Intelligent reclaimed water treatment method

Abstract

The application relates to the technical field of reclaimed water treatment, and discloses an intelligent reclaimed water treatment method, real-time water quality data and historical water quality data of reclaimed water to be treated are acquired; a water quality scoring model is established according to the historical water quality data, the real-time water quality data is input into the water quality scoring model, and scores of various influence factors of water quality are output; through the scores of various influence factors of water quality, a comprehensive score of the reclaimed water to be treated is generated; a reclaimed water treatment model library is established, a corresponding reclaimed water treatment model is selected in the reclaimed water treatment model library through the comprehensive score of the reclaimed water to be treated, a reclaimed water treatment strategy is generated; the reclaimed water treatment strategy is executed, a reclaimed water treatment process is detected, and the reclaimed water treatment model library is updated according to a detection result. Through real-time monitoring and automatic adjustment of treatment parameters, the automation level of the treatment process is improved; this not only reduces manual intervention, but also helps to quickly respond to water quality changes and maintain the stability of treatment effects.

Description

Technical Field

[0001] This invention relates to the field of greywater treatment technology, and in particular to an intelligent greywater treatment method. Background Technology

[0002] Currently, greywater treatment technology has become an important component of urban water management. Continuous technological innovation and optimization have made greywater treatment more efficient and economical, and applicable to various scenarios, including residential communities, commercial buildings, and industrial parks. Greywater reuse not only improves the recycling rate of water resources but also contributes to ecological environmental protection and sustainable urban development.

[0003] Greywater treatment technology is of great significance for alleviating water shortages, protecting the ecological environment, and promoting sustainable economic and social development. With the popularization of the technology and policy support, greywater reuse is expected to play a role in more fields and become an indispensable part of water resource management. Furthermore, advancements in greywater treatment technology can help raise public awareness of the importance of water resource protection and recycling, promoting a green development philosophy throughout society. Summary of the Invention

[0004] The purpose of this invention is to obtain real-time parameters of the wastewater to be treated by setting monitoring time nodes, generate a comprehensive score of the current wastewater through an evaluation model, and dynamically adjust the wastewater treatment strategy to achieve efficient optimization and dynamic adaptation of the wastewater treatment process.

[0005] To achieve the above objectives, the present invention provides an intelligent greywater treatment method, comprising:

[0006] Establish a water quality evaluation model based on historical data;

[0007] Multiple monitoring time nodes are established to obtain real-time parameters of the reclaimed water to be treated at the current monitoring time node, and a comprehensive score of the reclaimed water at the current monitoring time node is generated based on the reclaimed water index evaluation model.

[0008] Based on the comprehensive score of reclaimed water at the current monitoring time point, the current reclaimed water treatment strategy and the current reclaimed water monitoring cycle are set.

[0009] In some embodiments of this application, when establishing a reclaimed water index evaluation model based on historical data, the following are included:

[0010] Obtain historical water quality data D, where D = [G,S,P,O];

[0011] Historical water quality data D contains multiple types of sub-data sets, where G represents the particle size data set in reclaimed water, S represents the salinity data set in reclaimed water, P represents the pH value data set in reclaimed water, and O represents the oxygen value data set in reclaimed water.

[0012] G=[g1,g2…gi…gn]; S=[s1,s2…si…sn]; P=[p1,p2…pi…pn];

[0013] O = [o1, o2, ..., oi, ..., on];

[0014] gi represents the particle size data in the water measured in the i-th time, si represents the salinity data in the water measured in the i-th time, pi represents the pH value data in the water measured in the i-th time, oi represents the oxygen value data in the water measured in the i-th time, and n represents the total number of samplings.

[0015] Based on historical water quality data D, the values ​​of each type of sub-data are divided into intervals, generating multiple interval gradients. Values ​​are then assigned to these interval gradients to obtain the evaluation value Z for each type of water quality index, Z = (Z0...). G Z S Z P Z O );

[0016] Among them, Z G Z is an index used to score particle size data in water. S Z is an index used to score the salinity data in water. P Z is an index used to score the pH value data in water. O The index score is given to the oxygen content data in the water.

[0017] In some embodiments of this application, generating the comprehensive score of reclaimed water at the current monitoring time point includes:

[0018] Construct a historical water quality data matrix A[a ij ] n×4 ;

[0019] a ij This represents the score of the j-th data indicator in the i-th sample.

[0020] via a ij Calculate the weight p of the j-th data indicator in the i-th sample. ij :

[0021]

[0022] Calculate the entropy weight e of the j-th data indicator j :

[0023]

[0024] Calculate the coefficient of variation g of the j-th data indicator. j :

[0025] g j =1-e j(j = 1, 2, 3, 4);

[0026] Calculate the weight w of the j-th data indicator. j :

[0027]

[0028] The water quality scoring model is used to obtain the scores Z of various data indicators of the reclaimed water to be treated, and the comprehensive score TS of the reclaimed water to be treated is calculated based on the scores Z of various data indicators of the reclaimed water.

[0029] TS = W1 * Z G +W2*Z S +W3*Z P +W4*Z O ;

[0030] Wherein, W1 is the weight of the particle size data in the water, W2 is the weight of the salinity data in the water, W3 is the weight of the pH value data in the water, and W4 is the weight of the oxygen value data in the water.

[0031] In some embodiments of this application, setting the current greywater treatment strategy and the current greywater monitoring cycle includes:

[0032] Based on the comprehensive water quality score (TS), the water quality status of the reclaimed water to be treated is determined, the corresponding monitoring cycle is set, and the time interval from the current monitoring time node to the next time node is determined.

[0033] The index score Z for obtaining the particle size data in the water at the current monitoring time point. G The index score Z of the salinity data in the water S ;

[0034] If the particle size data in the water is scored Z G The index score Z of the salinity data in the water S Once all thresholds are reached, the deep processing stage begins.

[0035] The pretreatment stage includes: removal of particulate impurities from the greywater and desalination of the greywater;

[0036] Obtain particle size change ΔZ during the removal of particulate impurities from greywater. G And the salinity data ΔZ during the desalination process of greywater. S ;

[0037] Generate correction value Z for particle size data in water G ', Z G '=(Z G -ΔZ G );

[0038] Correction value Z for salinity data during water desalination S ', Z S '=(Z S -ΔZ S );

[0039] Based on granularity data variation ΔZ G and salinity data ΔZ S Generate water quality correction parameter B;

[0040] Based on water quality correction parameter B, the pH correction value Z in the water is generated. P 'and the correction value Z for oxygen content in water' O ';

[0041] The deep processing stage includes:

[0042] Water quality acid-base balance treatment is carried out based on the corrected pH value in the water;

[0043] Deoxygenation treatment is performed based on the corrected oxygen content in the water.

[0044] In some embodiments of this application, setting the corresponding monitoring period includes:

[0045] The average value μ of the comprehensive water quality score TS and the standard deviation σ of the comprehensive water quality score TS in the historical data are generated by using historical data.

[0046] Water quality status is classified based on the mean μ and standard deviation σ. Based on the classification of water quality status, the update time node for the comprehensive score of reclaimed water is determined.

[0047] Based on water purification experiments, the adjustment range of the sampling frequency (u) was determined. min ,u max );

[0048] u min u is the minimum scaling factor for the sampling frequency. max This is the maximum scaling factor for the sampling frequency;

[0049] If TS∈(μ-σ,μ+σ), then the current water to be tested is defined as Grade I water, the current water quality sampling frequency T0 is maintained, and the remaining amount of deoxygenating agent and Grade I acid-base reagent is predicted.

[0050] If TS∈(0,μ-σ), then the current water to be tested is defined as secondary reclaimed water, and the sampling frequency T of the current water quality is corrected.

[0051]

[0052] m is the current water quality sampling frequency scaling factor.

[0053] T = T0 / (1+m);

[0054] If TS∈(μ+σ,+∞), then the current water to be tested is defined as Class III water, and the sampling frequency T of the current water quality is corrected.

[0055] T = T0 / (1-m).

[0056] In some embodiments of this application, the removal of particulate impurities from greywater includes:

[0057] The threshold Z for particle size data in water is set based on water usage indicators. G (min);

[0058] And based on the current particle size data in the water, an index score Z is given. G Calculate the amount of coagulant to be added;

[0059] Real-time monitoring of particle size changes ΔZ during water particulate impurity removal process G When the particle size data in water is scored Z... G When the target threshold is reached, residual colloids and suspended solids are removed by filtration.

[0060] Obtain the change in granularity data ΔZ at this time. G (max).

[0061] In some embodiments of this application, the calculation of the amount of coagulant added includes:

[0062] The coagulant addition test was conducted to obtain the coagulant unit volume addition coefficient k0.

[0063] Calculate the amount of coagulant Q to be added;

[0064] Q = k0 * Z G *V;

[0065] V represents the volume of water to be treated.

[0066] In some embodiments of this application, the desalination of the greywater includes:

[0067] The threshold value Z for salinity in water is set based on water usage indicators. S (min);

[0068] The Z-index is based on the current salinity data in the water. S Calculate the time required for water desalination in reverse osmosis.

[0069] Real-time monitoring of salinity changes ΔZ during the desalination process of recycled water S When the salinity data in the water is scored Z... S When the indicator threshold is reached, obtain the change in granular data ΔZ at this point.S (max).

[0070] In some embodiments of this application, a pH correction value Z is generated in the water. P 'and the correction value Z for oxygen content in water' O 'Time' includes:

[0071] Based on granularity data variation ΔZ G and salinity data ΔZ S Generate water quality correction parameters B for the advanced treatment stage, B = (B1, B2);

[0072] B1=k1*ΔZ G +k2*ΔZ S ;

[0073] B2=k3*ΔZ G +k4*ΔZ S ;

[0074] k1 is the correction constant for pH value caused by changes in particle size data, k2 is the correction constant for pH value caused by changes in salinity data, k3 is the correction constant for oxygen content caused by changes in particle size data, and k4 is the correction constant for oxygen content caused by salinity data and k2.

[0075] Generate pH correction value Z in water P ', Z P =Z P -B1;

[0076] And the correction value Z for oxygen content in water O ', Z O =Z O -B2;

[0077] Wherein, B1 is the pH correction parameter for the pretreatment stage, and B2 is the oxygen content correction parameter for the pretreatment stage.

[0078] In some embodiments of this application, the deep processing stage includes:

[0079] Calculate the minimum index score Z for pH data in water at the current monitoring time. P (min)

[0080] Z P (min)=Z P -B1=Z P -(k1*ΔZ G (max)+k2*ΔZ S (max));

[0081] Calculate the minimum index score Z for oxygen content data in water at the current monitoring time. O (min)

[0082] Z O (min)=Z O -B2=Z O -(k3*ΔZ G (max)+k4*ΔZ S (max));

[0083] The minimum index score Z is based on the pH value data of the water. P (min), add acid and alkali reagents to adjust the pH value of the water to the target value;

[0084] The minimum index score Z is based on the oxygen content data in the water. O (min), add oxygen remover to control the oxygen content in the water to the target level.

[0085] Compared with existing technologies, the intelligent greywater treatment method of this invention has the following advantages:

[0086] Establish a water quality scoring model to quickly and accurately assess water quality; the water quality scoring model not only provides a quantitative water quality assessment tool, but also provides strong support for subsequent wastewater treatment decisions.

[0087] By analyzing historical water quality data, a water quality scoring model can be established to quantitatively assess water quality. The calculation of the comprehensive score takes into account the weights and coefficients of variation of multiple water quality parameters, making the score more comprehensive and objective.

[0088] Analyzing the overall score (TS) allows for a more accurate classification of water quality status, enabling the implementation of corresponding monitoring and treatment strategies for different levels of reclaimed water. This hierarchical management helps to address water quality issues in a targeted manner and improve overall treatment effectiveness.

[0089] By dynamically adjusting the sampling frequency, we can ensure the accuracy of monitoring and the timeliness of processing, and also rationally plan the use of reagents to achieve the goal of optimal resource allocation.

[0090] By updating the greywater treatment model library and providing feedback on the differences between the treatment effect and expectations, the model library can be continuously evolved, ensuring the long-term effectiveness and adaptability of greywater treatment. Attached Figure Description

[0091] Figure 1 This is a flowchart of an intelligent greywater treatment method disclosed in an embodiment of the present invention. Detailed Implementation

[0092] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0093] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0094] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0095] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0096] like Figure 1 As shown in the figure, an embodiment of the present invention discloses an intelligent greywater treatment method, comprising:

[0097] Establish a water quality evaluation model based on historical data;

[0098] Multiple monitoring time nodes are established to obtain real-time parameters of the reclaimed water to be treated at the current monitoring time node, and a comprehensive score of the reclaimed water at the current monitoring time node is generated based on the reclaimed water index evaluation model.

[0099] Based on the comprehensive score of reclaimed water at the current monitoring time point, the current reclaimed water treatment strategy and the current reclaimed water monitoring cycle are set.

[0100] Example 1:

[0101] When establishing a reclaimed water index evaluation model based on historical data, the following should be included:

[0102] Obtain historical water quality data D, where D = [G,S,P,O];

[0103] Historical water quality data D contains multiple types of sub-data sets, where G represents the particle size data set in reclaimed water, S represents the salinity data set in reclaimed water, P represents the pH value data set in reclaimed water, and O represents the oxygen value data set in reclaimed water.

[0104] G=[g1,g2…gi…gn]; S=[s1,s2…si…sn]; P=[p1,p2…pi…pn];

[0105] O = [o1, o2, ..., oi, ..., on];

[0106] gi represents the particle size data in the water measured in the i-th time, si represents the salinity data in the water measured in the i-th time, pi represents the pH value data in the water measured in the i-th time, oi represents the oxygen value data in the water measured in the i-th time, and n represents the total number of samplings.

[0107] Based on historical water quality data D, the values ​​of each type of sub-data are divided into intervals, generating multiple interval gradients. Values ​​are then assigned to these interval gradients to obtain the evaluation value Z for each type of water quality index, Z = (Z0...). G Z S Z P Z O );

[0108] Among them, Z G Z is an index used to score particle size data in water. S Z is an index used to score the salinity data in water. P Z is an index used to score the pH value data in water. O The index score is given to the oxygen content data in the water.

[0109] In this embodiment, a water quality scoring model is established to quickly and accurately assess water quality.

[0110] Data preparation: Daily water quality testing data were collected over the past year, covering four key indicators: salinity (S), particle size (G), pH (P), and dissolved oxygen (O). Each indicator had over 365 independent samples.

[0111] Rating Interpretation: A lower rating (close to 0) indicates that the indicator deviates significantly from the average level, suggesting potential water quality problems. Conversely, a high rating (close to 1) indicates good or stable water quality.

[0112] Real-time monitoring and early warning: By comparing newly collected data with the results of the scoring model in real time, the system can quickly identify sudden deterioration in water quality, making it easy to take immediate countermeasures, such as increasing the intensity of chemical treatment or adjusting the physical filtration program.

[0113] Long-term trend analysis: Continuously recorded scores of various indicators help to observe the seasonal fluctuations in water quality or the degree of influence of external factors, thereby optimizing the overall greywater treatment strategy.

[0114] Through this detailed implementation process, the water quality scoring model not only provides a quantitative water quality assessment tool, but also provides strong support for subsequent wastewater treatment decisions.

[0115] Example 2:

[0116] When generating the comprehensive score for reclaimed water at the current monitoring time point, the following are included:

[0117] Construct a historical water quality data matrix A[a ij ] n×4 ;

[0118] a ij This represents the score of the j-th data indicator in the i-th sample.

[0119] via a ij Calculate the weight p of the j-th data indicator in the i-th sample. ij :

[0120]

[0121] Calculate the entropy weight e of the j-th data indicator j :

[0122]

[0123] Calculate the coefficient of variation g of the j-th data indicator. j :

[0124] g j =1-e j (j = 1, 2, 3, 4);

[0125] Calculate the weight w of the j-th data indicator. j :

[0126]

[0127] The water quality scoring model is used to obtain the scores Z of various data indicators of the reclaimed water to be treated, and the comprehensive score TS of the reclaimed water to be treated is calculated based on the scores Z of various data indicators of the reclaimed water.

[0128] TS = W1 * Z G +W2*Z S +W3*Z P +W4*Z O ;

[0129] Wherein, W1 is the weight of the particle size data in the water, W2 is the weight of the salinity data in the water, W3 is the weight of the pH value data in the water, and W4 is the weight of the oxygen value data in the water.

[0130] In this embodiment, by analyzing historical water quality data, a water quality scoring model is established to quantitatively assess water quality conditions, providing a scientific basis for greywater treatment. This model helps operators more accurately understand the multidimensional influencing factors of water quality, thereby optimizing treatment strategies.

[0131] The comprehensive score calculation considers the weights and coefficients of variation of multiple water quality parameters, making the score more comprehensive and objective. This method helps identify key water quality issues and provides guidance for resource allocation and prioritization.

[0132] Model library establishment and updating: The establishment of a greywater treatment model library allows for the selection of appropriate treatment models based on real-time water quality scores, and the updating of the model library based on treatment results. This dynamic adjustment mechanism ensures treatment efficiency and water quality safety;

[0133] Intelligent treatment methods improve the automation level of the treatment process by real-time monitoring and automatic adjustment of treatment parameters. This not only reduces human intervention but also helps to respond quickly to changes in water quality and maintain the stability of treatment results.

[0134] Example 3:

[0135] The setting of the current greywater treatment strategy and the current greywater monitoring cycle includes:

[0136] Based on the comprehensive water quality score (TS), the water quality status of the reclaimed water to be treated is determined, the corresponding monitoring cycle is set, and the time interval from the current monitoring time node to the next time node is determined.

[0137] The index score Z for obtaining the particle size data in the water at the current monitoring time point. G The index score Z of the salinity data in the water S ;

[0138] If the particle size data in the water is scored Z G The index score Z of the salinity data in the water S Once all thresholds are reached, the deep processing stage begins.

[0139] The pretreatment stage includes: removal of particulate impurities from the greywater and desalination of the greywater;

[0140] Obtain particle size change ΔZ during the removal of particulate impurities from greywater. G And the salinity data ΔZ during the desalination process of greywater. S ;

[0141] Generate correction value Z for particle size data in water G ', Z G '=(Z G -ΔZ G );

[0142] Correction value Z for salinity data during water desalination S ', Z S '=(Z S -ΔZ S );

[0143] Based on granularity data variation ΔZ G and salinity data ΔZ S Generate water quality correction parameter B;

[0144] Based on water quality correction parameter B, the pH correction value Z in the water is generated. P 'and the correction value Z for oxygen content in water' O ';

[0145] The deep processing stage includes:

[0146] Water quality acid-base balance treatment is carried out based on the corrected pH value in the water;

[0147] Deoxygenation treatment is performed based on the corrected oxygen content in the water.

[0148] Example 4:

[0149] Setting the corresponding monitoring period includes:

[0150] The average value μ of the comprehensive water quality score TS and the standard deviation σ of the comprehensive water quality score TS in the historical data are generated by using historical data.

[0151] Water quality status is classified based on the mean μ and standard deviation σ. Based on the classification of water quality status, the update time node for the comprehensive score of reclaimed water is determined.

[0152] Based on water purification experiments, the adjustment range of the sampling frequency (u) was determined. min ,u max );

[0153] u min u is the minimum scaling factor for the sampling frequency. max This is the maximum scaling factor for the sampling frequency;

[0154] If TS∈(μ-σ,μ+σ), then the current water to be tested is defined as Grade I water, the current water quality sampling frequency T0 is maintained, and the remaining amount of deoxygenating agent and Grade I acid-base reagent is predicted.

[0155] If TS∈(0,μ-σ), then the current water to be tested is defined as secondary reclaimed water, and the sampling frequency T of the current water quality is corrected.

[0156]

[0157] m is the current water quality sampling frequency scaling factor.

[0158] T = T0 / (1+m);

[0159] If TS∈(μ+σ,+∞), then the current water to be tested is defined as Class III water, and the sampling frequency T of the current water quality is corrected.

[0160] T = T0 / (1-m).

[0161] In this embodiment, the sampling frequency can be dynamically adjusted based on the real-time water quality score, ensuring the accuracy of monitoring and the timeliness of treatment. Furthermore, reagent usage can be rationally planned to achieve optimal resource allocation. This tiered monitoring and treatment strategy provides a strong guarantee for the efficient and stable operation of the wastewater treatment system.

[0162] In this embodiment, statistical analysis of the average value μ and standard deviation σ of the comprehensive score TS can more accurately classify water quality states, thereby enabling the implementation of corresponding monitoring and treatment strategies for different levels of reclaimed water. This hierarchical management helps to address water quality problems in a targeted manner and improve the overall treatment effect.

[0163] Optimize resource allocation and treatment efficiency: Based on different water quality rating levels, monitoring and treatment resources can be rationally allocated to avoid using the same treatment standard for all reclaimed water, thereby improving resource utilization efficiency and treatment speed.

[0164] Strengthening early warning and emergency response capabilities: Through real-time monitoring and data analysis, digital twin technology can promptly detect water quality anomalies and trigger early warning mechanisms, which helps to quickly respond to potential water quality problems and reduce the impact of pollution incidents.

[0165] Promoting scientific and intelligent decision-making: The establishment and updating of model libraries, combined with data assimilation technology and simulation forecasting, provides decision support, making management measures more precise and effective. This contributes to maximizing the utilization of water resources and achieving sustainable development.

[0166] Enhancing the automation and intelligence of the system: Intelligent greywater treatment methods improve the automation level of water quality management, reduce manual intervention, and ensure the stability of treatment results by integrating advanced data analysis technology, automated control, and intelligent algorithms.

[0167] Example 5:

[0168] The process of removing particulate impurities from greywater includes:

[0169] The threshold Z for particle size data in water is set based on water usage indicators. G (min);

[0170] And based on the current particle size data in the water, an index score Z is given. G Calculate the amount of coagulant to be added;

[0171] Real-time monitoring of particle size changes ΔZ during water particulate impurity removal process G When the particle size data in water is scored Z... G When the target threshold is reached, residual colloids and suspended solids are removed by filtration.

[0172] Obtain the change in granularity data ΔZ at this time. G (max).

[0173] Example 6:

[0174] The calculation of the amount of coagulant to be added includes:

[0175] The coagulant addition test was conducted to obtain the coagulant unit volume addition coefficient k0.

[0176] Calculate the amount of coagulant Q to be added;

[0177] Q = k0 * Z G *V;

[0178] V represents the volume of water to be treated.

[0179] In this embodiment, the strategy is summarized.

[0180] The strategy for removing particulate matter from greywater aims to effectively reduce the content of colloids and suspended solids in water by combining coagulant dosage with subsequent filtration. Its core lies in accurately calculating the coagulant dosage based on water quality characteristic parameters (ZG), ensuring effective removal of impurities while avoiding reagent waste.

[0181] Calculation process for coagulant dosage

[0182] Determination of coagulant unit volume input coefficient (k)

[0183] Conduct coagulant addition tests to determine the unit volume dosage (k) required for a specific type of coagulant and its optimal flocculation effect under target water quality conditions, i.e., the coagulant dosage (g / m3) required per cubic meter (m3) of water to be treated.

[0184] Calculate the amount of coagulant to be added (Q).

[0185] Based on the measured coagulant unit volume input coefficient (k), combined with the volume of water to be treated (V) and water quality characteristic parameters (ZG), the total coagulant input (Q) is calculated.

[0186] Coagulation and sedimentation process

[0187] The calculated amount of coagulant is added to the water to be treated and stirred to disperse it evenly, promoting the formation of larger flocs and sedimentation of particulate matter.

[0188] Monitor whether the turbidity of the effluent after coagulation is lower than the preset threshold to ensure that the preliminary treatment meets the standards.

[0189] Deep filtering stage

[0190] If the turbidity of the effluent after coagulation meets the standard, it is further sent to a filter bed for deep filtration to remove residual microcolloids and suspended solids.

[0191] The selection and design of filter media should ensure that it can effectively trap smaller particles and ensure that the quality of the effluent meets the standards.

[0192] Example 7:

[0193] The desalination of the reclaimed water includes:

[0194] The threshold value Z for salinity in water is set based on water usage indicators. S (min);

[0195] The Z-index is based on the current salinity data in the water. S Calculate the time required for water desalination in reverse osmosis.

[0196] Real-time monitoring of salinity changes ΔZ during the desalination process of recycled water S When the salinity data in the water is scored Z... S When the indicator threshold is reached, obtain the change in granular data ΔZ at this point. S (max).

[0197] Example 8:

[0198] Generate pH correction value Z in water P 'and the correction value Z for oxygen content in water' O 'Time' includes:

[0199] Based on granularity data variation ΔZ G and salinity data ΔZ S Generate water quality correction parameters B for the advanced treatment stage, B = (B1, B2);

[0200] B1=k1*ΔZ G +k2*ΔZ S ;

[0201] B2=k3*ΔZ G +k4*ΔZ S ;

[0202] k1 is the correction constant for pH value caused by changes in particle size data, k2 is the correction constant for pH value caused by changes in salinity data, k3 is the correction constant for oxygen content caused by changes in particle size data, and k4 is the correction constant for oxygen content caused by salinity data and k2.

[0203] Generate pH correction value Z in water P ', Z P =Z P -B1;

[0204] And the correction value Z for oxygen content in water O ', Z O =Z O -B2;

[0205] Wherein, B1 is the pH correction parameter for the pretreatment stage, and B2 is the oxygen content correction parameter for the pretreatment stage.

[0206] In this embodiment, the desalination process of the greywater is as follows: First, a threshold value ZS(min) for salinity is set according to water usage indicators. Then, the desalination time of the greywater using reverse osmosis is calculated through real-time monitoring and data analysis. During the desalination process, the change in salinity data ΔZS is monitored in real time, and when the salinity index score ZS reaches the threshold, the change in particle size data ΔZS(max) is recorded.

[0207] Advanced treatment stage water quality correction: Based on changes in particle size and salinity data, a water quality correction parameter B is generated for the advanced treatment stage. This parameter is used to calculate the pH correction value ZP' and the oxygen correction value ZO'. These correction values ​​allow adjustment of the pH and oxygen content in the water to the target values.

[0208] Advanced treatment stage: After the pretreatment stage, the comprehensive score TS'' of the reclaimed water is calculated, and acid-base reagents and deoxygenating agents are added according to the minimum index score of the corrected pH value and oxygen content data to achieve the predetermined water quality standards.

[0209] In practical applications, these steps can be automated through integrated control systems and intelligent algorithms. For example, IoT technology can be used to monitor water quality parameters in real time, big data analytics can be used to optimize the treatment process, and cloud computing platforms can be used to centrally manage and analyze data. Furthermore, digital twin technology can be used to create virtual models of the greywater treatment system for more accurate prediction and control.

[0210] Example 9

[0211] The deep processing stage includes:

[0212] Calculate the minimum index score Z for pH data in water at the current monitoring time. P (min)

[0213] Z P (min)=Z P -B1=Z P -(k1*ΔZ G (max)+k2*ΔZ S (max));

[0214] Calculate the minimum index score Z for oxygen content data in water at the current monitoring time. O (min)

[0215] Z O (min)=Z O -B2=Z O -(k3*ΔZ G (max)+k4*ΔZ S (max));

[0216] The minimum index score Z is based on the pH value data of the water. P (min), add acid and alkali reagents to adjust the pH value of the water to the target value;

[0217] The minimum index score Z is based on the oxygen content data in the water. O (min), add oxygen remover to control the oxygen content in the water to the target level.

[0218] Intelligent greywater treatment methods, by integrating advanced information technology and automated control technology, not only improve the technical performance of water treatment, but also contribute to the sustainable use of water resources and environmental protection.

[0219] In this embodiment, the TS value is recorded to generate the dataset TSD: During the wastewater treatment process, samples are taken periodically to record the TS (Turbidity) value of the wastewater, generating a dataset TSD = [TS1, TS2, ..., TSt, ..., TSn] that changes over time, where each TSt represents the turbidity value at the t-th sampling after the start of treatment.

[0220] Actual rate of change VT calculation: Based on the TSD dataset, the actual rate of change of TS, VT, is calculated using differential or other numerical methods, reflecting the rate of turbidity change during the treatment process. Theoretical vs. actual deviation assessment: Theoretical rate of change VT' is obtained: Based on previous water purification experiments, the theoretical rate of change of TS, VT', is determined under ideal conditions. Error value MES calculation: VT and VT' are compared, and the deviation between them is calculated, i.e., error value MES = VT - VT'.

[0221] Set threshold E: Set an acceptable maximum deviation threshold E for MES.

[0222] Triggering manual intervention: If MES>E, it indicates that the treatment effect deviates significantly from the expectation and manual intervention is required.

[0223] Model library update

[0224] MES value and deviation analysis.

[0225] Parameter correction and new model generation: Based on the report content, the manual end analyzes the causes of deviations, adjusts control parameters, and generates a new wastewater treatment model.

[0226] Model library update: New models are added to the wastewater treatment model library after they have been verified to be effective.

[0227] This embodiment ensures continuous optimization of the greywater treatment model through real-time monitoring and dynamic adjustment, thereby improving treatment efficiency and water quality compliance rate. This data-driven model update mechanism not only enhances the performance of the greywater reuse system but also provides a more flexible and intelligent solution for water resource management.

[0228] The paper demonstrates the specific steps and effects of dynamically updating the greywater treatment model library. Through continuous data collection, analysis, and human intervention, the treatment model has been optimized and upgraded, which is of great significance for improving the stability and efficiency of the greywater treatment system.

[0229] Specific implementation of the dynamic update mechanism for the greywater treatment model library

[0230] Background and Objectives

[0231] In greywater treatment systems, changes in water quality parameters directly impact treatment effectiveness. This embodiment aims to improve overall system performance by monitoring turbidity (TS) changes during the treatment process in real time, evaluating treatment efficiency, and dynamically updating the greywater treatment model library based on the evaluation results.

[0232] Implementation steps

[0233] Data Recording and Analysis: Generating the TS Change Dataset (TSD)

[0234] Implementation details: The system automatically records turbidity values ​​during processing, forming a dataset.

[0235] TSD = [TS1, TS2, ..., TSt, ..., TSn], where each element TSt represents the turbidity value from the start of processing to the t-th sampling time, and n represents the total number of samplings. Implementation details for calculating the actual rate of change VT: Using numerical analysis methods, the actual rate of change VT of turbidity is calculated based on the TSD dataset. Implementation details for obtaining the theoretical rate of change VT': Referring to the results of water purification experiments, the theoretical rate of change VT' of turbidity under ideal conditions is obtained. Implementation details for calculating the error value MES: Comparing VT and VT', the difference between the two is calculated, i.e., MES. Implementation details for setting the threshold E and judgment: A maximum acceptable threshold E for the error value MES is set. When MES > E, the system determines that the current model's processing effect is poor.

[0236] Human intervention and model correction

[0237] Implementation details: Send alarm information to the human terminal, where professionals analyze the cause of the deviation, adjust control parameters, and design and verify a new processing model.

[0238] Implementation details: Add the new model to the greywater treatment model library for automatic use in subsequent treatment processes.

[0239] System performance improvement: By dynamically adjusting the model to ensure it matches the actual water quality conditions, the system effectively improves treatment efficiency and water quality compliance rate.

[0240] Cost savings: It avoids waste of resources and reduces processing costs.

[0241] Data-driven decision-making: Based on the analysis of real-time data, it provides accurate decision-making basis for human intervention.

[0242] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. An intelligent greywater treatment method, characterized in that, include: Establish a water quality evaluation model based on historical data; Multiple monitoring time nodes are established to obtain real-time parameters of the reclaimed water to be treated at the current monitoring time node, and a comprehensive score of the reclaimed water at the current monitoring time node is generated based on the reclaimed water index evaluation model. Based on the comprehensive score of reclaimed water at the current monitoring time point, set the current reclaimed water treatment strategy and the current reclaimed water monitoring cycle; When establishing a reclaimed water index evaluation model based on historical data, the following should be included: Obtain historical water quality data D, where D = [G, S, P, O]; Historical water quality data D contains multiple types of sub-data sets, where G represents the particle size data set in reclaimed water, S represents the salinity data set in reclaimed water, P represents the pH value data set in reclaimed water, and O represents the oxygen value data set in reclaimed water. G=[g1,g2…gi…gn]; S=[s1,s2…si…sn]; P=[p1,p2…pi…pn]; O=[o1,o2…oi…on]; gi represents the particle size data in the water measured in the i-th time, si represents the salinity data in the water measured in the i-th time, pi represents the pH value data in the water measured in the i-th time, oi represents the oxygen value data in the water measured in the i-th time, and n represents the total number of samplings. Based on historical water quality data D, the values ​​of each type of sub-data are divided into intervals, generating multiple interval gradients. Values ​​are then assigned to these interval gradients to obtain the evaluation value Z for each type of water quality index, Z = (Z0...). G Z S Z P Z O ); Among them, Z G Z is an index used to score particle size data in water. S Z is an index used to score the salinity data in water. P Z is an index used to score the pH value data in water. O Scoring indicators for oxygen content data in water; When generating the comprehensive score for reclaimed water at the current monitoring time point, the following are included: Construct a historical water quality data matrix A[a ij ] n×4 ; a ij This represents the score of the j-th data indicator in the i-th sample. via a ij Calculate the weight p of the j-th data indicator in the i-th sample. ij : ; Calculate the entropy weight e of the j-th data indicator j : (j=1,2,3,4)； Calculate the coefficient of variation g of the j-th data indicator. j : (j=1,2,3,4)； Calculate the weight w of the j-th data indicator. j : ； The water quality scoring model is used to obtain the scores Z for various data indicators of the reclaimed water to be treated, and the comprehensive score TS of the reclaimed water to be treated is calculated based on the scores Z. TS=W1*Z G +W2*Z S +W3*Z P +W4*Z O ; Wherein, W1 is the weight of the particle size data in the water, W2 is the weight of the salinity data in the water, W3 is the weight of the pH value data in the water, and W4 is the weight of the oxygen value data in the water. The setting of the current greywater treatment strategy and the current greywater monitoring cycle includes: Based on the comprehensive water quality score (TS), the water quality status of the reclaimed water to be treated is determined, the corresponding monitoring cycle is set, and the time interval from the current monitoring time node to the next time node is determined. The index score Z for obtaining the particle size data in the water at the current monitoring time point. G The index score Z of the salinity data in the water S ; If the particle size data in the water is scored Z G The index score Z of the salinity data in the water S Once all thresholds are reached, the deep processing stage begins. The pretreatment stage includes: removal of particulate impurities from the greywater and desalination of the greywater; Obtain particle size data changes during the removal of particulate impurities from greywater. Z G And data on salt content during the desalination process of greywater. Z S ; Generate correction value Z for particle size data in water G ', Z G '=(Z G - Z G ); Correction value Z for salinity data during water desalination S ', Z S '=(Z S - Z S ); Based on changes in granular data Z G and salinity data Z S Generate water quality correction parameter B; Based on water quality correction parameter B, the pH correction value Z in the water is generated. P 'and the correction value Z for oxygen content in water' O '; The deep processing stage includes: Water quality acid-base balance treatment is carried out based on the corrected pH value in the water; Deoxygenation treatment is performed based on the corrected oxygen content in the water; Setting the corresponding monitoring period includes: The average value µ of the comprehensive water quality score TS and the standard deviation σ of the comprehensive water quality score TS in the historical data are generated by using historical data. Water quality status is classified based on the mean value µ and the standard deviation σ. Based on the classification of water quality status, the update time node for the comprehensive score of reclaimed water is determined. Based on water purification experiments, the adjustment range of the sampling frequency (u) was determined. min ,u max ); u min u is the minimum scaling factor for the sampling frequency. max This is the maximum scaling factor for the sampling frequency; If TS∈(µ-σ,µ+σ), then the current water to be tested is defined as Grade I water, the current water quality sampling frequency T0 is maintained, and the remaining amount of deoxygenating agent and Grade I acid-base reagent is predicted. If TS∈(0,µ-σ), then the current water to be tested is defined as secondary reclaimed water, and the sampling frequency T of the current water quality is corrected. ； m is the current water quality sampling frequency scaling factor. ； If TS∈(µ+σ,+∞), then the current water to be tested is defined as Class III water, and the sampling frequency T of the current water quality is corrected. 。 2. The intelligent greywater treatment method as described in claim 1, characterized in that, The process of removing particulate impurities from greywater includes: The threshold Z for particle size data in water is set based on water usage indicators. G (min); And based on the current particle size data in the water, an index score Z is given. G Calculate the amount of coagulant to be added; Real-time monitoring of particle size changes during water particulate impurity removal Z G When the particle size data in water is scored Z... G When the target threshold is reached, residual colloids and suspended solids are removed by filtration. Obtain the change in granularity data at this time Z G (max).

3. The intelligent greywater treatment method as described in claim 2, characterized in that, The calculation of the amount of coagulant to be added includes: The coagulant addition test was conducted to obtain the coagulant unit volume addition coefficient k0. Calculate the amount of coagulant Q to be added; Q=k0*Z G *V; V represents the volume of water to be treated.

4. The intelligent greywater treatment method as described in claim 1, characterized in that, The desalination of the reclaimed water includes: The threshold value Z for salinity in water is set based on water usage indicators. S (min); The Z-index is based on the current salinity data in the water. S Calculate the time required for water desalination in reverse osmosis. Real-time monitoring of salinity changes during the desalination process of recycled water Z S When the salinity data in the water is scored Z... S When the indicator threshold is reached, obtain the change in granular data at that time. Z S (max).

5. The intelligent greywater treatment method as described in claim 1, characterized in that, Generate pH correction value Z in water P 'and the correction value Z for oxygen content in water' O 'Time' includes: Based on changes in granular data Z G and salinity data Z S Generate water quality correction parameters B for the advanced treatment stage, where B = (B1, B2); B1=k1* WITH G +k2* WITH S ; B2=k3* Z G +k4* Z S ; k1 is the correction constant for pH value caused by changes in particle size data, k2 is the correction constant for pH value caused by changes in salinity data, k3 is the correction constant for oxygen content caused by changes in particle size data, and k4 is the correction constant for oxygen content caused by salinity data and k2. Generate pH correction value Z in water P ', Z P '=Z P -B1; And the correction value Z for oxygen content in water O ', Z O '=Z O -B2; Wherein, B1 is the pH correction parameter for the pretreatment stage, and B2 is the oxygen content correction parameter for the pretreatment stage.

6. The intelligent greywater treatment method as described in claim 1, characterized in that, The deep processing stage includes: Calculate the minimum index score Z for pH data in water at the current monitoring time. P (min) WITH P (min)=Z P -B1=Z P -(k1* WITH G (max)+k2* WITH S (max)); Calculate the minimum index score Z for oxygen content data in water at the current monitoring time. O (min) Z O (min)=Z O -B2=Z O -(k3* Z G (max)+k4* Z S (max)); The minimum index score Z is based on the pH value data of the water. P (min), add acid and alkali reagents to adjust the pH value of the water to the target value; The minimum index score Z is based on the oxygen content data in the water. O (min), add oxygen remover to control the oxygen content in the water to the target level.

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