Reclaimed water reuse landscape water quality change monitoring method and system

Abstract

The invention belongs to the technical field of water quality monitoring, and discloses a reclaimed water reuse landscape water quality change monitoring method and system, and the method comprises the steps: obtaining original measurement data of a target index in a water body and a plurality of environment background parameters representing a water body environment background; analyzing and calculating a dynamic influence relationship between the original measurement data of the target index and each environment background parameter to obtain a state parameter representing a measurement deviation state of the target measurement device; determining a correction value corresponding to the current environment background and the measurement deviation state of the target measurement device by using a pre-established knowledge base according to the plurality of environment background parameters and the state parameters; correcting the original measurement data of the target index by using the correction value to obtain the corrected measurement data of the target index for evaluating the long-term influence of the reclaimed water on the aquatic ecological environment; an evaluation result is sent to a remote monitoring center; therefore, accurate measurement data can be provided, and reliable long-term influence evaluation is realized.

Description

Technical Field

[0001] This application relates to the technical field of water quality monitoring. Specifically, it relates to a method and system for monitoring the water quality changes of reclaimed water reused in landscapes. Background Art

[0002] As an important urban ecological infrastructure, the water quality of urban landscape water bodies is directly related to the urban environmental quality and the health of residents. In recent years, to alleviate the shortage of water resources, it has become a common practice to use reclaimed water as a supplementary water source for landscape water bodies. However, there may be trace amounts of specific substances in reclaimed water that have potential long-term cumulative or synergistic effects on the aquatic ecological environment, such as specific algal toxins or trace organic pollutants. Therefore, long-term and continuous water quality monitoring of landscape water bodies after reclaimed water reuse, especially for these indicators that are usually at low concentrations but sensitive to the aquatic ecosystem, is of great significance for scientifically evaluating the long-term cumulative impact of reclaimed water reuse on the aquatic ecological environment.

[0003] However, in the actual monitoring process, there are many technical challenges in the online and long-term measurement of these low-concentration and ecologically sensitive indicators. On the one hand, as an open system, the environmental background elements of landscape water bodies are extremely complex and change dynamically. There are a wide variety of background substances in the water body, such as natural or artificially introduced suspended solids, dissolved organic matter, and dissolved salts composed of different ions. Their concentrations and compositions are significantly affected by various factors such as seasons, climate, rainfall, and surrounding human activities, showing significant dynamic changes. These complex background substances may cause significant physical or chemical interference to the precision measurement devices used to measure the target indicators. For example, suspended solids may scatter or absorb light, affecting measurements based on optical principles; dissolved organic matter may cause spectral overlap or chemical reactions with the target substance; high salinity may affect the response characteristics of electrochemical sensors. The signals generated by these environmental background interferences are often much higher than the true signals of the target monitoring indicators and are prone to cross-interference with the target indicator signals, resulting in the original measurement data deviating seriously from the true concentration values of the target indicators. More challenging is that the interference effects of different environmental background parameters (such as turbidity, total dissolved organic matter, conductivity, temperature, pH, dissolved oxygen, etc.) on the target measurement device are not simply linear superpositions. There may be complex non-linear relationships and interactions between them, making it difficult to effectively eliminate interference through simple fixed compensation or linear models.

[0004] On the other hand, precision measurement devices used for online monitoring of these low-concentration indicators usually need to operate continuously outdoors in the water environment for a long time. Long-term exposure to the complex water environment will inevitably lead to the attenuation of the performance of the measurement device itself. For example, biofilm attachment, chemical adsorption or corrosion may occur on the surface of the sensor, resulting in a decrease in sensitivity, a slowdown in response speed or zero drift; the parameters of internal electronic components may also drift over time or with the environment, causing systematic deviations. This performance attenuation is cumulative, and its rate and degree may be closely related to environmental conditions such as water temperature, nutrient levels, flow velocity, etc. and the cumulative usage time of the device, showing complex dynamic change characteristics. This self-performance attenuation will cause systematic deviations in the measurement output signal or a decrease in measurement accuracy, and it is impossible to accurately reflect the true concentration level of the target indicator. Although traditional regular on-site manual calibration or maintenance can restore the sensor performance to a certain extent, it is costly, cumbersome to operate, and cannot respond to the continuously accumulating performance attenuation in real time, and it is even more impossible to provide continuous and reliable monitoring data during the interval between two calibrations.

[0005] In addition, the three factors of the change in the concentration of the target indicator caused by the introduction of reclaimed water, the complex and changeable background environmental interference, and the self-performance attenuation of the measurement device often act on the measurement process at the same time, resulting in the original monitoring data being the superposition result of the comprehensive influence of these factors. These effects are coupled and dynamically changing, making it extremely difficult to accurately distinguish the true concentration change of the target indicator caused by the introduction of reclaimed water from the original monitoring data, and effectively strip the measurement deviation caused by environmental interference and sensor self-attenuation. Even if the environmental background parameters and the state information of the measurement device can be obtained in real time, how to determine the correction amount that needs to be subtracted from the original measurement data in real time and accurately based on this information to effectively compensate for the complex and dynamically changing environmental interference and device self-attenuation is still a key technical problem to be solved urgently. Traditional compensation methods based on fixed correction factors, simple threshold judgments or offline calibration data are difficult to effectively cope with this complex monitoring scenario with multi-factor coupling and dynamic changes.

[0006] Therefore, in the specific application scenario where reclaimed water is reused for urban landscape water bodies and long-term monitoring of specific indicators with low concentration levels but significant impacts on the aquatic ecological environment is required to accurately evaluate the long-term impact of reclaimed water reuse on the aquatic ecological environment, there is an urgent need for a new water quality change monitoring method. This method should be able to, without interrupting online monitoring and without the need for frequent on-site manual calibration or maintenance, intelligently determine and apply dynamic correction amounts by real-time sensing of the water body environmental background and the state changes of the measuring device itself, and based on pre-established knowledge or rules, so as to effectively identify and remove the complex and dynamic impacts brought by environmental background interference and the performance decay of the measuring device itself, and finally reliably obtain the true concentration change data of the target indicators reflecting the independent effects of reclaimed water, providing reliable data support for accurately evaluating the long-term impact of reclaimed water reuse on the aquatic ecological environment.

[0007] In view of the above problems, the existing technologies need to be improved urgently. Summary of the Invention

[0008] The purpose of this application is to provide a monitoring method and system for water quality changes in reclaimed water reuse landscapes, which can provide accurate measurement data and achieve reliable long-term impact assessment.

[0009] In the first aspect, this application provides a monitoring method for water quality changes in reclaimed water reuse landscapes, which is used to monitor the long-term impact of reclaimed water on the aquatic ecological environment of urban landscape water bodies reused with reclaimed water. The steps of this method include:

[0010] S1. Through a target measuring device and multiple auxiliary measuring devices, obtain the original measurement data of the target indicators in the water body and multiple environmental background parameters characterizing the water body environmental background;

[0011] S2. Analyze and calculate the dynamic influence relationship between the original measurement data of the target indicators and each environmental background parameter to obtain a state parameter characterizing the measurement deviation state of the target measuring device;

[0012] S3. According to multiple environmental background parameters and the state parameter, use the pre-established knowledge base to determine the correction amount corresponding to the current environmental background and the measurement deviation state of the target measuring device; the knowledge base includes a pre-constructed multi-dimensional correction look-up table or a preset correction rule set;

[0013] S4. Use the correction amount to correct the original measurement data of the target indicators to obtain the corrected measurement data of the target indicators, which is used to evaluate the long-term impact of reclaimed water on the aquatic ecological environment;

[0014] S5. Send the evaluation result of the long-term impact of reclaimed water on the aquatic ecological environment to the remote monitoring center.

[0015] Preferably, step S1 includes:

[0016] S101. Collect the original measurement data of the target indicators in the water body at a preset frequency through the target measuring device; the target indicators are specific algal toxins or specific trace organic pollutants;

[0017] S102. Collect multiple environmental background parameters respectively through the multiple auxiliary measuring devices; the environmental background parameters include some or all of turbidity, total dissolved organic matter, conductivity, temperature, pH, dissolved oxygen;

[0018] S103. Perform time synchronization calibration and outlier processing on the original measurement data of the target indicators and the multiple environmental background parameters.

[0019] Preferably, step S2 includes:

[0020] S201. For the original measurement data of the target indicators, calculate its first derivative and second derivative to obtain the target indicator change rate and the target indicator change acceleration;

[0021] S202. For each environmental background parameter, calculate its Pearson correlation coefficient with the original measurement data of the target indicators, and screen out the environmental background parameters with the absolute value of the Pearson correlation coefficient greater than the preset correlation coefficient threshold as the key environmental background parameters;

[0022] S203. According to the target indicator change rate, the target indicator change acceleration and the key environmental background parameters, use the multiple linear regression algorithm to calculate the predicted value of the target indicators;

[0023] S204. Calculate the residual between the original measurement data of the target indicators and the predicted value, and calculate the mean and standard deviation of the residual;

[0024] S205. According to the mean and standard deviation of the residual, calculate the state parameters of the target measuring device; the state parameters include the residual mean offset and the residual standard deviation magnification factor.

[0025] Preferably, step S203 includes:

[0026] Construct an initial least squares regression model with the original measurement data of the target indicators as the dependent variable and the target indicator change rate, the target indicator change acceleration and each key environmental background parameter as the independent variables;

[0027] Solve the model parameters in the initial least squares regression model by minimizing the sum of squared residuals to obtain the final least squares regression model;

[0028] Substitute the target indicator change rate, the target indicator change acceleration and each key environmental background parameter into the final least squares regression model respectively to obtain the predicted value of the target indicators.

[0029] Preferably, step S205 includes:

[0030] Calculate the difference between the mean of the residuals and a preset reference mean as the initial residual mean offset;

[0031] Calculate the quotient of the standard deviation of the residuals and a preset reference standard deviation as the initial residual standard deviation magnification factor;

[0032] Measure or estimate the thickness of the biofilm on the surface of the target measuring device;

[0033] Calculate a correction term for the residual mean offset and a correction term for the residual standard deviation magnification factor based on the biofilm thickness;

[0034] Add the correction term for the residual mean offset to the initial residual mean offset to obtain the final residual mean offset; add the correction term for the residual standard deviation magnification factor to the initial residual standard deviation magnification factor to obtain the final residual standard deviation magnification factor.

[0035] Optionally, the knowledge base includes a pre-constructed multi-dimensional calibration lookup table;

[0036] Step S3 includes:

[0037] S301a. Compose a query vector using multiple environmental background parameters and status parameters at the current moment;

[0038] S302a. If there is a multi-dimensional index vector in the pre-constructed multi-dimensional calibration lookup table that is the same as the query vector, retrieve the corresponding calibration amount of the corresponding multi-dimensional index vector to obtain the calibration amount corresponding to the current environmental background and the measurement deviation state of the target measuring device;

[0039] S303a. If there is no multi-dimensional index vector in the pre-constructed multi-dimensional calibration lookup table that is the same as the query vector, determine multiple neighboring multi-dimensional index vectors according to the Euclidean distance between the query vector and each multi-dimensional index vector in the multi-dimensional calibration lookup table, and calculate the weighted average of the calibration amounts corresponding to each neighboring multi-dimensional index vector to obtain the calibration amount corresponding to the current environmental background and the measurement deviation state of the target measuring device.

[0040] Optionally, the knowledge base includes a preset calibration rule set;

[0041] Step S3 includes:

[0042] S301b. Extract the preset calibration rule set; the preset calibration rule set includes multiple calibration rules, and each calibration rule corresponds to a combination of one or more discretized environmental background parameter intervals and status parameter intervals, and the calibration amount confidence corresponding to this combination;

[0043] S302b. Map the multiple environmental background parameters and status parameters at the current moment to the corresponding discretized intervals in the preset correction rule set to obtain the current parameter interval combination;

[0044] S303b. If the preset correction rule set contains a correction rule that exactly matches the current parameter interval combination, calculate the correction amount corresponding to the current environmental background and the measurement deviation status of the target measuring device using the corresponding correction rule;

[0045] S304b. If the preset correction rule set does not contain a correction rule that exactly matches the current parameter interval combination, select multiple correction rules that partially match the current parameter interval combination according to the preset priority order, calculate the initial correction amounts respectively using the selected correction rules, and perform weighted average calculation on the initial correction amounts according to the confidence levels of the correction amounts of the selected correction rules to obtain the correction amount corresponding to the current environmental background and the measurement deviation status of the target measuring device.

[0046] Preferably, step S4 includes:

[0047] S401. Determine whether the original measurement data of the target index exceeds the preset range. If it exceeds, set the correction amount to zero;

[0048] S402. Subtract the correction amount from the original measurement data of the target index to obtain the corrected measurement data of the target index;

[0049] S403. Calculate the absolute value of the corrected measurement data of the target index. If the absolute value is less than the preset minimum value, set the corrected measurement data of the target index to the preset minimum value;

[0050] S404. According to the corrected measurement data of the target index, combined with the water quality evaluation standard, calculate the water quality index, and evaluate the long-term impact of reclaimed water on the aquatic ecological environment according to the water quality index.

[0051] Preferably, the water quality evaluation standard includes multiple evaluation indicators and the corresponding weight coefficients for each evaluation indicator;

[0052] Step S404 includes:

[0053] According to the water quality evaluation standard, screen out the key evaluation indicators related to the corrected measurement data of the target index, and calculate the comprehensive weight coefficient according to the weight coefficients of the key evaluation indicators;

[0054] Calculate the water quality index using the weighted index method according to the corrected measurement data of the target index and the comprehensive weight coefficient;

[0055] Compare the water quality index with the preset water quality grade classification standard to determine the water quality grade, and evaluate the long-term impact of reclaimed water on the aquatic ecological environment according to the water quality grade.

[0056] In a second aspect, the present application provides a monitoring system for the water quality change of reclaimed water for landscape use, which is used to monitor the long-term impact of reclaimed water on the aquatic ecological environment of the urban landscape water body reused with reclaimed water. The system includes a target measuring device, a plurality of auxiliary measuring devices, an edge computing device, and a remote monitoring center. The target measuring device, the auxiliary measuring devices, and the remote monitoring center are all communicatively connected to the edge computing device;

[0057] The target measuring device is used to measure the original measurement data of the target indicators in the water body and send it to the edge computing device;

[0058] The plurality of auxiliary measuring devices are used to measure a plurality of environmental background parameters characterizing the water body environment background and send them to the edge computing device;

[0059] The edge computing device is used to analyze and calculate the dynamic influence relationship between the original measurement data of the target indicators and each environmental background parameter to obtain a state parameter characterizing the measurement deviation state of the target measuring device; according to the plurality of environmental background parameters and the state parameter, using a pre-established knowledge base, determine a correction amount corresponding to the current environmental background and the measurement deviation state of the target measuring device; the knowledge base includes a pre-constructed multi-dimensional correction lookup table or a preset correction rule set; use the correction amount to correct the original measurement data of the target indicators to obtain the corrected target indicator measurement data for evaluating the long-term impact of reclaimed water on the aquatic ecological environment; send the evaluation result of the long-term impact of reclaimed water on the aquatic ecological environment to the remote monitoring center.

[0060] Beneficial effects: A monitoring method and system for the water quality change of reclaimed water for landscape use provided by the present application can reduce the influence of environmental interference and measurement deviation by dynamically correcting the original measurement data of the target indicators, can provide accurate measurement data, and realize reliable long-term impact evaluation. Description of the Drawings

[0061] Figure 1 It is a flowchart of the monitoring method for the water quality change of reclaimed water for landscape use provided by the embodiment of the present application.

[0062] Figure 2 It is a schematic structural diagram of the monitoring system for the water quality change of reclaimed water for landscape use provided by the embodiment of the present application.

[0063] Reference numeral description: 1, target measuring device; 2, auxiliary measuring device; 3, edge computing device; 4, remote monitoring center. Detailed Embodiments

[0064] The technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the present application usually described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application claimed, but only represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0065] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, terms such as "first" and "second" are only used for differential description and cannot be understood as indicating or implying relative importance.

[0066] Referring Figure 1 , the present application proposes a method for monitoring the water quality change of reclaimed water for landscape reuse, which is used to monitor the long-term impact of reclaimed water on the aquatic ecological environment of the urban landscape water body reused by reclaimed water. The steps of this method include:

[0067] S1. Through a target measuring device and a plurality of auxiliary measuring devices, obtain the original measurement data of the target index in the water body and a plurality of environmental background parameters characterizing the water body environmental background;

[0068] S2. Analyze and calculate the dynamic influence relationship between the original measurement data of the target index and each environmental background parameter to obtain a state parameter characterizing the measurement deviation state of the target measuring device;

[0069] S3. According to a plurality of environmental background parameters and the state parameter, use a pre-established knowledge base to determine a correction amount corresponding to the current environmental background and the measurement deviation state of the target measuring device; the knowledge base includes a pre-constructed multi-dimensional correction lookup table or a preset correction rule set;

[0070] S4. Use the correction amount to correct the original measurement data of the target index to obtain the corrected target index measurement data for evaluating the long-term impact of reclaimed water on the aquatic ecological environment;

[0071] S5. Send the evaluation result of the long-term impact of reclaimed water on the aquatic ecological environment to the remote monitoring center.

[0072] Among them, in step S1, a target measuring device can be used to collect the original measurement data of specific target indicators in the water body. For example, an on-line analyzer or a sensor can be used to measure the concentration of specific algal toxins or trace organic pollutants. At the same time, multiple auxiliary measuring devices can be used to collect multiple environmental background parameters characterizing the water body environment. For example, devices such as a turbidimeter, a total dissolved organic matter analyzer, a conductivity meter, a temperature sensor, a pH meter, and a dissolved oxygen sensor can be used to measure parameters such as the turbidity, total dissolved organic matter, conductivity, temperature, pH, and dissolved oxygen of the water body. These measuring devices can be deployed in the landscape water body to collect data continuously or periodically at a preset frequency.

[0073] Among them, in step S2, a data processing algorithm can be used to analyze the dynamic influence relationship between the original measurement data of the target indicator and each environmental background parameter. For example, statistical analysis, regression analysis, or machine learning methods can be used to model how the original measurement data changes with the change of the environmental background parameter. Based on this analysis, a state parameter characterizing the current measurement deviation state of the target measuring device can be calculated. These state parameters can quantify the influence degree of environmental background interference and the performance attenuation of the device itself on the measurement result. For example, it can be expressed as a systematic offset of the measurement value or an increase in random error. This step utilizes the original data and environmental parameters obtained in S1, reveals the laws of interference and attenuation through data analysis, and provides a quantitative basis for subsequent correction.

[0074] Among them, in step S3, the knowledge base can be a multi-dimensional correction lookup table, which stores the correction amounts corresponding to different combinations of environmental background parameters and device state parameters. It can also be a preset correction rule set, which contains rules for calculating or looking up correction amounts according to environmental parameters and state parameters. According to the current environmental background parameters obtained in S1 and the state parameters calculated in S2, querying or applying the knowledge base can determine a correction amount matching the current actual situation. This step converts the information in S1 and S2 into specific correction actions, realizing dynamic correction based on real-time perception.

[0075] Among them, in step S4, a mathematical operation can be used to apply the correction amount determined in S3 to the original measurement data of the target indicator obtained in S1. Usually, the correction amount is subtracted from the original measurement data to obtain the corrected measurement data of the target indicator. These corrected data are considered to be closer to the true concentration value of the target indicator, stripping off the errors caused by environmental interference and device attenuation. These corrected data are subsequently used to evaluate the long-term impact of reclaimed water on the aquatic ecological environment. For example, they can be compared with water quality evaluation standards to calculate the water quality index. This step is one of the core purposes of the method, improving the accuracy of monitoring results through data correction.

[0076] Among them, in step S5, specifically, a communication module can be used to send the evaluation result of the long-term impact of the reclaimed water obtained in S4 on the aquatic ecological environment to a remote monitoring center. The remote monitoring center can be a server, a cloud platform or a user terminal, which is used to receive, store, display and analyze monitoring data and evaluation results. This enables users to remotely and real-time understand the water quality status of the landscape water body and the impact of reclaimed water reuse, facilitating remote management and decision-making.

[0077] Specifically, this method obtains the original data of the target indicators and environmental background parameters through S1, directly facing the problem of inaccurate data caused by complex environmental background interference and device performance attenuation. Through S2, by analyzing the dynamic relationship between the original data and environmental parameters, the impacts of these interferences and attenuations are quantified, and the state parameters characterizing the deviation state of the device are obtained, which solves the technical problem of difficult to distinguish the real change from the interference superposition. Using the information of S1 and S2 and combining with the preset knowledge base, S3 intelligently determines the dynamic correction amount, overcoming the defect that the traditional fixed compensation method cannot cope with the dynamic complex scenario. S4 applies the determined correction amount to the original data to obtain more accurate corrected data, providing a basis for reliably evaluating the impact of reclaimed water. S5 sends the evaluation result to the remote center, realizing remote monitoring and management. The whole process forms a closed loop, from data acquisition, deviation analysis, dynamic correction to result evaluation and transmission, effectively improving the accuracy and reliability of the water quality monitoring of reclaimed water reused in landscape water bodies. Especially in monitoring low-concentration and ecologically sensitive indicators, it can more reliably evaluate the long-term impact of reclaimed water on the aquatic ecological environment, solving the problems of unreliable original data, difficult to cope with complex dynamic environments and device attenuation mentioned in the background technology.

[0078] As a preferred embodiment, the solution of the present application is specifically implemented as follows:

[0079] In a reclaimed water reused urban landscape water body, a set of water quality monitoring system is deployed. The target monitoring indicator is set as a specific algal toxin, such as microcystin-LR. The target measuring device uses an online microcystin analyzer, which is based on the principle of enzyme-linked immunosorbent assay and can measure the concentration of microcystin-LR in the water body in real time and output the original measurement data. At the same time, multiple auxiliary measuring devices are deployed, including a turbidity sensor, a total dissolved organic matter sensor, a conductivity sensor, a temperature sensor, a pH sensor and a dissolved oxygen sensor, which are used to measure environmental background parameters such as the turbidity, total dissolved organic matter, conductivity, temperature, pH and dissolved oxygen of the water body in real time. All measuring devices collect data at a frequency of once per hour and send the original measurement data and environmental background parameters to an edge computing device.

[0080] After receiving the data, the edge computing device executes step S2. It analyzes the dynamic change relationship between the original measurement data of microcystin-LR and various environmental background parameters (such as turbidity, total dissolved organic matter, temperature) over a period of history. For example, by establishing a regression model, it analyzes the deviation of the original measurement value of microcystin-LR from its expected value under different conditions of turbidity, total dissolved organic matter, and temperature. Based on this analysis, state parameters characterizing the measurement deviation state of the on-line analyzer are calculated. For example, one parameter represents the systematic offset of the current measurement value relative to the reference value, and another parameter represents the magnification factor of the fluctuation (noise level) of the measurement value relative to the reference level.

[0081] Next, the edge computing device executes step S3. A multi-dimensional calibration lookup table is pre-stored in the device. The index dimensions of this lookup table include turbidity, total dissolved organic matter, temperature, and the state parameters (offset and magnification factor) calculated in S2. The value of the lookup table is the corresponding calibration amount. The edge computing device uses the turbidity, total dissolved organic matter, temperature values collected at the current moment and the calculated state parameters to form a query vector, and looks up or calculates by interpolation in the lookup table to obtain the calibration amount corresponding to the current environmental background and device deviation state. For example, when the turbidity is high, the total dissolved organic matter is high, and the device state parameters show a positive offset, the lookup table will return a large negative calibration amount.

[0082] Then, the edge computing device executes step S4. The calibration amount determined in S3 is subtracted from the original measurement data of microcystin-LR collected at the current moment to obtain the calibrated measurement data of microcystin-LR. Using the calibrated data and combining with the preset water quality evaluation criteria (for example, stipulating the concentration threshold of microcystin-LR), the impact of reclaimed water on the aquatic ecological environment is evaluated. For example, it is judged whether the current water quality meets the ecological safety requirements.

[0083] Finally, the edge computing device executes step S5. The evaluation results are sent to the remote monitoring center through the network. The remote monitoring center receives and displays this information for managers to view and make decisions remotely.

[0084] Through the above technical solutions, this application solves the technical problem of accurately monitoring the water quality changes of specific low-concentration and ecologically sensitive indicators in the reclaimed water reused landscape water body in a complex dynamic environment. By dynamically calibrating the original measurement data, the measurement deviation caused by the interference of the complex dynamic environmental background and the performance attenuation of the measurement device itself is effectively stripped, so that the long-term impact of reclaimed water on the aquatic ecological environment can be reliably evaluated.

[0085] In some embodiments, step S1 includes:

[0086] S101. Collect the original measurement data of the target indicators in the water body at a preset frequency through the target measurement device; the target indicators are specific algal toxins or specific trace organic pollutants;

[0087] S102. Collect multiple environmental background parameters through the multiple auxiliary measurement devices respectively; the environmental background parameters include some or all of turbidity, total dissolved organic matter, conductivity, temperature, pH, dissolved oxygen;

[0088] S103. Perform time synchronization calibration and outlier processing on the original measurement data of the target indicators and the multiple environmental background parameters.

[0089] Among them, in step S101, the target measurement device refers to a precision measurement device for on-line monitoring of specific low-concentration and ecologically sensitive substances in the water body, and can be specifically implemented by an on-line spectral analyzer, an on-line fluorescence sensor, an on-line immunosensor, a miniaturized chromatography-mass spectrometry instrument, etc. The target indicators refer to specific substances that have potential long-term impacts on the aquatic ecological environment, specifically specific algal toxins or specific trace organic pollutants. Specific algal toxins such as microcystin, etc. Specific trace organic pollutants such as certain pesticides, drug residues, endocrine disruptors, etc. The preset frequency can be set according to monitoring requirements, such as every hour, every minute or a shorter time interval. Thus, the continuity and timeliness of the target indicator data are ensured.

[0090] Among them, in step S102, the multiple auxiliary measurement devices refer to standard water quality sensors for measuring environmental factors that affect the performance of the target measurement device in the water body, and can be specifically implemented by a turbidity meter, a dissolved organic matter sensor (such as measuring dissolved organic carbon or ultraviolet absorbance), a conductivity meter, a temperature sensor, a pH meter, a dissolved oxygen sensor, etc. The environmental background parameters are the key factors affecting the measurement accuracy of the target measurement device. By collecting these parameters, it provides a data basis for subsequent analysis of the impact of the environmental background on the target indicator measurement, and generally collects data at the same preset frequency as the target measurement device.

[0091] Among them, in step S103, time synchronization calibration and outlier processing are performed on the original measurement data of the target index and multiple environmental background parameters. Time synchronization calibration means aligning the data from different measurement devices according to timestamps to ensure that the data at the same moment or close moments can correspond. Specifically, methods such as timestamp-based matching, interpolation, or nearest neighbor alignment can be used to achieve this. Outlier processing means identifying and processing the error or interference data points existing in the original measurement data. Specifically, statistical methods (such as detection based on standard deviation or interquartile range), threshold-based judgment, or machine learning methods can be used to identify outliers, and the outliers can be removed, replaced (such as using the mean, median, or interpolation), or marked, etc. Thereby, the quality and reliability of the original data are improved.

[0092] Through the above technical solution, the present application clarifies the specific acquisition methods, acquisition frequencies, and acquisition objects of the target index and environmental background parameters in the water body, and performs time synchronization calibration and outlier processing on the collected original data. Thereby, the quality and reliability of the original data are improved, providing a basis for accurately analyzing the influence of the environmental background on the measurement of the target index and for effective correction in the subsequent process, and further supporting the evaluation of the long-term impact of reclaimed water reuse on the aquatic ecological environment.

[0093] In some embodiments, step S2 includes:

[0094] S201. For the original measurement data of the target index, calculate its first derivative and second derivative to obtain the target index change rate and the target index change acceleration;

[0095] S202. For each environmental background parameter, calculate the Pearson correlation coefficient between it and the original measurement data of the target index, and screen out the environmental background parameters whose absolute value of the Pearson correlation coefficient is greater than the preset correlation coefficient threshold as the key environmental background parameters;

[0096] S203. According to the target index change rate, the target index change acceleration, and the key environmental background parameters, use the multiple linear regression algorithm to calculate the predicted value of the target index;

[0097] S204. Calculate the residual between the original measurement data of the target index and the predicted value, and calculate the mean and standard deviation of the residual;

[0098] S205. According to the mean and standard deviation of the residual, calculate the state parameters of the target measurement device; the state parameters include the residual mean offset and the residual standard deviation magnification factor.

[0099] Among them, in step S201, the difference method can be used to calculate the first derivative and the second derivative. For example, for time series data, the first derivative can be approximately calculated by dividing the data difference between adjacent time points by the time interval, and the second derivative can be approximately calculated by dividing the difference of the first derivatives between adjacent time points by the time interval.

[0100] Among them, in step S202, the Pearson correlation coefficient between each environmental background parameter and the original measurement data of the target index is calculated. Specifically, it can be implemented using the standard Pearson correlation coefficient calculation formula, which measures the linear correlation degree between two variables. The environmental background parameters with the absolute value of the Pearson correlation coefficient greater than the preset correlation coefficient threshold are selected as the key environmental background parameters. The preset correlation coefficient threshold can be determined based on historical data analysis or expert experience, such as set to 0.5 or 0.6.

[0101] Among them, in step S203, a multiple linear regression model can be constructed. The original measurement data of the target index is used as the dependent variable, and the change rate of the target index, the change acceleration of the target index, and each key environmental background parameter are used as independent variables. The model coefficients are solved by methods such as the least squares method, and then the independent variable values at the current moment are substituted into the model to calculate the predicted value.

[0102] Among them, in step S204, it can be implemented using standard statistical methods. The residual is the original measurement data minus the predicted value. The mean residual is the arithmetic mean of the residual sequence, and the standard deviation of the residual is the standard deviation of the residual sequence. The mean and standard deviation of the residual can be calculated within a sliding time window. For example, the data of the past 24 hours is selected for calculation.

[0103] Among them, in step S205, it can be implemented using a preset calculation rule. For example, the mean residual offset can be calculated as the difference between the current mean residual and the preset reference mean (such as the zero point determined by device calibration or data during the stable period), and the magnification factor of the standard deviation of the residual can be calculated as the quotient of the current standard deviation of the residual and the preset reference standard deviation (such as the standard deviation determined by device calibration or data during the stable period). These state parameters quantify the systematic deviation and random volatility of the measuring device.

[0104] By calculating the dynamic change characteristics (change rate, change acceleration) of the target indicator itself, identifying the environmental factors (key environmental background parameters) significantly related to the change of the target indicator measurement value, and constructing a model based on this information to predict the "normal" state value of the target indicator, this solution can effectively separate the effects of environmental interference and device deviation on the original measurement data. The residual between the original measurement data and the predicted value mainly reflects the deviation caused by environmental interference and device deviation that the model fails to explain. Statistical analysis of the residuals (calculating the mean and standard deviation) and converting these statistical characteristics into quantified state parameters (residual mean offset and residual standard deviation magnification factor) can accurately characterize the current deviation state of the measuring device. These quantified state parameters, together with the environmental background parameters, can provide direct and effective information input for subsequent determination of the correction amount and correction of the original measurement data, thereby improving the accuracy of the monitoring data and solving the problem in the prior art that it is difficult to accurately quantify the measurement deviation.

[0105] Through the above technical solution, this application can specifically and effectively analyze and extract the state parameters from the complex and variable original measurement data and environmental background parameters that can accurately characterize the measurement deviation state of the target measuring device, solve the problem in the prior art that it is difficult to accurately quantify the measurement deviation, and provide key input for subsequent precise correction.

[0106] Preferably, step S203 may include:

[0107] Construct an initial least squares regression model with the original measurement data of the target indicator as the dependent variable and the target indicator change rate, target indicator change acceleration, and each key environmental background parameter as the independent variables;

[0108] Solve the model parameters in the initial least squares regression model by minimizing the sum of squared residuals to obtain the final least squares regression model;

[0109] Substitute the target indicator change rate, target indicator change acceleration, and each key environmental background parameter into the final least squares regression model respectively to obtain the predicted value of the target indicator.

[0110] Among them, the initial least squares regression model can be implemented in the form of a linear equation. For example, Y = β0 + β1*X1 + β2*X2 +... + βn*Xn + ε, where Y represents the dependent variable (the original measurement data of the target indicator), X1 to Xn represent the independent variables (the target indicator change rate, target indicator change acceleration, and each key environmental background parameter), β0 is the intercept term, β1 to βn are the weight coefficients of each independent variable, and ε is the error term.

[0111] Among them, solving for the model parameters (such as β0 to βn and ε) in the initial least squares regression model by minimizing the sum of squared residuals means using the least squares method as the method for estimating model parameters. Specifically, it can be achieved based on historical monitoring data (including historical original measurement data and various historical independent variables) by solving the normal equations or using optimization algorithms such as gradient descent. The goal of the least squares method is to find a set of model parameters that minimize the sum of the squares of the differences (i.e., residuals) between the model prediction values and the actual original measurement data. By minimizing the sum of squared residuals, optimal model fitting parameters can be obtained, and these parameters reflect the degree of linear influence of each independent variable on the dependent variable.

[0112] Among them, the rate of change of the target indicator, the acceleration of change of the target indicator, and each key environmental background parameter are respectively substituted into the final least squares regression model, so as to calculate the predicted value of the target indicator at the current moment. This predicted value is the output of the model based on the relationship between the independent variable and the dependent variable learned from historical data.

[0113] Through the above technical solution, the present application provides a method for specifically constructing and solving a multiple linear regression model for calculating the predicted value of the target indicator, thereby improving the prediction accuracy.

[0114] Preferably, step S205 includes:

[0115] Calculating the difference between the mean of the residuals and a preset reference mean as the initial residual mean offset;

[0116] Calculating the quotient of the standard deviation of the residuals and a preset reference standard deviation as the initial residual standard deviation magnification factor;

[0117] Measuring or estimating the thickness of the biofilm on the surface of the target measuring device;

[0118] Calculating a correction term for the residual mean offset and a correction term for the residual standard deviation magnification factor according to the biofilm thickness;

[0119] Adding the correction term for the residual mean offset to the initial residual mean offset to obtain the final residual mean offset; adding the correction term for the residual standard deviation magnification factor to the initial residual standard deviation magnification factor to obtain the final residual standard deviation magnification factor.

[0120] Among them, by statistically analyzing the residuals between the original measurement data of the target indicator and the predicted value over a period of time, calculating their average value, and comparing this average value with a pre-set reference value representing the residual mean in the ideal state, the difference is the parameter for preliminarily quantifying the systematic deviation of the measuring device. The preset reference mean can be the residual mean obtained by statistically analyzing a large amount of experimental data in the ideal state when the device is brand new or just calibrated, and is usually close to zero.

[0121] Among them, by statistically analyzing the dispersion degree of residuals over a period of time, calculating its standard deviation, and comparing this standard deviation with a pre-set reference value representing the standard deviation of residuals in an ideal state, the quotient is the parameter for preliminarily quantifying the change in the random deviation or noise level of the measuring device. The pre-set reference standard deviation can be the standard deviation of residuals obtained through statistical analysis of a large amount of experimental data in the ideal state when the device is brand new or just calibrated.

[0122] Among them, the thickness of the biofilm on the surface of the target measuring device is a key piece of information reflecting the attenuation state of the measuring device itself. The biofilm thickness can be obtained in various ways. For example, an optical sensor can be used to directly measure the thickness of the biofilm attached to the sensor surface; a pressure sensor can be used to measure the change in pressure loss when the fluid passes through the sensor surface to indirectly estimate the biofilm thickness; or it can be estimated based on water environment parameters (such as temperature, nutrient concentration, flow rate) and the cumulative operation time of the device, using a pre-established estimation model (which can be a mathematical function or a deep learning model).

[0123] Among them, calculating the correction term for the mean offset of residuals and the magnification factor for the standard deviation of residuals based on the biofilm thickness can be achieved by referring to a pre-established relationship table between the biofilm thickness and the deviation correction amount, or by calculating through a mathematical model (such as a linear model, a polynomial model, or a more complex non-linear model), which takes the biofilm thickness as the input and outputs the corresponding correction term for the mean offset of residuals and the magnification factor for the standard deviation of residuals. These relationships or models can be established through controlled experiments under laboratory conditions, or trained and optimized through data analysis and machine learning methods during actual operation.

[0124] Among them, adding the correction term for the mean offset of residuals to the initial mean offset of residuals to obtain the final mean offset of residuals, and the addition can be simple addition.

[0125] Among them, adding the magnification factor for the standard deviation of residuals to the initial magnification factor for the standard deviation of residuals to obtain the final magnification factor for the standard deviation of residuals, and the addition can be simple addition.

[0126] When calculating the state parameters of the target measurement device, this solution not only considers the environmental background interference and random fluctuations reflected by the residuals between the original measurement data of the target indicators and the predicted values, but also further introduces the key factor of the self-attenuation of the target measurement device - the biofilm thickness. By measuring or estimating the biofilm thickness and correcting the initial state parameters (residual mean offset and residual standard deviation magnification factor) calculated based on the residuals according to the biofilm thickness, the final state parameters that can more comprehensively reflect the measurement deviation state are obtained. This method decouples and synthesizes the influences of environmental background factors (indirectly reflected by comparing residuals with predicted values) and device self-attenuation factors (directly reflected by biofilm thickness), enabling the calculated state parameters to more accurately distinguish and quantify measurement deviations from different sources. Thus, this solution can more precisely characterize the true measurement deviation state of the target measurement device under complex water environment and long-term operation conditions, providing a more reliable input for determining the correction amount based on environmental background parameters and state parameters subsequently, thereby improving the accuracy of correction and ultimately enhancing the reliability of the assessment of the long-term impact of reclaimed water reuse on the aquatic ecological environment.

[0127] Through the above technical solution, this application solves the problem in the prior art that only relying on residuals to calculate state parameters cannot fully reflect the deviation caused by the self-attenuation of the device. By introducing the measurement or estimation of the biofilm thickness and correcting the initial state parameters calculated based on the residuals according to the biofilm thickness, more comprehensive and accurate state parameters are obtained, which can better characterize the measurement deviation state of the target measurement device and provide a more reliable basis for subsequent correction.

[0128] In some embodiments, the knowledge base includes a pre-constructed multi-dimensional correction lookup table;

[0129] Step S3 includes:

[0130] S301a. Compose a query vector with multiple environmental background parameters and state parameters at the current moment;

[0131] S302a. If there is a multi-dimensional index vector in the pre-constructed multi-dimensional correction lookup table that is the same as the query vector, retrieve the correction amount corresponding to the corresponding multi-dimensional index vector to obtain the correction amount corresponding to the current environmental background and the measurement deviation state of the target measurement device;

[0132] S303a. If there is no multi-dimensional index vector in the pre-constructed multi-dimensional correction lookup table that is the same as the query vector, determine multiple neighboring multi-dimensional index vectors according to the Euclidean distance between the query vector and each multi-dimensional index vector of the multi-dimensional correction lookup table, and calculate the weighted average of the correction amounts corresponding to each neighboring multi-dimensional index vector to obtain the correction amount corresponding to the current environmental background and the measurement deviation state of the target measurement device.

[0133] Among them, this method uses a pre-constructed multi-dimensional calibration lookup table as the knowledge base. The lookup table contains multiple multi-dimensional index vectors, each index vector corresponding to a specific combination of environmental background parameters and state parameters, and storing the calibration amount related to this combination.

[0134] Among them, step S301a constructs a query vector, which is composed of the currently monitored environmental background parameter values and the calculated state parameter values. This query vector is the input for matching or searching in the lookup table.

[0135] Among them, step S302a performs an exact matching search. The system checks whether there is a multi-dimensional index vector in the lookup table that is exactly the same as the current query vector. If an exactly matching index is found, the calibration amount corresponding to this index is directly extracted. Thus, when the environment and sensor state are consistent with the typical situations recorded in the lookup table, the directly corresponding calibration amount can be obtained.

[0136] Among them, step S303a processes the non-exact matching situation. When there is no index in the lookup table that exactly matches the query vector, the system calculates the Euclidean distances between the current query vector and all the multi-dimensional index vectors in the lookup table. Based on these distances, multiple neighboring multi-dimensional index vectors that are closest to the query vector are determined (for example, all multi-dimensional index vectors with Euclidean distances less than a preset distance threshold are determined as neighboring multi-dimensional index vectors, or the N multi-dimensional index vectors with the smallest Euclidean distances are determined as neighboring multi-dimensional index vectors, where N is a preset quantity). Then, a weighted average calculation is performed on the calibration amounts corresponding to these neighboring vectors. The weights can be determined according to the Euclidean distances. For example, the closer the neighboring vector is in terms of Euclidean distance, the greater its weight. Through this weighted average, even if the current environment and sensor state are not exactly recorded in the lookup table, an interpolation estimate can be made based on the calibration amounts of neighboring known states to obtain the calibration amount applicable to the current state. This improves the applicability of the calibration method to the actual continuously varying parameter space.

[0137] By combining direct search and weighted average based on neighboring vectors, this method can handle the continuous changes in the environmental background and the state of the measuring device during actual monitoring, and obtain the calibration amount applicable to the current actual situation from the discrete lookup table. Thus, the influence of environmental interference and measuring device deviation on the original measurement data can be effectively compensated, and the accuracy of the measured data of the target index after calibration can be improved, so as to more reliably evaluate the long-term impact of reclaimed water on the aquatic ecological environment.

[0138] In some other embodiments, the knowledge base includes a preset calibration rule set;

[0139] Step S3 includes:

[0140] S301b. Extract the preset calibration rule set; the preset calibration rule set includes multiple calibration rules, and each calibration rule corresponds to a combination of one or more discretized environmental background parameter intervals and state parameter intervals, as well as the calibration quantity confidence corresponding to this combination;

[0141] S302b. Map the multiple environmental background parameters and state parameters at the current moment to the corresponding discretized intervals in the preset calibration rule set to obtain the current parameter interval combination;

[0142] S303b. If the preset calibration rule set includes a calibration rule that exactly matches the current parameter interval combination, then calculate the calibration quantity corresponding to the current environmental background and the measurement deviation state of the target measuring device using the corresponding calibration rule;

[0143] S304b. If the preset calibration rule set does not include a calibration rule that exactly matches the current parameter interval combination, then select multiple calibration rules that partially match the current parameter interval combination according to the preset priority order, calculate the initial calibration quantities respectively using the selected calibration rules, and perform weighted average calculation on the initial calibration quantities according to the calibration quantity confidence of the selected calibration rules to obtain the calibration quantity corresponding to the current environmental background and the measurement deviation state of the target measuring device.

[0144] Among them, in the preset calibration rule set, each rule is a condition-result pair. The condition is the parameter interval combination, and the result is the calibration quantity (or calculation method) and the confidence. The discretization of parameters can divide continuous parameter values into a finite number of intervals. For example, the temperature can be divided into intervals such as "low temperature", "normal temperature", "high temperature", etc. Specifically, methods such as equal-distance division, equal-frequency division, or division based on expert knowledge can be used to achieve this. The calibration quantity confidence is a value that measures the reliability of the calibration quantity determined by this rule. Specifically, it can be represented by a floating point number between 0 and 1. The larger the value, the more reliable it is.

[0145] Among them, in S302b, mapping the multiple environmental background parameters and state parameters at the current moment to the corresponding discretized intervals in the preset calibration rule set to obtain the current parameter interval combination means comparing the real-time obtained environmental background parameter values and state parameter values with the discretized intervals defined in the rule set, determining the interval to which each parameter belongs, and combining these intervals to form a discretized description representing the current state. Specifically, a lookup table or conditional judgment statement can be used to implement the mapping of parameter values to intervals.

[0146] Among them, in S303b, check whether there is a rule in the rule set whose parameter interval combination is exactly the same as the current parameter interval combination. If found, directly apply the calibration quantity or calibration quantity calculation method provided by this rule to determine the final calibration quantity. Specifically, methods such as hash lookup or index lookup can be used to quickly locate the exactly matching rule.

[0147] Among them, in S304b, when there is no completely matching rule, the system will look for a rule that is partially consistent with the current parameter interval combination. A partial match can be defined as a rule that matches a subset of parameters. For example, if the current combination includes the intervals of parameters A, B, and C, a partially matching rule may only include the intervals of parameters A and B. The preset priority order can be based on the importance of the parameters, the degree of matching, or other predefined criteria, and can be specifically implemented using a rule sorting list or a scoring mechanism. For example, a priority level value can be assigned to each parameter in advance. From the parameter interval combinations in the preset calibration rule set, extract the parameter interval combinations that match the intervals of at least one parameter in the current parameter interval combination as alternative parameter interval combinations. Calculate the sum of the priority level values of the parameters that match the current parameter interval combination in each alternative parameter interval combination, and then select the calibration rule corresponding to the alternative parameter interval combination whose sum of priority level values exceeds the preset threshold for subsequent calibration amount calculation.

[0148] After selecting the partially matching rules, use the calibration amounts or calculation methods provided by these rules to obtain multiple initial calibration amounts respectively. Then, use the confidence levels of the calibration amounts associated with these rules as weights to perform a weighted average on these initial calibration amounts to obtain the final calibration amount, which can be specifically implemented using a weighted average formula. This processing method enables the calibration process to make full use of this multi-dimensional real-time information. Even when the rule set does not completely cover all possible states, it can estimate the calibration amount by integrating the information of relevant rules, improving the accuracy of calibration and the adaptability to complex and changing environments.

[0149] Specifically, the present application provides a specific method for determining the correction amount based on a preset correction rule set, which is an implementation method when the knowledge base adopts the rule set. This method first extracts the preset correction rule set, which associates the discretized environmental background parameter intervals and state parameter intervals combinations with the correction amount confidence, laying a foundation for subsequent processing. Then, the real-time environmental background parameters and state parameters obtained at the current moment are mapped to the discretized intervals defined in the rule set to form the current parameter intervals combination. Next, it is checked whether there is a correction rule in the rule set that exactly matches the current parameter intervals combination. If there is an exactly matching rule, the rule is directly applied to calculate the correction amount to ensure fast and accurate correction in known scenarios. If there is no exactly matching rule, multiple correction rules that partially match the current parameter intervals combination are selected in the preset priority order. For each selected partially matching rule, an initial correction amount is calculated, and these initial correction amounts are weighted and averaged according to the correction amount confidence of the rule to finally obtain the correction amount. This way of handling incomplete matching situations enables the system to comprehensively utilize the information of multiple relevant rules, and through confidence weighting, makes the more reliable rules have a greater impact on the final result. Thus, even when the rule set does not fully cover all complex and changeable monitoring scenarios, a reasonable correction amount estimate can still be provided. Compared with the problem that constructing a comprehensive lookup table may require a large amount of data and there are limitations in lookup interpolation, the rule set-based method can more flexibly handle complex and changeable monitoring environments and sensor states through logical rules and partial matching processing, improving the accuracy and robustness of the correction.

[0150] Through the above technical solution, the present application provides a method for determining the correction amount based on a preset correction rule set. By discretizing multi-dimensional environmental background parameters and state parameters and constructing a rule set, it realizes the logical description of complex parameter relationships. By finding exactly matching or partially matching rules and using the confidence of the rules for weighted averaging, a reasonable correction amount can still be determined when not all possible states are fully covered. This overcomes the deficiencies of traditional lookup table methods in terms of data volume requirements and interpolation limitations, improves the flexibility and robustness of the correction process, and enables more accurate estimation and compensation of measurement deviations caused by environmental interference and device self-attenuation in complex and changeable monitoring scenarios.

[0151] In some embodiments, step S4 includes:

[0152] S401. Determine whether the original measurement data of the target index exceeds the preset range. If it exceeds, set the correction amount to zero;

[0153] S402. Subtract the correction amount from the original measurement data of the target index to obtain the corrected target index measurement data;

[0154] S403. Calculate the absolute value of the corrected target index measurement data. If the absolute value is less than the preset minimum value, set the corrected target index measurement data to the preset minimum value.

[0155] S404. According to the corrected target index measurement data, combined with the water quality evaluation standard, calculate the water quality index, and evaluate the long-term impact of reclaimed water on the aquatic ecological environment based on the water quality index.

[0156] Among them, in step S401, the preset range can be determined according to the physical characteristics of the target index, the design specifications of the measuring device, and the requirements of the actual application scenario. The preset range can be set from 0 to a certain upper limit value, and this upper limit value is much higher than the expected maximum value within the normal monitoring range. If the original measurement data is higher than this upper limit or lower than 0 (for concentration indicators that are usually non-negative), the data is considered abnormal. If the original measurement data exceeds this preset range, the correction amount used for correction is set to zero, avoiding applying a correction model that may be trained or determined based on normal data in the case where the original data itself is extremely abnormal, thus introducing new errors or amplifying the abnormality of the original data.

[0157] Among them, in step S402, by subtracting this correction amount from the original measurement data, the aim is to strip the influence of these interferences and biases, making the corrected data closer to the true concentration value of the target index.

[0158] Among them, in step S403, the preset minimum value can be set according to the detection limit of the target index, the environmental background value, or the ecological significance. For example, for a certain trace organic pollutant, its preset minimum value can be set to its method detection limit or a minimum threshold with ecological significance. The corrected data should theoretically be non-negative (such as concentration), but in actual calculations, due to reasons such as fluctuations in the original data and estimation errors in the correction amount, the corrected result may be negative or a very small positive value close to zero. Forcing the data with an absolute value less than the preset minimum value to be set to the preset minimum value solves the problem of non-physical negative values or values lower than the actual measurable / meaningful threshold in the corrected data, ensuring the physical rationality of the data and the effectiveness of subsequent evaluations.

[0159] Among them, in step S404, the water quality evaluation criteria may include thresholds for specific indicators, grade divisions, or combined evaluation rules with other indicators. The water quality index is a method that converts single or multiple water quality parameters into a comprehensive value or grade, facilitating the understanding and comparison of water quality conditions. For example, according to the corrected target indicator concentration, by referring to the national or local water quality standards, the water quality grade to which the indicator belongs can be determined. Or, this indicator can be combined with other relevant indicators (such as dissolved oxygen, pH, etc.), and a comprehensive water quality index can be calculated through weighted average or other algorithms. Based on the calculated water quality index or the determined water quality grade, the long-term impact of reclaimed water reuse on the aquatic ecological environment can be evaluated, such as judging whether there are cumulative risks or whether the ecological protection requirements are met. This step utilizes the more accurate and reliable corrected data after the aforementioned processing, making the evaluation results based on this data more scientific and persuasive. The processing of steps S401 to S403 ensures that the data input to S404 has been processed for outliers and non-physical values, thereby improving the reliability of the final evaluation results.

[0160] Specifically, this solution provides a specific correction and evaluation process by introducing S401 to S404. Step S401 processes extreme outliers by determining whether the original measurement data exceeds the preset range, avoiding correction when the data itself is already untrustworthy. Step S402 performs the core correction operation, subtracting the correction amount. Step S403 processes the possible negative values or extremely low values in the corrected data to ensure the physical meaning of the data. Step S404 clarifies how to use the corrected data in combination with the water quality evaluation criteria for the final evaluation. These steps cooperate with each other to form a complete and robust data post-processing and evaluation process. This solution adds processing links for outliers and non-physical values, improving the accuracy and rationality of the correction results. At the same time, it clarifies the method for water quality evaluation based on the corrected data, making the evaluation process more standardized and reliable. This refinement and improvement of step S4 enable the entire monitoring method to provide more reliable data support and evaluation results when dealing with complex environmental interferences and device deviations and finally evaluating the long-term impact of reclaimed water.

[0161] Through the above technical solution, this application effectively processes the possible extreme outliers and non-physical values by adding the judgment of the range of the original measurement data and the limitation of the minimum value of the corrected data during the correction process, improving the accuracy and rationality of the correction results. At the same time, it clarifies the steps for water quality evaluation based on the processed data in combination with the water quality evaluation criteria, making the evaluation process of the long-term impact of reclaimed water reuse on the aquatic ecological environment more standardized and reliable, and avoiding deviations or unreliable results caused by data anomalies or unclear evaluation methods.

[0162] In some possible embodiments, the water quality evaluation criteria include multiple evaluation indicators and the weight coefficients corresponding to each evaluation indicator;

[0163] Step S404 includes:

[0164] According to the water quality evaluation criteria, filter out the key evaluation indicators related to the corrected measured data of the target indicator, and calculate the comprehensive weight coefficient according to the weight coefficients of the key evaluation indicators;

[0165] According to the corrected measured data of the target indicator and the comprehensive weight coefficient, calculate the water quality index by using the weighted index method;

[0166] Compare the water quality index with the preset water quality grade classification criteria to determine the water quality grade, and evaluate the long-term impact of the reclaimed water on the aquatic ecological environment according to the water quality grade.

[0167] Among them, the water quality evaluation criteria refer to the norms or regulations used to measure the water body quality, which include multiple different evaluation indicators, such as dissolved oxygen, pH, turbidity, specific pollutant concentration, etc., and a weight coefficient reflecting its importance is assigned to each evaluation indicator. Specifically, it can be achieved by using the water environment quality standards, industry standards issued by the state or local governments, or internal standards formulated for specific application scenarios. These standards usually list the index names, limit requirements and weights in tabular form.

[0168] Filtering out the key evaluation indicators related to the corrected measured data of the target indicator means identifying and selecting the evaluation indicators that are directly or closely related to the currently monitored target indicator (such as specific algal toxins or trace organic pollutants) from the water quality evaluation criteria containing multiple evaluation indicators. Specifically, it can be achieved by means of index name matching, index category attribution or preset association rules. For example, if the target indicator is a certain algal toxin, then select the evaluation items related to this algal toxin or related algal indicators in the screening criteria.

[0169] Calculating the comprehensive weight coefficient according to the weight coefficients of the key evaluation indicators means calculating a comprehensive weight coefficient according to the weight values corresponding to the selected key evaluation indicators in the original water quality evaluation criteria. Specifically, it can be achieved by summing, averaging or weighted averaging the weight coefficients of the selected key evaluation indicators. For example, if two key indicators are selected, and their weights are W1 and W2 respectively, then the comprehensive weight coefficient can be calculated as W1 + W2 or (W1 + W2) / 2. This comprehensive weight coefficient reflects the relative importance of the currently monitored target indicator in the entire water quality evaluation system and provides a basis for calculating the water quality index in the follow-up.

[0170] Calculating the water quality index using the weighted index method means combining the calibrated measured data of the target indicators and the previously calculated comprehensive weight coefficients through a mathematical method to obtain a quantified water quality index. Specifically, the calibrated measured data of the target indicators can be standardized (for example, compared with the standard limit values), and then the standardized values are multiplied by the comprehensive weight coefficients, or other formulas that conform to the principle of weighted index calculation can be used, such as Index = (Standardized measurement value) * (Comprehensive weight coefficient). This method takes into account the importance of the indicators, making the calculated water quality index better represent the status and influence of the target indicators in the water quality evaluation system.

[0171] Comparing the water quality index with the preset water quality grade classification standard to determine the water quality grade means comparing the calculated water quality index with the previously stipulated water quality grade classification standard. Specifically, a series of threshold ranges of the water quality index can be set, with each range corresponding to a specific water quality grade (for example, an index less than 10 is excellent, 10 - 30 is good, 30 - 60 is medium, and greater than 60 is poor), and then it is judged which range the calculated water quality index falls into to determine the corresponding water quality grade.

[0172] Evaluating the long - term impact of reclaimed water on the aquatic ecological environment based on the water quality grade means making judgments and descriptions about the possible long - term impacts of reclaimed water reuse on the water ecological environment based on the determined water quality grade. Specifically, it can be achieved by associating different water quality grades with the preset ecological impact descriptions. For example, an excellent or good water quality grade may indicate a relatively small or acceptable long - term impact, while a poor water quality grade may suggest potential long - term accumulation or adverse impacts, requiring further attention or measures.

[0173] Specifically, this solution aims to address the problem of how to effectively combine the corrected measurement data of a single target indicator with a complex water quality evaluation standard that includes multiple indicators and weights, so as to conduct a more accurate and comprehensive long-term impact assessment. First, it is clarified that the water quality evaluation standard is not a single threshold, but a comprehensive system that includes multiple evaluation indicators and their importance levels (weights). This provides a basis for subsequent comprehensive evaluations based on the data of a single target indicator. Then, during the evaluation process, the evaluation process is elaborated. The first step is to screen out the key evaluation indicators directly related to the measurement data of the target indicator currently being monitored according to the water quality evaluation standard. This is because the water quality evaluation standard may cover various pollutants or water quality parameters, while this method monitors specific algal toxins or trace organic pollutants. Therefore, only the part of the evaluation standard related to these specific substances needs to be concerned. Then, according to the weights corresponding to these screened key evaluation indicators in the entire water quality evaluation standard, a comprehensive weight coefficient is calculated. This comprehensive weight coefficient reflects the relative importance of the currently monitored target indicator in the entire water quality evaluation system, provides a reasonable weight basis for subsequent calculation of the water quality index, and avoids the one-sidedness that may be brought about by simply applying the weight of a single indicator. The second step is to calculate the water quality index using the weighted index method based on the corrected measurement data of the target indicator and the comprehensive weight coefficient calculated previously. The weighted index method is a commonly used multi-index comprehensive evaluation method. By combining the measured value of a single target indicator with its corresponding comprehensive weight coefficient, the measured value of this indicator can be transformed into a quantitative index that reflects its impact on the overall water quality. This method takes into account the importance level of the indicator, making the calculated water quality index more representative of the status and impact of this target indicator in the water quality evaluation system. The last step is to compare the calculated water quality index with the preset water quality grade classification standard to determine the water quality grade of the current water body. The water quality grade classification standard usually maps the water quality index to different grades, making the evaluation results more intuitive and understandable. By determining the water quality grade, the long-term impact degree of reclaimed water on the aquatic ecological environment can be more clearly understood, providing support for subsequent management and decision-making. This evaluation method based on the weighted index and water quality grade can evaluate the long-term impact of reclaimed water reuse on the aquatic ecological environment more comprehensively and scientifically than simply comparing the measured value of a single indicator with a certain threshold. By using the corrected measurement data of the target indicator obtained through the aforementioned steps, this solution can conduct evaluations based on data that is closer to the real situation, thereby improving the accuracy of the evaluation. Combining the corrected data with an evaluation standard that includes multiple indicators and weights, through calculating the comprehensive weight coefficient and the weighted index, the measured value of a single target indicator can be reflected in a broader water quality evaluation system, overcoming the limitations of judging only based on a single indicator threshold and providing a more representative water quality evaluation result.

[0174] Reference Figure 2, this application provides a monitoring system for the water quality change of reclaimed water reuse in landscape, which is used to monitor the long-term impact of reclaimed water on the aquatic ecological environment of the urban landscape water body reused by reclaimed water. The system includes a target measurement device 1, multiple auxiliary measurement devices 2, an edge computing device 3, and a remote monitoring center 4. The target measurement device 1, the auxiliary measurement devices 2, and the remote monitoring center 4 are all communicatively connected to the edge computing device 3;

[0175] The target measurement device 1 is used to measure the original measurement data of the target indicators in the water body and send it to the edge computing device 3;

[0176] Multiple auxiliary measurement devices 2 are used to measure multiple environmental background parameters characterizing the water body environment background and send them to the edge computing device 3;

[0177] The edge computing device 3 is used to analyze and calculate the dynamic influence relationship between the original measurement data of the target indicators and each environmental background parameter to obtain a state parameter characterizing the measurement deviation state of the target measurement device; according to multiple environmental background parameters and the state parameter, using the pre-established knowledge base, determine the correction amount corresponding to the current environmental background and the measurement deviation state of the target measurement device; the knowledge base includes a pre-constructed multi-dimensional correction lookup table or a preset correction rule set; use the correction amount to correct the original measurement data of the target indicators to obtain the corrected target indicator measurement data, which is used to evaluate the long-term impact of reclaimed water on the aquatic ecological environment; send the evaluation result of the long-term impact of reclaimed water on the aquatic ecological environment to the remote monitoring center 4 (the specific process can refer to the corresponding steps in the previous text).

[0178] Among them, the system realizes the monitoring task by integrating hardware units with different functions. The target measurement device 1 is deployed in the water body and is responsible for collecting the original measurement data of specific target indicators, such as specific algal toxins or trace organic pollutants. A plurality of auxiliary measurement devices 2 are also deployed in or near the water body to collect parameters reflecting the water body environmental background, such as turbidity, total dissolved organic matter, conductivity, temperature, pH, dissolved oxygen, etc. These devices transmit the collected data to the edge computing device 3 through communication connections. The edge computing device 3, as the core processing unit, receives data from the target measurement device and the auxiliary measurement devices. It performs data analysis and calculation tasks, including analyzing the dynamic relationship between the original data of the target indicators and the environmental background parameters, and calculating the state parameters reflecting the measurement deviation state of the target measurement device. The edge computing device 3 stores or accesses a pre-established knowledge base internally, and the knowledge base can be a multi-dimensional calibration lookup table or a preset calibration rule set. The edge computing device 3 determines the calibration amount for correcting the original measurement data of the target indicators by using the knowledge base based on the current environmental background parameters and the calculated state parameters. The calibration amount is applied to the original data to generate the calibrated measurement data of the target indicators. The edge computing device 3 uses the calibrated data to evaluate the long-term impact of reclaimed water on the aquatic ecological environment and sends the evaluation results to the remote monitoring center 4. The remote monitoring center 4 receives and processes these evaluation results for remote monitoring and management. Thus, the system realizes the functions of data collection, local processing, calibration, and result transmission.

[0179] In some specific embodiments, the monitoring system is deployed in an urban landscape lake. The target measurement device 1 employs an on-line fluorescence sensor for measuring the concentration of specific algal toxins (such as microcystins) in the water. The plurality of auxiliary measurement devices 2 include a turbidity sensor, a fluorescence probe for dissolved organic matter (DOM), a conductivity sensor, a temperature sensor, a pH sensor, and a dissolved oxygen sensor, which are integrated in a multi-parameter probe. These sensors are connected to an edge computing device 3 through an RS485 interface, and the device is an industrial-grade embedded computer. The edge computing device 3 communicates with the remote monitoring center 4 through a 4G or 5G network.

[0180] The above are only the embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A monitoring method for the water quality change of reclaimed water reused in landscapes, which is used to monitor the long-term impact of reclaimed water on the aquatic ecological environment of urban landscape water bodies reused with reclaimed water, and is characterized in that, The steps of this method include: S1. Obtain the original measurement data of the target index in the water body and multiple environmental background parameters characterizing the water body environment background through the target measurement device and multiple auxiliary measurement devices; S2. Analyze and calculate the dynamic influence relationship between the original measurement data of the target index and each environmental background parameter to obtain the state parameter characterizing the measurement deviation state of the target measurement device; S3. According to multiple environmental background parameters and state parameters, use the pre-established knowledge base to determine the correction amount corresponding to the current environmental background and the measurement deviation state of the target measurement device; the knowledge base includes a pre-constructed multi-dimensional correction lookup table or a preset correction rule set; S4. Use the correction amount to correct the original measurement data of the target index to obtain the corrected target index measurement data for evaluating the long-term impact of reclaimed water on the aquatic ecological environment; S5. Send the evaluation result of the long-term impact of reclaimed water on the aquatic ecological environment to the remote monitoring center.

2. The monitoring method for the change of the quality of reclaimed water reuse landscape water according to claim 1, characterized in that, Step S1 includes: S101. Collect the original measurement data of the target index in the water body at a preset frequency through the target measurement device; the target index is a specific algal toxin or a specific trace organic pollutant; S102. Collect multiple environmental background parameters through the multiple auxiliary measurement devices respectively; the environmental background parameters include some or all of turbidity, total dissolved organic matter, conductivity, temperature, pH, dissolved oxygen; S103. Perform time synchronization calibration and outlier processing on the original measurement data of the target index and multiple environmental background parameters.

3. The method for monitoring the water quality change of a reclaimed water reuse landscape according to claim 1, characterized in that, Step S2 includes: S201. Calculate the first derivative and the second derivative of the original measurement data of the target index to obtain the target index change rate and the target index change acceleration; S202. Calculate the Pearson correlation coefficient between each environmental background parameter and the original measurement data of the target index, and screen out the environmental background parameters with the absolute value of the Pearson correlation coefficient greater than the preset correlation coefficient threshold as the key environmental background parameters; S203. According to the target index change rate, the target index change acceleration and the key environmental background parameters, use the multiple linear regression algorithm to calculate the predicted value of the target index; S204. Calculate the residual between the original measurement data of the target index and the predicted value, and calculate the mean and standard deviation of the residual; S205. Calculate the state parameter of the target measurement device according to the mean and standard deviation of the residual; the state parameter includes the residual mean offset and the residual standard deviation magnification factor.

4. A method for monitoring the water quality change of a reclaimed water reuse landscape according to claim 3, characterized in that, Step S203 includes: Construct an initial least squares regression model with the original measurement data of the target index as the dependent variable and the target index change rate, the target index change acceleration and each key environmental background parameter as the independent variables; Solve the model parameters in the initial least squares regression model by minimizing the sum of squared residuals to obtain the final least squares regression model; Substitute the target index change rate, the target index change acceleration and each key environmental background parameter into the final least squares regression model respectively to obtain the predicted value of the target index.

5. A monitoring method for the water quality change of reclaimed water for landscape reuse according to claim 3, characterized in that Step S205 includes: Calculate the difference between the mean of the residual and the preset reference mean as the initial residual mean offset; Calculate the quotient of the standard deviation of the residual and the preset reference standard deviation as the initial amplification factor of the residual standard deviation; Measure or estimate the thickness of the biofilm on the surface of the target measuring device; Calculate the correction term for the mean offset of the residual and the correction term for the amplification factor of the residual standard deviation based on the biofilm thickness; Add the correction term for the mean offset of the residual to the initial mean offset of the residual to obtain the final mean offset of the residual; add the correction term for the amplification factor of the residual standard deviation to the initial amplification factor of the residual standard deviation to obtain the final amplification factor of the residual standard deviation.

6. The monitoring method for the change of the quality of reclaimed water reuse landscape water according to claim 1, characterized in that, The knowledge base includes a pre-constructed multi-dimensional calibration look-up table; Step S3 includes: S301a. Compose a query vector using multiple environmental background parameters and status parameters at the current moment; S302a. If there is a multi-dimensional index vector in the pre-constructed multi-dimensional calibration look-up table that is the same as the query vector, obtain the calibration amount corresponding to the current environmental background and the measurement deviation state of the target measuring device by retrieving the calibration amount corresponding to the corresponding multi-dimensional index vector; S303a. If there is no multi-dimensional index vector in the pre-constructed multi-dimensional calibration look-up table that is the same as the query vector, determine multiple neighboring multi-dimensional index vectors based on the Euclidean distance between the query vector and each multi-dimensional index vector in the multi-dimensional calibration look-up table, and calculate the weighted average of the calibration amounts corresponding to each neighboring multi-dimensional index vector to obtain the calibration amount corresponding to the current environmental background and the measurement deviation state of the target measuring device.

7. A method for monitoring the water quality change of reclaimed water for landscape reuse according to claim 1, characterized in that, The knowledge base includes a preset calibration rule set; Step S3 includes: S301b. Extract the preset calibration rule set; the preset calibration rule set contains multiple calibration rules, and each calibration rule corresponds to a combination of one or more discretized environmental background parameter intervals and status parameter intervals, as well as the confidence level of the calibration amount corresponding to this combination; S302b. Map the multiple environmental background parameters and status parameters at the current moment to the corresponding discretized intervals in the preset calibration rule set to obtain the current parameter interval combination; S303b. If the preset calibration rule set contains a calibration rule that exactly matches the current parameter interval combination, calculate the calibration amount corresponding to the current environmental background and the measurement deviation state of the target measuring device using the corresponding calibration rule; S304b. If the preset calibration rule set does not contain a calibration rule that exactly matches the current parameter interval combination, select multiple calibration rules that partially match the current parameter interval combination in accordance with the preset priority order, calculate the initial calibration amounts respectively using the selected calibration rules, and perform a weighted average calculation on the initial calibration amounts according to the confidence levels of the calibration amounts of the selected calibration rules to obtain the calibration amount corresponding to the current environmental background and the measurement deviation state of the target measuring device.

8. A method for monitoring the water quality change of reclaimed water for landscape reuse according to claim 1, characterized in that, Step S4 includes: S401. Determine whether the original measurement data of the target index exceeds the preset range. If it exceeds, set the calibration amount to zero; S402. Subtract the calibration amount from the original measurement data of the target index to obtain the calibrated measurement data of the target index; S403. Calculate the absolute value of the corrected target index measurement data. If the absolute value is less than the preset minimum value, set the corrected target index measurement data to the preset minimum value; S404. Calculate the water quality index based on the corrected target index measurement data and in combination with the water quality evaluation criteria, and evaluate the long-term impact of reclaimed water on the aquatic ecological environment according to the water quality index.

9. A method for monitoring the water quality change of reclaimed water reused in landscapes according to claim 8, characterized in that, The water quality evaluation criteria include multiple evaluation indicators and the corresponding weight coefficients for each evaluation indicator; Step S404 includes: According to the water quality evaluation criteria, screen out the key evaluation indicators related to the corrected target index measurement data, and calculate the comprehensive weight coefficient according to the weight coefficients of the key evaluation indicators; Calculate the water quality index using the weighted index method based on the corrected target index measurement data and the comprehensive weight coefficient; Compare the water quality index with the preset water quality grade classification standard to determine the water quality grade, and evaluate the long-term impact of reclaimed water on the aquatic ecological environment according to the water quality grade.

10. A monitoring system for the change of reclaimed water reuse landscape water quality, which is used to monitor the long-term impact of reclaimed water on the aquatic ecological environment of the urban landscape water body for reclaimed water reuse, is characterized in that, The system includes a target measurement device, multiple auxiliary measurement devices, an edge computing device, and a remote monitoring center. The target measurement device, the auxiliary measurement devices, and the remote monitoring center are all communicatively connected to the edge computing device; The target measurement device is used to measure the original measurement data of the target index in the water body and send it to the edge computing device; Multiple auxiliary measurement devices are used to measure multiple environmental background parameters characterizing the water body environment background and send them to the edge computing device; The edge computing device is used to analyze and calculate the dynamic influence relationship between the original measurement data of the target index and each environmental background parameter to obtain a state parameter characterizing the measurement deviation state of the target measurement device; according to the multiple environmental background parameters and the state parameter, use the pre-established knowledge base to determine the correction amount corresponding to the current environmental background and the measurement deviation state of the target measurement device; the knowledge base includes a pre-constructed multi-dimensional correction lookup table or a preset correction rule set; use the correction amount to correct the original measurement data of the target index to obtain the corrected target index measurement data for evaluating the long-term impact of reclaimed water on the aquatic ecological environment; send the evaluation result of the long-term impact of reclaimed water on the aquatic ecological environment to the remote monitoring center.

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