A Smart Dosing Control Method and System for Industrial Wastewater Treatment Plants Based on Multi-Parameter Adaptation

Abstract

This invention discloses an intelligent dosing control method and system for industrial wastewater treatment plants based on multi-parameter adaptive dosing, relating to the field of industrial wastewater control technology. By deploying water quality monitoring devices within a multi-parameter acquisition location group Pos, an original multi-parameter feature vector group Vec is constructed. Based on this, a regional dosing efficiency deficiency factor matrix Mat is calculated, thereby achieving quantitative identification of dosing response failure areas in the spatial structure of industrial wastewater treatment. Combined with a preset response failure judgment threshold Tau, the proportion of response failure areas Rtd is calculated, providing a quantitative indicator for identifying locally ineffective or inefficient dosing areas. The regional dosing efficiency deficiency factor matrix Mat is used to differentiate the original dosing plan dose vector Cref, generating a corrected dose vector Adj, achieving true spatially differentiated dosing control and effectively avoiding resource waste and reduced treatment efficiency caused by uneven dosing diffusion in traditional schemes.

Description

Technical Field

[0001] This invention relates to the field of industrial wastewater control technology, specifically to an intelligent dosing control method and system for industrial wastewater treatment plants based on multi-parameter adaptive control. Background Technology

[0002] In modern industrial systems, water treatment technology, as an important branch of environmental engineering, serves as a bridge connecting industrial production and ecological environmental protection. Industrial wastewater treatment, as a core area within water treatment, is a key focus for process optimization research and the application of intelligent technologies due to its complex pollutant composition, drastic fluctuations, and significant treatment challenges. In specific industrial wastewater treatment scenarios, chemical dosing control systems are often deployed in sedimentation tanks, coagulation tanks, or neutralization reaction units, undertaking crucial tasks such as pH adjustment, heavy metal removal, and suspended particulate removal.

[0003] In traditional chemical dosing control processes, it is often assumed that the added chemicals will rapidly and evenly diffuse into the water body, participating in subsequent reactions such as neutralization, coagulation, and sedimentation. However, in actual operation, due to complex hydraulic factors such as uneven water flow velocity distribution, local turbulent structures, dead zones in the tank, or asymmetric inflow and outflow, some areas may not be adequately mixed with the added chemicals or may not react with the pollutants at all, forming "reaction failure zones." In these areas, even if sensors indicate that the chemicals have been added and reached the set dosage, the actual reaction efficiency may be far lower than expected due to insufficient diffusion or the existence of hydraulic blind spots.

[0004] Meanwhile, traditional control strategies often lack the ability to perceive and respond to such spatial non-uniformity. In such systems, parameters such as pH, conductivity, and ORP are usually collected at a single measuring point, making it difficult to reflect the spatial distribution of the agent throughout the entire tank. More importantly, the existence of these "failure zones" not only leads to insufficient efficacy but also induces a chain reaction of control misjudgments. This can cause the system to judge the efficacy as substandard based on global feedback, thereby further increasing the dosage, ultimately resulting in increased energy consumption, higher operating costs, and problems such as side reactions or overtreatment. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent dosing control method and system for industrial wastewater treatment plants based on multi-parameter adaptive dosing, solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a smart dosing control method for industrial wastewater treatment plants based on multi-parameter adaptive control, comprising the following steps:

[0007] S1. By deploying water quality monitoring devices in the wastewater treatment multi-parameter acquisition location group Pos, the original environmental response parameters of each wastewater treatment multi-parameter acquisition location group Pos are collected, and the original multi-parameter feature vector group Vec of the industrial wastewater treatment system is constructed.

[0008] S2. Based on the constructed original multi-parameter feature vector group Vec, construct the regional efficacy residual factor matrix Mat of the wastewater treatment multi-parameter acquisition location group Pos;

[0009] S3. Based on the regional drug efficacy deficiency factor matrix Mat and the preset response failure judgment threshold Tau, calculate the proportion of response failure areas Rtd, which is used to identify areas with low drug efficacy utilization in the wastewater treatment multi-parameter collection location group Pos.

[0010] S4. Based on the regional drug efficacy deficiency factor matrix Mat and the original dosing plan dose vector Cref, a modified dose vector Adj is generated to form a spatially differentiated dosing strategy for the multi-parameter acquisition location group Pos of wastewater treatment.

[0011] S5. Using the modified dose vector Adj and the drug response efficiency function Eta, calculate the comprehensive dosing response effectiveness index Eff to determine the overall effectiveness of the current dosing strategy.

[0012] Preferably, S1 includes S11 and S12;

[0013] S11. At each sampling point Pnt∈Pos in the preset multiple wastewater treatment multi-parameter acquisition location group Pos, a water quality monitoring device is deployed to collect the original environmental response parameters of each wastewater treatment multi-parameter acquisition location group Pos in real time.

[0014] The water quality monitoring device includes a water flow velocity sensor, a pH sensor, and a conductivity sensor;

[0015] The original environmental response parameters include the water flow velocity Vel(Pnt,t) at sampling point Pnt at time t, the pH value Phv(Pnt,t) at sampling point Pnt at time t, and the conductivity Con(Pnt,t) at sampling point Pnt at time t.

[0016] Preferably, in step S12, based on the acquired original environmental response parameters, the rate of change of response per unit time is calculated for each sampling point Pnt. Specifically, this is obtained by calculating the rate of change between time t and time t-Δt, where Δt represents the time interval, thus forming a derivative parameter that reflects the characteristics of instantaneous disturbance.

[0017] The derivative parameters include the change in pH value at sampling point Pnt at time t, Dph(Pnt,t), and the change in conductivity at sampling point Pnt at time t, Dct(Pnt,t).

[0018] By integrating derivative parameters and original environmental response parameters, the original multi-parameter feature vector group Vec(Pnt,t) = {Dph(Pnt,t), Dct(Pnt,t), Vel(Pnt,t), Phv(Pnt,t), Con(Pnt,t)} of the sampling point Pnt in the industrial wastewater treatment system at time t is constructed.

[0019] Preferably, S2 includes S21 and S22;

[0020] S21. Based on the water flow velocity Vel(Pnt,t), pH change Dph(Pnt,t), and conductivity change rate Dct(Pnt,t) in the original multi-parameter feature vector group Vec(Pnt,t) of the sampling point Pnt at time t, construct the reagent reaction efficiency function Eta(Pnt,t) of the sampling point Pnt at time t.

[0021] Preferably, in step S22, the drug response efficiency function Eta(Pnt,t) of all sampling points Pnt at time point t is normalized, and the maximum value is extracted and compared with the drug response efficiency function Eta(Pnt,t) of each sampling point Pnt at time point t to obtain the regional drug efficacy deficiency factor Mat(pnt) of each sampling point Pnt. The regional drug efficacy deficiency factors Mat(pnt) of all sampling points Pnt are integrated to construct a matrix representing the degree of regional drug efficacy decay of each sampling point, and the regional drug efficacy deficiency factor matrix Mat is obtained.

[0022] Preferably, S3 includes S31 and S32;

[0023] S31. Based on the regional pharmacodynamic deficiency factor matrix Mat and the preset response failure judgment threshold Tau, determine whether each sampling point Pnt meets the response failure condition and obtain the response failure status label Flg of each sampling point Pnt.

[0024] The response failure status label Flg of the sampling point Pnt is obtained through the following response failure conditions:

[0025] When the regional drug efficacy deficiency factor Mat(pnt) of sampling point Pnt is greater than or equal to the response failure judgment threshold Tau, the response failure status label Flg(Pnt) of sampling point Pnt is obtained as 1, indicating that sampling point Pnt is a region with unqualified drug efficacy utilization.

[0026] When the regional drug efficacy deficiency factor Mat(pnt) of sampling point Pnt is less than the response failure judgment threshold Tau, the response failure status label Flg(Pnt) of sampling point Pnt is obtained as 0, indicating that sampling point Pnt is a region with qualified drug efficacy utilization.

[0027] S32. By extracting the number of sampling points Pnt with response failure state label Flg=1 for all marked sampling points Pnt in the regional drug efficacy deficiency factor matrix Mat, and then calculating the proportion between them and the wastewater treatment multi-parameter collection location group Pos, the proportion of the reaction failure area Rtd is obtained.

[0028] Preferably, S4 includes S41;

[0029] S41. Based on the regional drug efficacy deficiency factor matrix Mat and the original dosing plan dose vector Cref, a modified dose vector Adj(Pnt) is generated for each sampling point Pnt. This vector is used to modify the dosing strategy for the sampling point Pnt, forming a spatially differentiated dosing strategy for the multi-parameter collection location group Pos in wastewater treatment.

[0030] Wherein, the original dosing plan dose vector Cref represents the preset dosing dose for industrial wastewater;

[0031] The corrected dose vector Adj(Pnt) for the sampling point Pnt is obtained using the following formula:

[0032] ;

[0033] In the formula, Cref(Pnt) represents the original dosing plan dose vector of the sampling point Pnt, and Alp represents the dose correction adjustment coefficient, which is used to adjust the sensitivity of the degree of incompleteness to dose correction. The specific value is set by the user.

[0034] Preferably, S5 includes S51 and S52;

[0035] S51. Based on the obtained modified dose vector Adj and the drug response efficiency function Eta, calculate the comprehensive drug dosing response effectiveness index Eff;

[0036] The comprehensive dosing response effectiveness index Eff is obtained through the following calculation formula:

[0037] ;

[0038] In the formula, Eta(Pnt,t) represents the drug response efficiency function of sampling point Pnt at time point t, and Mat(pnt) represents the regional pharmacodynamic deficiency factor of sampling point Pnt.

[0039] Preferably, in step S52, the overall dosing response effectiveness index Eff is compared with the preset system response evaluation threshold Thr, and the execution of the dosing control strategy is triggered based on the comparison result to determine the overall effectiveness of the current dosing strategy.

[0040] The execution of the dosing control strategy is triggered by the following comparison mechanism:

[0041] When the comprehensive dosing response effectiveness index Eff < the system response evaluation threshold Thr, the current dosing response effect is deemed unqualified, indicating that the current original dosing plan dose vector Cref reflects the efficiency unqualified. Steps S1 to S4 are executed again to generate a new corrected dose vector Adj and a second comparison is performed.

[0042] When the comprehensive dosing response effectiveness index Eff ≥ the system response evaluation threshold Thr, the current dosing response effect is deemed qualified, and the dosing dose is the generated modified dose vector Adj. At the same time, the modified dose vector Adj can be used as an adaptive update reference for the original dosing plan dose vector Cref to form a new version of the original dosing plan dose vector, and the original dosing plan dose vector Cref can continue to be used.

[0043] A multi-parameter adaptive intelligent dosing control system for industrial wastewater treatment plants includes a water quality data acquisition module, a regional dosing efficacy detection module, a regional dosing efficacy response determination module, a correction module, and a decision-making module.

[0044] The water quality data acquisition module collects the original environmental response parameters of each wastewater treatment multi-parameter acquisition location group Pos by deploying water quality monitoring devices in the wastewater treatment multi-parameter acquisition location group Pos, and constructs the original multi-parameter feature vector group Vec of the industrial wastewater treatment system.

[0045] The regional efficacy detection module constructs the regional efficacy residual factor matrix Mat of the wastewater treatment multi-parameter acquisition location group Pos based on the constructed original multi-parameter feature vector group Vec;

[0046] The regional drug efficacy response determination module calculates the proportion of response failure areas Rtd based on the regional drug efficacy defect factor matrix Mat and the preset response failure determination threshold Tau, which is used to identify areas with low drug efficacy utilization in the wastewater treatment multi-parameter acquisition location group Pos.

[0047] The correction module generates a corrected dose vector Adj based on the regional drug efficacy deficiency factor matrix Mat and the original dosing plan dose vector Cref, forming a spatially differentiated dosing strategy for the multi-parameter acquisition location group Pos of wastewater treatment.

[0048] The decision module uses the modified dose vector Adj and the drug response efficiency function Eta to calculate the comprehensive dosing response effectiveness index Eff and determine the overall effectiveness of the current dosing strategy.

[0049] This invention provides a method and system for intelligent dosing control in industrial wastewater treatment plants based on multi-parameter adaptive control, which has the following beneficial effects:

[0050] (1) By deploying water quality monitoring devices in the multi-parameter acquisition location group Pos of wastewater treatment, an original multi-parameter feature vector group Vec is constructed, and the regional drug efficacy deficiency factor matrix Mat is calculated on this basis, thereby realizing the quantitative identification of "drug response failure area" in the spatial structure of industrial wastewater treatment. Subsequently, combined with the preset response failure judgment threshold Tau, the proportion of response failure area Rtd is calculated, providing a quantitative indicator for identifying local dosing ineffective or inefficient areas. The original dosing plan dose vector Cref is differentiated and modified using the regional drug efficacy deficiency factor matrix Mat to generate the modified dose vector Adj, realizing true "spatial differentiated dosing control", effectively avoiding the resource waste and treatment efficiency reduction caused by uneven dosing diffusion in traditional schemes. Finally, by constructing a comprehensive dosing response effectiveness index Eff, and combining the comparison results of this index with the system response evaluation threshold Thr, the modified dose vector Adj is dynamically adjusted, thereby forming a closed-loop control mechanism with response assessment, strategy judgment, and adaptive correction.

[0051] (2) Based on the combined judgment mechanism of the regional pharmacodynamic inefficiency factor matrix Mat and the response failure judgment threshold Tau, the proportion of response failure areas Rtd, which is statistically derived from the response failure status label Flg(Pnt) of all sampling points Pnt, not only quantifies the overall proportion of inefficient areas of the system, but also provides a macroscopic judgment threshold for whether to trigger the correction strategy. When the judgment is valid, the correction dose vector Adj, constructed by combining the regional pharmacodynamic inefficiency factor matrix Mat with the original dosing plan dose vector Cref, enables the system to accurately adjust the dosage according to the degree of inefficiency of each sampling point Pnt, forming a dosing control scheme with spatially differentiated structure. This mechanism significantly improves the control system's ability to handle "regional response inefficiency", enabling the dosing strategy to evolve from "uniform dosage" to "dynamic adjustment according to response performance", effectively avoiding problems such as local over-dosing and insufficient dose in the boundary area.

[0052] (3) By constructing and calculating the comprehensive dosing response effectiveness index Eff, a global and quantitative evaluation of the current dosing response status of the entire wastewater treatment system is realized, and the overall effectiveness of the current dosing strategy can be identified autonomously. When the dosing response does not meet the standard, steps S1 to S4 are automatically backtracked to regenerate the corrected dose vector Adj(Pnt) and verify its response performance again. When the dosing response meets the expected standard, the system not only actually executes the generated corrected dose vector Adj(Pnt), but also uses it as a feedback basis to dynamically optimize the original dosing plan dose vector Cref(Pnt), realizing the continuous evolution of control experience. This breaks the limitations of traditional industrial dosing systems that rely on fixed dose rules and lack data feedback judgment. It constructs an intelligent closed-loop control framework that drives strategy evolution with response performance and quantifies the quality of the strategy with system-level indicators, which greatly improves the adaptability, self-learning ability and stability of the control strategy. It is especially suitable for dynamic treatment scenarios with frequent fluctuations in wastewater quality and complex pollutant structures. Attached Figure Description

[0053] Figure 1 This is a schematic diagram illustrating the steps of an intelligent dosing control method for industrial wastewater treatment plants based on multi-parameter adaptive control, as described in this invention.

[0054] Figure 2 This is a schematic diagram of a multi-parameter adaptive intelligent dosing control system for industrial wastewater treatment plants according to the present invention.

[0055] Figure 3 This is a schematic diagram of the regional pharmacodynamic deficiency factor matrix Mat. Detailed Implementation

[0056] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0057] Example 1

[0058] This invention provides a smart dosing control method for industrial wastewater treatment plants based on multi-parameter adaptive control. Please refer to [link / reference]. Figure 1 This includes the following steps:

[0059] S1. By deploying water quality monitoring devices in the wastewater treatment multi-parameter acquisition location group Pos, the original environmental response parameters of each wastewater treatment multi-parameter acquisition location group Pos are collected, and the original multi-parameter feature vector group Vec of the industrial wastewater treatment system is constructed.

[0060] S2. Based on the constructed original multi-parameter feature vector group Vec, construct the regional efficacy residual factor matrix Mat of the wastewater treatment multi-parameter acquisition location group Pos;

[0061] S3. Based on the regional drug efficacy deficiency factor matrix Mat and the preset response failure judgment threshold Tau, calculate the proportion of response failure areas Rtd, which is used to identify areas with low drug efficacy utilization in the wastewater treatment multi-parameter collection location group Pos.

[0062] S4. Based on the regional drug efficacy deficiency factor matrix Mat and the original dosing plan dose vector Cref, a modified dose vector Adj is generated to form a spatially differentiated dosing strategy for the multi-parameter acquisition location group Pos of wastewater treatment.

[0063] S5. Using the modified dose vector Adj and the drug response efficiency function Eta, calculate the comprehensive dosing response effectiveness index Eff to determine the overall effectiveness of the current dosing strategy.

[0064] In this embodiment, a water quality monitoring device deployed within the multi-parameter acquisition location group Pos of wastewater treatment is used to construct an original multi-parameter feature vector group Vec. Based on this, a regional drug efficacy deficiency factor matrix Mat is calculated, thereby realizing the quantitative identification of "drug response failure areas" in the spatial structure of industrial wastewater treatment. Subsequently, combined with a preset response failure judgment threshold Tau, the proportion of response failure areas Rtd is calculated, providing a quantitative indicator for identifying local dosing ineffective or inefficient areas. The regional drug efficacy deficiency factor matrix Mat is used to differentiate the original dosing plan dose vector Cref, generating a corrected dose vector Adj, realizing true "spatial differentiated dosing control". This effectively avoids resource waste and treatment efficiency reduction caused by uneven dosing diffusion in traditional schemes. Finally, by constructing a comprehensive dosing response effectiveness index Eff and combining the comparison results of this index with the system response evaluation threshold Thr, the decision on whether to execute the corrected dose vector Adj is dynamically adjusted, thus forming a closed-loop control mechanism with response assessment, strategy judgment, and adaptive correction. Therefore, this not only significantly improves the spatial uniformity of the dosing reaction and the synergy of the system response, but also solves the key problems in existing technologies, such as the difficulty in identifying reaction dead zones, the difficulty in quantifying dosing failure areas, and the inability to reconstruct control strategies for spatial heterogeneity.

[0065] Example 2

[0066] Specifically: S1 includes S11 and S12;

[0067] S11. At each sampling point Pnt∈Pos in the preset multiple wastewater treatment multi-parameter acquisition location group Pos, a water quality monitoring device is deployed to collect the original environmental response parameters of each wastewater treatment multi-parameter acquisition location group Pos in real time.

[0068] The water quality monitoring device includes a water flow velocity sensor, a pH sensor, and a conductivity sensor;

[0069] The original environmental response parameters include the water flow velocity Vel(Pnt,t) at sampling point Pnt at time t, the pH value Phv(Pnt,t) at sampling point Pnt at time t, and the conductivity Con(Pnt,t) at sampling point Pnt at time t.

[0070] S12. Based on the acquired original environmental response parameters, calculate the rate of change of response per unit time for each sampling point Pnt. Specifically, the rate of change between time t and time t-Δt is obtained by calculating the rate of change between time t and time t-Δt, where Δt represents the time interval, forming a derivative parameter that reflects the characteristics of instantaneous disturbance.

[0071] The derivative parameters include the change in pH value at sampling point Pnt at time t, Dph(Pnt,t), and the change in conductivity at sampling point Pnt at time t, Dct(Pnt,t).

[0072] The pH change Dph(Pnt,t) at the sampling point Pnt over time t is obtained using the following formula:

[0073] ;

[0074] The rate of change of conductivity Dct(Pnt,t) at the sampling point over time t is obtained using the following formula:

[0075] ;

[0076] By integrating derivative parameters and original environmental response parameters, the original multi-parameter feature vector group Vec(Pnt,t) = {Dph(Pnt,t), Dct(Pnt,t), Vel(Pnt,t), Phv(Pnt,t), Con(Pnt,t)} of the sampling point Pnt in the industrial wastewater treatment system at time t is constructed.

[0077] In this embodiment, real-time acquisition of raw environmental response parameters, including water flow velocity Vel(Pnt,t), pH value Phv(Pnt,t), and conductivity Con(Pnt,t), not only achieves spatially distributed monitoring of the wastewater system's operating status but also introduces dynamic response characteristics in the time dimension. By calculating the rate of change of sampling point Pnt between time t and time t−Δt, derivative parameters such as pH change Dph(Pnt,t) and conductivity change Dct(Pnt,t) are constructed, integrating "instantaneous disturbance behavior" as diagnostic basis data into the wastewater treatment process control. Based on the joint expression of derivative parameters and raw environmental response parameters, the constructed raw multi-parameter feature vector group Vec(Pnt,t) can comprehensively characterize the dynamic state of water quality at a specific time point, thus providing extremely high temporal sensitivity and parameter linkage for subsequent regional efficacy response evaluation and control strategy optimization. Compared with existing technologies that rely solely on static concentration values ​​or single-parameter triggering for dosing decisions, this feature vector construction method significantly enhances the system's ability to perceive subtle changes in water quality, possesses stronger predictability and feedforward control potential, and is particularly suitable for industrial wastewater treatment scenarios with narrow reaction windows and fast disturbance responses.

[0078] Example 3

[0079] Please see Figure 1 and Figure 3 Specifically: S2 includes S21 and S22;

[0080] S21. Based on the water flow velocity Vel(Pnt,t), pH change Dph(Pnt,t), and conductivity change rate Dct(Pnt,t) in the original multi-parameter feature vector group Vec(Pnt,t) at time t of sampling point Pnt, construct the drug response efficiency function Eta(Pnt,t) of sampling point Pnt at time t, which reflects the response efficiency of unit dose at sampling point Pnt. The higher the value, the more significant the drug effect of sampling point Pnt.

[0081] The drug reaction efficiency function Eta(Pnt,t) at sampling point Pnt at time point t is constructed in the following way:

[0082] ;

[0083] In the formula, Eps represents a minimum constant used to prevent division by zero, and Lam represents a weighting coefficient used to adjust the contribution ratio of pH change Dph(Pnt,t) and conductivity change Dct(Pnt,t).

[0084] S22. Normalize the drug response efficiency function Eta(Pnt,t) of all sampling points Pnt at time point t, and compare the maximum value with the drug response efficiency function Eta(Pnt,t) of each sampling point Pnt at time point t to obtain the regional drug efficacy deficiency factor Mat(pnt) of each sampling point Pnt. Integrate the regional drug efficacy deficiency factors Mat(pnt) of all sampling points Pnt to construct a matrix representing the degree of regional drug efficacy decay of each sampling point, and obtain the regional drug efficacy deficiency factor matrix Mat.

[0085] The regional pharmacodynamic deficiency factor matrix Mat is obtained through the following calculation formula:

[0086] ;

[0087] In the formula, Mat(Pnt) represents the regional pharmacodynamic residual factor of the sampling point Pnt relative to the maximum drug response efficiency function Eta(Pnt,t), and max represents the maximum value function, which is used to extract the maximum drug response efficiency function Eta(Pnt,t) at time t.

[0088] Example illustration of constructing the drug response efficiency function Eta(Pnt,t) and the regional pharmacodynamic residual factor matrix Mat:

[0089] Taking three sampling points Pnt=1, Pnt=2, and Pnt=3 set in an industrial wastewater treatment section as an example, the original multi-parameter feature vector group Vec(Pnt,t) at the same time point t includes the following parameter values:

[0090] Sampling point Pnt=1: water flow velocity Vel(Pnt,t)=0.85; pH change Dph(Pnt,t)=0.18; conductivity change rate Dct(Pnt,t)=0.11;

[0091] Sampling point Pnt=2: water flow velocity Vel(Pnt,t)=0.42; pH change Dph(Pnt,t)=0.05; conductivity change rate Dct(Pnt,t)=0.03;

[0092] Sampling point Pnt=3: water flow velocity Vel(Pnt,t)=0.73; pH change Dph(Pnt,t)=0.12; conductivity change rate Dct(Pnt,t)=0.07;

[0093] The formula for calculating the drug reaction efficiency function Eta(Pnt,t) at sampling point Pnt at time t is as follows:

[0094] Sampling point Pnt=1: Eta(Pnt=1,t)=0.235 / 0.851≈0.276;

[0095] Sampling point Pnt=2: Eta(Pnt=2,t)=0.065 / 0.421≈0.1544;

[0096] Sampling point Pnt=3: Eta(Pnt=3,t)=0.155 / 0.731≈0.212;

[0097] The Eta(Pnt,t) values ​​from the three sampling points are normalized to obtain the regional pharmacodynamic deficiency factor Mat(Pnt) for each sampling point.

[0098] Sampling point Pnt=1: Mat(Pnt=1,t)=1-0.276 / 0.276=0;

[0099] Sampling point Pnt=2: Mat(Pnt=2,t)=1-0.1544 / 0.2760≈0.44;

[0100] Sampling point Pnt=3: Mat(Pnt=3,t)=1-0.212 / 0.276≈0.23;

[0101] In this embodiment, by introducing a drug reaction efficiency function Eta(Pnt,t) at the sampling point Pnt, with water flow velocity Vel(Pnt,t), pH change Dph(Pnt,t), and conductivity change Dct(Pnt,t) as core parameters, real-time modeling of the reaction activity of a unit dose of drug at different treatment locations is achieved. This function not only quantifies the strength of the reaction efficiency but also controls the relative contribution ratio of the two disturbance sources, pH and conductivity, to the drug efficacy performance using an adjustment coefficient Lam. Compared with existing technologies that judge drug efficacy solely based on changes in terminal water quality, this function has stronger local resolution and real-time response sensitivity. Furthermore, by using a maximum value normalization method to characterize the differences in the drug reaction efficiency function Eta(Pnt,t) at all sampling points Pnt, a regional drug efficacy deficiency factor Mat(Pnt) is constructed, thereby realizing the quantitative expression of the spatial decay of drug efficacy within the system. The pharmacodynamic deficiency factor matrix Mat in this region enables the system not only to identify the problem of "how much drug to add", but also to identify the problem of "which areas are ineffective or inefficient in drug addition". This greatly enhances the system's ability to identify reaction dead zones and potential waste areas, fills the key gap in traditional dosing control that lacks spatial response difference expression methods, and provides a data-driven response benchmark for subsequent dosing correction strategies.

[0102] Example 4

[0103] Specifically: S3 includes S31 and S32;

[0104] S31. Based on the regional pharmacodynamic deficiency factor matrix Mat and the preset response failure judgment threshold Tau, determine whether each sampling point Pnt meets the response failure condition and obtain the response failure status label Flg of each sampling point Pnt.

[0105] The response failure status label Flg of the sampling point Pnt is obtained through the following response failure conditions:

[0106] When the regional drug efficacy deficiency factor Mat(pnt) of sampling point Pnt is greater than or equal to the response failure judgment threshold Tau, the response failure status label Flg(Pnt) of sampling point Pnt is obtained as 1, indicating that sampling point Pnt is a region with unqualified drug efficacy utilization.

[0107] When the regional drug efficacy deficiency factor Mat(pnt) of sampling point Pnt is less than the response failure judgment threshold Tau, the response failure status label Flg(Pnt) of sampling point Pnt is obtained as 0, indicating that sampling point Pnt is a region with qualified drug efficacy utilization.

[0108] S32. By extracting the number of sampling points Pnt with response failure state label Flg=1 for all marked sampling points Pnt in the regional drug efficacy deficiency factor matrix Mat, and then calculating the proportion between them and the wastewater treatment multi-parameter collection location group Pos, the proportion of the reaction failure area Rtd is obtained.

[0109] S4 includes S41;

[0110] S41. Based on the regional drug efficacy deficiency factor matrix Mat and the original dosing plan dose vector Cref, a modified dose vector Adj(Pnt) is generated for each sampling point Pnt. This vector is used to modify the dosing strategy for the sampling point Pnt, forming a spatially differentiated dosing strategy for the multi-parameter collection location group Pos in wastewater treatment.

[0111] Wherein, the original dosing plan dose vector Cref represents the preset dosing dose for industrial wastewater;

[0112] The corrected dose vector Adj(Pnt) for the sampling point Pnt is obtained using the following formula:

[0113] ;

[0114] In the formula, Cref(Pnt) represents the original dosing plan dose vector of the sampling point Pnt, and Alp represents the dose correction adjustment coefficient, which is used to adjust the sensitivity of the degree of incompleteness to dose correction. The specific value is set by the user.

[0115] In this embodiment, based on the combined judgment mechanism of the regional pharmacodynamic deficiency factor matrix Mat and the response failure judgment threshold Tau, this invention introduces a response failure status label Flg(Pnt) for the first time in industrial wastewater treatment dosing control to mark whether the sampling point Pnt has a problem of low pharmacodynamic response. Compared with the traditional "overall error scoring" or "global concentration deviation" method, the introduction of the status label Flg(Pnt) enables the system to have point-by-point response judgment capability, thereby effectively identifying local dead zones or inefficient response areas. Furthermore, the proportion of inefficient response areas Rtd, calculated based on the response failure status labels Flg(Pnt) of all sampling points Pnt, not only quantifies the overall proportion of inefficient areas in the system, but also provides a macroscopic judgment threshold for whether to trigger a correction strategy. When the judgment is valid, the correction dose vector Adj, constructed by combining the regional pharmacodynamic deficiency factor matrix Mat with the original dosing plan dose vector Cref, enables the system to accurately adjust the dosage according to the degree of deficiency of each sampling point Pnt, forming a dosing control scheme with spatially differentiated structure. This mechanism significantly improves the control system's ability to handle "regional response ineffectiveness," enabling the dosing strategy to evolve from "uniform dosage" to "dynamic adjustment based on response performance." It effectively avoids problems such as local overdosing and insufficient dosage in boundary areas, and solves the core shortcomings of traditional systems in dealing with spatial drug efficacy heterogeneity, such as crude control methods and insensitive feedback.

[0116] Example 5

[0117] Specifically: S5 includes S51 and S52;

[0118] S51. Based on the obtained modified dose vector Adj and the drug response efficiency function Eta, calculate the comprehensive drug dosing response effectiveness index Eff;

[0119] The comprehensive dosing response effectiveness index Eff is obtained through the following calculation formula:

[0120] ;

[0121] In the formula, Eta(Pnt,t) represents the drug response efficiency function of sampling point Pnt at time point t, and Mat(pnt) represents the regional pharmacodynamic deficiency factor of sampling point Pnt.

[0122] S52. Compare the comprehensive dosing response effectiveness index Eff with the preset system response evaluation threshold Thr, and trigger the execution of the dosing control strategy based on the comparison result to determine the overall effectiveness of the current dosing strategy.

[0123] The execution of the dosing control strategy is triggered by the following comparison mechanism:

[0124] When the comprehensive dosing response effectiveness index Eff < the system response evaluation threshold Thr, the current dosing response effect is deemed unqualified, indicating that the current original dosing plan dose vector Cref reflects the efficiency unqualified. Steps S1 to S4 are executed again to generate a new corrected dose vector Adj and a second comparison is performed.

[0125] When the comprehensive dosing response effectiveness index Eff ≥ the system response evaluation threshold Thr, the current dosing response effect is deemed qualified, and the dosing dose is the generated modified dose vector Adj. At the same time, the modified dose vector Adj can be used as an adaptive update reference for the original dosing plan dose vector Cref to form a new version of the original dosing plan dose vector, and the original dosing plan dose vector Cref can continue to be used.

[0126] In this embodiment, by constructing and calculating the comprehensive dosing response effectiveness index Eff, a global and quantitative evaluation of the current dosing response status of the entire wastewater treatment system is achieved. This not only considers the reagent reaction efficiency function Eta(Pnt,t) at the sampling point Pnt, but also integrates the spatial failure characteristics of the regional residual effect factor Mat(Pnt), effectively avoiding the blind spot in traditional control systems where "excellent local response masks global failure." Through the comparison mechanism between the comprehensive dosing response effectiveness index Eff and the system response evaluation threshold Thr, the system can autonomously identify the overall effectiveness of the current dosing strategy. When the dosing response fails to meet the standard, steps S1 to S4 are automatically backtracked to regenerate the corrected dose vector Adj(Pnt) and verify its response performance again. When the dosing response meets the expected standard, the system not only actually executes the generated corrected dose vector Adj(Pnt), but also uses it as feedback to dynamically optimize the original dosing plan dose vector Cref(Pnt), realizing the continuous evolution of control experience. This breaks the limitations of traditional industrial dosing systems that rely on fixed dose rules and lack data feedback for judgment. It constructs an intelligent closed-loop control framework that drives strategy evolution with response performance and quantifies the quality of the strategy with system-level indicators, greatly improving the adaptability, self-learning ability and stability of the control strategy. It is especially suitable for dynamic treatment scenarios with frequent fluctuations in wastewater quality and complex pollutant structures.

[0127] Example 6

[0128] A multi-parameter adaptive intelligent dosing control system for industrial wastewater treatment plants, please refer to... Figure 2 Specifically, it includes a water quality data acquisition module, a regional efficacy detection module, a regional efficacy response determination module, a correction module, and a decision-making module;

[0129] The water quality data acquisition module collects the original environmental response parameters of each wastewater treatment multi-parameter acquisition location group Pos by deploying water quality monitoring devices in the wastewater treatment multi-parameter acquisition location group Pos, and constructs the original multi-parameter feature vector group Vec of the industrial wastewater treatment system.

[0130] The regional efficacy detection module constructs the regional efficacy residual factor matrix Mat of the wastewater treatment multi-parameter acquisition location group Pos based on the constructed original multi-parameter feature vector group Vec;

[0131] The regional drug efficacy response determination module calculates the proportion of response failure areas Rtd based on the regional drug efficacy defect factor matrix Mat and the preset response failure determination threshold Tau, which is used to identify areas with low drug efficacy utilization in the wastewater treatment multi-parameter acquisition location group Pos.

[0132] The correction module generates a corrected dose vector Adj based on the regional drug efficacy deficiency factor matrix Mat and the original dosing plan dose vector Cref, forming a spatially differentiated dosing strategy for the multi-parameter acquisition location group Pos of wastewater treatment.

[0133] The decision module uses the modified dose vector Adj and the drug response efficiency function Eta to calculate the comprehensive dosing response effectiveness index Eff and determine the overall effectiveness of the current dosing strategy.

[0134] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-parameter adaptive based intelligent dosing control method for industrial wastewater treatment plants, characterized in that: The method comprises the following steps: S1, collecting original environmental response parameters of each sewage treatment multi-parameter collection position group Pos by laying water quality monitoring devices in the sewage treatment multi-parameter collection position group Pos, and constructing an original multi-parameter feature vector group Vec of the industrial sewage treatment system; S2, constructing a regional drug efficacy deficiency factor matrix Mat of the sewage treatment multi-parameter collection position group Pos based on the constructed original multi-parameter feature vector group Vec; S3, calculating a reaction failure region proportion Rtd according to the regional drug efficacy deficiency factor matrix Mat and in combination with a preset response failure judgment threshold Tau, for identifying a region with low drug efficacy utilization rate in the sewage treatment multi-parameter collection position group Pos; S4, generating a corrected dose vector Adj based on the regional drug efficacy deficiency factor matrix Mat and an original dosing plan dose vector Cref, and forming a spatial differentiated dosing strategy for the sewage treatment multi-parameter collection position group Pos; S5, calculating a comprehensive dosing response effectiveness index Eff by using the corrected dose vector Adj and a drug reaction efficiency function Eta, and judging the overall effectiveness of the current dosing strategy.

2. The multi-parameter adaptive based intelligent dosing control method for industrial wastewater treatment plant of claim 1, wherein: The S1 comprises S11 and S12; S11, laying water quality monitoring devices at each sampling point Pnt in the preset plurality of sewage treatment multi-parameter collection position groups Pos to collect original environmental response parameters of each sewage treatment multi-parameter collection position group Pos in real time; The water quality monitoring device comprises a water flow velocity sensor, a pH value sensor, and a conductivity sensor; The original environmental response parameters comprise water flow velocity Vel(Pnt,t) of the sampling point Pnt at time t, pH value Phv(Pnt,t) of the sampling point Pnt at time t, and conductivity Con(Pnt,t) of the sampling point Pnt at time t.

3. The multi-parameter adaptive based intelligent dosing control method for industrial wastewater treatment plant of claim 2, wherein: S12, calculating the response change rate per unit time for each sampling point Pnt based on the obtained original environmental response parameters, specifically by calculating the change rate between time t and time t-△t, wherein △t represents a time interval, to obtain a derivative parameter reflecting instantaneous disturbance characteristics; The derivative parameter comprises pH value change amount Dph(Pnt,t) of the sampling point Pnt at time t and conductivity change rate Dct(Pnt,t) of the sampling point Pnt at time t; By integrating the derivative parameter and the original environmental response parameter, an original multi-parameter feature vector group Vec(Pnt,t) of the sampling point Pnt at time t in the industrial sewage treatment system is constructed, and the original multi-parameter feature vector group Vec(Pnt,t)={Dph(Pnt,t), Dct(Pnt,t), Vel(Pnt,t), Phv(Pnt,t), Con(Pnt,t)}.

4. The multi-parameter adaptive based intelligent dosing control method for industrial wastewater treatment plant of claim 3, wherein: The S2 comprises S21 and S22; S21, constructing a drug reaction efficiency function Eta(Pnt,t) of the sampling point Pnt at time t based on water flow velocity Vel(Pnt,t), pH value change amount Dph(Pnt,t), and conductivity change rate Dct(Pnt,t) in the original multi-parameter feature vector group Vec(Pnt,t) of the sampling point Pnt at time t.

5. The multi-parameter adaptive based intelligent dosing control method for industrial wastewater treatment plant of claim 4, wherein: S22, normalizing the medicament reaction efficiency function Eta(Pnt, t) of all sampling points Pnt at the time point t, and obtaining a regional drug efficacy deficiency factor Mat(pnt) of each sampling point Pnt by extracting the maximum value and comparing the medicament reaction efficiency function Eta(Pnt, t) of each sampling point Pnt at the time point t, integrating the regional drug efficacy deficiency factors Mat(pnt) of all sampling points Pnt to construct a matrix representing the attenuation degree of the regional drug efficacy of each sampling point, and obtaining the regional drug efficacy deficiency factor matrix Mat.

6. The multi-parameter adaptive based intelligent dosing control method for industrial wastewater treatment plant of claim 5, wherein: The S3 includes S31 and S32. S31, determining whether each sampling point Pnt meets the response failure condition according to the regional drug efficacy deficiency factor matrix Mat and combining a preset response failure judgment threshold Tau, and obtaining a response failure state label Flg of each sampling point Pnt. The response failure state label Flg of the sampling point Pnt is obtained by the following response failure condition: When the regional drug efficacy deficiency factor Mat(pnt) of the sampling point Pnt is greater than or equal to the response failure judgment threshold Tau, the response failure state label Flg(Pnt) of the sampling point Pnt is obtained as 1, indicating that the sampling point Pnt is a region with unqualified drug utilization rate; When the regional drug efficacy deficiency factor Mat(pnt) of the sampling point Pnt is less than the response failure judgment threshold Tau, the response failure state label Flg(Pnt) of the sampling point Pnt is obtained as 0, indicating that the sampling point Pnt is a region with qualified drug utilization rate; S32, obtaining the proportion of the number of sampling points Pnt with the response failure state label Flg=1 in the regional drug efficacy deficiency factor matrix Mat to the sewage treatment multi-parameter collection position group Pos, and obtaining the reaction failure region proportion Rtd.

7. The multi-parameter adaptive based intelligent dosing control method for industrial wastewater treatment plant of claim 6, wherein: The S4 includes S41. S41, generating a corrected dose vector Adj(Pnt) of each sampling point Pnt based on the regional drug efficacy deficiency factor matrix Mat and the original dosing plan dose vector Cref, for correcting the dosing strategy of the sampling point Pnt, and forming a spatially differentiated dosing strategy for the sewage treatment multi-parameter collection position group Pos; Wherein, the original dosing plan dose vector Cref represents the preset dosing dose of the industrial wastewater; The corrected dose vector Adj(Pnt) of the sampling point Pnt is obtained by the following calculation formula: ； Wherein, Cref(Pnt) represents the original dosing plan dose vector of the sampling point Pnt, Alp represents a dose correction adjustment coefficient, which is specifically used to adjust the sensitivity of the deficiency degree to the dose correction, and the specific value is set by the user.

8. The multi-parameter adaptive based intelligent dosing control method for industrial wastewater treatment plant of claim 7, wherein: The S5 includes S51 and S52. S51, calculating a comprehensive dosing response effectiveness index Eff based on the obtained corrected dose vector Adj and the medicament reaction efficiency function Eta.

9. The multi-parameter adaptive based intelligent dosing control method for industrial wastewater treatment plant of claim 8, wherein: S52, comparing the comprehensive dosing response effectiveness index Eff with a preset system response evaluation threshold Thr, and triggering the execution of the dosing control strategy according to the comparison result to determine the overall effectiveness of the current dosing strategy The execution of the dosing control strategy is triggered by the following comparison mechanism: When the comprehensive dosing response effectiveness index Eff is less than the system response evaluation threshold Thr, it is determined that the current dosing response effect is unqualified, indicating that the original dosing plan dose vector Cref reflects unqualified efficiency, and the steps S1 to S4 are executed again to generate a new modified dose vector Adj, and secondary comparison is performed; When the comprehensive dosing response effectiveness index Eff is greater than or equal to the system response evaluation threshold Thr, it is determined that the current dosing response effect is qualified, and the dosing dose executed is the generated modified dose vector Adj, and the modified dose vector Adj can be used as an adaptive update reference for the original dosing plan dose vector Cref to form a new version of the original dosing plan dose vector, and the original dosing plan dose vector Cref is continued to be used.

10. An intelligent dosing control system for industrial wastewater treatment plants based on multi-parameter self-adaptation, applied to the intelligent dosing control method for industrial wastewater treatment plants based on multi-parameter self-adaptation in any one of claims 1-9, characterized in that: The system comprises a water quality data acquisition module, a regional drug efficacy detection module, a regional drug efficacy response determination module, a correction module and a decision module. The water quality data acquisition module acquires original environmental response parameters of each sewage treatment multi-parameter acquisition position group Pos by arranging water quality monitoring devices in the sewage treatment multi-parameter acquisition position group Pos, and constructs an original multi-parameter feature vector group Vec of the industrial sewage treatment system. The regional drug efficacy detection module constructs a regional drug efficacy deficiency factor matrix Mat of the sewage treatment multi-parameter acquisition position group Pos based on the constructed original multi-parameter feature vector group Vec; The regional drug efficacy response determination module calculates a response failure area proportion Rtd according to the regional drug efficacy deficiency factor matrix Mat combined with a preset response failure determination threshold Tau, for identifying an area with low drug efficacy utilization rate in the sewage treatment multi-parameter acquisition position group Pos; The correction module generates a modified dose vector Adj based on the regional drug efficacy deficiency factor matrix Mat and the original dosing plan dose vector Cref, and forms a spatial differentiated dosing strategy for the sewage treatment multi-parameter acquisition position group Pos; The decision module calculates a comprehensive dosing response effectiveness index Eff by using the modified dose vector Adj and a drug reaction efficiency function Eta, and determines the overall effectiveness of the current dosing strategy.

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