quality index of the future time period based on the deep learning model, and update the water quality index at each moment in the future time period by using a Kalman filter algorithm embedded in a gated cyclic unit; the Kalman filter algorithm embedded in a gated cyclic unit sets three gated cyclic units, which respectively replace the state transition matrix, the process noise matrix and the measurement noise matrix in the Kalman filter; The water pollution early warning module is configured to: conduct single factor evaluation of water quality, comprehensive pollution index evaluation and trend analysis of water quality index changes based on updated water quality indicators for future time periods, and issue early warnings.

[0055] It should be noted here that each module in this embodiment corresponds to each step in Example 1 one by one, and the specific implementation process is the same, which will not be repeated here.

[0056] Embodiment 3 This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of a water quality monitoring and early warning method as described in the above-mentioned embodiment 1 are implemented.

[0057] Embodiment 4 This embodiment provides a computer device, such as Figure 4 As shown, it includes a computer-readable storage medium 1003, a processor 1001, a communication interface 1002, and a computer program stored on the computer-readable storage medium 1003 and executable on the processor 1001, wherein the processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 can be connected via a bus or other means. The communication interface 1002 is used to receive and send data, and when the processor 1001 executes the program, the steps in the water quality monitoring and early warning method described in the first embodiment are implemented.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A water quality monitoring and early warning method, characterized in that: include: Obtain water quality indicators at several consecutive moments in the current period; Based on the water quality indicators of the current period, a deep learning model is used to predict the water quality indicators at each moment in the future period; Based on the water quality index of the future time period predicted by the deep learning model, the Kalman filter algorithm embedded with the gated loop unit is used for optimization to update the water quality index at each moment in the future time period; the Kalman filter algorithm embedded with the gated loop unit sets three gated loop units to replace the state transition matrix, process noise matrix and measurement noise matrix in the Kalman filter respectively; Based on the updated water quality indicators for future time periods, single factor evaluation of water quality, comprehensive pollution index evaluation and trend analysis of water quality indicator changes are carried out, and early warnings are issued.

2. A water quality monitoring and early warning method as claimed in claim 1, characterized in that: The water quality indicators are hydrogen ion concentration index, total nitrogen, total phosphorus, dissolved oxygen, conductivity, ammonia nitrogen, turbidity, permanganate index and / or water temperature.

3. A water quality monitoring and early warning method as claimed in claim 1, characterized in that: In the Kalman filter algorithm embedded with the gated loop unit, the input of the first gated loop unit is the predicted value of the water quality index at the previous moment, the first gated loop unit generates a current state as the input of the second gated loop unit, the second gated loop unit generates a process covariance, the water quality index at the current moment predicted by the deep learning model is input into the third gated loop unit to generate a measurement covariance, the current state, the water quality index at the current moment predicted by the deep learning model, the process covariance and the measurement covariance enter the Kalman filter algorithm to update the predicted value of the water quality index at the current moment.

4. A water quality monitoring and early warning method as claimed in claim 1, characterized in that: The single factor evaluation of water quality is as follows: Calculate the excess multiple ,in, C i is the predicted value of the i-th water quality index, S i is the evaluation standard value of the i-th water quality index; when P i When >1, a water quality index exceeds the standard signal is issued and the excess multiple is output.

5. A water quality monitoring and early warning method as claimed in claim 1, characterized in that: The comprehensive pollution index evaluation is as follows: Calculate the comprehensive pollution index ,in, is the excess multiple of the i-th water quality index, m is the number of water quality indexes; when P < 0.7, no warning signal is generated; when 0.7 ≤ P <1, a potential pollution signal is issued in combination with the single factor evaluation results of water quality; when 1 ≤ P <2, a water pollution warning signal is issued; when P When the air quality index is ≥ 2, a serious pollution warning signal will be issued.

6. A water quality monitoring and early warning method as claimed in claim 1, characterized in that: The water quality index change trend analysis is as follows: if the water quality index sequence at each moment in the updated future period is X={ }, n is the number of moments in the future period, calculate the trend change of water quality concentration ; ; ; ;when Z MK > 0 When the concentration increases, an early warning signal is issued; when Z MK = 0 , no warning signal is generated; when Z MK < 0 , no warning signal is generated.

7. A water quality monitoring and early warning system, characterized in that: include: A water quality data acquisition module is configured to: acquire water quality indicators at a number of consecutive moments in a current period; The water quality prediction module based on deep learning is configured to: based on the water quality index of the current period, use the deep learning model to predict the water quality index at each moment in the future period; A data assimilation module is configured to: optimize the water quality index of the future time period based on the deep learning model, and update the water quality index at each moment in the future time period by using a Kalman filter algorithm embedded in a gated cyclic unit; the Kalman filter algorithm embedded in a gated cyclic unit sets three gated cyclic units, which respectively replace the state transition matrix, the process noise matrix and the measurement noise matrix in the Kalman filter; The water pollution early warning module is configured to: conduct single factor evaluation of water quality, comprehensive pollution index evaluation and trend analysis of water quality index changes based on updated water quality indicators for future time periods, and issue early warnings.

8. A water quality monitoring and early warning system as claimed in claim 7, characterized in that: The water quality indicators are hydrogen ion concentration index, total nitrogen, total phosphorus, dissolved oxygen, conductivity, ammonia nitrogen, turbidity, permanganate index and / or water temperature.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in a water quality monitoring and early warning method as described in any one of claims 1 to 6 are implemented.

10. A computer device comprising a computer-readable storage medium, a processor, and a computer program stored in the computer-readable storage medium and executable on the processor, characterized in that: When the processor executes the program, the steps of a water quality monitoring and early warning method as described in any one of claims 1-6 are implemented.

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