Intelligent flow dividing control method for intercepting well based on water quality soft measurement technology

Abstract

The invention relates to the technical field of intelligent interception of a drainage system, in particular to an intelligent flow dividing control method of an intercepting well based on a water quality soft measurement technology, which comprises the following steps: establishing a binary classification mechanism based on a quantization threshold value; determining a corresponding relation between the real-time comprehensive water quality index and the reference comprehensive water quality index; establishing a rapid inversion model based on characteristic indexes; and carrying out intelligent diversion control on the intercepting well based on the rapid inversion model. According to the method, the problem of poor data reliability in the prior art is solved, and real-time and accurate'tagging 'classification of incoming water attributes is realized through indirect inversion of the indexes which are easy to measure and the indexes which are difficult to measure.

Description

A Smart Diversion Control Method for Interception Wells Based on Water Quality Soft Measurement Technology Technical Field

[0001] This invention relates to the field of intelligent interception technology for drainage systems, and in particular to an intelligent diversion control method for interception wells based on water quality soft measurement technology. Background Technology

[0002] With the deepening of urbanization and increased efforts in water environment management in my country, improving the quality and efficiency of urban drainage systems has become a focus of attention in the field of municipal engineering. In the old urban areas of many Chinese cities, combined sewer systems still exist extensively. Furthermore, even in many drainage systems that have undergone rainwater and sewage separation renovations, misconnections and mixing of rainwater and sewage frequently occur. Interception wells, as key hubs connecting urban drainage networks, receiving water bodies (such as rivers and lakes), and sewage treatment plants, directly determine the quality of the final water environment and the operational efficiency of the sewage treatment system through their scientific operation and scheduling.

[0003] In traditional interceptor well control systems, management relies primarily on physical sensors such as level gauges and rain gauges for extensive control. The typical control logic is as follows: on sunny days, all sewage in the interceptor well is diverted to the sewage network and transported to the sewage treatment plant; on rainy days, when the water level in the network exceeds a set threshold or the rainfall reaches a certain standard, the overflow outlet is opened to directly discharge the mixed water into the receiving water body to prevent urban flooding.

[0004] However, existing technologies that rely solely on water level or rainfall for judgment have significant limitations and cannot accurately reflect the degree of pollution in incoming water. For example, "initial rainwater" at the beginning of rainfall often carries a large amount of surface pollutants, with concentrations even higher than domestic sewage. If it is discharged directly simply because the water level rises, it will cause serious water pollution. Conversely, in the later stages of rainfall or when groundwater infiltration is high, the water quality is relatively clean. If it is still blindly intercepted and sent to sewage treatment plants, it will not only strain valuable pipe networks and treatment plant capacity but also result in excessively low influent concentrations, affecting the biochemical treatment efficiency of sewage treatment plants.

[0005] Although some technologies have attempted to introduce conventional online water quality monitoring instruments such as chemical oxygen demand (COD) and ammonia nitrogen into interception wells to assist in decision-making, these instruments are usually bulky, expensive, rely on chemical reagent reactions, have long analysis cycles (usually tens of minutes), and have serious lag, which cannot meet the rapid response requirements of interception wells to sudden changes in water flow.

[0006] Furthermore, the internal environment of interception wells is harsh, damp, and contains a large amount of suspended impurities, making conventional precision water quality instruments prone to clogging, damage, or frequent maintenance, and compromising data reliability. Therefore, there is an urgent need for a method that can overcome these shortcomings, utilize low-cost, maintenance-free detection techniques to quickly and accurately determine the type of incoming water (whether it is highly polluted or low-polluted), and construct a multi-dimensional control logic prioritizing safety and water quality. This would enable intelligent and precise diversion of water from interception wells, effectively reducing the pollution load entering rivers. Summary of the Invention

[0007] The purpose of this invention is to provide an intelligent diversion control method for intercepting wells based on water quality soft measurement technology, which solves the problem of poor data reliability in existing technologies. By indirectly inverting difficult-to-measure indicators through easily measurable indicators, it achieves real-time and accurate "labeling" classification of incoming water attributes.

[0008] To achieve the above objectives, this invention provides an intelligent diversion control method for intercepting wells based on water quality soft measurement technology, comprising the following steps: S1, establishing a binary classification mechanism based on quantization thresholds; S2, determining the real-time comprehensive water quality index. Compared with the benchmark comprehensive water quality index The corresponding relationship; S3, establish a fast inversion model based on feature indicators; S4, carry out intelligent diversion control of intercepting wells based on the fast inversion model.

[0009] In some embodiments of this application, in S1, the binary classification mechanism uses the real-time comprehensive water quality index and the benchmark comprehensive water quality index as the discrimination criteria. and The numerical comparison results determine the direction of water flow.

[0010] In some embodiments of this application, in S2, the real-time comprehensive water quality index is determined. Compared with the benchmark comprehensive water quality index The correspondence includes: based on the concentration levels of various pollutant indicators in actual water bodies, using... The same comprehensive water quality index calculation model is used to calculate the real-time evaluation value, which is then determined as the real-time comprehensive water quality index. .

[0011] In some embodiments of this application, in S2, utilizing and The same comprehensive water quality index calculation model is used to calculate the real-time evaluation value, which is then determined as the real-time comprehensive water quality index. This includes: weighting the various single-factor water quality indicator indices to obtain the comprehensive water quality index. .

[0012] In some embodiments of this application, in step S2, the various single-factor water quality labeling indices are weighted and calculated to obtain a comprehensive water quality index. Includes: S21, Calculation of single-factor water quality labeling index To obtain the real-time monitoring concentration of the j-th pollutant index in the water within the interception well, a single-factor identification index is used. The calculation formula is: ;in, The measured concentration or standard limit of the j-th indicator. For the j-th index, based on its concentration The corresponding water quality category This represents the specific position of the j-th indicator within its category interval. For the first The lower limit of the Class III water quality standard, For the first The upper limit of the Class II water quality standard; S22, for different application scenarios, select either a weighted average model of key indicators or a weighted model based on the Nemerow index to calculate the water quality according to the key management requirements of the receiving water body. The formula for calculating the weighted average model of key indicators is: ;in, This represents the total number of indicators involved in the evaluation. The number of key pollution indicators, A single-factor identifier index for the selected key pollution indicators, where, This is a repeated weighting term used to increase the weight of key indicators in the overall evaluation; the calculation formula for the weighted model based on the Nemerow index is: ;in, This represents the total number of indicators involved in the evaluation. It is the maximum value among all single-factor identifier indices participating in the evaluation, used to ensure that the comprehensive index can be effectively raised when a single indicator exceeds the limit significantly.

[0013] In some embodiments of this application, in S2, the single-factor water quality labeling index The structural definition is: .

[0014] In some embodiments of this application, in S2, when used for calculation hour, Let j be the measured concentration of the j-th index; when used for calculation hour, This represents the standard limit for the j-th indicator.

[0015] In some embodiments of this application, in step S3, establishing a fast inversion model based on feature indicators includes: S31, simultaneously measuring the easily measurable indicator Y and physicochemical indicator X of the water sample according to the calculation results of steps S1-S2, and calculating the corresponding current water quality index W based on the physicochemical indicator X, and constructing a mapping dataset of easily measurable indicator Y and current water quality index W; S32, establishing a highly correlated inversion function model between easily measurable indicator Y and current water quality index W based on the mapping dataset and machine learning algorithm.

[0016] In some embodiments of this application, in step S4, the intelligent diversion control of the intercepting well based on the fast inversion model includes: during the actual operation phase of the intercepting well, the system first performs a liquid level safety scan and obtains the real-time water level through the liquid level gauge. And in accordance with the preset flood prevention warning water level Perform a comparison; if If the system determines the situation to be "high water volume risk type," then regardless of water quality, the system will forcibly enter the flood prevention emergency mode, prioritizing the opening of overflow valves for direct discharge of floodwater to ensure urban safety; if The system enters water quality discrimination mode. At this time, the easily measurable index Y of the real-time collected incoming water is substituted into the highly correlated inversion function model to obtain the current water quality index W. The system automatically compares the current water quality index W with... Perform a comparison, if If it is determined to be a type that can be directly arranged, then It was determined to be a type that requires interception and diversion.

[0017] The advantages and beneficial effects of this invention compared to the prior art are as follows: By synchronously collecting a large amount of data in actual application scenarios and establishing a mapping relationship using statistical analysis and machine learning algorithms, the system can automatically select a set of Y indicators that are most correlated with the target X indicator, eliminate redundant variables, and thus construct a high-precision water quality inversion model, realizing real-time and accurate estimation of X indicator values ​​using Y indicator data acquired in seconds.

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0019] Figure 1 is a flowchart of an intelligent diversion control method for intercepting wells based on water quality soft measurement technology in an embodiment of the present invention; Figure 2 is a flowchart of inflow type discrimination and intelligent diversion control in an embodiment of the present invention; Figure 3 is a schematic diagram of the determination logic of the benchmark comprehensive water quality index in an embodiment of the present invention; Figure 4 is a schematic diagram of the fitting effect of the water quality inversion model in an embodiment of the present invention; Figure 5 is a schematic diagram of the operation effect of an embodiment of the present invention during a typical rainfall process. Detailed Implementation

[0020] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product is in use. They are used only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," and "connect" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] The primary technical problem to be solved by this invention is to overcome the lack of rapid and accurate water quality identification methods in existing interception well control technologies, which leads to the defects of coarse interception strategies and inability to take into account both the water environment's receiving capacity and the load of the sewage treatment system. This invention provides a method for accurate identification and diversion control of interception well inflow type based on rapid inversion of water quality indicators.

[0022] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0023] As shown in Figure 1, the present invention provides an intelligent diversion control method for intercepting wells based on water quality soft measurement technology, including the following steps: S1, establishing a binary classification mechanism based on quantization threshold.

[0024] S2. Determine the real-time comprehensive water quality index. Compared with the benchmark comprehensive water quality index The correspondence.

[0025] S3. Establish a fast inversion model based on feature indicators.

[0026] S4. Intelligent diversion control of interception wells based on fast inversion model.

[0027] In some embodiments of this application, in S1, addressing the fundamental scientific problem of identifying the type of water flowing into intercepting wells—namely, how to accurately determine whether the water body belongs to "clean water that can be directly discharged into environmental water bodies (such as rivers)" or "polluted water that must be diverted to sewage treatment plants or regulating ponds"—this invention abandons traditional qualitative judgment methods and proposes a quantitative discrimination standard based on numerical comparison. This binary classification mechanism uses a real-time comprehensive water quality index and a benchmark comprehensive water quality index as the discrimination criteria. and The numerical comparison results determine the direction of water flow.

[0028] In some embodiments of this application, in S2, the real-time comprehensive water quality index is determined. Compared with the benchmark comprehensive water quality index The correspondence includes: based on the concentration levels of various pollutant indicators in actual water bodies, using... The same comprehensive water quality index calculation model is used to calculate the real-time evaluation value, which is then determined as the real-time comprehensive water quality index. .

[0029] In some embodiments of this application, in S2, utilizing and The same comprehensive water quality index calculation model is used to calculate the real-time evaluation value, which is then determined as the real-time comprehensive water quality index. This includes: weighting the various single-factor water quality indicator indices to obtain the comprehensive water quality index. .

[0030] In some embodiments of this application, in step S2, the various single-factor water quality labeling indices are weighted and calculated to obtain a comprehensive water quality index. Includes: S21, Calculation of single-factor water quality labeling index To obtain the real-time monitoring concentration of the j-th pollutant index in the water within the interception well, a single-factor identification index is used. The calculation formula is: ;in, The measured concentration or standard limit of the j-th indicator. For the j-th index, based on its concentration The corresponding water quality category This represents the specific position of the j-th indicator within its category interval. For the first The lower limit of the Class III water quality standard, For the first The upper limit of the Class II water quality standard; S22, for different application scenarios, select either a weighted average model of key indicators or a weighted model based on the Nemerow index to calculate the water quality according to the key management requirements of the receiving water body. The formula for calculating the weighted average model of key indicators is: ;in, This represents the total number of indicators involved in the evaluation. The number of key pollution indicators, A single-factor identifier index for the selected key pollution indicators, where, This is a repeated weighting term used to increase the weight of key indicators in the overall evaluation; the calculation formula for the weighted model based on the Nemerow index is: ;in, This represents the total number of indicators involved in the evaluation. It is the maximum value among all single-factor identifier indices participating in the evaluation, used to ensure that the comprehensive index can be effectively raised when a single indicator exceeds the limit significantly.

[0031] In some embodiments of this application, in S2, the single-factor water quality labeling index The structural definition is: .

[0032] In some embodiments of this application, in S2, when used for calculation hour, Let j be the measured concentration of the j-th index; when used for calculation hour, This represents the standard limit for the j-th indicator.

[0033] The benchmark comprehensive water quality index The threshold is used to determine whether water quality meets the standards. The method for determining it is as follows: based on the relevant national or local emission standards (such as the Class A or Class III water body standards in the "Urban Wastewater Treatment Plant Pollutant Discharge Standard"), select the standard limits of several key pollutant indicators (denoted as X indicators, including but not limited to ammonia nitrogen, total phosphorus, etc.), and derive the fixed constant values ​​using a preset comprehensive water quality index calculation model.

[0034] Correspondingly, real-time comprehensive water quality index This is a numerical value characterizing the actual pollution level of incoming water. It is defined as: based on the concentration levels of various pollutant indicators (X indicators) in the actual water body, using... The real-time evaluation value is calculated using the same comprehensive water quality index calculation model.

[0035] In some embodiments of this application, in step S3, establishing a rapid inversion model based on feature indicators includes: S31, based on the calculation results of steps S1-S2, simultaneously measuring the key physicochemical indicators X and easily measurable indicators Y within the water quality standard of the water sample, and calculating the corresponding current water quality index W based on the physicochemical indicators X, and constructing a mapping dataset of easily measurable indicators Y and current water quality index W; S32, based on the mapping dataset and machine learning algorithms, establishing a highly correlated inversion function model between easily measurable indicators Y and current water quality index W.

[0036] Given the technical bottlenecks in practical engineering, such as long response time, easy probe contamination, and large data drift in directly monitoring physicochemical indicators (X indicators) like ammonia nitrogen and total phosphorus, resulting in both inaccuracy and slow measurement speed, this invention proposes an indirect inversion method based on alternative indicators. This method selects physical or optical indicators with fast response speed, low detection cost, and good stability as alternative variables.

[0037] In some embodiments of this application, in step S4, the intelligent diversion control of the intercepting well based on the fast inversion model includes: during the actual operation phase of the intercepting well, the system first performs a liquid level safety scan and obtains the real-time water level through the liquid level gauge. And in accordance with the preset flood prevention warning water level Perform a comparison; if If the system determines the situation to be "high water volume risk type," then regardless of water quality, the system will forcibly enter the flood prevention emergency mode, prioritizing the opening of overflow valves for direct discharge of floodwater to ensure urban safety; if The system enters water quality discrimination mode. At this time, the easily measurable index Y of the real-time collected incoming water is substituted into the highly correlated inversion function model to obtain the current water quality index W. The system automatically compares the current water quality index W with... Perform a comparison, if If it is determined to be a type that can be directly arranged, then It was determined to be a type that requires interception and diversion.

[0038] The process of water inflow type identification and intelligent diversion control in this embodiment is shown in Figure 2.

[0039] The determination of the key pollutant indicators (i.e., X indicators) in this invention strictly adheres to the core requirements and legal standards of national environmental protection supervision. Specifically, it aims to ensure that the control logic of the interception well is consistent with the assessment system of the receiving water body or sewage treatment plant. These indicators specifically include chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), suspended solids (SS), total nitrogen (TN), ammonia nitrogen (NH3-N), and total phosphorus (TP), which respectively represent the total amount of organic pollutants, biodegradability, physical turbidity, and nutrient load in the water body, and are the fundamental basis for determining whether the water quality meets the discharge standards.

[0040] To address the issues of difficulty in online monitoring and high maintenance costs of the aforementioned X indicator, this invention further screened and identified a set of rapid alternative indicators (i.e., Y indicators) that can respond quickly, measure accurately, and require no chemical reagents. The selection of the Y index primarily focuses on the ultraviolet-visible spectral characteristics. Utilizing the absorption principle of organic matter at specific wavelengths, absorbance at characteristic wavelengths such as 254nm, 260nm, 270nm, 275nm, 280nm, 285nm, 295nm, and 365nm (A254, A260, etc.) was selected as the core variable. At the same time, in order to eliminate the interference of particulate matter scattering and to analyze complex organic components, absorbance ratios at specific wavelengths were also introduced as composite indicators, specifically covering A254 / A365, A254 / A436, A280 / A665, A300 / A400, A340 / A254, A400 / A600, A465 / A665, A210 / A254, A220 / A254, and A254 / A203, etc.

[0041] This invention collects a large amount of data synchronously in practical application scenarios, establishes a mapping relationship using statistical analysis and machine learning algorithms, and the system can automatically select a set of Y indicators that are most correlated with the target X indicator, eliminate redundant variables, and thus construct a high-precision water quality inversion model, realizing real-time and accurate estimation of X indicator values ​​using Y indicator data acquired in seconds.

[0042] The following experimental verification is carried out with reference to specific embodiments.

[0043] This embodiment uses the intercepting well at the end of a combined sewer system in a city as an application scenario to demonstrate the construction and operation of a rapid water type identification and intelligent diversion system based on the fusion of multispectral and conventional water quality indicators.

[0044] The first step in this embodiment is to construct a localized water quality inversion model database, which requires periodic on-site water sampling and laboratory analysis. Technicians need to collect representative water samples at the inlet of the target intercepting well under different meteorological conditions, such as dry weather, early rainfall, mid-rainfall, and late rainfall, to ensure that the samples cover the full range of water quality fluctuations, from high-concentration sewage to low-concentration rainwater. The collected water samples will be immediately sent to the laboratory for simultaneous testing. The testing will be conducted in two dimensions: The first dimension is to determine the true concentration of key pollutant indicators (X indicators) according to national standard methods. Specifically, this includes determining chemical oxygen demand (COD) using the potassium dichromate method, determining ammonia nitrogen (NH3-N) using Nessler's reagent spectrophotometry, and determining total phosphorus (TP) and suspended solids (SS). These data will serve as the "true values" for model training. The second dimension is to determine the rapid surrogate indicators (Y indicators) of the same sample using a UV-Vis full-band scanner and a multi-parameter water quality probe. This will record absorbance data at characteristic wavelengths such as 254nm and 365nm, as well as physical parameters such as turbidity, conductivity, pH value, and temperature. These data will serve as the "input variables" for the model.

[0045] After acquiring sufficient paired datasets of "X-indicator - Y-indicator", the system proceeds to the data processing and model building stage. First, based on a pre-defined comprehensive water quality index calculation formula (e.g., using the Nemerow index method or weighted average method), the system calculates the real-time comprehensive water quality index for each water sample using the concentration values ​​of various X-indicators measured in the laboratory. This reduces multi-dimensional pollution parameters into a single evaluation value. Subsequently, machine learning algorithms such as Partial Least Squares (PLS) or Support Vector Machine (SVM) are used, with the Y index (spectral absorbance and physical parameters) as the independent variable, and the calculated comprehensive water quality index (…). The algorithm uses Y as the dependent variable for regression training. During training, the algorithm automatically selects the spectral bands (such as A254) and auxiliary parameters (such as turbidity) that are most sensitive to changes in water quality, and removes redundant variables with low correlation or collinearity. Finally, it establishes a high-precision comprehensive water quality index that can be output simply by inputting an easily measurable Y value. The mathematical inversion model.

[0046] At the same time, a benchmark comprehensive water quality index (CQI) was established for control decision-making. This is a key step in this embodiment. The settings are not static, but dynamically selected based on the environmental function and protection objectives of the surface water area to which the overflow from the intercepting well is received, as well as the treatment process requirements of the area, strictly corresponding to the classification standards in the "Discharge Standard of Pollutants for Urban Wastewater Treatment Plants". The benchmark comprehensive water quality index ( The determination logic is shown in Figure 3. In specific implementation, the type of receiving water body and protection target are first verified, and the standard level to be adopted is determined according to the following rules: (1) When the effluent from the intercepting well is introduced into rivers and lakes with small dilution capacity for urban landscape water use and general reclaimed water use, the pollutant limit corresponding to the A standard of the first-level standard is selected as the basis for calculation; (2) When the effluent is discharged into the Class III functional water area of ​​GB3838 surface water (excluding the designated drinking water source protection area and swimming area), the Class II functional water area of ​​GB3097 seawater, and closed or semi-closed water areas such as lakes and reservoirs, the pollutant limit corresponding to the B standard of the first-level standard is selected; (3) When the effluent is discharged into the Class IV and V functional water areas of GB3838 surface water or the Class III and IV functional sea areas of GB3097 seawater, the pollutant limit corresponding to the second-level standard is selected; (4) For towns that are not key control basins and not water source protection areas, if the first-level enhanced treatment process is adopted, the pollutant limit corresponding to the third-level standard is selected.

[0047] After determining the specific applicable standard level, substitute the standard concentration values ​​of each individual pollutant (such as COD, NH3-N, etc.) corresponding to that level into the same comprehensive water quality index calculation model mentioned above. The fixed value obtained is the judgment threshold for that interception well. .

[0048] At this point, the software-level model and discrimination criteria are ready; the next step is the on-site deployment of the hardware system. An online monitoring terminal integrating an ultraviolet-visible spectroscopy probe and multi-parameter water quality sensors will be installed inside the interception well. Simultaneously, a high-precision level gauge (such as an ultrasonic level gauge or a submersible level gauge) will be installed to monitor the real-time water level within the well. The terminal has a self-cleaning function to adapt to the harsh downhole environment and is connected to a field programmable logic controller (PLC) via an industrial bus.

[0049] Once the system is officially put into operation, the PLC controller executes the following dual-judgment logic: First judgment: hydraulic safety judgment (prevention of waterlogging).

[0050] The system reads the liquid level gauge data in real time. And compare it with the preset warning water level threshold. Compare them.

[0051] (1) If This indicates that the current water inflow is too high, and the pipe network faces the risk of overflow or flooding. At this time, the system determines the inflow type as "non-dischargeable but must be directly discharged." The PLC controller immediately executes the highest priority "flood prevention emergency mode," forcibly opening the overflow valves to the fullest extent, and closing or reducing the interception valves according to the sewage treatment plant's capacity to discharge floodwater as quickly as possible. In this mode, no further water quality assessment is performed to ensure urban drainage safety.

[0052] (2) If This indicates that the current hydraulic load is within a safe range, and the system enters the second stage of judgment.

[0053] The second level of judgment: water quality compliance judgment.

[0054] Once the system is officially operational, the online monitoring terminal scans the spectral characteristics and physical parameters of the water flowing through the intercepting well in real time at a frequency of seconds (e.g., once every 30 seconds), thus acquiring Y-index data in real time. Upon receiving this raw data, the PLC controller immediately invokes its internally stored mathematical inversion model to calculate the current real-time comprehensive water quality index within milliseconds. Next, the controller executes the core decision-making logic: it calculates the value in real time... Compared with the preset benchmark comprehensive water quality index A comparison is performed. Based on the comparison results, the system automatically issues action commands to the actuators to achieve precise diversion of the interception well. Specifically, when the calculated... The data shows that the water quality is worse than When the pollution level exceeds the discharge standard of the receiving water body, the system determines that the incoming water is "highly polluted water" (such as dry sewage or initial rainwater). The PLC immediately instructs the interception valve to open and the overflow valve to close, diverting the entire volume of water to the sewage network for transport to the sewage treatment plant. Conversely, when the pollution level is lower than the discharge standard of the receiving water body, the system determines that the incoming water is "highly polluted water" (such as dry sewage or initial rainwater). The PLC immediately instructs the interception valve to open and the overflow valve to close, diverting the entire volume of water to the sewage network for transport to the sewage treatment plant. The numerical values ​​show that the water quality is better than or equal to When the system determines that the incoming water is "clean water" (such as rainwater or clean groundwater seepage), the PLC instructs the intercepting valve to close and the overflow valve to open, directly discharging the water to the receiving water body. In this way, the present invention achieves refined scheduling based on environmental capacity and regulatory standards, protecting the water environment of specific functional areas while effectively reducing the ineffective load on wastewater treatment plants. Figure 4 is a schematic diagram of the fitting effect of the water quality inversion model in this embodiment of the invention, and Figure 5 is a schematic diagram of the operational effect of this embodiment of the invention during a typical rainfall process.

[0055] In this application, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. In case of any inconsistency, the meaning set forth in this specification or derived from the content described herein shall prevail. Furthermore, the terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit the scope of this application.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for intelligent diversion control of intercepting wells based on water quality soft measurement technology, characterized in that, Includes the following steps: S1. Establish a binary classification mechanism based on quantization threshold; S2. Determine the real-time comprehensive water quality index. Compared with the benchmark comprehensive water quality index The corresponding relationship; S3, establish a fast inversion model based on feature indicators; S4, carry out intelligent diversion control of intercepting wells based on the fast inversion model.

2. The intelligent diversion control method for intercepting wells based on water quality soft measurement technology according to claim 1, characterized in that, In S1, the binary classification mechanism uses the real-time comprehensive water quality index and the benchmark comprehensive water quality index as the discrimination criteria. and The numerical comparison results determine the direction of water flow.

3. The intelligent diversion control method for intercepting wells based on water quality soft measurement technology according to claim 2, characterized in that, In step S2, the real-time comprehensive water quality index is determined. Compared with the benchmark comprehensive water quality index The correspondence includes: based on the concentration levels of various pollutant indicators in actual water bodies, using... The same comprehensive water quality index calculation model is used to calculate the real-time evaluation value, which is then determined as the real-time comprehensive water quality index. 。 4. The intelligent diversion control method for intercepting wells based on water quality soft measurement technology according to claim 3, characterized in that, In S2, using and The same comprehensive water quality index calculation model is used to calculate the real-time evaluation value, which is then determined as the real-time comprehensive water quality index. This includes: weighting the various single-factor water quality indicator indices to obtain the comprehensive water quality index. 。 5. The intelligent diversion control method for intercepting wells based on water quality soft measurement technology according to claim 4, characterized in that, In step S2, the individual water quality indicator indices are weighted and calculated to obtain the comprehensive water quality index. Includes: S21, Calculation of single-factor water quality labeling index To obtain the real-time monitoring concentration of the j-th pollutant index in the water within the interception well, a single-factor identification index is used. The calculation formula is: ;in, The measured concentration or standard limit of the j-th indicator. For the j-th index, based on its concentration The corresponding water quality category This represents the specific position of the j-th indicator within its category interval. For the first The lower limit of the Class III water quality standard, For the first The upper limit of the Class II water quality standard; S22, for different application scenarios, select either a weighted average model of key indicators or a weighted model based on the Nemerow index to calculate the water quality according to the key management requirements of the receiving water body. The formula for calculating the weighted average model of key indicators is: ;in, This represents the total number of indicators involved in the evaluation. The number of key pollution indicators, A single-factor identifier index for the selected key pollution indicators, where, This is a repeated weighting term used to increase the weight of key indicators in the overall evaluation; the calculation formula for the weighted model based on the Nemerow index is: ;in, This represents the total number of indicators involved in the evaluation. It is the maximum value among all single-factor identifier indices participating in the evaluation, used to ensure that the comprehensive index can be effectively raised when a single indicator exceeds the limit significantly.

6. The intelligent diversion control method for intercepting wells based on water quality soft measurement technology according to claim 5, characterized in that, In S2, the single-factor water quality labeling index The structural definition is: 。 7. The intelligent diversion control method for intercepting wells based on water quality soft measurement technology according to claim 6, characterized in that, In S2, when used for calculation hour, Let j be the measured concentration of the j-th index; when used for calculation hour, This represents the standard limit for the j-th indicator.

8. The intelligent diversion control method for intercepting wells based on water quality soft measurement technology according to claim 7, characterized in that, In step S3, establishing a rapid inversion model based on feature indicators includes: S31, based on the calculation results of steps S1-S2, simultaneously measuring the easily measurable indicator Y and physicochemical indicator X of the water sample, and calculating the corresponding current water quality index W based on the physicochemical indicator X, and constructing a mapping dataset of easily measurable indicator Y and current water quality index W; S32, based on the mapping dataset and machine learning algorithm, establishing a highly correlated inversion function model between easily measurable indicator Y and current water quality index W.

9. The intelligent diversion control method for intercepting wells based on water quality soft measurement technology according to claim 8, characterized in that, In step S4, the intelligent diversion control of the intercepting well based on the fast inversion model includes: during the actual operation phase of the intercepting well, the system first performs a liquid level safety scan and obtains the real-time water level through the liquid level gauge. And in accordance with the preset flood prevention warning water level Perform a comparison; if If the system determines the situation to be "high water volume risk type," then regardless of water quality, the system will forcibly enter the flood prevention emergency mode, prioritizing the opening of overflow valves for direct discharge of floodwater to ensure urban safety; if The system enters water quality discrimination mode. At this time, the easily measurable index Y of the real-time collected incoming water is substituted into the highly correlated inversion function model to obtain the current water quality index W. The system automatically compares the current water quality index W with... Perform a comparison, if If it is determined to be a type that can be arranged in a straight line, then It was determined to be a type that requires interception and diversion.

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