Intelligent water quality monitoring method and system

Abstract

The invention discloses an intelligent water quality monitoring method and system, and relates to the technical field of environmental monitoring, and the method comprises the steps: firstly obtaining water quality information and potential time sequence information of each position in a target water area, and obtaining an information set through preprocessing; then, according to the information set, analyzing the charge unbalance condition of each position in the target water area so as to determine an unbalance hot spot area and an unbalance non-hot spot area, respectively analyzing the relevance between the charge unbalance condition in the unbalance hot spot area and the unbalance non-hot spot area and each parameter in the water quality information, and determining a coupling area through comparison; and finally, verifying the association condition between the charge imbalance index and the corresponding parameter in the coupling region, obtaining a parameter set, and setting a timestamp for triggering an early warning signal for each parameter in the parameter set so as to formulate a corresponding treatment means.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and specifically to an intelligent water quality monitoring method and system. Background Art

[0002] With the acceleration of the industrialization and urbanization processes, the problem of water body pollution has become increasingly complex and diverse. Traditional water quality monitoring mainly focuses on conventional indicators such as chemical substance concentration, pH value, dissolved oxygen, and chemical oxygen demand. However, at the microscopic level, the impact of the charge distribution state in water bodies on the ecosystem has gradually emerged. Especially in terms of microbial activity and ecosystem stability, an imbalance in charge distribution may lead to abnormal microbial metabolism and changes in community structure, thereby threatening the health of the entire aquatic ecosystem, but this has been long ignored. Therefore, there is an urgent need for an intelligent water quality monitoring method that can accurately monitor the charge distribution and its ecological impact.

[0003] Currently, the existing water quality monitoring means have significant deficiencies in evaluating the impact of charge distribution on the ecosystem. On the one hand, there is a lack of effective monitoring techniques for charge distribution imbalance, making it difficult to accurately obtain the distribution signals of microscale charged particles in water bodies and unable to quantify the degree of charge imbalance. On the other hand, the long-term impact of charge distribution imbalance on the ecosystem is ignored. For example, in some eutrophic water bodies, due to the lack of monitoring and analysis of charge distribution, it is difficult to accurately explain the internal relationship between the changes in microbial community structure and the process of water body eutrophication, resulting in difficulty in timely warning of the stress risk caused by water quality deterioration to the ecosystem. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an intelligent water quality monitoring method and system, which solves the problems in the above background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent water quality monitoring method, including the following steps, S1: Obtain the water quality information and potential time series information at each position in the target water area, and after preprocessing, obtain an information set; S2: According to the information set, analyze the charge imbalance situation at each position in the target water area to determine the imbalance hot spots and non-hot spots, and respectively analyze the correlation between the charge imbalance situation and each parameter in the water quality information in the imbalance hot spots and non-hot spots. After comparison, determine the coupling area; S3: Verify the correlation between the charge imbalance index and the corresponding parameters in the coupling area, obtain a parameter set, and set a time stamp for triggering a warning signal for each parameter in the parameter set to formulate corresponding treatment measures.

[0006] Preferably, sensor group nodes and multiple groups are arranged in the target water area according to a specified grid size A nanoelectrode array is used to obtain water quality information and potential time series information at various positions in the target water area; Three-point calibration is performed using standard solutions. The electrodes are respectively placed in standard solutions of three different concentrations, and the corresponding potential values are measured. By recording the potential change amount and ion concentration change amount under different standard solutions, the sensitivity coefficient of each electrode is calculated; the sensitivity coefficient is used to reflect the response ability of the electrode to ion concentration changes.

[0007] By using wavelet threshold denoising and baseline drift correction on the water quality information and potential time series information, a processed information set is obtained. According to the sensitivity coefficient of each electrode, the corresponding potential value in the information set is converted into charge concentration, and after normalization processing, charge time series information is obtained.

[0008] Preferably, according to the charge time series information, the average value and standard deviation of the charge concentration of all electrodes are calculated, and then through the average value and standard deviation of the charge concentration, the charge imbalance index of each electrode array is calculated, expressed as: , is the charge imbalance index of the corresponding electrode array at time t, is the standard deviation of the charge concentration of the corresponding electrode array at time t, is the average value of the charge concentration of the corresponding electrode array at time t, is a positive number; Through the charge imbalance index of the corresponding electrode array at time t, a time-varying index sequence of the corresponding electrode array is obtained. The time-varying index sequence is used to record the change of the charge imbalance index of the corresponding electrode array at different times.

[0009] Preferably, the charge imbalance index corresponding to the position coordinates of each electrode array is collected, and Kriging interpolation method is used to reconstruct the charge imbalance index on the grid of the entire target water area. Specifically: according to the grid range of the entire target water area, the points to be interpolated are determined. According to the spatial correlation between the position of each point to be interpolated and the position coordinates of each collected electrode array, the charge imbalance index of the point to be interpolated is obtained, expressed as: , is the charge imbalance index of the corresponding point to be interpolated at time t, i is the number of the electrode array to be collected, n is the total number of the electrode arrays to be collected, is the charge imbalance index of the i-th electrode array at time t, is the weight coefficient of the i-th electrode array; After reconstruction, a charge imbalance heat map at each moment is obtained, and threshold segmentation is performed on the charge imbalance heat map; Compare the charge imbalance index at each position in the charge imbalance heat map with a preset threshold. If the charge imbalance index exceeds the threshold, mark the corresponding position as an imbalance hot spot area; otherwise, mark the corresponding position as a non-hot spot area of imbalance. Extract the coordinates of the imbalance hot spot areas to obtain a set of hot spot area coordinates.

[0010] Preferably, based on the set of hot spot area coordinates, determine a set of non-hot spot area coordinates, and extract the water quality information of the set of hot spot area coordinates and the set of non-hot spot area coordinates from the information aggregation, and extract the charge imbalance index of the set of hot spot area coordinates and the set of non-hot spot area coordinates from the time-varying index sequence. Combine the set of hot spot area coordinates and the set of non-hot spot area coordinates to generate a set of area coordinates. In the set of hot spot area coordinates and the set of non-hot spot area coordinates, respectively, statistically calculate the correlation coefficients between the charge imbalance index and each parameter in the water quality information. Specifically: use each parameter in the time-varying index sequence and the water quality information at the corresponding time as inputs and substitute them into the correlation coefficient calculation function. The formula is: , where is the correlation coefficient between the charge imbalance index and the corresponding parameter, t is the time number, m is the duration in the time-varying index sequence, is the mean value of the charge imbalance index in the time-varying index sequence, is the value of the corresponding parameter in the water quality information at time t, is the mean value of the corresponding parameter; After taking the absolute value of the correlation coefficients between the charge imbalance index and all parameters in the water quality information and calculating the mean value, construct a stress coupling coefficient. The formula is: , is the stress coupling coefficient of the corresponding set of area coordinates, G is the total number of correlation coefficients in the corresponding set of area coordinates, g is the coordinate number in the corresponding set of area coordinates, K is the total number of parameters, and k is the parameter number, is the correlation coefficient between the charge imbalance index at coordinate g in the corresponding set of area coordinates and the k-th parameter B.

[0011] Preferably, respectively determine the stress coupling coefficients in the set of hot spot area coordinates and the set of non-hot spot area coordinates, and perform subtraction calculation on the stress coupling coefficients in the set of hot spot area coordinates and the set of non-hot spot area coordinates to obtain an impact difference value. If the impact difference value exceeds a preset difference threshold, mark the imbalance hot spot area as a coupling area; otherwise, it is not a coupling area. The coupling area is used to indicate the stress risk faced by the water quality in the corresponding area.

[0012] Preferably, according to the determined coupling area, verify the correlation between the charge imbalance index and the corresponding parameter in the coupling area. Specifically, it includes: Set the maximum lag order according to the sampling frequency. Based on the maximum lag order, construct autoregressive terms with only the corresponding parameters to predict the corresponding parameters and autoregressive terms with the charge imbalance index added simultaneously, which are specifically expressed as follows: Autoregressive terms with only the corresponding parameters to predict the corresponding parameters: ; Autoregressive terms with the charge imbalance index added simultaneously: ; In the formula, P is the maximum lag order, p is the lag index, is the autoregressive coefficient, is the first residual term, is the regression coefficient of the charge imbalance index at time t - p, is the charge imbalance index at time t - p, is the second residual term, is the value of the corresponding parameter at time t - p; Calculate the sum of squared residuals respectively to obtain the sum of squared residuals of the first residual term and the sum of squared residuals of the second residual term; Based on the sum of squared residuals of the first residual term and the sum of squared residuals of the second residual term, construct the F statistic, and the formula is: , where F is the F statistic, is the sum of squared residuals of the first residual term, is the sum of squared residuals of the second residual term, is the sample size; If the F statistic exceeds the critical value of the distribution, then reject the null hypothesis that the charge imbalance index does not Granger cause the corresponding parameter, indicating that there is a causal relationship between the charge imbalance index and the corresponding parameter, and the charge imbalance index is in the position of cause in the causal relationship. If the F statistic does not exceed the critical value of the distribution, then it indicates that there is no causal relationship between the charge imbalance index and the corresponding parameter.

[0013] Preferably, according to the maximum lag order, verify the correlation for different lag orders to draw a curve graph of the F statistic changing with the lag order, and find the target F statistic from the curve graph according to the changing trend in the curve graph.

[0014] Statistically analyze the parameters that have a causal relationship with the charge imbalance index to generate a parameter set, and find the target F statistic related to each parameter in the parameter set.

[0015] Preferably, the steps for obtaining the target F statistic include: Take three different lag orders with an adjacent relationship in the curve graph as a comparison group, and obtain several groups of comparison groups through statistics; Calculate the mean and standard deviation of the F-statistics corresponding to the respective lag orders in each comparison group, and select the comparison group with the largest mean and the smallest standard deviation as the target group; select the F-statistic with the smallest mean difference corresponding to it in the target group as the target F-statistic, and use the lag order corresponding to the target F-statistic as the best time delay for the charge imbalance index to affect the corresponding parameter. Based on the best time delay for the charge imbalance index to affect the corresponding parameter, obtain the time stamp for triggering the warning signal in advance when the corresponding parameter is abnormal, and formulate corresponding treatment measures within the time period of this best time delay.

[0016] An intelligent water quality monitoring system, comprising: The information acquisition module is used to acquire the water quality information and potential time series information at each location in the target water area, and obtain an information set after preprocessing. The area identification module is used to analyze the charge imbalance situation at each location in the target water area according to the information set to determine the imbalance hot spot area and the non-imbalance hot spot area, and respectively analyze the correlation between the charge imbalance situation and each parameter in the water quality information in the imbalance hot spot area and the non-imbalance hot spot area. After comparison, determine the coupling area. The setting module is used to verify the correlation between the charge imbalance index and the corresponding parameter in the coupling area, obtain a parameter set, and set the time stamp for triggering the warning signal for each parameter in the parameter set respectively to formulate corresponding treatment measures.

[0017] The present invention provides an intelligent water quality monitoring method and system, which have the following beneficial effects: (1) By performing threshold segmentation on the reconstructed charge imbalance heat map, this method can accurately identify the imbalance hot spots. For example, in lake ecological monitoring, this method was successfully used to locate the charge imbalance hot spots caused by pollutant accumulation in the bay. Compared with the traditional methods relying on manual experience or simple data statistics, this method can quickly and accurately determine the areas that need key attention, extract the coordinate set of the hot spots, provide a clear target for subsequent in-depth research and targeted treatment, and further improve the efficiency and accuracy of the monitoring work. In S2, the coordinate set of the non-hot spots is determined through the coordinate set of the hot spots, and the two are combined to generate the regional coordinate set. At the same time, the water quality information and charge imbalance index of the corresponding regions are extracted, which can systematically compare and analyze the relationship between charge imbalance and various water quality parameters in different regions. By constructing the stress coupling coefficient and comparing the coefficient differences between the hot spots and non-hot spots, and using the influence difference value and difference threshold to judge the coupling area, the areas where the water quality is at risk of stress can be accurately identified. For example, in the monitoring scenario of urban inland rivers, the coupling area with strong correlation between charge imbalance and parameters such as dissolved oxygen and ammonia nitrogen caused by industrial sewage discharge can be quickly located. This accurate identification makes the water quality warning no longer blind, and can issue alarms for the areas where there are real risks. The calculation of the stress coupling coefficient quantifies the correlation degree between charge imbalance and water quality parameters, making the ecological stress relationship clearer and more definite. At the same time, by determining the coupling area, the key treatment areas and key influencing parameters can be clarified, avoiding the blindness of treatment measures, making the treatment resources more reasonably allocated, and improving the treatment efficiency.

[0018] (2) By constructing the lag autoregressive terms of only the corresponding parameters to predict the corresponding parameters and adding the lag autoregressive terms of the charge imbalance index at the same time, and using the F statistic for Granger causality test, it can accurately determine whether there is a causal relationship between the charge imbalance index and water quality parameters in the coupling area. Compared with the traditional correlation analysis, it can more accurately reveal the essential connection between variables. After determining the causal relationship, a more scientific warning mechanism can be set based on this. When it is clear that the charge imbalance index is the cause of the changes in some water quality parameters, the future change trend of the corresponding water quality parameters can be accurately predicted when the charge imbalance index shows abnormal fluctuations. For example, in river monitoring, if it is verified that the charge imbalance index will cause a significant decrease in dissolved oxygen after 3 hours, then when the charge imbalance index is monitored to increase abnormally, a warning of a decrease in dissolved oxygen can be issued 3 hours in advance. The accuracy and reliability of the warning are greatly improved. Compared with fuzzy warnings, it can reserve sufficient response time for relevant departments and reduce ecological risks.

[0019] (3) According to the maximum lag order, verify the correlation for different lag orders and plot a curve graph showing the change of the F statistic with the lag order. This step is to comprehensively observe the change in the correlation degree between the charge imbalance index and the corresponding parameters under different time lags, and visually present the dynamic evolution of the relationship between the two with the trend of the curve, providing data visualization support for subsequent analysis. Generate a parameter set for the parameters that have a causal relationship with the charge imbalance index, and find the relevant target F statistic for each parameter in the parameter set. This step aims to determine all water quality parameters affected by the charge imbalance index and find the F statistic that can relatively significantly reflect this causal relationship, providing a key basis for subsequent determination of the optimal time delay and warning timestamp. By taking three adjacent different lag orders in the curve graph as a comparison group, calculate the mean and standard deviation of the F statistics of each comparison group, select the comparison group with the largest mean and the smallest standard deviation as the target group, and then determine the F statistic with the smallest difference from the mean in the target group as the target F statistic. The corresponding lag order is the relatively better time delay for the charge imbalance index to affect the corresponding parameter. This series of operations is to accurately screen out the time points when the charge imbalance index has a significant and relatively stable impact on each parameter from many possible time lags, so as to provide the most accurate time basis for early warning and treatment. Based on the relatively better time delay for the charge imbalance index to affect the corresponding parameter, obtain the timestamp for triggering the early warning signal in advance when the corresponding parameter is abnormal, and formulate corresponding treatment measures within this time period. This step transforms the previous analysis results into practical applications, realizes the early warning and targeted treatment of water quality anomalies, and improves the practicability and effectiveness of the water quality monitoring system. Description of the Drawings

[0020] Figure 1 Schematic flowchart of an intelligent water quality monitoring method of the present invention; Figure 2 Logic diagram of an intelligent water quality monitoring method of the present invention; Figure 3 Curve graph showing the change of the F statistic with the lag order in an intelligent water quality monitoring method of the present invention; Figure 4 Block diagram of an intelligent water quality monitoring system of the present invention. Detailed Embodiments

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Embodiment 1

[0023] Please refer to Figures 1 to 3 , the present invention provides an intelligent water quality monitoring method, including the following steps, S1: Obtain the water quality information and potential time series information at each position in the target water area, and after preprocessing, obtain an information set; S2: According to the information set, analyze the charge imbalance situation at each position in the target water area to determine the imbalance hot spot area and the imbalance non-hot spot area, respectively analyze the correlation between the charge imbalance situation and each parameter in the water quality information in the imbalance hot spot area and the imbalance non-hot spot area, and after comparison, determine the coupling area; S3: Verify the correlation between the charge imbalance index and the corresponding parameters in the coupling area, obtain a parameter set, and set a time stamp for triggering a warning signal for each parameter in the parameter set to formulate corresponding treatment measures.

[0024] In this embodiment, by obtaining the water quality information and potential time series information at each position in the target water area through step S1 and performing preprocessing to obtain an information set, multi-dimensional data covering physical, chemical, and charge characteristics can be comprehensively collected. For example, in the monitoring of the water area near an industrial park, not only conventional water quality parameters such as pH value and dissolved oxygen are obtained, but also potential time series information is collected through a nanoelectrode array, providing a rich and accurate data basis for subsequent analysis. Compared with traditional single-index monitoring, it can more accurately reflect the true state of water quality.

[0025] Based on the information set obtained in step S1, step S2 analyzes the charge imbalance situation, determines the imbalance hot spot area and the non-hot spot area, and studies the correlation between the charge imbalance and each water quality parameter to determine the coupling area. This kind of analysis can accurately locate the areas that have a significant impact on the ecosystem. For example, during the monitoring of a certain lake, this step successfully identified the bay as the charge imbalance hot spot area, and it was found that there is a strong correlation between the charge imbalance in this area and the growth of algae (characterized by the chlorophyll-a content), that is, the coupling area, indicating the direction for subsequent targeted monitoring and treatment, and further avoiding blindness.

[0026] Step S3 verifies the correlation between the charge imbalance index and the corresponding parameters in the coupling area, obtains a parameter set and sets a warning time stamp, and formulates treatment measures. For example, during the monitoring of a certain river, it is determined that there is a causal relationship between parameters such as dissolved oxygen and ammonia nitrogen and the charge imbalance and they are included in the parameter set. According to the analysis, the dissolved oxygen will significantly decrease about 6 hours after the charge imbalance, so a time stamp for triggering a warning signal 5 hours in advance is set. Once the monitoring data reaches the warning condition, the system automatically starts the oxygenation equipment and adjusts the discharge strategy of the sewage treatment plant, realizing scientific warning and precise treatment. Compared with traditional lagged treatment, the treatment efficiency is improved and the risk of ecological damage is reduced.

[0027] In summary, step S2 is based on S1. Using the information set obtained from S1, it deeply analyzes the charge imbalance situation in the target water area, determines the imbalance hotspots and non-hotspot areas, and further explores the correlation between charge imbalance and water quality parameters to screen out the coupling areas. This step is equivalent to sifting through a vast amount of data to find the key areas and factors that affect water quality and the ecosystem, thereby determining the targets for subsequent precise research and treatment. Step S3 then deeply verifies and applies the coupling areas determined in S2. By verifying the correlation situation, it obtains a parameter set, sets the warning timestamp and treatment measures. This step is the key to transforming the previous analysis results into practical applications, realizing a closed-loop from monitoring, analysis to warning and treatment, ensuring that the entire water quality monitoring system can effectively function and timely respond to problems brought about by water quality changes.

[0028] Example 2

[0029] Please refer to Figure 1 , specifically: Sensor group nodes and multiple groups of nanoelectrode arrays are arranged in the target water area according to the specified grid size and installed on a floating ball bracket with adjustable depth to ensure sampling at three layers of 0.5 m, 1.5 m, and 2.5 m. Each node is equipped with a microfiltration flow cell. After removing large particle impurities, the potential signal is transmitted to the edge computing platform in real time through optical fiber or wireless link; the system regularly automatically calibrates the sensitivity coefficient using a standard solution. The array deployment not only achieves spatial redundancy but also improves data reliability.

[0030] Obtain the water quality information and potential time series information at each position in the target water area; the potential time series represents the position of each electrode array in the target water area and the potential value at the sampling moment; Among them, the grid size can be set to a 5-meter by 5-meter grid; Each electrode array contains multiple microelectrodes that can simultaneously measure the local current and potential. Through this deployment method, it is possible to obtain the distribution signals of charged particles at different positions and micro-scales in the water body, forming a comprehensive perception of the charge distribution in the water body.

[0031] Three-point calibration is performed using a standard solution (known ion concentration), that is, the electrode is placed in three different concentrations of standard solutions respectively, and the corresponding potential values are measured. By recording the potential change amount and ion concentration change amount under different standard solutions, the sensitivity coefficient of each electrode is calculated; the sensitivity coefficient reflects the response ability of the electrode to ion concentration changes and is used to convert the measured potential signal into the actual charge concentration.

[0032] The method for obtaining the sensitivity coefficient is as follows: calculate the ratio of the potential change amount of the corresponding electrode under different standard solutions to the ion concentration change amount, and after multiple verifications, take the average value of multiple groups of calculation results to obtain the sensitivity coefficient; through the results of multiple calibrations, reduce the influence of single measurement errors, so that the finally obtained sensitivity value is more representative and stable.

[0033] By using wavelet threshold denoising and baseline drift correction on water quality information and potential time series information, an information set after processing is obtained, and according to the sensitivity coefficient of each electrode, the corresponding potential value in the information set of potential time series information is converted into charge concentration, and after normalization processing, charge time series information is obtained. That is to say, the corresponding potential signal is converted into a physical quantity that can directly reflect the charge distribution in the water body, which is convenient for subsequent analysis and processing; By converting the corresponding potential value into charge concentration, a physical quantity that can directly reflect the charge distribution in the water body is obtained, which is convenient for subsequent analysis and processing.

[0034] Wavelet transform can decompose a signal into different frequency scales. By setting a threshold, the wavelet coefficients corresponding to high-frequency noise are set to zero or subjected to shrinkage processing, and then the signal is reconstructed through inverse wavelet transform, thereby removing high-frequency interference. For example, high-frequency noises such as electromagnetic interference that may exist in the water body can be effectively removed by this method, making the potential signal smoother and more accurate.

[0035] According to the charge time series information, calculate the average value and standard deviation of the charge concentration of all electrodes, and then calculate the charge imbalance index of each electrode array through the average value and standard deviation of the charge concentration, which is expressed as: , is the charge imbalance index of the corresponding electrode array at time t, is the standard deviation of the charge concentration of the corresponding electrode array at time t, is the average value of the charge concentration of the corresponding electrode array at time t, is a positive number, a very small positive number (such as ), which is used to ensure that the denominator is not zero and ensure the stability of numerical calculation; The charge imbalance index is used to quantify the degree of charge distribution imbalance in the area covered by the entire electrode array. Among them, the aforementioned standard deviation of the charge concentration reflects the degree of dispersion of the charge concentration. The larger the standard deviation, the greater the difference in charge concentration between different electrodes, and the average value represents the average level of charge concentration in this area. By dividing the standard deviation by the average value, the obtained charge imbalance index can comprehensively reflect the degree of deviation of the charge distribution from the uniform state. The larger the index value, the more serious the charge distribution imbalance, and the smaller the index value, the more balanced the charge distribution means.

[0036] Specifically, the charge imbalance index can reflect changes in processes such as ion migration and material transformation in water bodies. For example, when a water body is polluted, the ionic components of the pollutants will change the distribution of the original ions in the water body, resulting in a change in the charge imbalance index. By monitoring this index, it is possible to indirectly understand whether the water body is polluted and the degree of pollution.

[0037] Obtain the time-varying index sequence of the corresponding electrode array through the charge imbalance index of the corresponding electrode array at time t. The time-varying index sequence is used to record the changes in the charge imbalance index of the corresponding electrode array at different times. Through this sequence, the evolution trend of the charge distribution imbalance degree over time can be observed.

[0038] In this embodiment, a standard solution is used for three-point calibration to obtain the sensitivity coefficient of each electrode, ensuring the accuracy of electrode measurement. For example, when monitoring the water area near an industrial wastewater discharge outlet, the accurate sensitivity coefficient can accurately convert the potential signal into the charge concentration, enabling the monitoring data to truly reflect the charge distribution of the water body and providing a reliable basis for subsequent analysis.

[0039] The node is built-in with a microfiltration flow cell to remove large particles. Combined with preprocessing means such as wavelet threshold denoising and baseline drift correction, the reliability and stability of the data are effectively improved. Taking river monitoring as an example, large particles such as sediment in the water will interfere with electrode measurement, and the microfiltration flow cell can filter impurities in advance; while the signal noise and baseline drift caused by environmental factors are eliminated after wavelet threshold denoising and baseline drift correction, making the finally obtained charge time series information more accurate.

[0040] In addition, the system regularly uses a standard solution to automatically calibrate the sensitivity coefficient, avoiding measurement errors caused by the performance degradation of the electrode after long-term use, and ensuring the stability and consistency of long-term monitoring data. Based on the processed charge time series information, the charge imbalance index and the time-varying index sequence are calculated, which can quantify the degree of charge distribution imbalance and monitor its dynamic changes. For example, in the monitoring of an urban landscape lake, by analyzing the time-varying index sequence, the evolution trend of the charge imbalance degree of the lake water over time can be grasped in real time. If it is found that the charge imbalance index continues to rise during a certain period, combined with other water quality parameters, it can be timely judged whether there is an increase in pollution or an abnormal ecosystem, providing key data support for water quality early warning and treatment.

[0041] At the same time, the spatial redundancy brought by the array deployment further improves the reliability of the data. Even if some nodes fail, the continuity and integrity of the monitoring can still be ensured through the data of other nodes.

[0042] Example 3

[0043] Please refer to Figure 1 and Figure 2, specifically: collect the charge imbalance index corresponding to the position coordinates of each electrode array, and use Kriging interpolation to reconstruct the charge imbalance index on the grid of the entire target water area. Specifically: according to the grid range of the entire target water area, determine the points to be interpolated, and obtain the charge imbalance index of the points to be interpolated based on the spatial correlation between the position of each point to be interpolated and the position coordinates of each collected electrode array, expressed as: , is the charge imbalance index of the corresponding point to be interpolated at time t, i is the number of the electrode array to be collected, and n is the total number of the electrode arrays to be collected. is the charge imbalance index of the i-th electrode array at time t. is the weight coefficient of the i-th electrode array, and this coefficient reflects the degree of spatial correlation between the point to be interpolated and the known points; Through this method, the discrete CDII values of the electrode arrays can be made continuous on the grid of the entire water area, and the estimated value of the charge distribution imbalance index at each position at time t can be obtained.

[0044] After reconstruction, obtain the charge imbalance heat map at each moment, and perform threshold segmentation on the charge imbalance heat map; Compare the charge imbalance index at each position in the charge imbalance heat map with a pre-set threshold. If the charge imbalance index exceeds the threshold, mark the corresponding position as an imbalance hot spot area, otherwise mark the corresponding position as an imbalance non-hot spot area; Extract the coordinates of the imbalance hot spot areas to obtain the hot spot area coordinate set.

[0045] Through spatio-temporal heat map reconstruction and hot spot identification, visually display the distribution of charge distribution imbalance in space and time, locate the areas with high imbalance risks, and provide information in the spatial dimension for subsequent assessment of ecological impacts.

[0046] In this embodiment, by using Kriging interpolation to reconstruct the discrete charge imbalance index of the electrode arrays on the grid of the entire target water area, the data of finite sampling points can be transformed into continuous spatial distribution data. For example, in the monitoring of a river that is 10 kilometers long and 500 meters wide, only partial point data can be obtained relying on discrete electrode arrays. However, after interpolation reconstruction, the estimated value of the charge imbalance index of each grid point on the entire river plane can be obtained, forming a continuous charge imbalance heat map. This visual presentation method enables researchers and managers to intuitively understand the spatial distribution of charge imbalance, and it is easier to discover potential water quality problem areas compared with discrete data.

[0047] Perform threshold segmentation on the reconstructed charge imbalance heat map. Based on a preset threshold, such as the outlier boundary determined through historical data statistical analysis, the charge imbalance hot spot area can be accurately identified. For example, in the monitoring of a certain lake ecological reserve, through this method, a charge imbalance hot spot area caused by sewage discharge in the center of the lake was successfully located. The charge imbalance index in this area far exceeded the threshold, providing accurate location information for the environmental protection department to take targeted treatment measures in a timely manner.

[0048] Extract the coordinate set of the imbalance hot spot area, which provides a clear research object for subsequent in-depth analysis of the correlation between charge imbalance and water quality parameters, ecological stress effects, etc. For example, when studying the impact of charge imbalance on aquatic biological communities, biological samples can be collected for the hot spot area and non-hot spot area respectively, and comparative analysis can be carried out in combination with charge imbalance data, making the research more targeted and scientific. It avoids blind research in the entire water area, effectively saving manpower, material resources and time costs, and at the same time improving the reliability and application value of research results.

[0049] Example 4

[0050] Please refer to Figure 1 and Figure 2 , specifically: Based on the coordinate set of the hot spot area, determine the coordinate set of the non-hot spot area, and extract the water quality information of the coordinate set of the hot spot area and the coordinate set of the non-hot spot area from the information aggregation, and extract the charge imbalance index of the coordinate set of the hot spot area and the coordinate set of the non-hot spot area from the time-varying index sequence; Combine the coordinate set of the hot spot area and the coordinate set of the non-hot spot area to generate a regional coordinate set; In the coordinate set of the hot spot area and the coordinate set of the non-hot spot area, respectively, statistically calculate the correlation coefficients between the charge imbalance index and each parameter in the water quality information. Specifically: Take each parameter in the time-varying index sequence and the water quality information at the corresponding time as inputs, and substitute them into the correlation coefficient calculation function. The formula is: , where is the correlation coefficient between the charge imbalance index and the corresponding parameter, t is the time number, m is the duration in the time-varying index sequence, is the mean value of the charge imbalance index in the time-varying index sequence, is the value of the corresponding parameter in the water quality information at time t, is the mean value of the corresponding parameter; The aforementioned correlation coefficient between the charge imbalance index and the corresponding parameter, where the corresponding parameter refers to various water quality parameters in the water quality information. Specifically, each parameter in the water quality information includes but is not limited to pH value, dissolved oxygen, and plankton count; The correlation coefficient between the charge imbalance index and the corresponding parameter is a statistical indicator used to measure the closeness of the linear relationship between the two. Specifically: if the correlation coefficient is greater than 0, it indicates a positive correlation between the charge imbalance index and the corresponding parameter, that is, when the charge imbalance index increases, the corresponding parameter also tends to increase; when the charge imbalance index decreases, the corresponding parameter also tends to decrease. The closer the correlation coefficient is to 1, the stronger the positive correlation between the two.

[0051] When the correlation coefficient is less than 0, it means that there is a negative correlation between the charge imbalance index and the corresponding parameter, that is, when the charge imbalance index increases, the corresponding parameter tends to decrease; when the charge imbalance index decreases, the corresponding parameter tends to increase. The closer the correlation is to -1, the stronger the negative correlation between the two.

[0052] If the correlation coefficient is close to 0, it indicates that there is no obvious linear correlation between the charge imbalance index and the corresponding parameter, and the changes of the two are independent of each other without an obvious pattern to follow.

[0053] By calculating the correlation coefficients between the charge imbalance index and each parameter in the water quality information, it can help analyze the connection between the charge imbalance phenomenon and the changes in water quality parameters, providing an important basis for further studying the potential relationship between the two and judging whether there is stress coupling.

[0054] Among them, the pH value directly measures the acidity and alkalinity of the water body through a pH meter. This parameter reflects the acid-base balance state of the water body and has an important impact on the metabolic activity, enzyme activity, and cell membrane permeability of microorganisms. Different microorganisms have different suitable pH ranges for growth. Dissolved oxygen is measured by the electrochemical probe method or the iodometric method. Dissolved oxygen is an essential substance for the respiration of aquatic organisms, and its content directly affects the metabolic activities of aerobic microorganisms in the water body and the composition and distribution of the aquatic biological community. The plankton count is obtained by regularly collecting water samples and classifying and counting the plankton (including phytoplankton and zooplankton) in them. As an important part of the water body ecosystem, the species composition, quantity change, and community structure of plankton can intuitively reflect the health status and stability of the ecosystem and are extremely sensitive to environmental changes.

[0055] After taking the absolute value of the correlation coefficients between the charge imbalance index and all parameters in the water quality information and calculating the mean value, a stress coupling coefficient is constructed. The formula is: , is the stress coupling coefficient of the corresponding regional coordinate set, G is the total number of correlation coefficients within the corresponding regional coordinate set, g is the coordinate number within the corresponding regional coordinate set, K is the total number of parameters, k is the parameter number, is the correlation coefficient between the charge imbalance index at coordinate g within the corresponding regional coordinate set and the kth parameter B.

[0056] In this way, regardless of whether the charge imbalance index is positively or negatively correlated with each parameter, as long as the correlation is significant, it will be reflected in the stress coupling coefficient. The larger the stress coupling coefficient, the stronger the coupling degree between the charge imbalance and the disturbance of ecological parameters, that is, the more significant the impact of the unbalanced charge distribution on the ecosystem; conversely, the smaller the stress coupling coefficient, the weaker the correlation between the two, and the relatively smaller the impact of the charge imbalance on the ecosystem.

[0057] The stress coupling coefficient is an index used to comprehensively measure the tightness of the correlation between the charge imbalance index and each parameter in the water quality information. Its construction is based on the correlation coefficients between the charge imbalance index and each parameter in the water quality information. The stress coupling coefficient can comprehensively consider the relationship between the charge imbalance index and multiple water quality parameters, rather than just a single parameter. By processing all the correlation coefficients, a value that can reflect the overall coupling degree is obtained, so as to comprehensively evaluate the comprehensive impact of the charge imbalance phenomenon on the water quality. Of course, it can also be used to compare the stress coupling degrees of different regional coordinate sets. For example, when comparing the stress coupling coefficients of the hot spot area coordinate set and the non-hot spot area coordinate set, it can be judged which area has a closer relationship between the charge imbalance index and the water quality parameters, and then analyze the differences in the impact of charge imbalance on the water quality in different regions.

[0058] Respectively determine the stress coupling coefficients in the hot spot area coordinate set and the non-hot spot area coordinate set, and perform subtraction calculation on the stress coupling coefficients in the hot spot area coordinate set and the non-hot spot area coordinate set to obtain the impact difference value. If the impact difference value exceeds the pre-set difference threshold, mark the imbalance hot spot area as the coupling area, otherwise it is not the coupling area; The coupling area is used to indicate the stress risk faced by the water quality in the corresponding area.

[0059] After determining the coupling area through the stress coupling coefficient, the relationship between the charge imbalance and the water quality parameters in these areas can be studied in depth, providing a scientific basis for formulating more effective water quality governance and protection measures. For example, if it is found that the stress coupling coefficient in a certain area is relatively high, indicating that the charge imbalance in this area has a greater impact on the water quality, it is necessary to focus on and take measures to adjust the charge distribution to improve the water quality.

[0060] In this embodiment, based on the determined set of coordinates of the hot spots, the set of coordinates of the non-hot spots is determined, and the water quality information of the corresponding regions is extracted from the information set. At the same time, the corresponding charge imbalance index is extracted from the time-varying index sequence. Finally, the sets of coordinates of the hot spots and non-hot spots are combined into a set of regional coordinates. This step mainly provides structured data for subsequent analysis, integrating the key data of different regions together. Compared with the existing water quality monitoring methods, the existing water quality monitoring often only focuses on the overall water quality status or simply analyzes a single region, lacking systematic integration and comparison of data between different regions. By clearly dividing the hot spots and non-hot spots and integrating the relevant data, this step can analyze the differences in water quality characteristics of different regions in more detail, laying a foundation for in-depth study of the relationship between charge imbalance and water quality parameters.

[0061] The set of coordinates of the hot spots is a set of coordinates of the regions with a relatively high degree of charge imbalance determined through preliminary analysis. These regions are the key objects of concern in the study of water quality problems.

[0062] The set of coordinates of the non-hot spots is a set of coordinates of the regions with a relatively low degree of charge imbalance, opposite to the hot spots, serving as a comparison reference region.

[0063] The set of regional coordinates is the set after combining the set of coordinates of the hot spots and the set of coordinates of the non-hot spots, covering the coordinate information of all regions to be analyzed in the target water area.

[0064] In the set of coordinates of the hot spots and the set of coordinates of the non-hot spots, the correlation coefficients between the charge imbalance index and each parameter in the water quality information are respectively calculated. Through the correlation coefficient calculation function, the time-varying index sequence (reflecting the change of the charge imbalance index over time) and the water quality information parameters at the corresponding time are used as inputs to calculate the correlation coefficient between the two, so as to measure the linear correlation degree between the charge imbalance index and each water quality parameter. Compared with the existing technical effects, the existing technology usually only analyzes the water quality parameters separately or simply observes the surface relationship between the parameters, making it difficult to accurately quantify the correlation degree between charge imbalance and other water quality parameters. By accurately calculating the correlation coefficient, this step can scientifically reveal the potential relationship between charge imbalance and each water quality parameter, providing a quantitative basis for further analysis.

[0065] The correlation coefficient is an index used to measure the linear correlation degree between two variables, with a value range between -1 and 1. The closer its absolute value is to 1, the stronger the linear correlation between the two variables; approaching 0 indicates a weaker linear correlation. A positive correlation means that the two variables change in the same trend, and a negative correlation means that the change trends are opposite.

[0066] The time-varying index sequence is a sequence that records the changes in the charge imbalance index of the electrode array at different times. Through this sequence, the evolution trend of the degree of charge distribution imbalance over time can be observed, providing data for analyzing the relationship between charge imbalance and other parameters in the time dimension.

[0067] After taking the absolute value of the correlation coefficients between the charge imbalance index and all parameters in the water quality information and calculating the mean value, a stress coupling coefficient is constructed. This coefficient comprehensively reflects the overall correlation degree between the charge imbalance and each parameter in the water quality information, and is used to evaluate the stress degree of the charge imbalance on the water ecosystem. Compared with the effects of the prior art, the prior art lacks a quantitative assessment of the comprehensive correlation between multiple water quality parameters and charge imbalance, making it difficult to comprehensively evaluate the stress on the water ecosystem. In this step, by constructing the stress coupling coefficient, the coupling degree between the charge imbalance and the water quality parameters can be measured as a whole, providing a more comprehensive index for judging the water quality ecological risk. For example: In lake monitoring, for a certain area, after calculating the correlation coefficients between the charge imbalance index and multiple water quality parameters such as pH, dissolved oxygen, and ammonia nitrogen, the stress coupling coefficient is constructed. If the coefficient value is large, it indicates that there is a strong correlation between the charge imbalance and multiple water quality parameters in this area, meaning that the water quality ecosystem in this area is under a high degree of stress from the charge imbalance and needs to be focused on.

[0068] The stress coupling coefficient is a comprehensive index obtained by processing the correlation coefficients between the charge imbalance index and each parameter in the water quality information. The larger its value, the stronger the coupling degree between the charge imbalance and the disturbance of the ecological parameters, that is, the more significant the impact of the charge distribution imbalance on the ecosystem, and it is used to quantitatively evaluate the stress degree of the water ecosystem.

[0069] The stress coupling coefficients in the hot spot area coordinate set and the non-hot spot area coordinate set are determined respectively, and the difference between the two is calculated to obtain the impact difference value. The impact difference value is compared with a pre-set difference threshold. If it exceeds the threshold, the imbalance hot spot area is marked as the coupling area, indicating that the water quality in this area faces a high stress risk. Compared with the effects of the prior art, the prior art is difficult to accurately identify which areas have a more significant impact of charge imbalance on the water quality ecosystem and is difficult to achieve targeted monitoring and governance. In this step, by locking the high-risk areas, a clear goal is provided for taking effective measures in the follow-up. For example: In the monitoring of a certain reservoir, by calculating the stress coupling coefficients of the hot spot area and the non-hot spot area, it is found that the difference between the two is large and exceeds the set threshold, so the hot spot area is marked as the coupling area. This prompts the management personnel that due to the strong coupling relationship between the charge imbalance and the water quality parameters in this area, it faces a high ecological risk and needs to strengthen monitoring and take timely governance measures, such as adjusting the water flow or putting in purification substances.

[0070] The coupling region is the region determined by comparing the stress coupling coefficients of the hot spot region and the non-hot spot region. There is a strong correlation between the charge imbalance and water quality parameters within this region, indicating that the water quality in the corresponding region faces the risk of stress, and it is the region that requires key attention and treatment. The influence difference value is the difference between the stress coupling coefficients in the coordinate sets of the hot spot region and the non-hot spot region, which is used to measure the difference in the degree of influence of the charge imbalance on water quality parameters between the hot spot region and the non-hot spot region, and is an important basis for judging the coupling region.

[0071] Example 5

[0072] Please refer to Figure 1 and Figure 2 , specifically: According to the determined coupling region, verify the correlation between the charge imbalance index and the corresponding parameters within the coupling region, specifically including: Set the maximum lag order. For example, according to the sampling frequency and ecological response characteristics, the maximum lag order can be set to 12 or 24; Based on the maximum lag order, respectively construct a lag autoregressive term that only uses the lagged autoregressive terms of the corresponding parameters to predict the corresponding parameters and a lag autoregressive term that simultaneously adds the lagged autoregressive term of the charge imbalance index, specifically expressed as: Using only the lagged autoregressive terms of the corresponding parameters to predict the corresponding parameters: ; Simultaneously adding the lagged autoregressive term of the charge imbalance index: ; In the formula, P is the maximum lag order, that is, the maximum time step length to be traced back, p is the lag index, ranging from 1 to P, representing the p-th moment forward, is the autoregressive coefficient, representing the linear influence weight of the parameter value at the p-th moment forward on the parameter B value at the current moment t, is the first residual term (prediction error), reflecting the remaining unexplained part when only using the lagged terms of the corresponding parameter B itself for prediction, is the regression coefficient of the charge imbalance index at the t - p moment, representing the influence weight of the charge imbalance index at the p-th moment forward on the charge imbalance index at the current moment, is the charge imbalance index at the moment t - p, used to test the additional explanatory power of the charge perturbation on the corresponding parameter, is the second residual term, reflecting the part that remains unexplained after simultaneously considering the corresponding parameter and the lagged terms of the charge imbalance index, is the value of the corresponding parameter at the moment t - p, which is the autoregressive input to the current corresponding parameter value; is the value of the corresponding parameter at the moment t - p, which is the autoregressive input to the current corresponding parameter value; Among them, the lag autoregressive term is a content in time series analysis used to describe the relationship between the past values of a variable itself and its current value. By considering the lagged values of the variable, the lag autoregressive term can capture the dynamic change law in time series data. For the corresponding parameters, it can describe the dependence relationship between the parameters themselves at different time points, helping to analyze the evolution trend of the parameters over time. For example, if the autoregressive coefficient of the lag autoregressive term of a water quality parameter (such as pH) is large, it indicates that the current value of this parameter is largely affected by its past values, that is, it has a strong time series correlation.

[0073] When the lag autoregressive term of the charge imbalance index is added, the influence of the charge imbalance index on the corresponding parameter can be further explored. By analyzing the magnitude and significance, the direction and degree of the effect of the charge imbalance index on the corresponding parameter at different lag orders can be understood. If the at a certain lag order is significantly non-zero, it indicates that the charge imbalance index at this moment has a significant effect on the current corresponding parameter, thus revealing the potential causal relationship between the charge imbalance and the corresponding parameter.

[0074] Calculate the sum of squared residuals respectively to obtain the sum of squared residuals of the first residual term and the sum of squared residuals of the second residual term; Based on the sum of squared residuals of the first residual term and the sum of squared residuals of the second residual term, construct the F statistic, and the formula is: , where F is the F statistic, is the sum of squared residuals of the first residual term, is the sum of squared residuals of the second residual term, is the sample size, which refers to the total number of time series data points used to establish the prediction of the corresponding parameter using only the lag autoregressive term of the corresponding parameter and when adding the lag autoregressive term of the charge imbalance index at the same time. The in the denominator is the adjustment term of the degrees of freedom. The role of this adjustment term is to adjust the sample size and model complexity to more accurately reflect the distribution characteristics of the statistic; Among them, the numerator part reflects the degree of improvement in the model's explanatory ability (i.e., the reduction in the sum of squared residuals) after adding the lag term of the charge imbalance index. Dividing by P is to eliminate the influence of the difference in the number of parameters. The denominator part represents the average residual including both the parameter and the lag term of the charge imbalance index, that is, the average residual of the unrestricted model, which is used to standardize the change amount of the numerator; The F statistic is used to test whether the charge imbalance index has a significant predictive ability for the corresponding parameter; If the F statistic exceeds If the F-statistic exceeds the critical value of the F-distribution, the null hypothesis that the charge imbalance index does not Granger cause the corresponding parameter is rejected, indicating that there is a causal relationship between the charge imbalance index and the corresponding parameter, and the charge imbalance index is in the position of cause in the causal relationship. If the F-statistic does not exceed the critical value of the F-distribution, it indicates that there is no causal relationship between the charge imbalance index and the corresponding parameter.

[0075] The critical value of the F-distribution can be obtained in the following way: First, clarify the degrees of freedom. Here, the F-distribution has two degrees of freedom. The first degree of freedom is P; the second degree of freedom is T - 2p - 1, where T is the sample size. Then, according to the given significance level, such as a significance level of 0.05, look up the corresponding critical value in the F-distribution table. The F-distribution table usually lists the critical values corresponding to different significance levels for different combinations of degrees of freedom. First, find the row and column with degrees of freedom P and T - 2p - 1, and the value at their intersection is the critical value of the F-distribution at this significance level.

[0076] Among them, is an F-distribution, where P and T - 2p - 1 are the two degrees of freedom of this distribution; In the field of water quality monitoring, traditional methods mostly focus on the correlation analysis between water quality indicators, but correlation is not equivalent to causation. By clarifying the causal relationship and response lag time between the charge imbalance index and each parameter, it is of great significance for early warning of ecological stress and optimizing governance strategies.

[0077] Of course, the above steps can be repeated, but swap the roles of the charge imbalance index and the corresponding parameter, that is, check whether the corresponding parameter is in the position of cause in the causal relationship. If so, it is a two-way coupling.

[0078] In this embodiment, the maximum lag order is set according to the sampling frequency. The purpose is to determine the maximum time step of the retrospective data and delimit the time range for subsequent model construction. On this basis, a lag autoregressive term prediction using only the corresponding parameter and a lag autoregressive term adding the charge imbalance index are constructed. The former is used to reflect the influence of the historical data of the parameter itself on the current value, and the latter explores whether the charge imbalance index can additionally explain the parameter change. For example, when monitoring the dissolved oxygen in a river, if the sampling frequency is once per hour and the maximum lag order is set to 6, the restricted model only uses the dissolved oxygen in the river in the past 6 hours to predict the current dissolved oxygen in the river, and the unrestricted model adds the contemporaneous charge imbalance index to analyze its influence on the prediction of the dissolved oxygen in the river.

[0079] Calculate the sum of squared residuals of the two models respectively. Its function is to measure the deviation degree between the predicted value and the actual value of the model. The smaller the value, the better the fitting effect of the model. In the above example of river dissolved oxygen monitoring, calculating the sum of squared residuals in the prediction model using only the lag autoregressive terms of the corresponding parameters can evaluate the prediction accuracy based only on the historical data of dissolved oxygen. The sum of squared residuals in the model with the lag autoregressive term of the charge imbalance index reflects the fitting effect of the model after adding the charge imbalance index.

[0080] The sum of squared residuals is an important basic data for constructing the F-statistic subsequently. Based on the sum of squared residuals of the two models, the F-statistic is constructed to test whether the charge imbalance index has a significant impact on the corresponding parameters. Compare the F-statistic with the critical value. If the F-statistic exceeds the critical value, the original hypothesis is rejected, indicating that there is a causal relationship between the charge imbalance index and the corresponding parameters, that is, the charge imbalance will cause the change of this parameter; otherwise, there is no causal relationship. For example, in the monitoring of lake chlorophyll-a content, if the calculated F-statistic is significant, it means that the charge imbalance index is one of the reasons affecting the change of chlorophyll-a content. The F-statistic is used to quantify the significance of the causal relationship, and the original hypothesis is the initial hypothesis condition for the test.

[0081] Compared with the prior art, this method scientifically verifies the causal relationship between the charge imbalance index and water quality parameters from the time series dimension through rigorous hypothesis testing and model comparison, avoiding misjudgment caused by relying only on correlation analysis. For example, traditional methods may find that the pH value of a certain water area is related to the charge imbalance index, but cannot determine the causal direction; while this method can clearly judge whether the charge imbalance affects the pH value or the change of the pH value causes the charge imbalance, providing a more accurate basis for water quality treatment and improving the scientificity and effectiveness of water quality monitoring and treatment.

[0082] Example 6

[0083] Please refer to Figure 1 and Figure 3 , specifically: according to the maximum lag order, verify the correlation situation for different lag orders to draw a curve graph of the F-statistic changing with the lag order, and find the target F-statistic from the curve graph according to the changing trend in the curve graph.

[0084] Statistically analyze the parameters that have a causal relationship with the charge imbalance index, generate a parameter set, and find the target F-statistic related to each parameter in the parameter set.

[0085] The steps for obtaining the target F-statistic include: Take three different lag orders with an adjacent relationship in the curve graph as a comparison group, and obtain several groups of comparison groups through statistics; Calculate the mean and standard deviation of the F-statistics corresponding to the respective lag orders in each comparison group, and select the comparison group with the largest mean and the smallest standard deviation as the target group; select the F-statistic with the smallest mean difference corresponding to it in the target group as the target F-statistic, and use the lag order corresponding to the target F-statistic as the best time delay for the charge imbalance index to affect the corresponding parameter. Based on the best time delay for the charge imbalance index to affect the corresponding parameter, obtain the time stamp for triggering the warning signal in advance for the abnormality of the corresponding parameter, and formulate corresponding control measures within the time period of this best time delay.

[0086] According to the lag length, that is, within the time period of this best time delay, formulate and deploy control strategies such as oxygenation and drug administration in advance to achieve early intervention in the change of microbial activity.

[0087] Please refer to Figure 3 ,in Figure 3 Among them, the lag orders from lag order 1 to lag order 7 are all lag days. By taking three different lag orders with adjacent relationships in the curve graph as a comparison group, it can be seen that in Figure 3 the lag orders from lag order 1 to lag order 3 form a comparison group, the lag orders from lag order 2 to lag order 4 form a comparison group, the lag orders from lag order 3 to lag order 5 form a comparison group, and so on. It can be seen that there are five comparison groups. By calculating the mean and standard deviation of the F-statistics corresponding to the respective lag orders in each comparison group, select the comparison group with the largest mean and the smallest standard deviation as the target group. From Figure 3 it can be seen that Figure 3 the target group in

[0088] In this embodiment, by verifying different lag orders, comprehensively analyze the correlation between the charge imbalance index and the corresponding parameters at different time delays. The curve graph of the F-statistic changing with the lag order can visually present the change trend of this relationship. For example, when studying the relationship between the dissolved oxygen parameter in water quality and the charge imbalance index, different lag orders correspond to different days. Through verification, it can be understood how the charge imbalance index a few days ago affects the current dissolved oxygen.

[0089] Statistically analyze the parameters that have a causal relationship with the charge imbalance index to form a parameter set, and clarify which parameters are substantially related to the charge imbalance index to make the subsequent analysis more targeted. For example, among multiple water quality parameters, it is determined that the acidity, dissolved oxygen, etc. have a causal relationship with the charge imbalance index and include them in the parameter set.

[0090] Take three adjacent different lag orders in the curve graph as a comparison group, and calculate the mean and standard deviation of the F-statistics in each comparison group. The mean reflects the average level of the F-statistics in this group, and the standard deviation reflects the degree of dispersion of the data. For example, there are multiple comparison groups of three lag orders, calculate the mean and standard deviation of their F-statistics. If the mean of a certain group is large and the standard deviation is small, it means that the overall F-statistics of this group are relatively high and stable. Select the comparison group with the largest mean and the smallest standard deviation as the target group, and select the F-statistic with the smallest difference from the mean as the target F-statistic, which can find a relatively representative and stable F-statistic, corresponding to the situation where the charge imbalance index has the most significant impact on the corresponding parameter.

[0091] The lag order corresponding to the target F-statistic is the relatively better time delay for the charge imbalance index to affect the corresponding parameter, which clarifies the specific time interval for the parameter to be affected by the charge imbalance index, providing a key basis for early warning and treatment. For example, if the relatively better time delay is 4 days, it means that the abnormality of the corresponding parameter can be predicted 4 days in advance according to the change of the charge imbalance index. According to the relatively better time delay, the timestamp for triggering the early warning signal for the abnormality of the corresponding parameter can be obtained, allowing the manager to have enough time to take countermeasures. At the same time, corresponding treatment measures can be formulated within this time period to specifically solve the possible water quality problems and improve the scientificity and effectiveness of water quality management. Compared with the prior art, this method can more accurately determine the causal relationship between the charge imbalance index and the water quality parameter and the optimal impact time delay, providing a more accurate basis for water quality monitoring and treatment, helping to give early warning and take effective measures to reduce the harm caused by water quality deterioration.

[0092] Example 7

[0093] Please refer to Figure 4 , specifically: An intelligent water quality monitoring system, including: The information acquisition module is used to acquire the water quality information and potential time series information at each location in the target water area, and after preprocessing, obtain the information set; The area identification module is used to analyze the charge imbalance situation at each location in the target water area according to the information set to determine the imbalance hot spot area and the non-imbalance hot spot area, and respectively analyze the correlation between the charge imbalance situation and each parameter in the water quality information in the imbalance hot spot area and the non-imbalance hot spot area. After comparison, determine the coupling area; The setting module is used to verify the correlation between the charge imbalance index and the corresponding parameter in the coupling area, obtain the parameter set, and set the timestamp for triggering the early warning signal for each parameter in the parameter set respectively to formulate corresponding treatment measures.

[0094] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent water quality monitoring method, characterized in that: The following steps are included: S1: Obtain water quality information and potential time series information at each location in the target water area, and obtain an information set after preprocessing; S2: Analyze the charge imbalance at each location in the target waters according to the information set to determine the imbalance hotspot area and imbalance non-hotspot area, analyze the correlation between the charge imbalance in the imbalance hotspot area and imbalance non-hotspot area and the parameters in the water quality information, and determine the coupling area after comparison; S3: Verify the correlation between the charge imbalance index and the corresponding parameters in the coupling region, obtain the parameter set, and set the timestamp for triggering the early warning signal for each parameter in the parameter set to formulate corresponding governance measures.

2. An intelligent water quality monitoring method according to claim 1, characterized in that: Deploy sensor group nodes and multiple groups according to the specified grid size in the target water area. Nanoelectrode array to obtain water quality information and potential time series information at each location in the target water area; Three-point calibration is performed using standard solutions, whereby the electrodes are placed in three standard solutions of different concentrations, respectively, and the corresponding potential values ​​are measured. By recording the potential change and ion concentration change under different standard solutions, the sensitivity coefficient of each electrode is calculated and obtained; the sensitivity coefficient is used to reflect the response ability of the electrode to changes in ion concentration; The water quality information and potential time series information are subjected to wavelet threshold denoising and baseline drift correction to obtain a processed information set. The potential time series information in the information set is converted into charge concentration according to the sensitivity coefficient of each electrode, and the charge time series information is obtained after normalization.

3. An intelligent water quality monitoring method according to claim 2, characterized in that: According to the charge time series information, the average and standard deviation of the charge concentration of all electrodes are calculated, and then the charge imbalance index of each electrode array is calculated by the average and standard deviation of the charge concentration, which is expressed as: , is the charge imbalance index of the corresponding electrode array at time t, is the standard deviation of the charge concentration of the corresponding electrode array at time t, is the mean charge concentration of the corresponding electrode array at time t, is a positive number; The time-varying index sequence of the corresponding electrode array is obtained through the charge imbalance index of the corresponding electrode array at time t, and the time-varying index sequence is used to record the change of the charge imbalance index of the corresponding electrode array at different times.

4. An intelligent water quality monitoring method according to claim 3, characterized in that: The charge imbalance index corresponding to each electrode array position coordinate is collected, and the charge imbalance index is reconstructed on the grid of the entire target water area using the Kriging interpolation method. Specifically, the interpolation point is determined according to the grid range of the entire target water area, and the charge imbalance index of the interpolation point is obtained according to the spatial correlation between the position of each interpolation point and the collected coordinates of each electrode array position, which is expressed as: , is the charge imbalance index of the corresponding interpolation point at time t, i is the number of the electrode array to be collected, n is the total number of electrode arrays to be collected, is the charge imbalance index of the ith electrode array at time t, is the weight coefficient of the i-th electrode array; After reconstruction, the charge imbalance heat map at each moment is obtained, and the charge imbalance heat map is threshold segmented; The charge imbalance index at each position in the charge imbalance heat map is compared with a preset threshold. If the charge imbalance index exceeds the threshold, the corresponding position is marked as an imbalance hotspot area, otherwise the corresponding position is marked as an imbalance non-hotspot area; The coordinates of the imbalance hot spot area are extracted to obtain the coordinate set of the hot spot area.

5. An intelligent water quality monitoring method according to claim 4, characterized in that: Based on the hotspot area coordinate set, the non-hotspot area coordinate set is determined, and the water quality information of the hotspot area coordinate set and the non-hotspot area coordinate set is extracted from the information cluster, and the charge imbalance index of the hotspot area coordinate set and the non-hotspot area coordinate set is extracted from the time-varying indicator sequence; Combining the hotspot area coordinate set with the non-hotspot area coordinate set to generate an area coordinate set; In the hot spot area coordinate set and the non-hot spot area coordinate set, the correlation coefficients between the charge imbalance index and each parameter in the water quality information are respectively calculated. Specifically, the time-varying index sequence and each parameter in the water quality information at the corresponding time are used as inputs to substitute into the correlation coefficient calculation function. The formula is: , where is the correlation coefficient between the charge imbalance index and the corresponding parameter, t is the time number, m is the duration in the time-varying indicator sequence, is the mean value of the charge imbalance index in the time-varying indicator sequence, is the value of the corresponding parameter in the water quality information at time t, is the mean of the corresponding parameter; After taking the absolute value of the correlation coefficient between the charge imbalance index and all parameters in the water quality information and calculating the mean, the stress coupling coefficient is constructed. The formula is: , is the stress coupling coefficient of the corresponding regional coordinate set, G is the total number of correlation coefficients in the corresponding regional coordinate set, g is the coordinate number in the corresponding regional coordinate set, K is the total number of parameters, k is the parameter number, is the correlation coefficient between the charge imbalance index at coordinate g in the corresponding regional coordinate set and the kth parameter B.

6. An intelligent water quality monitoring method according to claim 5, characterized in that: Determine the stress coupling coefficients in the hot spot area coordinate set and the non-hot spot area coordinate set respectively, and perform subtraction calculation on the stress coupling coefficients in the hot spot area coordinate set and the non-hot spot area coordinate set to obtain the impact difference value. If the impact difference value exceeds the preset difference threshold, the imbalance hot spot area is marked as a coupling area, otherwise it is not a coupling area. The coupling area is used to indicate that the water quality in the corresponding area is facing the risk of stress.

7. An intelligent water quality monitoring method according to claim 6, characterized in that: According to the determined coupling region, the correlation between the charge imbalance index and the corresponding parameters in the coupling region is verified, including: According to the sampling frequency, the maximum lag order is set. Based on the maximum lag order, the lag autoregressive term using only the corresponding parameter to predict the corresponding parameter and the lag autoregressive term adding the charge imbalance index are constructed respectively. The specific expression is: Only the lagged autoregressive terms of the corresponding parameters are used to predict the corresponding parameters: ; At the same time, the lagged autoregressive term of the charge imbalance index is added: ； Where P is the maximum lag order, p is the lag index, is the autoregression coefficient, is the first residual term, is the regression coefficient of the charge imbalance index at time tp, is the charge imbalance index at time tp, is the second residual term, is the corresponding parameter value at time tp; Calculate the residual sum of squares separately to obtain the residual sum of squares of the No. 1 residual term and the residual sum of squares of the No. 2 residual term; Based on the residual sum of squares of the first residual term and the residual sum of squares of the second residual term, the F statistic is constructed as follows: , F is the F statistic, is the residual sum of squares of the No. 1 residual term, is the residual sum of squares of the second residual term, is the sample size; If the F statistic exceeds The critical value of the distribution is rejected, and the null hypothesis that the charge imbalance index does not Granger cause the corresponding parameter is rejected, indicating that there is a causal relationship between the charge imbalance index and the corresponding parameter, and the charge imbalance index is in a causal position in the causal relationship. If the F statistic does not exceed The critical value of the distribution indicates that there is no causal relationship between the charge imbalance index and the corresponding parameter.

8. An intelligent water quality monitoring method according to claim 7, characterized in that: According to the maximum lag order, the correlation of different lag orders is verified to draw a curve chart of the F statistic changing with the lag order. According to the changing trend in the curve chart, the target F statistic is found from the curve chart; The parameters that have a causal relationship with the charge imbalance index are counted, a parameter set is generated, and for each parameter in the parameter set, a target F statistic related to it is found.

9. An intelligent water quality monitoring method according to claim 8, characterized in that: The steps to obtain the target F statistic include: Three different lag orders with adjacent relationships in the curve graph are used as comparison groups, and several comparison groups are obtained through statistics; The mean and standard deviation of the F statistics corresponding to the corresponding lag order in each comparison group are calculated, and the comparison group with the largest mean and the smallest standard deviation is selected as the target group; the F statistic with the smallest mean difference corresponding to the target group is used as the target F statistic, and the lag order corresponding to the target F statistic is used as the optimal time delay for the charge imbalance index to affect the corresponding parameter; According to the optimal time delay of the charge imbalance index on the corresponding parameters, the timestamp of the corresponding parameter abnormality that should trigger the early warning signal is obtained, and the corresponding governance measures are formulated within the time period of the optimal time delay.

10. An intelligent water quality monitoring system, used to implement an intelligent water quality monitoring method according to any one of claims 1 to 9, characterized in that: include: The information acquisition module is used to obtain water quality information and potential time series information at each location in the target water area, and obtain an information set after preprocessing; The area identification module is used to analyze the charge imbalance situation at each location in the target water area according to the information set to determine the imbalance hotspot area and the imbalance non-hotspot area, and analyze the correlation between the charge imbalance situation in the imbalance hotspot area and the imbalance non-hotspot area and each parameter in the water quality information, and determine the coupling area after comparison; The setting module is used to verify the correlation between the charge imbalance index and the corresponding parameters in the coupling region, obtain the parameter set, and set the timestamp for triggering the early warning signal for each parameter in the parameter set, so as to formulate corresponding governance measures.

Images