Modularized whole-process high-quality direct drinking water treatment system and control method

Abstract

The invention discloses a modularized full-process high-quality direct drinking water treatment system and a control method, and belongs to the technical field of direct drinking water treatment.The modularized full-process high-quality direct drinking water treatment system is characterized in that an online sensor is deployed at a water inlet of a flocculation basin, raw water turbidity, temperature and flow parameters are collected in real time, and qualified samples are screened to construct a standardized water quality characteristic data set in combination with historical water quality and agent adding records; based on the data set, performing multi-dimensional clustering on historical water quality parameters, generating a characteristic working condition cluster, calculating a three-dimensional efficiency index, generating a dynamic dosage reference interval by using a sliding window confidence interval and a time decay factor, establishing a mapping relation between water quality characteristics and medicament dosage, and generating a graded dosage strategy; the transmembrane pressure difference and the membrane flux change rate are continuously monitored at the water inlet end in the membrane treatment stage, the membrane pollution trend is predicted through time sequence analysis, the prediction result is corrected according to the agent adding deviation, the precipitation effluent turbidity and the membrane inflow COD data, a cleaning early warning signal is generated, and graded response is performed according to the pollution degree.

Description

Technical Field

[0001] The present invention relates to the technical field of direct drinking water treatment, and in particular to a modular full-process high-quality direct drinking water treatment system and a control method thereof. Background Technique

[0002] The direct drinking water treatment system is a core facility to ensure the water safety of the public. Its treatment process usually includes flocculation, sedimentation, filtration, and disinfection and other links: the raw water first enters the flocculation tank, and the suspended solids and colloidal particles in the water are coagulated into larger flocs by adding flocculants; subsequently, in the sedimentation tank, the flocs settle due to gravity, realizing solid-liquid separation; in the filtration stage, the remaining tiny suspended solids in the water are further removed; finally, the pathogenic microorganisms are killed through the disinfection process to ensure that the quality of the effluent meets the drinking water standard.

[0003] However, although the existing technology has improved the treatment effect of direct drinking water to a certain extent, there are still many problems and challenges in practical applications: each treatment unit of the traditional water plant operates independently, and the connection efficiency is low, resulting in a long and complex treatment process, increased energy consumption and limited overall treatment efficiency; at the same time, the dosing of chemicals lacks a dynamic regulation mechanism, mostly relying on fixed empirical values or simple feedback control, and it is difficult to accurately adapt the dosing amount according to the real-time fluctuations of water quality, which not only causes waste of chemicals and increases operating costs, but also leads to unstable sedimentation effects due to dosing deviations, indirectly increasing the load of the subsequent membrane treatment link; especially when the flocculation effect is poor, the remaining tiny flocs and colloidal substances will accelerate the pollution of the membrane module, and the existing technology fails to effectively establish a dynamic relationship between chemical dosing and membrane pollution, lacking forward-looking prediction and active intervention in the development trend of membrane pollution, resulting in a shortened membrane cleaning cycle, reduced service life, and further pushing up the operation and maintenance costs. Summary of the Invention

[0004] The purpose of the present invention is to provide a modular full-process high-quality direct drinking water treatment system and a control method thereof to solve the problems raised in the above background technique.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: A modular full-process high-quality direct drinking water treatment control method, including: Step S100: Deploy an online sensor at the inlet of the flocculation tank to collect the turbidity, temperature, and flow parameters of the raw water in real time, synchronously collect historical water quality data and the corresponding chemical dosing records, and screen out qualified samples that meet the constraint conditions to form a standardized water quality characteristic data set; Step S200: Based on the standardized water quality characteristic data set, perform multi-dimensional clustering on the historical water quality parameters to generate characteristic working condition clusters, calculate the three-dimensional efficiency indexes of each cluster, and generate a dynamic dosing amount reference interval through a sliding window confidence interval and a time decay factor; Step S300: Establish a mapping relationship between water quality characteristics and chemical dosing amounts, and dynamically generate a hierarchical dosing strategy that matches the current water quality fluctuation characteristics; Step S400: After chemical dosing and sedimentation treatment, continuously monitor the transmembrane pressure difference and the change rate of membrane flux at the inlet end of the membrane treatment stage. Predict the development trend of membrane fouling through time-series data analysis; and dynamically correct the prediction results based on the deviation of chemical dosing results, the turbidity of sedimentation effluent, and the COD data of membrane inlet water. Generate a cleaning warning signal in advance based on the corrected results, and trigger responses according to the degree of fouling classification.

[0006] Further, step S100 includes: Step S101: Use the on-line turbidimeter, temperature sensor, and electromagnetic flowmeter deployed at the inlet of the flocculation tank to set the sampling frequency to obtain the raw water turbidity, temperature, and flow rate data in real time; all sensor data is transmitted to the central controller through analog signals for noise filtering and data normalization processing to form a standardized water quality characteristic data set; Step S102: Collect the water quality parameters at the inlet of the flocculation tank and the corresponding chemical dosing amount data in the past year. The water quality parameters include turbidity, pH, temperature, COD, dissolved oxygen, ammonia nitrogen, residual chlorine, and conductivity; the corresponding chemical dosing amount data includes multi-point chlorine dosing data and alum dosing data; divide the qualified area according to the pre-set factory residual chlorine threshold and pre-filtration turbidity threshold as constraints, screen the sample data, and construct a historical sample data set.

[0007] Further, step S200 includes: Step S201: Use the K-means++ algorithm to perform multi-dimensional clustering on the historical sample data set. By identifying the combined characteristics of water quality parameters, including 8-dimensional parameters of turbidity, pH, temperature, COD, dissolved oxygen, ammonia nitrogen, residual chlorine, and conductivity, divide the historical data points with similar operating conditions into independent clusters. Each cluster is associated with the multi-point chlorine dosing data sequence {C1, C2,..., C m} and the alum dosing data sequence {A1, A2,..., A m}, where C m represents the chlorine dosage applied at multiple points at the m-th sampling moment, and A m represents the flocculant dosage applied at multiple points at the m-th sampling moment; Establish a three-dimensional performance evaluation matrix for each cluster, and quantitatively characterize the treatment performance from three dimensions, including the residual chlorine decay coefficient, the flocculation efficiency index, and the chemical utilization rate; The residual chlorine decay coefficient is obtained by fitting the 24-hour residual chlorine decay curve, reflecting the decay characteristics of the disinfection effect over time, and is expressed as: K = ΔY / ΔT, where K represents the decay rate of the residual chlorine concentration per unit time, ΔY is the change in the residual chlorine concentration within 24 hours, and ΔT is the time interval, which is fixed at 24 hours; The flocculation efficiency index is calculated based on the difference between the influent turbidity and the settled water turbidity to evaluate the turbidity removal effect, and is expressed as: η = (T0 - T1) / T0, where η represents the removal efficiency of the flocculant for turbidity, T0 represents the influent turbidity of the flocculation tank, and T1 represents the effluent turbidity of the sedimentation tank; The drug utilization rate reflects the matching degree between the actual drug consumption and the dosage, and is expressed as: μ = Σ(C target ×Q j ) / Σ(C j ×Q j )), where μ represents the effective utilization ratio of the drug, C target represents the set dosage concentration, Q j is the instantaneous flow rate at the jth sampling moment, and C j is the drug dosage corresponding to the jth sampling moment; Step S202: After completing the performance evaluation, the moving average is calculated for the drug dosage sequence within each cluster using the sliding window algorithm: MA(t) = 1 / wΣ t i=t-n C i ), where MA(t) represents the moving average of the drug dosage for the w sampling points before the tth moment, n represents the size of the sliding window, t represents the current sampling moment, Ci represents the drug dosage for the ith time period, and the value range of i is [t - n, t], representing the data sequence traced back n cycles from the current moment t; The dynamic confidence coefficient Z(t) is introduced to construct the confidence interval [MA - Z(t)σ, MA + Z(t)σ] to form a dynamically adjusted dosage benchmark range; where Z(t) is adaptively adjusted according to the formula: Z(t) = Z0×[1 + α×(CV(t) / CV0 - 1)], where Z0 is the set benchmark coefficient, CV(t) is the coefficient of variation of the water quality parameters within the current window, CV0 is the historical average coefficient of variation, α is the adjustment parameter, and α ∈ [0.3, 0.7]; the coefficient of variation of the water quality parameters = the standard deviation of the n data points within the current sliding window / the moving average within the current sliding window; Finally, the time decay factor is introduced to dynamically correct the weight of historical data, and the weight of stale data is reduced through the exponential decay mechanism, which is expressed as: λ = e (-Δt / τ) ), where λ represents the time decay factor, and Δt is the time difference between the data recording moment and the current moment.

[0008] Furthermore, step S300 includes: Step S301: For each cluster, calculate the mean vector of its 8-dimensional water quality parameters, denoted as X c ; Extract the central value of the dynamic dosing amount benchmark range of this cluster in Step S202 as the benchmark dosing amount, denoted as D c = [V c , A c ; where V c is the benchmark chlorine dosage, and A c is the benchmark alum dosage; Establish a mapping function f from the water quality feature vector X to the chemical dosing amount D. For any real-time water quality feature vector X now , calculate its Euclidean distance d(X c , X now ) from all cluster centroids X c , and select the cluster to which the centroid with the minimum distance belongs: ; where f(X) represents the mapping function, Dp represents the benchmark chemical dosing amount corresponding to the p-th cluster, and Lp represents the water quality feature space of the p-th cluster, and the space is an Euclidean space region centered on the centroid X c with the within-cluster standard deviation as the radius; Step S302: According to the Euclidean distance d between the real-time water quality parameter X now and the centroid X c of the matching cluster, design a hierarchical response strategy: When the real-time water quality parameter X now satisfies d(X now , X c ) <= ε1, directly adopt the benchmark dosing amount D c , where ε1 is the stability threshold, set to 0.8 times the standard deviation of the within-cluster Euclidean distance; When ε1 < d(X now , X c ) <= ε2, trigger linear interpolation adjustment, according to the formula: ; where is the adjustment coefficient, is the gradient of the mapping function, and ε2 is the fluctuation threshold, set to 1.5 times the standard deviation of the within-cluster Euclidean distance; When d(X now , X c ) > ε2, initiate an emergency response: call the dosing amount of the nearest neighbor cluster as the initial value, and generate a correction amount ΔD PID based on the real-time feedback of the filtered turbidity and the residual chlorine in the effluent through a PID controller: ; where e(t) = [(T target - Tfilter ),(Cl target -Cl actual )], T target represents the set turbidity target value, T filter represents the real-time monitored value of the turbidity after filtration, Cl target represents the set residual chlorine target value, Cl actual represents the real-time monitored value of the residual chlorine in the effluent, K p , K i , K d represents the controller parameters.

[0009] Further, step S400 includes: Step S401: Real-time collect the transmembrane pressure difference through the pressure sensors at the inlet and outlet ends of the membrane module, synchronously monitor the membrane flux through the electromagnetic flowmeter, and obtain the influent chemical oxygen demand through the COD on-line monitor, denoted as the variable COD 进水 ; Calculate the average transmembrane pressure difference per hour, the membrane flux decay rate, and the average COD, and generate a time series data set; The average transmembrane pressure difference calculates the average value by the arithmetic mean method; The membrane flux decay rate first calculates the average membrane flux of the current hour, and then compares it with the initial membrane flux to obtain the membrane flux decay rate = (1 - average value / initial membrane flux) × 100%; The average COD is calculated by the weighted average method, with a higher weight assigned to the most recent sampling, and equal weights assigned to the remaining sampling points; Use the Kalman filter algorithm to suppress the noise of the original data, eliminate sensor drift and electromagnetic interference; Calibrate the parameter reference range based on historical operation data; Step S402: Select the transmembrane pressure difference data sequence of the previous 24 hours before the current moment as the core input feature, combine the membrane flux decay rate and the average influent COD, and construct a membrane fouling prediction model through a double-layer LSTM network. The network structure uses two hidden layers, each layer containing 64 neurons, followed by a fully connected layer to output the predicted values of the transmembrane pressure difference at the next 4 time points; Re-extract the full-condition operation data of the past 6 months from the time series data set, and divide it into a training set and a test set at a ratio of 8:2 to train the model; Based on the predicted values of the transmembrane pressure difference and the change rate, combine the membrane flux decay rate, set thresholds to divide the pollution risk into high, medium, and low levels, and set corresponding response measures; Step S403: Obtain the chemical agent dosage data, including recording the chlorine dosage and the alum dosage, which are respectively set as C 实际 , A 实际 ; For the deviation degree of the dosage from the reference value, calculate the chlorine dosage deviation ratio respectively: D L = ∣C 实际 - V C ∣ / V C and the alum dosage deviation ratio D F = A C ∣A实际 -A C ∣ / A C , then take the larger value of the two as the comprehensive index D of the dosing deviation 综合 =max(D L , D F ); at the outlet end of the sedimentation tank, collect the turbidity of the sedimented water through the deployed on-line turbidimeter at the same sampling frequency as the sensor at the inlet end of the flocculation tank, and set it as T 沉淀 , and compare it with the set standard value T 标准 of the turbidity of the sedimented water; the exceeding standard situation of the turbidity of the sedimented water is calculated by the exceeding standard multiple D T =max(0, T 沉淀 - T 标准 ) / T 标准 to measure. If the turbidity of the sedimented water does not exceed the standard, then D T = 0; in addition, set the upper limit of the reasonable range of the organic load according to historical data and experience, and set it as COD 上限 ; calculate the exceeding standard ratio D COD = max(0, COD 进水 - COD 上限 ) / COD 上限 . If the influent COD does not exceed the upper limit, then D COD = 0; finally, comprehensively consider the above comprehensive index D 综合 of the dosing deviation, the exceeding standard multiple D T of the turbidity, and the exceeding standard ratio D COD of the organic load to generate the risk correction factor R; adopt the weighted summation method, set the weights according to the influence degree of each factor on the membrane fouling rate, set the weight of the comprehensive index of the dosing deviation as w1, the weight of the exceeding standard multiple of the turbidity as w2, and the weight of the exceeding standard ratio of the organic load as w3, and w1 + w2 + w3 = 1; through the formula R = w1×D 综合 + w2×D T + w3×D COD ; calculate the risk correction factor; this correction factor is positively correlated with the membrane fouling rate, that is, the larger the R value, the higher the risk and the faster the rate of membrane fouling; input the calculated risk correction factor R into the membrane fouling prediction model to linearly correct the predicted value of the transmembrane pressure difference and dynamically adjust the threshold of the pollution risk level; when it is predicted that the transmembrane pressure difference in the next 1 hour will exceed the set threshold, automatically generate a cleaning warning signal, and the signal content includes the predicted over-limit time point, the specific transmembrane pressure difference value, the pollution risk level and the corresponding response measures; after the warning is triggered, the model will incorporate the operation data after cleaning into the historical data set and dynamically update the parameter benchmark range.

[0010] A modular full-process high-quality direct drinking water treatment system, which includes a data acquisition module, a data analysis module, a hierarchical dosing module, and a membrane fouling control module; The data acquisition module deploys online sensors at the inlet of the flocculation tank to collect the turbidity, temperature, and flow parameters of the raw water in real time, synchronously collects historical water quality data and the corresponding chemical dosing records, screens out qualified samples that meet the constraint conditions, and forms a standardized water quality characteristic data set; Based on the standardized water quality characteristic data set, the data analysis module performs multi-dimensional clustering on the historical water quality parameters, generates characteristic operating condition clusters, calculates the three-dimensional efficiency indexes of each cluster, and generates a dynamic dosing benchmark interval through a sliding window confidence interval and a time decay factor; The hierarchical dosing module establishes a mapping relationship between water quality characteristics and chemical dosing amounts, and dynamically generates a hierarchical dosing strategy that matches the current water quality fluctuation characteristics; After chemical dosing and sedimentation treatment, the membrane fouling control module continuously monitors the transmembrane pressure difference and the change rate of membrane flux at the inlet end of the membrane treatment stage, and predicts the development trend of membrane fouling through time series data analysis; and dynamically corrects the prediction results based on the deviation of chemical dosing results, the turbidity of sedimentation effluent, and the COD data of membrane inlet water, generates a cleaning warning signal in advance based on the corrected results, and triggers responses according to the degree of pollution classification.

[0011] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: Through multi-dimensional clustering and efficiency evaluation methods, the present invention accurately captures the relationship between water quality characteristics and chemical dosing, optimizes the dosing strategy, realizes the precise adaptation of chemical dosing, effectively reduces chemical waste and operating costs, and at the same time solves the problem of unstable effluent water quality caused by unreasonable chemical dosing in traditional technologies; The present invention constructs a double-layer LSTM network membrane fouling prediction model, combines real-time monitoring data of membrane components (transmembrane pressure difference, membrane flux, COD) and risk correction factors (chemical dosing deviation, excessive turbidity of sedimentation water, excessive organic load), realizes the accurate prediction and dynamic correction of the development trend of membrane fouling, can generate a cleaning warning signal 1 hour in advance, and triggers response measures according to the degree of pollution classification, significantly prolongs the service life of membrane components, and at the same time reduces manual intervention and meets higher standard water quality requirements.

[0012] By combining the clustering of multi-dimensional water quality characteristic data and dynamic adjustment strategies, the present invention accurately adjusts the chemical dosing amount to achieve precise hierarchical response; at the same time, through the membrane fouling prediction model and risk correction mechanism, the membrane fouling risk is effectively reduced and the service life of the membrane is improved. The present invention breaks through the limitations of traditional water treatment methods, improves the automation and intelligence level of the water treatment process, and thus improves the accuracy and efficiency of water quality management. Description of the Drawings

[0013] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the accompanying drawings: Figure 1 is a method flow chart of a modular full-process high-quality direct drinking water treatment control method. Specific embodiments

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0015] Please refer to Figure 1 , the present invention provides a technical solution: a modular full-process high-quality direct drinking water treatment control method, including: Step S100: Deploy online sensors at the inlet of the flocculation tank to collect the turbidity, temperature, and flow parameters of the raw water in real time, synchronously collect historical water quality data and the corresponding chemical dosage records, and screen out qualified samples that meet the constraint conditions to form a standardized water quality characteristic data set; Step S200: Based on the standardized water quality characteristic data set, perform multi-dimensional clustering on the historical water quality parameters to generate characteristic working condition clusters, calculate the three-dimensional efficiency indexes of each cluster, and generate a dynamic dosage benchmark interval through a sliding window confidence interval and a time decay factor; Step S300: Establish a mapping relationship between the water quality characteristics and the chemical dosage, and dynamically generate a hierarchical dosing strategy that matches the current water quality fluctuation characteristics; Step S400: After chemical dosing and sedimentation treatment, continuously monitor the transmembrane pressure difference and the change rate of membrane flux at the inlet end of the membrane treatment stage, and predict the development trend of membrane fouling through time series data analysis; and dynamically correct the prediction results based on the deviation of the chemical dosing results, the turbidity of the sedimentation effluent, and the membrane inlet COD data, generate a cleaning warning signal in advance based on the corrected results, and trigger responses according to the degree of fouling classification.

[0016] Step S100 includes: Step S101: Use the online turbidity meter, temperature sensor, and electromagnetic flowmeter deployed at the inlet of the flocculation tank to set the sampling frequency to obtain the turbidity, temperature, and flow data of the raw water in real time; all sensor data is transmitted to the central controller through analog signals for noise filtering and data normalization processing to form a standardized water quality characteristic data set; Step S102: Collect the water quality parameters at the inlet of the flocculation tank and the corresponding chemical dosing data in the past year. The water quality parameters include turbidity, pH, temperature, COD, dissolved oxygen, ammonia nitrogen, residual chlorine, and conductivity. The corresponding chemical dosing data includes multi-point chlorine dosing data and alum dosing data. Divide the qualified area according to the preset outlet residual chlorine threshold and pre-filtration turbidity threshold as constraints, screen the sample data, and construct a historical sample dataset.

[0017] Step S200 includes: Step S201: Use the K-means++ algorithm to perform multi-dimensional clustering on the historical sample dataset. By identifying the combined characteristics of water quality parameters, including 8-dimensional parameters of turbidity, pH, temperature, COD, dissolved oxygen, ammonia nitrogen, residual chlorine, and conductivity, divide the historical data points with similar operating conditions into independent clusters. Each cluster is associated with a multi-point chlorine dosing data sequence {C1, C2,..., C m} and an alum dosing data sequence {A1, A2,..., A m}, where C m represents the chlorine dosage added at multiple points at the m-th sampling moment, and A m represents the flocculant dosage added at multiple points at the m-th sampling moment; Establish a three-dimensional performance evaluation matrix for each cluster, and quantitatively characterize the treatment performance from three dimensions, including the residual chlorine decay coefficient, flocculation efficiency index, and chemical utilization rate; The residual chlorine decay coefficient is obtained by fitting the 24-hour residual chlorine decay curve, which reflects the decay characteristics of the disinfection effect over time, and is expressed as: K = ΔY / ΔT, where K represents the decay rate of the residual chlorine concentration per unit time, ΔY is the change in the residual chlorine concentration within 24 hours, and ΔT is the time interval, which is fixed at 24 hours; The flocculation efficiency index is calculated based on the difference between the influent turbidity and the settled water turbidity to evaluate the turbidity removal effect, and is expressed as: η = (T0 - T1) / T0, where η represents the removal efficiency of the flocculant for turbidity, T0 represents the influent turbidity of the flocculation tank, and T1 represents the effluent turbidity of the sedimentation tank; The chemical utilization rate reflects the matching degree between the actual chemical consumption and the dosing amount, and is expressed as: μ = Σ(C target × Q j ) / Σ(C j × Q j ), where μ represents the effective utilization ratio of the chemical, C target represents the set dosing concentration, Q j is the instantaneous flow rate at the j-th sampling moment, and C j is the chemical dosing amount corresponding to the j-th sampling moment; Step S202: After completing the performance evaluation, use the sliding window algorithm to calculate the moving average of the chemical dosing sequence within each cluster: MA(t) = 1 / w Σ t i=t-n C i , where MA(t) represents the moving average of the chemical dosing amounts at the w sampling points before time t, n represents the size of the sliding window, t represents the current sampling time, Ci represents the chemical dosing amount in the i-th time period, where the value range of i is [t - n, t], indicating the data sequence traced back n periods from the current time t; Introduce the dynamic confidence coefficient Z(t) to construct the confidence interval [MA - Z(t)σ, MA + Z(t)σ] to form a dynamically adjusted dosing amount reference range; where Z(t) is adaptively adjusted according to the formula: Z(t) = Z0 × [1 + α × (CV(t) / CV0 - 1)], where Z0 is the set reference coefficient, CV(t) is the coefficient of variation of the water quality parameters within the current window, CV0 is the historical average coefficient of variation, α is the adjustment parameter, α ∈ [0.3, 0.7]; the coefficient of variation of the water quality parameters = the standard deviation of the n data points within the current sliding window / the moving average within the current sliding window; Finally, introduce the time decay factor to dynamically correct the weights of historical data, and reduce the weights of stale data through the exponential decay mechanism, expressed as: λ = e (-Δt / τ) , where λ represents the time decay factor, and Δt is the time difference between the data recording time and the current time.

[0018] Step S300 includes: Step S301: For each cluster, calculate the mean vector of its 8-dimensional water quality parameters, denoted as X c ; Extract the central value of the dynamically adjusted dosing amount reference range of this cluster in Step S202 as the reference dosing amount, denoted as D c = [V c , A c ; where V c is the reference chlorine dosing amount, and A[[ID=##]] c is the reference alum dosing amount; Establish a mapping function f from the water quality feature vector X to the chemical dosing amount D. For any real-time water quality feature vector X now , calculate its Euclidean distance d(X c , X now ) from all cluster centroids X c , and select the cluster to which the centroid with the minimum distance belongs: ; Among them, f(X) represents the mapping function, Dp represents the reference chemical dosing amount corresponding to the p-th cluster, and Lp represents the water quality feature space of the p-th cluster, and the space is centered on the centroid X cAn Euclidean space region centered at [center] with the standard deviation within the cluster as the radius; Step S302: According to the real-time water quality parameter X now and the Euclidean distance d from the centroid X c of the matching cluster group, design a hierarchical response strategy: When the real-time water quality parameter X now satisfies d(X now , X c ) <= ε1, directly adopt the reference dosing amount D c , where ε1 is the stability threshold, set to 0.8 times the standard deviation of the Euclidean distance within the cluster; When ε1 < d(X now , X c ) <= ε2, trigger linear interpolation adjustment, according to the formula: ; where is the adjustment coefficient, is the gradient of the mapping function, and ε2 is the fluctuation threshold, set to 1.5 times the standard deviation of the Euclidean distance within the cluster; When d(X now , X c ) > ε2, initiate an emergency response: call the dosing amount of the nearest neighbor cluster group as the initial value, and based on the real-time feedback of the filtered turbidity and the residual chlorine in the effluent, generate a correction amount ΔD PID : ; where e(t) = [(T target - T filter ), (Cl target - Cl actual )], T target represents the set turbidity target value, T filter represents the real-time monitored value of the filtered turbidity, Cl target represents the set residual chlorine target value, Cl actual represents the real-time monitored value of the residual chlorine in the effluent, and K p , K i , K d represent the controller parameters.

[0019] Step S400 includes: Step S401: Real-time collect the transmembrane pressure difference through the pressure sensors at the inlet and outlet ends of the membrane module, synchronously monitor the membrane flux through the electromagnetic flowmeter, and obtain the influent chemical oxygen demand through the COD on-line monitor, denoted as the variable COD 进水; Calculate the average transmembrane pressure difference per hour, the membrane flux decay rate, and the average COD value to generate a time-series data set; the average transmembrane pressure difference is calculated by the arithmetic mean method; the membrane flux decay rate first calculates the average membrane flux of the current hour and then compares it with the initial membrane flux to obtain the membrane flux decay rate = (1 - average value / initial membrane flux) × 100%; the average COD value is calculated by the weighted average method, with a higher weight assigned to the most recent sampling and equal weights assigned to the remaining sampling points; use the Kalman filter algorithm to suppress noise in the original data and eliminate sensor drift and electromagnetic interference; calibrate the parameter reference range based on historical operation data; Step S402: Select the transmembrane pressure difference data sequence of the previous 24 hours before the current moment as the core input feature, combine the membrane flux decay rate and the average influent COD value, and construct a membrane fouling prediction model through a double-layer LSTM network. The network structure uses two hidden layers, each containing 64 neurons, followed by a fully connected layer to output the predicted transmembrane pressure difference values for the next 4 time points; extract the full-condition operation data of the past 6 months from the time-series data set and divide it into a training set and a test set at a ratio of 8:2 to train the model; based on the predicted transmembrane pressure difference values and the change rate, combine the membrane flux decay rate, set thresholds to divide the pollution risk into high, medium, and low levels, and set corresponding response measures; Step S403: Obtain the chemical dosage data, including recording the chlorine dosage and the alum dosage, which are set as C 实际 、A 实际 ; For the deviation degree of the dosage from the reference value, calculate the chlorine dosage deviation ratio respectively: D L = ∣C 实际 - V C ∣ / V C and the alum dosage deviation ratio D F = A C ∣A 实际 - A C ∣ / A C , and then take the larger value of the two as the comprehensive dosage deviation index D 综合 = max(D L , D F ); At the outlet end of the sedimentation tank, collect the turbidity of the sedimented water through the deployed on-line turbidimeter at the same sampling frequency as the sensor at the inlet end of the flocculation tank, which is set as T 沉淀 , and compare it with the set sedimented water turbidity standard value T 标准 ; The turbidity exceeding standard situation of the sedimented water is measured by calculating the turbidity exceeding standard multiple D T = max(0, T 沉淀 - T 标准 ) / T 标准 . If the turbidity of the sedimented water does not exceed the standard, then D T= 0; In addition, set the upper limit of the reasonable range of the organic load according to historical data and experience, and set it as COD 上限 ; Calculate the over-standard ratio D of the organic load COD = max(0, COD 进水 - COD 上限 ) / COD 上限 , if the influent COD does not exceed the upper limit, then D COD = 0; Finally, comprehensively consider the above dosing deviation comprehensive index D 综合 , the turbidity over-standard multiple D T , and the organic load over-standard ratio D COD to generate a risk correction factor R; adopt the method of weighted summation, set the weights according to the influence degree of each factor on the membrane fouling rate, set the weight of the dosing deviation comprehensive index as w1, the weight of the turbidity over-standard multiple as w2, and the weight of the organic load over-standard ratio as w3, and w1 + w2 + w3 = 1; through the formula R = w1 × D 综合 + w2 × D T + w3 × D COD ; Calculate the risk correction factor; this correction factor is positively correlated with the membrane fouling rate, that is, the larger the R value, the higher the risk of membrane fouling and the faster the rate; input the calculated risk correction factor R into the membrane fouling prediction model to linearly correct the predicted value of the transmembrane pressure difference and dynamically adjust the threshold of the pollution risk level; when it is predicted that the transmembrane pressure difference in the next 1 hour will exceed the set threshold, automatically generate a cleaning warning signal, and the signal content includes the predicted over-limit time point, the specific transmembrane pressure difference value, the pollution risk level, and the corresponding response measures; after the warning is triggered, the model incorporates the operation data after cleaning into the historical data set and dynamically updates the parameter benchmark range.

[0020] Embodiment of the present invention: Step S100: In a direct drinking water treatment plant with a reservoir water source, deploy an online turbidity meter (measurement range 0.01 - 100 NTU), a temperature sensor (accuracy ±0.5 °C), and an electromagnetic flowmeter (range 0 - 1000 m³ / h) at the inlet of the flocculation tank to collect raw water turbidity, temperature, and flow data in real time at a sampling frequency of 5 minutes; the sensor data is transmitted to the central controller through a 4 - 20 mA analog signal, and after removing noise through Gaussian filtering, it is normalized to form a standardized water quality characteristic data set; synchronously collect historical data for the past year, including 8 - dimensional water quality parameters such as turbidity, pH, temperature, and the corresponding chlorine and alum dosing amounts; with the constraints of the residual chlorine at the outlet ≥ 0.8 mg / L and the turbidity before filtration ≤ 5 NTU, screen qualified samples to construct a historical sample data set containing 100,000 records; Step S200: Use the K-means++ algorithm to cluster the historical sample dataset with 8-dimensional parameters, and divide the water quality conditions into 12 independent clusters; taking a high turbidity cluster as an example, the corresponding alum dosage data sequence is {8 - 12 mg / L}, and the chlorine dosage data sequence is {1.0 - 1.5 mg / L}; calculate the three-dimensional efficiency index for this cluster: Residual chlorine decay coefficient: By fitting the 24-hour residual chlorine decay curve, we get K = 0.05 / h, that is, the residual chlorine concentration decays by 5% per hour; Flocculation efficiency index: The influent turbidity T0 = 20 NTU, and the settled water turbidity T1 = 1 NTU, so η=(20 - 1) / 20 = 95%; Reagent utilization rate: Set C target = 1.0 mg / L, and the measured Σ(C target ×Q j ) = 1000 mg, Σ(C j ×Q j ) = 1200 mg, so μ = 83.3%; Through the sliding window algorithm, the window size is set to n = 24, calculate the moving average of the dosage MA(t) = 10 mg / L, and combine the dynamic confidence coefficient Z(t) = 1.5 (the current coefficient of variation CV(t) = 0.2, the historical average CV0 = 0.15, = 0.5), to construct the confidence interval [10 - 1.5×1, 10 + 1.5×1] = [8.5, 11.5] mg / L; introduce the time decay factor λ = e (-Δt / 24) (Δt is the number of days between data), the weight of the data 30 days ago decays to 0.286, and generate a dynamic dosing reference interval; Step S300: When the raw water turbidity = 15 NTU, pH = 7.2, and temperature = 25 °C at a certain moment, calculate the real-time water quality feature vector X now and the Euclidean distance from the centroid of each cluster, match to the high turbidity cluster (d = 0.8ε1), and directly use the reference alum dosage of 10 mg / L and chlorine dosage of 1.2 mg / L; If the raw water turbidity suddenly rises to 25 NTU (d = 1.2ε2), then trigger linear interpolation adjustment: D = 10 + 0.3×∇f(X now ), where the gradient ∇f = 0.5, and the adjusted alum dosage = 10 + 0.3×0.5×(25 - 20) = 10.75 mg / L; the chlorine dosage is synchronously adjusted to 1.2 + 0.3×0.5×(0.9 - 0.8) = 1.25 mg / L; If the turbidity continues to rise to 35 NTU (d > 1.2ε2), start the emergency response: taking the dosing amount of the nearest neighbor cluster of 12 mg / L as the initial value, based on the real-time feedback of the filtered turbidity (measured 0.3 NTU, target value 0.1 NTU) and the residual chlorine in the effluent (measured 0.5 mg / L, target value 0.9 mg / L), calculate the correction amount through the PID controller to obtain the final alum dosage and the final chlorine dosage; Step S400: In the membrane treatment stage, collect the transmembrane pressure difference (TMP = 20 kPa), membrane flux (80 L / (m²·h)), and influent COD (50 mg / L) in real time, and calculate: Average transmembrane pressure difference: 20 kPa; Membrane flux decay rate: (1 - 80 / 100)×100% = 20%; Average COD: 52 mg / L after weighted average (the weight of the most recent sampling is set to 0.4); Predict the TMP for the next 4 hours to be 22, 24, 26, and 28 kPa respectively through the double-layer LSTM network, and the initial risk level is medium; synchronously obtain the chemical dosing deviation (alum dosage 11.8 mg / L, benchmark 10 mg / L, D L =(11.8 - 10) / 10 = 18%), the turbidity of the sedimented water (T 沉淀 = 0.5 NTU, T 标准 = 0.3 NTU, D T =(0.5 - 0.3) / 0.3 = 66.7%), the COD exceeding standard ratio (COD 进水 = 52 mg / L, COD 上限 = 50 mg / L, D COD =(52 - 50) / 50 = 4%), calculate the risk correction factor R = 0.4×0.18 + 0.4×0.667 + 0.2×0.04 = 0.3468; After inputting R into the model, the corrected predicted value is 22×(1 + 0.3468) = 29.63 kPa, and the corrected sequence is [29.63, 32.32, 35.02, 37.71] kPa; the original risk level is medium (corresponding to 25 - 30 kPa), but the corrected predicted value will jump from 22 kPa to 29.63 kPa within 1 hour, and the change rate exceeds the threshold of 3 kPa / h. It is determined that the pollution risk has increased sharply, and the level is upgraded from medium to high; generate a cleaning warning signal 1 hour in advance, the content of which includes: "It is predicted that the transmembrane pressure difference will be close to the threshold (29.63 kPa / 30 kPa) 1 hour later and will exceed the limit to 32.32 kPa 2 hours later", and it is recommended to immediately start chemical enhanced backwashing; after cleaning, incorporate the operation data (TMP = 18 kPa, membrane flux = 90 L / (m²·h)) into the historical data set and dynamically update the parameter reference range.

[0021] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, in any aspect, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A modular full-process high-quality direct drinking water treatment control method, characterized in that: The method includes: Step S100: Deploy online sensors at the inlet of the flocculation tank to collect the turbidity, temperature, and flow parameters of the raw water in real time. Synchronously collect historical water quality data and the corresponding chemical dosage records, and screen out qualified samples that meet the constraint conditions to form a standardized water quality characteristic data set. Step S200: Based on the standardized water quality characteristic data set, perform multi-dimensional clustering on the historical water quality parameters to generate characteristic operating condition clusters, calculate the three-dimensional efficiency indexes of each cluster, and generate a dynamic dosing benchmark interval through a sliding window confidence interval and a time decay factor. Step S300: Establish a mapping relationship between water quality characteristics and chemical dosages, and dynamically generate a hierarchical dosing strategy that matches the current water quality fluctuation characteristics. Step S400: After chemical dosing and sedimentation treatment, continuously monitor the transmembrane pressure difference and the change rate of membrane flux at the inlet end of the membrane treatment stage, and predict the development trend of membrane fouling through time series data analysis. Dynamically correct the prediction results based on the deviation of chemical dosing results, the turbidity of the sedimentation effluent, and the COD data of the membrane inlet water. Generate a cleaning warning signal in advance based on the corrected results, and trigger responses according to the degree of fouling classification.

2. The modular full-process high-quality direct drinking water treatment control method according to claim 1, wherein: The said Step S100 includes: Step S101: Use an online turbidimeter, temperature sensor, and electromagnetic flowmeter deployed at the inlet of the flocculation tank to set the sampling frequency to obtain the turbidity, temperature, and flow data of the raw water in real time. All sensor data is transmitted to the central controller through analog signals for noise filtering and data normalization processing to form a standardized water quality characteristic data set. Step S102: Collect the water quality parameters and their corresponding chemical dosage data at the inlet of the flocculation tank in the past year. The water quality parameters include turbidity, pH, temperature, COD, dissolved oxygen, ammonia nitrogen, residual chlorine, and conductivity. The corresponding chemical dosage data includes multi-point chlorine addition data and alum addition data. Divide the qualified area according to the pre-set factory residual chlorine threshold and pre-filtration turbidity threshold as constraint conditions, screen the sample data, and construct a historical sample data set.

3. A modular full-process high-quality direct drinking water treatment control method according to claim 1, characterized in that: The said Step S200 includes: Step S201: Use the K-means++ algorithm to perform multi-dimensional clustering on the historical sample dataset. By identifying the combined characteristics of water quality parameters, including 8-dimensional parameters such as turbidity, pH, temperature, COD, dissolved oxygen, ammonia nitrogen, residual chlorine, and conductivity, the historical data points with similar operating condition characteristics are divided into independent clusters. Each cluster is associated with the multi-point chlorine addition data sequences {C1, C2,..., C m} and the coagulant addition data sequences {A1, A2,..., A m}, where C m represents the chlorine dosage added at multiple points at the m-th sampling moment, and A m represents the coagulant dosage added at multiple points at the m-th sampling moment; Establish a three-dimensional efficiency evaluation matrix for each cluster, and quantitatively characterize the treatment efficiency from three dimensions, including the residual chlorine decay coefficient, the flocculation efficiency index, and the chemical utilization rate. The residual chlorine decay coefficient is obtained by fitting the 24-hour residual chlorine decay curve, which reflects the decay characteristics of disinfection effect over time, and is expressed as: K = ΔY / ΔT, where K represents the decay rate of residual chlorine concentration per unit time, ΔY is the change in residual chlorine concentration within 24 hours, and ΔT is the time interval, which is fixed at 24 hours. The flocculation efficiency index is calculated based on the difference between the inlet turbidity and the sedimentation water turbidity to evaluate the turbidity removal effect, and is expressed as: η = (T0 - T1) / T0, where η represents the removal efficiency of the flocculant for turbidity, T0 represents the inlet turbidity of the flocculation tank, and T1 represents the outlet turbidity of the sedimentation tank. The utilization rate of the agent reflects the matching degree between the actual consumption and the dosage of the agent, and is expressed as: μ = Σ(C target ×Q j ) / Σ(C j ×Q j ), where μ represents the effective utilization ratio of the agent, C target represents the set dosage concentration, Q j is the instantaneous flow rate at the j-th sampling moment, and C j is the dosage of the agent corresponding to the j-th sampling moment; Step S202: After completing the performance evaluation, the moving average is calculated for the chemical dosing sequence within each cluster using a sliding window algorithm: MA(t) = 1 / w Σ t i=t-n C i , where MA(t) represents the moving average of the chemical dosing amounts at the w sampling points before time t, n represents the size of the sliding window, t represents the current sampling time, Ci represents the chemical dosing amount in the i-th time period, where the value range of i is [t - n, t], indicating the data sequence traced back n periods from the current time t; Introduce a dynamic confidence coefficient Z(t) to construct a confidence interval [MA - Z(t)σ, MA + Z(t)σ], forming a dynamically adjusted dosing benchmark range; where Z(t) is adaptively adjusted according to the formula: Z(t)=Z0×[1 + α×(CV(t) / CV0 - 1)], where Z0 is the set benchmark coefficient, CV(t) is the coefficient of variation of water quality parameters within the current window, CV0 is the historical average coefficient of variation, α is the adjustment parameter, and α ∈ [0.3, 0.7]; the coefficient of variation of the water quality parameters = the standard deviation of n data points within the current sliding window / the moving average within the current sliding window; Finally, a time decay factor is introduced to dynamically correct the weights of historical data, and the weights of stale data are reduced through an exponential decay mechanism, which is expressed as: λ = e (-Δt / τ) , where λ represents the time decay factor and Δt is the time difference between the data recording time and the current time.

4. A modular full-process high-quality direct drinking water treatment control method according to claim 1, characterized in that: The step S300 includes: Step S301: For each cluster, calculate the mean vector of its 8-dimensional water quality parameters, denoted as X c ; Extract the central value of the dynamic dosing amount benchmark range of this cluster in step S202 as the benchmark dosing amount, denoted as D c =[V c , A c ; where V c is the benchmark chlorine dosage, and A c is the benchmark alum dosage; Establish a mapping function f from the water quality feature vector X to the chemical dosage D, for any real-time water quality feature vector X now , calculate its Euclidean distance d(X c , X now , X c ) with all cluster centroids, and select the cluster to which the centroid with the minimum distance belongs: ； Among them, f(X) represents the mapping function, Dp represents the benchmark chemical dosage corresponding to the p-th cluster group, and Lp represents the water quality characteristic space of the p-th cluster group. The space is an Euclidean space region centered at the centroid X c and with the within-cluster standard deviation as the radius; Step S302: Design a hierarchical response strategy based on the Euclidean distance d between the real-time water quality parameter X now and the centroid X c of the matching cluster: When the real-time water quality parameter X now satisfies d(X now , X c ) <= ε1, directly adopt the reference dosage D c , where ε1 is the stability threshold, set to 0.8 times the standard deviation of the Euclidean distance within the cluster; When ε1 < d(X now , X c ) <= ε2, trigger linear interpolation adjustment according to the formula: ; where is the adjustment coefficient, is the gradient of the mapping function, ε2 is the fluctuation threshold, set to 1.5 times the standard deviation of the Euclidean distance within the cluster; When d(X now , X c ) > ε2, initiate an emergency response: call the dosing amount of the nearest neighbor cluster as the initial value, and based on the real-time feedback of the filtered turbidity and the residual chlorine in the effluent, generate a correction amount ΔD PID : ； where e(t) = [(T target - T filter ), (Cl target - Cl actual )], T target represents the set turbidity target value, T filter represents the real-time monitored value of the turbidity after filtration, Cl target represents the set residual chlorine target value, Cl actual represents the real-time monitored value of the residual chlorine in the effluent, K p , K i , K d represent the controller parameters.

5. A modular full-process high-quality direct drinking water treatment control method according to claim 1, characterized in that: The step S400 includes: Step S401: Real-time collect the transmembrane pressure difference through the pressure sensors at the water inlet and outlet ends of the membrane module, synchronously monitor the membrane flux through an electromagnetic flowmeter, and obtain the influent chemical oxygen demand through a COD on-line monitor, denoted as the variable COD 进水 ; Calculate the average transmembrane pressure difference, membrane flux decay rate, and COD average value per hour to generate a time series data set; The average transmembrane pressure difference is calculated by the arithmetic mean method; The membrane flux decay rate first calculates the average membrane flux of the current hour, and then compares it with the initial membrane flux to obtain the membrane flux decay rate = (1 - average value / initial membrane flux) × 100%; The COD average value is calculated by the weighted average method, with a higher weight assigned to the most recent sampling, and equal weights assigned to the remaining sampling points; Use the Kalman filter algorithm to suppress noise in the original data, eliminate sensor drift and electromagnetic interference; Calibrate the parameter reference range based on historical operation data; Step S402: Select the transmembrane pressure difference data sequence of the previous 24 hours before the current moment as the core input feature, combine the membrane flux decay rate and the average influent COD, and construct a membrane fouling prediction model through a double-layer LSTM network. The network structure uses two hidden layers, each containing 64 neurons, followed by a fully connected layer to output the predicted values of the transmembrane pressure difference at the next 4 time points; Re-extract the full-condition operation data of the past 6 months from the time-series data set, and divide it into a training set and a test set at a ratio of 8:2 to train the model; Based on the predicted values of the transmembrane pressure difference and the change rate, combined with the membrane flux decay rate, set thresholds to divide the pollution risk into three levels: high, medium, and low, and set corresponding response measures; Step S403: Obtain the data of chemical dosage, including recording the chlorine dosage and alum dosage, which are respectively set as C 实际 , A 实际 ; For the deviation degree of the dosage from the reference value, calculate the chlorine dosage deviation ratio respectively: D L = ∣C 实际 - V C ∣ / V C and the alum dosage deviation ratio D F = A C ∣A 实际 - A C ∣ / A C , and then take the larger value of the two as the comprehensive index D 综合 of the dosage deviation = max(D L , D F ); At the outlet end of the sedimentation tank, collect the turbidity of the sedimented water through the deployed on-line turbidimeter at the same sampling frequency as the sensor at the inlet end of the flocculation tank, which is set as T 沉淀 , and compare it with the set standard value T 标准 of the sedimented water turbidity; The exceeding standard situation of the sedimented water turbidity is measured by calculating the turbidity exceeding standard multiple D T = max(0, T 沉淀 - T 标准 ) / T 标准 , if the sedimented water turbidity does not exceed the standard, then D T = 0; In addition, set the upper limit of the reasonable range of the organic load according to historical data and experience, which is set as COD 上限 ; Calculate the organic load exceeding standard ratio D COD = max(0, COD 进水 - COD 上限 ) / COD 上限 , if the influent COD does not exceed the upper limit, then D COD = 0; Finally, comprehensively generate the risk correction factor R from the above comprehensive index D 综合 of the dosage deviation, the turbidity exceeding standard multiple D T , and the organic load exceeding standard ratio D COD ; Adopt the weighted summation method, set the weights according to the influence degree of each factor on the membrane fouling rate, set the weight of the comprehensive index of the dosage deviation as w1, the weight of the turbidity exceeding standard multiple as w2, and the weight of the organic load exceeding standard ratio as w3, and w1 + w2 + w3 = 1; Through the formula R = w1×D 综合 + w2×D T + w3×D COD , a risk correction factor is calculated; the calculated risk correction factor R is input into the membrane fouling prediction model to linearly correct the predicted value of the transmembrane pressure difference and dynamically adjust the threshold value of the pollution risk level; when it is predicted that the transmembrane pressure difference in the next 1 hour will exceed the set threshold, a cleaning warning signal is automatically generated, and the signal content includes the predicted overlimit time point, the specific transmembrane pressure difference value, the pollution risk level, and the corresponding response measures; after the warning is triggered, the model incorporates the operation data after cleaning into the historical data set and dynamically updates the parameter benchmark range.

6. A modular full-process high-quality direct drinking water treatment system, characterized in that: The system includes a data acquisition module, a data analysis module, a hierarchical dosing module, and a membrane fouling control module; The data acquisition module deploys online sensors at the inlet of the flocculation tank to collect the turbidity, temperature, and flow parameters of the raw water in real time, synchronously collect historical water quality data and the corresponding chemical dosing records, screen out qualified samples that meet the constraint conditions, and form a standardized water quality characteristic data set; The data analysis module performs multi-dimensional clustering on the historical water quality parameters based on the standardized water quality characteristic data set, generates characteristic operating condition clusters, calculates the three-dimensional efficiency indexes of each cluster, and generates a dynamic dosing benchmark interval through a sliding window confidence interval and a time decay factor; The hierarchical dosing module establishes a mapping relationship between water quality characteristics and chemical dosing amounts, and dynamically generates a hierarchical dosing strategy that matches the current water quality fluctuation characteristics; After chemical dosing and sedimentation treatment, the membrane fouling control module continuously monitors the transmembrane pressure difference and the membrane flux change rate at the inlet end of the membrane treatment stage, and analyzes the time series data to predict the development trend of membrane fouling; And dynamically correct the prediction results based on the deviation of the chemical dosing results, the turbidity of the sedimentation effluent, and the membrane influent COD data, generate a cleaning warning signal in advance based on the corrected results, and trigger responses according to the pollution level classification.

7. A modular full-process high-quality direct drinking water treatment system according to claim 6, characterized in that: The data acquisition module includes a real-time data acquisition unit and a historical data processing unit; The real-time data acquisition unit collects raw water parameters in real time through sensors at the inlet of the flocculation tank, and forms a standardized data set after filtering and normalization; The historical data processing unit collects water quality parameters and chemical dosage amounts in the past year, screens qualified samples through the set thresholds of residual chlorine at the factory outlet and turbidity before filtration, and constructs a historical sample set.

8. A modular full-process high-quality direct drinking water treatment system according to claim 6, characterized in that: The data analysis module includes a clustering and efficiency evaluation unit and a dynamic benchmark generation unit; The clustering and efficiency evaluation unit uses the K-means++ algorithm to cluster 8-dimensional water quality parameters, generates operating condition clusters, and calculates three-dimensional indicators of the residual chlorine decay coefficient, flocculation efficiency index, and chemical utilization rate; The dynamic benchmark generation unit calculates the moving average of the dosage amount through the sliding window algorithm, and generates a dosage amount benchmark interval in combination with the dynamic confidence coefficient and the time decay factor.

9. A modular full-process high-quality direct drinking water treatment system according to claim 6, characterized in that: The hierarchical dosing module includes an intelligent matching unit and a hierarchical response unit; The intelligent matching unit calculates the Euclidean distance between the real-time water quality characteristics and the cluster centroid, matches the most similar operating condition cluster, and obtains the benchmark dosing amount; The hierarchical response unit triggers a three-level response according to the distance threshold, including direct dosing in the steady state, linear interpolation in the wave state, and abnormal emergency PID control.

10. A modular full-process high-quality direct drinking water treatment system according to claim 6, characterized in that: The membrane fouling control module includes a membrane parameter monitoring unit and a membrane fouling warning unit; The membrane parameter monitoring unit collects data on the transmembrane pressure difference, membrane flux, and COD of the membrane module, and generates a time series data set after Kalman filtering; The membrane fouling warning unit constructs a membrane fouling prediction model through a double-layer LSTM network to predict the transmembrane pressure difference in the future time period, dynamically corrects the prediction result based on the deviation of the chemical dosing result, the turbidity of the sedimentation effluent, and the membrane inlet COD data, and automatically generates a cleaning warning and response strategy in combination with the pollution risk classification.

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