A modular full-process high-quality direct drinking water treatment system and control method

Abstract

The present invention discloses a modular full-process high-quality direct drinking water treatment system and control method, which belongs to the technical field of direct drinking water treatment. Online sensors are deployed at the water inlet of the flocculation tank to collect raw water turbidity, temperature, and flow parameters in real time. Combined with historical water quality and reagent addition records, qualified samples are screened to construct a standardized water quality characteristic data set; based on the data set, historical water quality parameters are multidimensionally clustered to generate characteristic operating condition clusters, and three-dimensional performance indicators are calculated. A dynamic dosage reference interval is generated using a sliding window confidence interval and a time decay factor, a mapping relationship between water quality characteristics and reagent addition amounts is established, and a graded dosage strategy is generated; during the membrane treatment stage, the transmembrane pressure difference and the membrane flux change rate are continuously monitored at the water inlet end, the membrane pollution trend is predicted through time series analysis, and the prediction result is corrected according to the reagent addition deviation, the turbidity of the precipitated water, and the COD data of the membrane inlet water, a cleaning early warning signal is generated, and a graded response is given according to the degree of pollution.

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. Background Art

[0002] The direct drinking water treatment system is a core facility to ensure the safety of public drinking water. Its treatment process usually includes flocculation, sedimentation, filtration and disinfection. The raw water first enters the flocculation tank, and the suspended matter and colloidal particles in the water are agglomerated into larger flocs by adding flocculants. Then, in the sedimentation tank, the flocs settle due to gravity to achieve solid-liquid separation. The filtration stage further removes the tiny suspended matter remaining in the water. Finally, the disinfection process kills pathogenic microorganisms to ensure that the effluent water quality meets the drinking water standards.

[0003] However, although the existing technology has improved the treatment effect of direct drinking water to a certain extent, it still faces many problems and challenges in actual application: the various treatment units of traditional water plants operate independently, and the connection efficiency is low, resulting in lengthy and complicated treatment processes, increased energy consumption and limited overall treatment efficiency; at the same time, the addition of chemicals lacks a dynamic control mechanism, and mostly relies on fixed experience values ​​or simple feedback control, making it difficult to accurately adapt the dosage according to the real-time fluctuations in water quality. This not only causes waste of chemicals and rising operating costs, but also causes unstable precipitation effects due to deviations in dosage, indirectly increasing the load on subsequent membrane treatment links; especially when the flocculation effect is poor, the residual tiny flocs and colloidal substances will accelerate the fouling of membrane components, and the existing technology has failed to effectively establish a dynamic correlation between chemical addition and membrane fouling, and lacks forward-looking predictions and proactive intervention in the development trend of membrane fouling, resulting in shortened membrane cleaning cycles and reduced service life, further pushing up 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 control method to solve the problems raised in the above background technology.

[0005] In order 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, comprising:

[0006] Step S100: Deploy online sensors at the water inlet of the flocculation tank to collect the turbidity, temperature, and flow parameters of the raw water in real time, simultaneously collect historical water quality data and corresponding reagent dosage records, screen out qualified samples that meet the constraints, and form a standardized water quality characteristic data set;

[0007] Step S200: Based on the standardized water quality characteristic data set, multidimensional clustering is performed on historical water quality parameters to generate characteristic operating condition clusters, three-dimensional performance indicators of each cluster are calculated, and a dynamic dosage reference interval is generated using a sliding window confidence interval and a time decay factor;

[0008] Step S300: Establishing a mapping relationship between water quality characteristics and reagent dosage, and dynamically generating a graded dosing strategy that matches the current water quality fluctuation characteristics;

[0009] Step S400: After the reagent addition and precipitation treatment, the transmembrane pressure difference and the membrane flux change rate are continuously monitored at the water inlet end of the membrane treatment stage, and the development trend of membrane pollution is predicted through time series data analysis; and the prediction results are dynamically corrected based on the deviation of the reagent addition results, the turbidity of the precipitation effluent and the COD data of the membrane inlet water. Based on the correction results, a cleaning warning signal is generated in advance, and a response is triggered according to the degree of pollution.

[0010] Furthermore, step S100 includes:

[0011] Step S101: By deploying an online turbidity meter, temperature sensor, and electromagnetic flowmeter at the water inlet of the flocculation tank, the sampling frequency is set to obtain real-time raw water turbidity, temperature, and flow data; all sensor data are transmitted to the central controller via analog signals, noise filtering and data normalization are performed to form a standardized water quality characteristic data set;

[0012] Step S102: Collect water quality parameters and corresponding reagent dosage data at the flocculation tank inlet over the past year. The water quality parameters include turbidity, pH, temperature, COD, dissolved oxygen, ammonia nitrogen, residual chlorine, and conductivity. The corresponding reagent dosage data includes multi-point chlorination data and alum addition data. Based on the pre-set factory residual chlorine threshold and pre-filtration turbidity threshold as constraints, the qualified area is divided, sample data is screened, and a historical sample data set is constructed.

[0013] Furthermore, step S200 includes:

[0014] Step S201: Use K-means++ algorithm to perform multidimensional clustering on the historical sample data set. By identifying the combined characteristics of water quality parameters, including turbidity, pH, temperature, COD, dissolved oxygen, ammonia nitrogen, residual chlorine, and conductivity, a total of 8 parameters, historical data points with similar operating conditions are divided into independent clusters. Each cluster is associated with the multi-point chlorination data sequence {C1, C2, ..., C m} and the alum-added data sequence {A1, A2, ..., A m}, where C m A represents the chlorine dosage added at multiple points at the mth sampling time. m Indicates the flocculant dosage added at multiple points at the mth sampling moment;

[0015] A three-dimensional performance evaluation matrix was established for each cluster to quantitatively characterize the treatment efficiency from three dimensions, including residual chlorine attenuation coefficient, flocculation efficiency index, and chemical utilization rate.

[0016] The residual chlorine attenuation coefficient is obtained by fitting the 24-hour residual chlorine attenuation curve, reflecting the attenuation characteristics of the disinfection effect over time, and is expressed as: K=ΔY / ΔT, where K represents the attenuation 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.

[0017] The flocculation efficiency index is calculated based on the difference between the influent turbidity and the sedimentation water turbidity to evaluate the turbidity removal effect, and is expressed as: η = (T0-T1) / T0, where η represents the turbidity removal efficiency of the flocculant, T0 represents the turbidity of the influent of the flocculation tank, and T1 represents the turbidity of the effluent of the sedimentation tank;

[0018] The agent utilization rate reflects the matching degree between the actual agent consumption and the dosage, which 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 Indicates the set dosage concentration, Q j is the instantaneous flow at the jth sampling moment, C j is the dosage of the agent corresponding to the jth sampling moment;

[0019] Step S202: After the efficacy evaluation is completed, the sliding window algorithm is used to calculate the moving average of the drug administration sequence in each cluster: MA(t)=1 / wΣ t i=t-n C i , where MA(t) represents the moving average of the dosage of the reagent at w sampling points before time t, n represents the sliding window size, t represents the current sampling time, Ci represents the dosage of the reagent in the i-th time period, and the value range of i is [tn,t], which represents the data sequence tracing back to n periods from the current time t;

[0020] 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 reference range; wherein Z(t) is adaptively adjusted according to the formula: Z(t)=Z0×[1+α×(CV(t) / CV0-1)], wherein Z0 is the set reference coefficient, CV(t) is the coefficient of variation of the water quality parameter in 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 parameter = the standard deviation of the n data points in the current sliding window / the moving average in the current sliding window;

[0021] Finally, the time decay factor is introduced to dynamically modify the weight of historical data, and the weight of outdated data is reduced through the exponential decay mechanism, which is expressed as: λ=e(-Δt / τ) , where λ represents the time attenuation factor, and Δt is the time difference between the data recording time and the current time.

[0022] Furthermore, step S300 includes:

[0023] Step S301: For each cluster, calculate the mean vector of its 8-dimensional water quality parameters, denoted as X c Extract the center value of the dynamic dosage reference range of the cluster in step S202 as the reference dosage, recorded as D c =[V c , A c ]; where V c is the base chlorine dosage, A c The amount of alum added as the base;

[0024] Establish a mapping function f from water quality feature vector X to reagent dosage D, and for any real-time water quality feature vector X now , calculate its centroid X with all clusters c The Euclidean distance d(X now ,X c ), select the cluster with the smallest distance to the centroid:

[0025] ;

[0026] Where f(X) represents the mapping function, Dp represents the reference dosage of the p-th cluster, Lp represents the water quality characteristic space of the p-th cluster, and the space is centered on the center of mass X. c The Euclidean space region with the center as and the standard deviation within the cluster as the radius;

[0027] Step S302: According to the real-time water quality parameter X now and matching cluster centroid X c The Euclidean distance d is used to design a hierarchical response strategy:

[0028] When the real-time water quality parameter X now Satisfy d(X now ,X c )<=ε1, directly use the standard dosage D c , where ε1 is the stability threshold, which is set to 0.8 times the standard deviation of the intra-cluster Euclidean distance;

[0029] When ε1 <d(X now ,X c )<=ε2, linear interpolation adjustment is triggered according to the formula: ;in is the adjustment coefficient, is the mapping function gradient, ε2 is the fluctuation threshold, which is set to 1.5 times the standard deviation of the intra-cluster Euclidean distance;

[0030] When d(X now ,X c )>ε2, the emergency response is initiated: the dosage of the nearest neighbor cluster is called as the initial value, and the correction value ΔD is generated by the PID controller based on the real-time feedback of the turbidity after filtration and the residual chlorine in the effluent. PID :

[0031] ;

[0032] where e(t)=[(T target -T filter ),(Cl target -Cl actual )],T target Indicates the set turbidity target value, T filter Indicates the real-time monitoring value of turbidity after filtration, Cl target Indicates the set residual chlorine target value, Cl actual Indicates the real-time monitoring value of residual chlorine in the outlet water, K p , K i , K d Represents controller parameters.

[0033] Furthermore, step S400 includes:

[0034] Step S401: The transmembrane pressure difference is collected in real time by the pressure sensors at the water inlet and outlet of the membrane module, the membrane flux is monitored synchronously by the electromagnetic flowmeter, and the chemical oxygen demand of the influent is obtained by the COD online monitor, which is set as the variable COD 进水 ; Calculate the hourly mean transmembrane pressure difference, membrane flux decay rate and COD mean to generate a time series data set; the mean transmembrane pressure difference is calculated by arithmetic averaging; the membrane flux decay first calculates the mean membrane flux of the current hour, and then compares it with the initial membrane flux to obtain the membrane flux decay rate = (1-membrane flux mean / initial membrane flux) × 100%; the COD mean is calculated by weighted averaging, giving a higher weight to the most recent sampling, and the remaining sampling points are assigned equal weights; the Kalman filter algorithm is used to suppress noise on the raw data to eliminate sensor drift and electromagnetic interference; the parameter reference range is calibrated based on historical operating data;

[0035] Step S402: Select the transmembrane pressure difference data sequence for the 24 hours before the current moment as the core input feature. Combined with the membrane flux decay rate and the mean influent COD value, a membrane fouling prediction model is constructed using a two-layer LSTM network. The network structure uses two hidden layers, each containing 64 neurons, followed by a fully connected layer to output the transmembrane pressure difference prediction value for the next four time points. Re-extract the full operating condition data for the past 6 months from the time series data set and divide it into a training set and a test set in an 8:2 ratio to train the model. Based on the predicted transmembrane pressure difference value and the rate of change, combined with the membrane flux decay rate, a threshold is set to divide the pollution risk into three levels: high, medium and low, and corresponding response measures are set.

[0036] Step S403: Obtain the dosage data of the reagents, including recording the amount of chlorine and alum added, which are set as C 实际 、A 实际 ; For the deviation between the dosage and the reference value, calculate the deviation ratio of the chlorine dosage: D L =|C 实际 -V C ∣ / V C And the deviation ratio of alum addition amount D F =A C ∣A 实际 -A C ∣ / A C , and then take the larger value of the two as the comprehensive index of dosage deviation D 综合 =max(D L ,D F ); At the outlet of the sedimentation tank, the turbidity of the sedimentation water is collected by the deployed online turbidity meter at the same sampling frequency as the sensor at the inlet of the flocculation tank, and is set to T 沉淀 and compare it with the set turbidity standard value T of the sediment water 标准 For comparison; the turbidity of sediment water exceeds the standard by calculating the turbidity exceeding the standard multiple D T =max(0,T 沉淀 -T 标准 ) / T 标准 To measure, if the turbidity of the sediment water does not exceed the standard, then D T =0; In addition, the upper limit of the reasonable range of organic load is set based on historical data and experience, which is set as COD 上限 ; Calculate the organic load exceeding the 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, the above dosage deviation comprehensive index D 综合 , turbidity exceeds the standard multiple D T , organic load exceeding the standard ratio DCOD Generate risk correction factor R; adopt weighted summation method, set weight according to the influence degree of each factor on membrane fouling rate, set the weight of dosage deviation comprehensive index as w1, the weight of turbidity exceeding standard multiple as w2, the weight of organic load exceeding standard ratio as w3, and w1+w2+w3=1; through formula R=w1×D 综合 +w2×D T +w3×D COD ; Calculate the risk correction factor; the 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, perform linear correction on the predicted transmembrane pressure difference, and dynamically adjust the threshold of the pollution risk level; when it is predicted that the transmembrane pressure difference will exceed the set threshold in the next 1 hour, a cleaning warning signal is automatically generated, 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.

[0037] A modular full-process high-quality direct drinking water treatment system, which includes a data acquisition module, a data analysis module, a graded dosing module and a membrane fouling control module;

[0038] The data acquisition module deploys online sensors at the water inlet of the flocculation tank to collect the turbidity, temperature and flow parameters of the raw water in real time, simultaneously collects historical water quality data and corresponding reagent dosage records, screens out qualified samples that meet the constraints, and forms a standardized water quality characteristic data set;

[0039] The data analysis module performs multidimensional clustering on historical water quality parameters based on a standardized water quality characteristic data set, generates characteristic operating condition clusters, calculates three-dimensional performance indicators for each cluster, and generates a dynamic dosage reference interval using a sliding window confidence interval and a time decay factor;

[0040] The graded dosing module establishes a mapping relationship between water quality characteristics and reagent dosage, and dynamically generates a graded dosing strategy that matches the current water quality fluctuation characteristics;

[0041] After the reagent addition and precipitation treatment, the membrane pollution control module continuously monitors the transmembrane pressure difference and membrane flux change rate at the water inlet end of the membrane treatment stage, and predicts the development trend of membrane pollution through time series data analysis; and dynamically corrects the prediction results based on the deviation of the reagent addition results, the turbidity of the precipitation effluent and the COD data of the membrane inlet water. Based on the correction results, a cleaning early warning signal is generated in advance, and a response is triggered according to the degree of pollution.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention uses multidimensional clustering and performance evaluation methods to accurately capture the relationship between water quality characteristics and chemical dosage, optimize the dosage strategy, achieve precise adaptation of chemical dosage, effectively reduce chemical waste and lower operating costs, and solve the problem of unstable effluent water quality caused by unreasonable chemical dosage in traditional technologies.

[0044] The present invention constructs a double-layer LSTM network membrane fouling prediction model. Combining the real-time monitoring data of membrane components (transmembrane pressure difference, membrane flux, COD) and risk correction factors (deviation in reagent dosage, excessive turbidity of sediment water, and excessive organic load), it achieves accurate prediction and dynamic correction of the development trend of membrane fouling. It can generate a cleaning warning signal within 1 hour and trigger response measures according to the degree of pollution, significantly extending the service life of the membrane components, while reducing manual intervention and meeting higher standards of water quality.

[0045] The present invention combines the clustering and dynamic adjustment strategy of multi-dimensional water quality characteristic data to accurately adjust the dosage of reagents and achieve accurate graded response; at the same time, through the membrane pollution prediction model and risk correction mechanism, it effectively reduces the risk of membrane pollution and increases the service life of the membrane. 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0047] Figure 1 It is a method flow chart of a modular full-process high-quality direct drinking water treatment control method. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0049] See also Figure 1 The present invention provides a technical solution: a modular full-process high-quality direct drinking water treatment control method, comprising:

[0050] Step S100: Deploy online sensors at the water inlet of the flocculation tank to collect the turbidity, temperature, and flow parameters of the raw water in real time, simultaneously collect historical water quality data and corresponding reagent dosage records, screen out qualified samples that meet the constraints, and form a standardized water quality characteristic data set;

[0051] Step S200: Based on the standardized water quality characteristic data set, multidimensional clustering is performed on historical water quality parameters to generate characteristic operating condition clusters, three-dimensional performance indicators of each cluster are calculated, and a dynamic dosage reference interval is generated using a sliding window confidence interval and a time decay factor;

[0052] Step S300: Establishing a mapping relationship between water quality characteristics and reagent dosage, and dynamically generating a graded dosing strategy that matches the current water quality fluctuation characteristics;

[0053] Step S400: After the reagent addition and precipitation treatment, the transmembrane pressure difference and the membrane flux change rate are continuously monitored at the water inlet end of the membrane treatment stage, and the development trend of membrane pollution is predicted through time series data analysis; and the prediction results are dynamically corrected based on the deviation of the reagent addition results, the turbidity of the precipitation effluent and the COD data of the membrane inlet water. Based on the correction results, a cleaning warning signal is generated in advance, and a response is triggered according to the degree of pollution.

[0054] Step S100 includes:

[0055] Step S101: By deploying an online turbidity meter, temperature sensor, and electromagnetic flowmeter at the water inlet of the flocculation tank, the sampling frequency is set to obtain real-time raw water turbidity, temperature, and flow data; all sensor data are transmitted to the central controller via analog signals, noise filtering and data normalization are performed to form a standardized water quality characteristic data set;

[0056] Step S102: Collect water quality parameters and corresponding reagent dosage data at the flocculation tank inlet over the past year. The water quality parameters include turbidity, pH, temperature, COD, dissolved oxygen, ammonia nitrogen, residual chlorine, and conductivity. The corresponding reagent dosage data includes multi-point chlorination data and alum addition data. Based on the pre-set factory residual chlorine threshold and pre-filtration turbidity threshold as constraints, the qualified area is divided, sample data is screened, and a historical sample data set is constructed.

[0057] Step S200 includes:

[0058] Step S201: Use K-means++ algorithm to perform multidimensional clustering on the historical sample data set. By identifying the combined characteristics of water quality parameters, including turbidity, pH, temperature, COD, dissolved oxygen, ammonia nitrogen, residual chlorine, and conductivity, a total of 8 parameters, historical data points with similar operating conditions are divided into independent clusters. Each cluster is associated with the multi-point chlorination data sequence {C1, C2, ..., C m} and the alum-added data sequence {A1, A2, ..., A m}, where C m A represents the chlorine dosage added at multiple points at the mth sampling time. m Indicates the flocculant dosage added at multiple points at the mth sampling moment;

[0059] A three-dimensional performance evaluation matrix was established for each cluster to quantitatively characterize the treatment efficiency from three dimensions, including residual chlorine attenuation coefficient, flocculation efficiency index, and chemical utilization rate.

[0060] The residual chlorine attenuation coefficient is obtained by fitting the 24-hour residual chlorine attenuation curve, reflecting the attenuation characteristics of the disinfection effect over time, and is expressed as: K=ΔY / ΔT, where K represents the attenuation 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.

[0061] The flocculation efficiency index is calculated based on the difference between the influent turbidity and the sedimentation water turbidity to evaluate the turbidity removal effect, and is expressed as: η = (T0-T1) / T0, where η represents the turbidity removal efficiency of the flocculant, T0 represents the turbidity of the influent of the flocculation tank, and T1 represents the turbidity of the effluent of the sedimentation tank;

[0062] The agent utilization rate reflects the matching degree between the actual agent consumption and the dosage, which 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 Indicates the set dosage concentration, Q j is the instantaneous flow at the jth sampling moment, C j is the dosage of the agent corresponding to the jth sampling moment;

[0063] Step S202: After the efficacy evaluation is completed, the sliding window algorithm is used to calculate the moving average of the drug administration sequence in each cluster: MA(t)=1 / wΣ t i=t-n C i , where MA(t) represents the moving average of the dosage of the reagent at w sampling points before time t, n represents the sliding window size, t represents the current sampling time, Ci represents the dosage of the reagent in the i-th time period, and the value range of i is [tn,t], which represents the data sequence tracing back to n periods from the current time t;

[0064] 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 reference range; wherein Z(t) is adaptively adjusted according to the formula: Z(t)=Z0×[1+α×(CV(t) / CV0-1)], wherein Z0 is the set reference coefficient, CV(t) is the coefficient of variation of the water quality parameter in 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 parameter = the standard deviation of the n data points in the current sliding window / the moving average in the current sliding window;

[0065] Finally, the time decay factor is introduced to dynamically modify the weight of historical data, and the weight of outdated data is reduced through the exponential decay mechanism, which is expressed as: λ=e (-Δt / τ) , where λ represents the time attenuation factor, and Δt is the time difference between the data recording time and the current time.

[0066] Step S300 includes:

[0067] Step S301: For each cluster, calculate the mean vector of its 8-dimensional water quality parameters, denoted as X c Extract the center value of the dynamic dosage reference range of the cluster in step S202 as the reference dosage, recorded as D c =[V c , A c ]; where V c is the base chlorine dosage, A c The amount of alum added as the base;

[0068] Establish a mapping function f from water quality feature vector X to reagent dosage D, and for any real-time water quality feature vector X now , calculate its centroid X with all clusters c The Euclidean distance d(X now ,X c ), select the cluster with the smallest distance to the centroid:

[0069] ;

[0070] Where f(X) represents the mapping function, Dp represents the reference dosage of the p-th cluster, Lp represents the water quality characteristic space of the p-th cluster, and the space is centered on the center of mass X. c The Euclidean space region with the center as and the standard deviation within the cluster as the radius;

[0071] Step S302: According to the real-time water quality parameter X now and matching cluster centroid X c The Euclidean distance d is used to design a hierarchical response strategy:

[0072] When the real-time water quality parameter Xnow Satisfy d(X now ,X c )<=ε1, directly use the standard dosage D c , where ε1 is the stability threshold, which is set to 0.8 times the standard deviation of the intra-cluster Euclidean distance;

[0073] When ε1 <d(X now ,X c )<=ε2, linear interpolation adjustment is triggered according to the formula: ;in is the adjustment coefficient, is the mapping function gradient, ε2 is the fluctuation threshold, which is set to 1.5 times the standard deviation of the intra-cluster Euclidean distance;

[0074] When d(X now ,X c )>ε2, the emergency response is initiated: the dosage of the nearest neighbor cluster is called as the initial value, and the correction value ΔD is generated by the PID controller based on the real-time feedback of the turbidity after filtration and the residual chlorine in the effluent. PID :

[0075] ;

[0076] where e(t)=[(T target -T filter ),(Cl target -Cl actual )],T target Indicates the set turbidity target value, T filter Indicates the real-time monitoring value of turbidity after filtration, Cl target Indicates the set residual chlorine target value, Cl actual Indicates the real-time monitoring value of residual chlorine in the outlet water, K p , K i , K d Represents controller parameters.

[0077] Step S400 includes:

[0078] Step S401: The transmembrane pressure difference is collected in real time by the pressure sensors at the water inlet and outlet of the membrane module, the membrane flux is monitored synchronously by the electromagnetic flowmeter, and the chemical oxygen demand of the influent is obtained by the COD online monitor, which is set as the variable COD 进水; Calculate the hourly mean transmembrane pressure difference, membrane flux decay rate and COD mean to generate a time series data set; the mean transmembrane pressure difference is calculated by arithmetic averaging; the membrane flux decay first calculates the mean membrane flux of the current hour, and then compares it with the initial membrane flux to obtain the membrane flux decay rate = (1-membrane flux mean / initial membrane flux) × 100%; the COD mean is calculated by weighted averaging, giving a higher weight to the most recent sampling, and the remaining sampling points are assigned equal weights; the Kalman filter algorithm is used to suppress noise on the raw data to eliminate sensor drift and electromagnetic interference; the parameter reference range is calibrated based on historical operating data;

[0079] Step S402: Select the transmembrane pressure difference data sequence for the 24 hours before the current moment as the core input feature. Combined with the membrane flux decay rate and the mean influent COD value, a membrane fouling prediction model is constructed using a two-layer LSTM network. The network structure uses two hidden layers, each containing 64 neurons, followed by a fully connected layer to output the transmembrane pressure difference prediction value for the next four time points. Re-extract the full operating condition data for the past 6 months from the time series data set and divide it into a training set and a test set in an 8:2 ratio to train the model. Based on the predicted transmembrane pressure difference value and the rate of change, combined with the membrane flux decay rate, a threshold is set to divide the pollution risk into three levels: high, medium and low, and corresponding response measures are set.

[0080] Step S403: Obtain the dosage data of the reagents, including recording the amount of chlorine and alum added, which are set as C 实际 、A 实际 ; For the deviation between the dosage and the reference value, calculate the deviation ratio of the chlorine dosage: D L =|C 实际 -V C ∣ / V C And the deviation ratio of alum addition amount D F =A C ∣A 实际 -A C ∣ / A C , and then take the larger value of the two as the comprehensive index of dosage deviation D 综合 =max(D L ,D F ); At the outlet of the sedimentation tank, the turbidity of the sedimentation water is collected by the deployed online turbidity meter at the same sampling frequency as the sensor at the inlet of the flocculation tank, and is set to T 沉淀 and compare it with the set turbidity standard value T of the sediment water 标准 For comparison; the turbidity of sediment water exceeds the standard by calculating the turbidity exceeding the standard multiple D T =max(0,T 沉淀 -T 标准 ) / T 标准 To measure, if the turbidity of the sediment water does not exceed the standard, then D T=0; In addition, the upper limit of the reasonable range of organic load is set based on historical data and experience, which is set as COD 上限 ; Calculate the organic load exceeding the 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, the above dosage deviation comprehensive index D 综合 , turbidity exceeds the standard multiple D T , organic load exceeding the standard ratio D COD Generate risk correction factor R; adopt weighted summation method, set weight according to the influence degree of each factor on membrane fouling rate, set the weight of dosage deviation comprehensive index as w1, the weight of turbidity exceeding standard multiple as w2, the weight of organic load exceeding standard ratio as w3, and w1+w2+w3=1; through formula R=w1×D 综合 +w2×D T +w3×D COD ; Calculate the risk correction factor; the 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, perform linear correction on the predicted transmembrane pressure difference, and dynamically adjust the threshold of the pollution risk level; when it is predicted that the transmembrane pressure difference will exceed the set threshold in the next 1 hour, a cleaning warning signal is automatically generated, 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.

[0081] Embodiments of the present invention:

[0082] Step S100: In a reservoir-source drinking water treatment plant, 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) are deployed at the flocculation tank inlet 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 via a 4-20 mA analog signal. After Gaussian filtering to remove noise, it is normalized to form a standardized water quality characteristic dataset. Historical data from the past year is simultaneously collected, including eight water quality parameters such as turbidity, pH, and temperature, as well as the corresponding chlorine and alum dosages. Qualified samples are screened using the constraints of residual chlorine ≥ 0.8 mg / L and pre-filtration turbidity ≤ 5 NTU to construct a historical sample dataset containing 100,000 records.

[0083] Step S200: Use the K-means++ algorithm to perform 8-dimensional parameter clustering on the historical sample data set, dividing the water quality conditions into 12 independent clusters. Taking a high turbidity cluster as an example, its corresponding alum addition data sequence is {8-12 mg / L}, and its chlorine addition data sequence is {1.0-1.5 mg / L}. Calculate the three-dimensional performance index for this cluster:

[0084] Residual chlorine decay coefficient: by fitting the 24-hour residual chlorine decay curve, we get K=0.05 / h, which means the residual chlorine concentration decays by 5% per hour;

[0085] Flocculation efficiency index: inlet turbidity T0 = 20 NTU, sedimentation water turbidity T1 = 1 NTU, η = (20-1) / 20 = 95%;

[0086] Drug utilization rate: set C target =1.0mg / L, measured Σ(C target ×Q j )=1000mg,Σ(C j ×Q j ) = 1200 mg, μ = 83.3%;

[0087] By using the sliding window algorithm, the window size is set to n=24, and the moving average of the dosage MA(t)=10 mg / L is calculated. Combined with the dynamic confidence coefficient Z(t)=1.5 (current coefficient of variation CV(t)=0.2, historical average CV0=0.15, =0.5), the confidence interval [10-1.5×1,10+1.5×1]=[8.5,11.5] mg / L is constructed; the time decay factor λ=e is introduced. (-Δt / 24) (Δt is the number of days between data), the weight of data from 30 days ago is decayed to 0.286, and a dynamic dosing benchmark interval is generated;

[0088] Step S300: When the raw water turbidity = 15NTU, pH = 7.2, and temperature = 25°C at a certain moment, calculate the real-time water quality feature vector X now The Euclidean distance from the centroid of each cluster was matched to the high turbidity cluster (d = 0.8ε1), and the standard alum addition amount of 10 mg / L and chlorine addition amount of 1.2 mg / L were directly used;

[0089] If the raw water turbidity suddenly rises to 25 NTU (d = 1.2ε2), the linear interpolation adjustment is triggered: D = 10 + 0.3 × ∇f (X now ), where the gradient ∇f = 0.5, the adjusted amount of alum added = 10 + 0.3 × 0.5 × (25-20) = 10.75 mg / L; the amount of chlorine added is simultaneously adjusted to 1.2 + 0.3 × 0.5 × (0.9-0.8) = 1.25 mg / L;

[0090] If the turbidity continues to rise to 35 NTU (d>1.2ε2), the emergency response is initiated: using the nearest neighbor cluster dosage of 12 mg / L as the initial value, based on the real-time feedback of the post-filtration turbidity (measured 0.3 NTU, target value 0.1 NTU) and the effluent residual chlorine (measured 0.5 mg / L, target value 0.9 mg / L), the PID controller calculates the correction amount to obtain the final alum dosage and the final chlorine dosage;

[0091] Step S400: During the membrane treatment phase, the transmembrane pressure (TMP = 20 kPa), membrane flux (80 L / (m²・h)), and COD influent (50 mg / L) are collected in real time to calculate:

[0092] Average transmembrane pressure difference: 20kPa;

[0093] Membrane flux attenuation rate: (1-80 / 100)×100%=20%;

[0094] COD average: weighted average is 52 mg / L (the weight of the most recent sampling is set to 0.4);

[0095] The two-layer LSTM network predicts that the TMP in the next four hours will be 22, 24, 26, and 28 kPa respectively, and the initial risk level is medium; the deviation of the reagent addition is obtained simultaneously (the amount of alum added is 11.8 mg / L, the base is 10 mg / L, and the D L =(11.8-10) / 10=18%), sedimentation water turbidity (T 沉淀 =0.5NTU,T 标准 =0.3NTU, D T =(0.5-0.3) / 0.3=66.7%), COD exceeding standard ratio (COD 进水 =52mg / L, COD 上限 =50mg / L, D COD =(52-50) / 50=4%), calculate the risk adjustment factor R=0.4×0.18+0.4×0.667+0.2×0.04=0.3468;

[0096] After inputting R into the model, the revised predicted value was 22 × (1 + 0.3468) = 29.63 kPa, resulting in a revised sequence of [29.63, 32.32, 35.02, 37.71] kPa. The original risk level was medium (corresponding to 25-30 kPa), but the revised predicted value jumped from 22 kPa to 29.63 kPa within one hour, with the rate of change exceeding the threshold of 3 kPa / h. This indicated a sharp increase in the contamination risk and raised the level from medium to high. A cleaning warning signal was generated one hour in advance, stating: "The transmembrane pressure is predicted to approach the threshold (29.63 kPa / 30 kPa) in one hour and exceed the limit to 32.32 kPa in two hours." It is recommended that chemical enhanced backwash be initiated immediately. After cleaning, operational data (TMP = 18 kPa, membrane flux = 90 L / (m²・h)) was incorporated into the historical dataset, dynamically updating the parameter baseline range.

[0097] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A modular full-process high-quality direct drinking water treatment control method, characterized by: The method comprises: Step S100: Deploy online sensors at the water inlet of the flocculation tank to collect the turbidity, temperature, and flow parameters of the raw water in real time, simultaneously collect historical water quality data and corresponding reagent dosage records, screen out qualified samples that meet the constraints, and form a standardized water quality characteristic data set; Step S200: Based on the standardized water quality characteristic data set, multidimensional clustering is performed on historical water quality parameters to generate characteristic operating condition clusters. Three-dimensional performance indicators of each cluster are calculated, including the residual chlorine attenuation coefficient, flocculation efficiency index, and reagent utilization rate. A dynamic dosage reference interval is generated using a sliding window confidence interval and a time decay factor. Step S300: Establishing a mapping relationship between water quality characteristics and reagent dosage, and dynamically generating a graded dosing strategy that matches the current water quality fluctuation characteristics; 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 center value of the dynamic dosage reference interval of the cluster in step S200 as the reference dosage, recorded as D c =[V c , A c ]; where V c is the base chlorine dosage, A c The amount of alum added as the base; Establish a mapping function f from water quality feature vector X to reagent dosage D, and for any real-time water quality feature vector X now , calculate its centroid X with all clusters c The Euclidean distance d(X now ,X c ), select the cluster with the smallest distance to the centroid: ； Where f(X) represents the mapping function, Dp represents the reference dosage of the p-th cluster, Lp represents the water quality characteristic space of the p-th cluster, and the space is centered on the center of mass X. c The Euclidean space region with the center as and the standard deviation within the cluster as the radius; Step S302: According to the real-time water quality parameter X now and matching cluster centroid X c The Euclidean distance d is used to design a hierarchical response strategy: When the real-time water quality parameter X now Satisfy d(X now ,X c )<=ε1, directly use the standard dosage D c , where ε1 is the stability threshold, which is set to 0.8 times the standard deviation of the intra-cluster Euclidean distance; When ε1 <d(X now ,X c )<=ε2, linear interpolation adjustment is triggered according to the formula: ;in is the adjustment coefficient, is the mapping function gradient, ε2 is the fluctuation threshold, which is set to 1.5 times the standard deviation of the intra-cluster Euclidean distance; When d(X now ,X c )>ε2, the emergency response is initiated: the dosage of the nearest neighbor cluster is called as the initial value, and the correction value ΔD is generated by the PID controller based on the real-time feedback of the turbidity after filtration and the residual chlorine in the effluent. PID : ； where e(t)=[(T target -T filter ),(Cl target -Cl actual )],T target Indicates the set turbidity target value, T filter Indicates the real-time monitoring value of turbidity after filtration, Cl target Indicates the set residual chlorine target value, Cl actual Indicates the real-time monitoring value of residual chlorine in the outlet water, K p , K i , K d Represents controller parameters; Step S400: After the reagent addition and precipitation treatment, the transmembrane pressure difference and the membrane flux change rate are continuously monitored at the water inlet end of the membrane treatment stage, and the development trend of membrane pollution is predicted by time series data analysis; and the prediction results are dynamically corrected based on the deviation of the reagent addition results, the turbidity of the precipitated water and the COD data of the membrane inlet water: the comprehensive index of the deviation between the actual reagent dosage and the benchmark dosage, the turbidity excess multiple of the turbidity of the precipitated water relative to the standard value, and the excess ratio of the COD data of the membrane inlet water relative to the upper limit of the reasonable range are calculated respectively, and the risk correction factor is generated by weighted summation to perform linear correction on the membrane pollution prediction result; based on the correction result, a cleaning warning signal is generated in advance, and a response is triggered according to the pollution degree level.

2. A modular full-process high-quality direct drinking water treatment control method according to claim 1, characterized in that: The step S100 includes: Step S101: By deploying an online turbidity meter, temperature sensor, and electromagnetic flowmeter at the water inlet of the flocculation tank, the sampling frequency is set to obtain real-time raw water turbidity, temperature, and flow data; all sensor data are transmitted to the central controller via analog signals, noise filtering and data normalization are performed to form a standardized water quality characteristic data set; Step S102: Collect water quality parameters and corresponding reagent dosage data at the flocculation tank inlet over the past year. The water quality parameters include turbidity, pH, temperature, COD, dissolved oxygen, ammonia nitrogen, residual chlorine, and conductivity. The corresponding reagent dosage data includes multi-point chlorination data and alum addition data. Based on the pre-set factory residual chlorine threshold and pre-filtration turbidity threshold as constraints, the qualified area is divided, sample data is screened, and a historical sample data set is constructed.

3. A modular full-process high-quality direct drinking water treatment control method according to claim 2, characterized in that: The step S200 includes: Step S201: Use K-means++ algorithm to perform multidimensional clustering on the historical sample data set. By identifying the combined characteristics of water quality parameters, including turbidity, pH, temperature, COD, dissolved oxygen, ammonia nitrogen, residual chlorine, and conductivity, a total of 8 parameters, historical data points with similar operating conditions are divided into independent clusters. Each cluster is associated with the multi-point chlorination data sequence {C1, C2, ..., C m } and the alum-added data sequence {A1, A2, ..., A m }, where C m A represents the chlorine dosage added at multiple points at the mth sampling time. m Indicates the flocculant dosage added at multiple points at the mth sampling moment; A three-dimensional performance evaluation matrix was established for each cluster to quantitatively characterize the treatment efficiency from three dimensions, including residual chlorine attenuation coefficient, flocculation efficiency index, and chemical utilization rate. The residual chlorine attenuation coefficient is obtained by fitting the 24-hour residual chlorine attenuation curve, reflecting the attenuation characteristics of the disinfection effect over time, and is expressed as: K=ΔY / ΔT, where K represents the attenuation 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 sedimentation water turbidity to evaluate the turbidity removal effect, and is expressed as: η = (T0-T1) / T0, where η represents the turbidity removal efficiency of the flocculant, T0 represents the turbidity of the influent of the flocculation tank, and T1 represents the turbidity of the effluent of the sedimentation tank; The reagent utilization rate reflects the matching degree between the actual consumption of the reagent and the dosage, which 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 Indicates the set dosage concentration, Q j is the instantaneous flow at the jth sampling moment, C j is the dosage of the agent corresponding to the jth sampling moment; Step S202: After the efficacy evaluation is completed, the sliding window algorithm is used to calculate the moving average of the drug administration sequence in each cluster: MA(t)=1 / wΣ t i=t-n C i , where MA(t) represents the moving average of the dosage of the reagent at w sampling points before time t, n represents the sliding window size, t represents the current sampling time, Ci represents the dosage of the reagent in the i-th time period, and the value range of i is [tn,t], which represents the data sequence tracing back to n periods from the current time 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 reference range; wherein Z(t) is adaptively adjusted according to the formula: Z(t)=Z0×[1+α×(CV(t) / CV0-1)], wherein Z0 is the set reference coefficient, CV(t) is the coefficient of variation of the water quality parameter in 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 parameter = the standard deviation of the n data points in the current sliding window / the moving average in the current sliding window; Finally, the time decay factor is introduced to dynamically modify the weight of historical data, and the weight of outdated data is reduced through the exponential decay mechanism, which is expressed as: λ=e (-Δt / τ) , where λ represents the time attenuation 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 3, characterized in that: The step S400 includes: Step S401: The transmembrane pressure difference is collected in real time by the pressure sensors at the water inlet and outlet of the membrane module, the membrane flux is monitored synchronously by the electromagnetic flowmeter, and the chemical oxygen demand of the influent is obtained by the COD online monitor, which is set as the variable COD 进水 ; Calculate the hourly mean transmembrane pressure difference, membrane flux attenuation rate and COD mean to generate a time series data set; the mean transmembrane pressure difference is calculated by arithmetic averaging; the membrane flux attenuation first calculates the mean membrane flux of the current hour, and then compares it with the initial membrane flux to obtain the membrane flux attenuation rate = (1-membrane flux mean / initial membrane flux) × 100%; the COD mean is calculated by weighted averaging, giving a higher weight to the most recent sampling, and the remaining sampling points are assigned equal weights; the Kalman filter algorithm is used to suppress noise on the raw data to eliminate sensor drift and electromagnetic interference; the parameter reference range is calibrated based on historical operating data; Step S402: Select the transmembrane pressure difference data sequence for the 24 hours before the current moment as the core input feature. Combined with the membrane flux decay rate and the mean influent COD value, a membrane fouling prediction model is constructed using a two-layer LSTM network. The network structure uses two hidden layers, each containing 64 neurons, followed by a fully connected layer to output the transmembrane pressure difference prediction value for the next four time points. Re-extract the full operating condition data for the past 6 months from the time series data set and divide it into a training set and a test set in an 8:2 ratio to train the model. Based on the predicted transmembrane pressure difference value and the rate of change, combined with the membrane flux decay rate, a threshold is set to divide the pollution risk into three levels: high, medium and low, and corresponding response measures are set. Step S403: Obtain the dosage data of the reagents, including recording the amount of chlorine and alum added, which are set as C 实际 、A 实际 ; For the deviation between the dosage and the reference value, calculate the deviation ratio of the chlorine dosage: D L =|C 实际 -V C ∣ / V C And the deviation ratio of alum addition amount D F =A C ∣A 实际 -A C ∣ / A C , and then take the larger value of the two as the comprehensive index of dosage deviation D 综合 =max(D L ,D F ); At the outlet of the sedimentation tank, the turbidity of the sedimentation water is collected by the deployed online turbidity meter at the same sampling frequency as the sensor at the inlet of the flocculation tank, and is set to T 沉淀 and compare it with the set turbidity standard value T of the sediment water 标准 For comparison; the turbidity of sediment water exceeds the standard by calculating the turbidity exceeding the standard multiple D T =max(0,T 沉淀 -T 标准 ) / T 标准 To measure, if the turbidity of the sediment water does not exceed the standard, then D T =0; In addition, the upper limit of the reasonable range of organic load is set based on historical data and experience, which is set as COD 上限 ; Calculate the organic load exceeding the 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, the above dosage deviation comprehensive index D 综合 , turbidity exceeds the standard multiple D T , organic load exceeding the standard ratio D COD Generate risk correction factor R; adopt weighted summation method, set weight according to the influence degree of each factor on membrane fouling rate, set the weight of dosage deviation comprehensive index as w1, the weight of turbidity exceeding standard multiple as w2, the weight of organic load exceeding standard ratio as w3, and w1+w2+w3=1; through formula R=w1×D 综合 +w2×D T +w3×D COD , calculate the risk correction factor; input the calculated risk correction factor R into the membrane fouling prediction model, perform linear correction on the predicted transmembrane pressure difference, and dynamically adjust the threshold of the pollution risk level; when it is predicted that the transmembrane pressure difference will exceed the set threshold in the next hour, a cleaning warning signal is automatically generated, and the signal content includes the predicted exceeding time point, specific transmembrane pressure difference value, pollution risk level and corresponding response measures; after the warning is triggered, the model will incorporate the post-cleaning operation data into the historical data set and dynamically update the parameter benchmark range.

5. A modular full-process high-quality direct drinking water treatment system, applied to a modular full-process high-quality direct drinking water treatment control method according to any one of claims 1 to 4, characterized in that: The system includes a data acquisition module, a data analysis module, a graded dosing module and a membrane fouling control module; The data acquisition module deploys online sensors at the water inlet of the flocculation tank to collect the turbidity, temperature and flow parameters of the raw water in real time, simultaneously collects historical water quality data and corresponding reagent dosage records, screens out qualified samples that meet the constraints, and forms a standardized water quality characteristic data set; The data analysis module performs multidimensional clustering on historical water quality parameters based on a standardized water quality characteristic data set, generates characteristic operating condition clusters, calculates three-dimensional performance indicators for each cluster, and generates a dynamic dosage reference interval using a sliding window confidence interval and a time decay factor; The graded dosing module establishes a mapping relationship between water quality characteristics and reagent dosage, and dynamically generates a graded dosing strategy that matches the current water quality fluctuation characteristics; After the reagent addition and precipitation treatment, the membrane fouling control module continuously monitors the transmembrane pressure difference and membrane flux change rate at the water inlet of the membrane treatment stage, and predicts the development trend of membrane fouling through time series data analysis; The prediction results are dynamically corrected based on the deviation of the reagent addition results, the turbidity of the sedimentation water and the COD data of the membrane inlet water. Based on the correction results, a cleaning early warning signal is generated in advance, and a response is triggered according to the degree of pollution.

6. A modular full-process high-quality direct drinking water treatment system according to claim 5, 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 the flocculation tank inlet sensor, and forms a standardized data set after filtering and normalization; The historical data processing unit collects water quality parameters and dosage of reagents in the past year, screens qualified samples by set factory residual chlorine and pre-filtration turbidity thresholds, and constructs a historical sample set.

7. The modular full-process high-quality direct drinking water treatment system according to claim 5, characterized in that: The data analysis module includes a clustering and performance evaluation unit and a dynamic benchmark generation unit; The clustering and performance evaluation unit uses the K-means++ algorithm to cluster the 8-dimensional water quality parameters, generate working condition clusters, and calculate the three-dimensional indicators of residual chlorine attenuation coefficient, flocculation efficiency index, and reagent utilization rate; The dynamic benchmark generation unit calculates the moving average value of the dosage by a sliding window algorithm, and generates a dosage benchmark interval by combining the dynamic confidence coefficient and the time decay factor.

8. The modular full-process high-quality direct drinking water treatment system according to claim 5, 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 cluster with the most similar working conditions, and obtains the reference dosage; The hierarchical response unit triggers three-level responses according to the distance threshold, including direct injection in steady state, linear interpolation in fluctuating state, and abnormal emergency PID control.

9. The modular full-process high-quality direct drinking water treatment system according to claim 5, characterized in that: The membrane pollution control module includes a membrane parameter monitoring unit and a membrane pollution early warning unit; The membrane parameter monitoring unit collects transmembrane pressure difference, membrane flux, and COD data of the membrane module, and generates a time series data set after Kalman filtering; The membrane pollution early warning unit constructs a membrane pollution prediction model through a double-layer LSTM network to predict the transmembrane pressure difference in the future time period and dynamically corrects the prediction results based on the deviation of the reagent addition results, the turbidity of the sedimentation water and the COD data of the membrane inlet water. In combination with the pollution risk classification, it automatically generates a cleaning warning and response strategy.

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