Intelligent water purification method and system based on water quality analysis

Abstract

This application relates to an intelligent water purification method and system based on water quality analysis, belonging to the field of water treatment technology. The method includes: acquiring the raw water turbidity and unit flow rate; determining a basic flocculant dosage based on the raw water turbidity and unit flow rate, and adding the basic flocculant dosage to the reaction zone; acquiring the flocculant particle size and post-treatment turbidity in the reaction zone; constructing a historical time interval and determining similarity parameters for each time point within the historical time interval based on the current raw water turbidity, raw water unit flow rate, and flocculant particle size, defining the time point corresponding to the largest similarity parameter as the similar time point, and acquiring similar turbidity based on the similar time point; determining the detection deviation turbidity based on the post-treatment turbidity and the similar detection turbidity; and outputting a device abnormality signal when the detection deviation turbidity is outside the permissible deviation range. This application has the function of improving the treatment effect of water purification operations.

Description

Technical Field

[0001] This application relates to the field of water treatment technology, and in particular to an intelligent water purification method and system based on water quality analysis. Background Technology

[0002] In the water purification process, water quality analysis is a crucial step in ensuring efficient purification. Different purification strategies must be adopted for different water quality conditions to achieve precise dosing and optimized process parameters. Especially when treating high-turbidity raw water, flocculants such as polyaluminum chloride (PAC) or polyacrylamide (PAM) are usually added to destabilize colloidal particles in the water and form flocs, which are then separated by sedimentation or filtration to achieve the purification purpose.

[0003] Currently, intelligent dosing control systems typically employ a feedforward-feedback approach to optimize flocculant dosage. Specifically, a turbidity sensor is installed at the inlet of the flocculation tank to monitor the raw water turbidity in real time and calculate the initial dosage based on an empirical model. Simultaneously, a turbidity sensor is added to the reaction zone of the flocculation tank to detect the water quality after flocculation, and a PID control algorithm is used to dynamically adjust the PAC / PAM dosage to ensure that the effluent turbidity meets the standards.

[0004] In the aforementioned technologies, due to the complex internal environment of the flocculation tank, such as floc adhesion, mechanical stirring impact, and chemical corrosion, the turbidity sensor in the reaction zone may experience measurement deviation or complete failure. However, the sensor itself does not have a self-testing function, which causes the system to dynamically optimize the flocculant based on erroneous data, resulting in poor water purification treatment effect and room for improvement. Summary of the Invention

[0005] To improve the treatment effect of water purification operations, this application provides an intelligent water purification method and system based on water quality analysis.

[0006] In a first aspect, this application provides an intelligent water purification method based on water quality analysis, employing the following technical solution:

[0007] A smart water purification method based on water quality analysis includes:

[0008] Obtain the raw water turbidity and raw water unit flow rate;

[0009] Based on the preset basic matching relationship, the basic flocculation dosage corresponding to the raw water turbidity and raw water unit flow rate is determined, and the flocculant of the basic flocculation dosage is added to the preset reaction zone;

[0010] The particle size of the flocculated particles and the turbidity of the subsequent water were measured in the reaction zone.

[0011] Construct a historical interval on a preset timeline, with the current time point as the endpoint and a width equal to the preset historical duration.

[0012] Based on the current raw water turbidity, raw water unit flow rate, and detected flocculent particle size, the similarity parameters for each time point in the historical interval are determined, and the time point corresponding to the largest similarity parameter is defined as the similar time point. The similar turbidity is then obtained based on the similar time point.

[0013] The difference between the turbidity of the subsequent water test and the turbidity of similar tests is calculated to determine the detection deviation turbidity.

[0014] Determine whether the detected turbidity is within the preset permissible deviation range;

[0015] If the detected turbidity is within the permissible deviation range, a normal operation signal will be output.

[0016] If the detected turbidity is outside the permissible deviation range, an abnormal signal will be output to the device.

[0017] Optionally, after determining parameters for similar situations, intelligent water purification methods based on water quality analysis may also include:

[0018] Determine whether the maximum similarity parameter is greater than the preset near similarity parameter;

[0019] If the maximum similarity parameter is greater than the near similarity parameter, then the time point corresponding to the maximum similarity parameter is defined as the similar time point.

[0020] If the maximum case similarity parameter is not greater than the near similarity parameter, then the time point corresponding to the maximum case similarity parameter is defined as the limit time point, and the limit detection turbidity is obtained based on the limit time point.

[0021] The combination of deviation parameters is determined by calculating the raw water turbidity, raw water unit flow rate, and detected flocculant particle size at the current time point and the extreme time point.

[0022] Within the historical interval, the raw water turbidity, raw water unit flow rate and detected floc size are used to determine the comparison parameter combination, and the two time points where the comparison parameter combination is consistent with the deviation parameter combination are defined as mutual binding points.

[0023] The difference between the turbidity of the water at the time points that are mutually dependent is used to calculate the dependent deviation turbidity, and the appropriate deviation turbidity is calculated based on all dependent deviation turbidities.

[0024] The similarity detection turbidity is determined by calculation based on the appropriate deviation turbidity and the limit detection turbidity.

[0025] Optionally, the step of calculating and determining the appropriate bias turbidity based on all bond bias turbidities includes:

[0026] Define the bond point that corresponds to the current time point among the two bond points as the control point, and define the situation similarity parameter determined at the control point as the control similarity parameter;

[0027] The control interval is determined based on the control point and the current time point, and the control confidence coefficient is calculated based on the control interval and control similarity parameters.

[0028] The weight of each tether bias turbidity is determined by calculation based on all control confidence coefficients.

[0029] The appropriate deviation turbidity is determined by calculating based on all the turbidity of the synergy deviations and their corresponding weights.

[0030] Optionally, if the maximum similarity parameter is greater than the near-similarity parameter, the intelligent water purification method based on water quality analysis also includes:

[0031] Determine if there is only one time point where the similarity parameter is greater than the nearest similarity parameter;

[0032] If there is only one time point where the similarity parameter is greater than the near similarity parameter, then the time point where the similarity parameter is greater than the near similarity parameter is defined as the similar time point.

[0033] If there is more than one time point where the similarity parameter is greater than the near similarity parameter, then the time points where the similarity parameter is greater than the near similarity parameter are combined to construct a set of similar times.

[0034] Historical turbidity is obtained from time points in a similar time set, and a similar turbidity range is constructed based on the historical turbidity and preset similar turbidity.

[0035] The number of internal times is determined by counting the time points corresponding to historical turbidity measurements within a similar turbidity range.

[0036] The number of internal time values ​​with the largest value is determined according to the preset sorting rules, and the turbidity range corresponding to this number of internal time values ​​is defined as the effective turbidity range. The similar detection turbidity is determined by calculating based on the historical detection turbidity within the effective turbidity range.

[0037] Optionally, after the number of internal time intervals is determined, the intelligent water purification method based on water quality analysis also includes:

[0038] Determine whether there exist at least two internal time ranges with the same number of times and similar maximum turbidity;

[0039] If there are no at least two turbidity ranges with the same number of internal time and the largest similar turbidity range, then the turbidity range corresponding to the largest number of internal time is defined as the effective turbidity range.

[0040] If there are at least two similar turbidity ranges with the same number of internal time and the largest number of internal time, then the similar turbidity range corresponding to the largest number of internal time is defined as the turbidity candidate range.

[0041] Representative similar parameters are calculated based on historical data showing that turbidity was within the turbidity candidate range at specific times.

[0042] Based on the sorting rules, the parameter with the largest value is determined as the representative similar parameter, and the turbidity candidate range corresponding to this representative similar parameter is defined as the effective turbidity range.

[0043] Optionally, after the device outputs an abnormal signal, the intelligent water purification method based on water quality analysis also includes:

[0044] The feedback flocculation dose corresponding to the similar detection turbidity is determined according to the preset feedback matching relationship, and the flocculant of the feedback flocculation dose is added to the reaction zone;

[0045] Secondary flocculation particle size is obtained in the reaction zone, and the difference between the secondary flocculation particle size and the detected flocculation particle size is calculated to determine the flocculation change particle size.

[0046] The required growth particle size is determined based on the preset particle size matching relationship to determine the corresponding feedback flocculation dosage.

[0047] Determine whether the flocculation change particle size is greater than the demand growth particle size;

[0048] If the flocculation change particle size is not greater than the required increase particle size, the comprehensive flocculation dose is determined by summing the feedback flocculation dose and the basic flocculation dose.

[0049] If the flocculation change particle size is greater than the required growth particle size, the basic flocculation dose is updated by summing the feedback flocculation dose and the basic flocculation dose. The current secondary flocculation particle size is updated to the new detection flocculation particle size. The similar detection turbidity is determined again based on the new basic flocculation dose and the basic flocculation dose, until the comprehensive flocculation dose is determined.

[0050] Optionally, if the flocculation change particle size is not greater than the required increase particle size, the intelligent water purification method based on water quality analysis also includes:

[0051] The percentage of particle size change is determined by calculation based on the changes in flocculation particle size and the increase in demand particle size.

[0052] The effective flocculant dosage is determined by calculation based on the proportion of particle size change and the feedback flocculant dosage.

[0053] The feedback flocculation dose is updated based on the effective flocculation dose and the preset risk-bearing dose.

[0054] Secondly, this application provides an intelligent water purification system based on water quality analysis, which adopts the following technical solution:

[0055] A smart water purification system based on water quality analysis includes:

[0056] The acquisition module is used to acquire the raw water turbidity and raw water unit flow rate;

[0057] The processing module, connected to the acquisition and judgment modules, is used for information storage and processing;

[0058] The judgment module, connected to the acquisition and processing modules, is used for judging information.

[0059] The processing module determines the basic flocculation dosage corresponding to the raw water turbidity and the raw water unit flow rate based on the preset basic matching relationship, and adds the basic flocculation dosage of flocculant to the preset reaction zone;

[0060] The acquisition module acquires the size of the detected flocculent particles and the turbidity of the subsequent water in the reaction zone.

[0061] The processing module constructs a historical interval on a preset timeline, with the current time point as the endpoint and a width of a preset historical duration.

[0062] The processing module determines the similarity parameters for each time point in the historical interval based on the current raw water turbidity, raw water unit flow rate, and detected flocculent particle size. It defines the time point corresponding to the largest similarity parameter as the similar time point and obtains the similar turbidity based on the similar time point.

[0063] The processing module calculates the difference between the turbidity detected in the downstream water and the turbidity detected in similar water to determine the detection deviation turbidity.

[0064] The judgment module determines whether the detected turbidity is within the preset permissible deviation range;

[0065] If the judgment module determines that the detected turbidity is within the permissible deviation range, the processing module outputs a normal operation signal;

[0066] If the judgment module determines that the detected turbidity is not within the permissible deviation range, the processing module outputs a device abnormality signal.

[0067] In summary, this application includes at least one of the following beneficial technical effects:

[0068] During water quality analysis, analyzing the turbidity of the water under similar historical conditions can reveal whether the current turbidity sensor is damaged. This reduces the likelihood of using erroneous data for dynamic optimization of flocculants and improves the treatment effect of water purification operations.

[0069] When the reference value of historical data is high, the situation of each historical data can be comprehensively analyzed to determine the similar detection turbidity with higher reliability, thereby improving the accuracy of data analysis;

[0070] When a turbidity sensor is found to be damaged, the effect of internal water purification can be determined by analyzing the changes in floc size, thereby improving the stability of water purification operations. Attached Figure Description

[0071] Figure 1 This is a flowchart of an intelligent water purification method based on water quality analysis.

[0072] Figure 2 This is a flowchart of the modules for an intelligent water purification method based on water quality analysis. Detailed Implementation

[0073] To make the purpose, technical solution, and advantages of this application clearer, the following is combined with Figures 1-2 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0074] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0075] This application discloses an intelligent water purification method based on water quality analysis, referring to... Figure 1 The method flow of the intelligent water purification method based on water quality analysis includes the following steps:

[0076] Step S100: Obtain the raw water turbidity and raw water unit flow rate.

[0077] The raw water turbidity is the water turbidity value obtained by the turbidity sensor installed at the inlet of the flocculation tank, and the raw water flow rate is the water flow rate entering the flocculation tank per unit time.

[0078] Step S101: Determine the basic flocculation dosage corresponding to the raw water turbidity and raw water unit flow rate according to the preset basic matching relationship, and add the flocculant of the basic flocculation dosage to the preset reaction zone.

[0079] The basic flocculant dosage is the initial dosage of flocculant used for raw water flocculation treatment. Different raw water turbidity and different raw water flow rates indicate different amounts of suspended solids that need to be treated, and the corresponding basic flocculant dosage will also be different. The basic matching relationship between the three can be determined by the staff in advance through multiple experiments. By adding the flocculant at the basic flocculant dosage to the reaction zone, the suspended solids in the raw water in the reaction zone can be flocculated.

[0080] Step S102: Obtain the particle size of the flocculated material and the turbidity of the subsequent water in the reaction zone.

[0081] The detected floc size is the average particle size of the flocs formed by the suspended solids, which can be obtained by a floc imaging instrument. The detected turbidity is the turbidity value of the water in the flocculation tank detected by the turbidity sensor installed in the reaction zone.

[0082] Step S103: Construct a historical interval on the preset timeline with the current time point as the endpoint and the width as the preset historical duration.

[0083] The time axis is a coordinate axis formed by combining various time points. This coordinate axis points from the time points that have been passed to the time points that have not yet been reached. The time points that have been passed are on the left side of the coordinate axis, and the left side of the coordinate axis is defined as the front side of the time axis. The historical duration is the total duration of historical water purification operations set by the staff. By constructing historical intervals, it is convenient to acquire and analyze data within the historical duration.

[0084] Step S104: Determine the similarity parameters for each time point in the historical interval based on the current raw water turbidity, raw water unit flow rate, and detected flocculent particle size, and define the time point corresponding to the largest similarity parameter as the similar time point, and obtain the similar detected turbidity based on the similar time point.

[0085] The similarity parameter reflects the degree of similarity between the water purification situation at a historical point in time and the current point in time. A higher value indicates a more similar water purification process between the two points. The calculation formula is as follows: ,in For parameters indicating similarity in circumstances, This represents the absolute value of the difference in turbidity of the raw water at two different time points. It is the absolute value of the difference in the unit flow rate of the raw water at two time points. This represents the absolute value of the difference between the detected flocculent particle size at two time points. The parameter values ​​preset for staff reflect the impact of deviations in raw water turbidity testing on the similarity of water purification treatment. The parameter values ​​preset for staff reflect the impact of deviations in raw water unit flow rate on the similarity of water purification treatment. The parameter value preset by the staff reflects the influence of the deviation of the detected flocculent particle size on the similarity of water purification treatment; the time point corresponding to the largest similarity parameter is defined as the similar time point to mark the time point most similar to the current water purification treatment situation; the similar detection turbidity is the turbidity value of the water in the reaction zone detected at the similar time point.

[0086] Step S105: Calculate the difference between the turbidity detected in the post-water test and the turbidity detected in similar tests to determine the detection deviation turbidity.

[0087] The detection deviation turbidity is the difference between the turbidity detected in the subsequent water and the turbidity detected in similar samples.

[0088] Step S106: Determine whether the detection deviation turbidity is within the preset permissible deviation range.

[0089] The permissible deviation range is the range within which the difference in turbidity between two tests is allowed, as set by the staff. The purpose of this judgment is to determine whether there are any abnormalities in the turbidity data obtained at the moment of testing.

[0090] Step S1061: If the detected turbidity is within the permissible deviation range, output a normal operation signal.

[0091] When the detected turbidity is within the permissible deviation range, it indicates that the detected turbidity data is relatively normal. At this time, a normal operation signal can be output to mark this situation.

[0092] Step S1062: If the detected turbidity is not within the permissible deviation range, output a device abnormality signal.

[0093] When the detected turbidity is outside the permissible deviation range, it indicates that the detected turbidity data is abnormal, meaning that the turbidity sensor has a data deviation or is damaged. In this case, the dosage of flocculant cannot be dynamically adjusted based on the data from the turbidity sensor. Therefore, an abnormal signal is output to identify this situation, thereby reducing the occurrence of incorrect adjustments and improving the treatment effect of water purification operations.

[0094] Once the parameters for similar conditions are determined, intelligent water purification methods based on water quality analysis also include:

[0095] Step S200: Determine whether the maximum similarity parameter is greater than the preset near similarity parameter.

[0096] The proximity similarity parameter is the minimum similarity parameter set by the staff to determine when the purification situation is considered to be highly similar. The purpose of the judgment is to determine whether the most similar water purification situation meets the similarity analogy requirements.

[0097] Step S2001: If the maximum case similarity parameter is greater than the near-similarity parameter, then the time point corresponding to the maximum case similarity parameter is defined as the similar time point.

[0098] When the maximum similarity parameter is greater than the nearest similarity parameter, it means that the most similar water purification situation meets the similarity analogy requirement. At this time, the corresponding time point can be defined as the similar time point.

[0099] Step S2002: If the maximum case similarity parameter is not greater than the near similarity parameter, then the time point corresponding to the maximum case similarity parameter is defined as the limit time point, and the limit detection turbidity is obtained based on the limit time point.

[0100] When the maximum similarity parameter is not greater than the nearest similarity parameter, it indicates that the most similar water purification situation cannot meet the analogy requirements of the current water purification situation, that is, it has no reference significance and needs further analysis; define the limit time point to mark different time points, and the limit detection turbidity is the turbidity of the water after detection at the limit time point.

[0101] Step S201: Calculate the combination of deviation parameters based on the raw water turbidity, raw water unit flow rate, and detected flocculent particle size at the current time point and the extreme time point.

[0102] The deviation parameter combination is a combination of the differences between various parameters at the current time point and the extreme time point. For example, at the current time point, the raw water turbidity is 100 NTU and the raw water unit flow rate is 10. The detected flocculant particle size was 0.5 mm, the raw water turbidity at the limiting time point was 80 NTU, and the raw water unit flow rate was 9. If the detected flocculent particle size is 0.4 mm, then the obtained deviation parameter combination is {20, 1, 0.1}.

[0103] Step S202: Determine the comparison parameter combination based on the raw water turbidity, raw water unit flow rate and detected flocculent particle size at any two time points in the historical interval, and define the two time points where the comparison parameter combination is consistent with the deviation parameter combination as mutual binding points.

[0104] The comparison parameter combination is a combination of the differences of various parameters between two time points, and the determination method is the same as that of the deviation parameter combination mentioned above. When the comparison parameter combination is consistent with the deviation parameter combination, it means that the water purification deviation at the two time points corresponding to the current comparison parameter combination is consistent with the water purification deviation at the limit time point and the current time point under the current situation. At this time, reference analysis can be carried out. Therefore, a checkpoint is defined to distinguish different time points.

[0105] Step S203: Calculate the difference between the turbidity of the water at the time points that are mutually dependent and determine the dependent deviation turbidity. Calculate the appropriate deviation turbidity based on all dependent deviation turbidities.

[0106] The turbidity deviation is the difference in turbidity detected in the water at the time points where the two points are mutually bound. The suitable turbidity deviation is the water quality turbidity deviation parameter that needs to be met under the theoretical conditions under the current deviation of various parameters. It can be calculated and determined by the average value of all turbidity deviations, or it can be determined through steps S300-S303.

[0107] Step S204: Calculate and determine the similarity detection turbidity based on the appropriate deviation turbidity and the limit detection turbidity.

[0108] The similarity detection turbidity is the value obtained by adding the appropriate deviation turbidity to the limit detection turbidity.

[0109] The steps for determining the appropriate bias turbidity based on all bond bias turbidities include:

[0110] Step S300: Define the bond point corresponding to the current time point among the two bond points as the control point, and define the situation similarity parameter determined at the control point as the control similarity parameter.

[0111] The bond point corresponding to the current time point is the time point between two bond points whose relationship is consistent with the relationship between the current time point and the limit time point. Defining a reference point can determine the specific reference point. At this time, defining the reference similarity parameter can distinguish the similarity parameter in different situations.

[0112] Step S301: Determine the control interval based on the control point and the current time point, and calculate the control confidence coefficient based on the control interval and control similarity parameters.

[0113] The control interval is the time interval between the control point and the current time point. The control confidence coefficient is a parameter reflecting the reliability of the data at the currently determined control point. The larger the value, the stronger the data reliability, and the more meaningful it is for data reference. The formula for calculating the control confidence coefficient is as follows: ,in As a reference confidence coefficient, To compare similar parameters, To compare the time interval, These are pre-defined weighted parameters that reflect the impact of similarity parameters on the reliability of the data. This is a preset weighting parameter that reflects the impact of the time interval between controls on the reliability of the data.

[0114] Step S302: Calculate the binding bias weights for each binding bias turbidity based on all control confidence coefficients.

[0115] The hindrance bias weight is the parameter value obtained by summing the confidence coefficients of a single control with the confidence coefficients of all controls.

[0116] Step S303: Calculate and determine the appropriate deviation turbidity based on all bond deviation turbidities and their corresponding bond deviation weights.

[0117] By multiplying all the turbidity of the entanglement bias by their corresponding weights and summing them all, a more reliable and appropriate turbidity of the bias can be obtained, thereby improving the accuracy of data analysis.

[0118] If the maximum similarity parameter is greater than the near-similarity parameter, intelligent water purification methods based on water quality analysis also include:

[0119] Step S400: Determine if there is only one case where the similarity parameter is greater than the time point of the closest similarity parameter.

[0120] The purpose of the judgment is to determine whether there is only one time point that meets the requirements.

[0121] Step S4001: If there is only one time point where the similarity parameter is greater than the near similarity parameter, then the time point where the similarity parameter is greater than the near similarity parameter is defined as the similar time point.

[0122] When there is only one time point where the similarity parameter is greater than the near similarity parameter, this time point can be directly defined as the similar time point for analysis.

[0123] Step S4002: If there is more than one time point where the similarity parameter is greater than the near similarity parameter, then combine the time points where the similarity parameter is greater than the near similarity parameter to construct a similar time set.

[0124] When there is more than one time point where the similarity parameter is greater than the near similarity parameter, it indicates that there are multiple time points with a high degree of similarity. At this time, further analysis can be performed to determine the accurate similarity detection turbidity. Therefore, a similar time set is constructed to extract data from the time points that meet the requirements.

[0125] Step S401: Obtain historical turbidity based on the time points in the similar time set, and construct a turbidity similar range based on the historical turbidity and the preset similar turbidity.

[0126] Historical turbidity refers to the turbidity of water measured at a time point within a similar time set. Similar turbidity is the maximum allowable difference in turbidity value set by staff when two turbidity values ​​are considered to be relatively close. The similar turbidity range is the numerical interval formed by adding and subtracting the similar turbidity from the historical turbidity, with each value being a value close to the current historical turbidity. One historical turbidity value corresponds to one similar turbidity range.

[0127] Step S402: Count the time points corresponding to historical turbidity measurements within a similar turbidity range to determine the number of internal time points.

[0128] The internal time count is the number of historical turbidity measurements within a single turbidity range.

[0129] Step S403: Determine the number of internal times with the largest value according to the preset sorting rules, define the turbidity range corresponding to the number of internal times as the effective turbidity range, and calculate the similar detection turbidity based on the historical detection turbidity within the effective turbidity range.

[0130] The sorting rules are methods set by staff to sort numerical values, such as the bubble sort method. By sorting rules, the number of internal times with the largest value can be determined, which means that the historical turbidity that appears in the current turbidity range is more reasonable. Therefore, it is defined as the effective turbidity range. By averaging all historical turbidity within the effective turbidity range, a similar detection turbidity with high data reliability can be obtained.

[0131] Once the internal time quantity is determined, intelligent water purification methods based on water quality analysis also include:

[0132] Step S500: Determine whether there are at least two internal time ranges with the same number of times and similar maximum turbidity.

[0133] The purpose of the judgment is to determine whether there are multiple turbidity ranges that meet the requirements, so as to determine the unique effective turbidity range.

[0134] Step S5001: If there are no at least two turbidity ranges with the same number of internal time and the largest similar turbidity range, then the turbidity range corresponding to the largest number of internal time is defined as the effective turbidity range.

[0135] When there are no at least two turbidity ranges with the same number of internal time and the largest similarity, it means that there is only one turbidity range that meets the requirements. In this case, it can be defined as the effective turbidity range.

[0136] Step S5002: If there are at least two similar turbidity ranges with the same number of internal time and the largest number of internal time, then the similar turbidity range corresponding to the largest number of internal time is defined as the turbidity candidate range.

[0137] When there are at least two turbidity ranges with the same number of internal time and the largest turbidity range, it indicates that there are multiple turbidity ranges that meet the requirements. Further analysis is needed at this time. Therefore, turbidity candidate ranges are defined to distinguish different turbidity ranges.

[0138] Step S501: Calculate the representative similar parameters based on the similarity parameters of historical turbidity detection times when the turbidity is within the turbidity candidate range.

[0139] The representative similarity parameter is the average of the similarity parameters for all historical turbidity detection time points where the turbidity is within the turbidity candidate range.

[0140] Step S502: Determine the representative similar parameter with the largest value according to the sorting rules, and define the turbidity candidate range corresponding to the representative similar parameter as the effective turbidity range.

[0141] The sorting rules determine that the largest value represents a similar parameter, which means that the data within the turbidity candidate range has high reference reliability. Therefore, it can be defined as the effective turbidity range.

[0142] Following the output of an abnormal signal from the device, intelligent water purification methods based on water quality analysis also include:

[0143] Step S600: Determine the feedback flocculation dose corresponding to the similar detection turbidity according to the preset feedback matching relationship, and add the flocculant of the feedback flocculation dose to the reaction zone.

[0144] The feedback flocculation dosage is the amount of flocculant that needs to be added again when the turbidity of the water after flocculation treatment is similar to the detected turbidity. Different similar detected turbidities correspond to different feedback flocculation dosages. The feedback matching relationship between the two is determined by the staff in advance through multiple tests. Further purification of water turbidity can be achieved by continuing to add flocculant.

[0145] Step S601: Obtain the secondary flocculation particle size in the reaction zone, and calculate the difference between the secondary flocculation particle size and the detected flocculation particle size to determine the flocculation change particle size.

[0146] The secondary flocculation particle size is the average particle size of the flocs in the flocculation tank after the flocculant is added again. The flocculation change particle size is the parameter value obtained by subtracting the detected flocculation particle size from the secondary flocculation particle size.

[0147] Step S602: Determine the required growth particle size corresponding to the feedback flocculation dosage based on the preset particle size matching relationship.

[0148] The required increase particle size is the theoretically possible increase in particle size after adding a feedback flocculation dose when the water turbidity does not meet the requirements but can still be treated. The required increase particle size corresponding to different feedback flocculation doses is determined in advance by the staff through experiments.

[0149] Step S603: Determine whether the flocculation change particle size is greater than the required increase particle size.

[0150] The purpose of the judgment is to determine whether the current changes in flocs meet the requirements of normal flocculation. In other words, the change in floc particle size reflects whether the water turbidity has been treated to meet the requirements.

[0151] Step S6031: If the flocculation change particle size is not greater than the required increase particle size, the comprehensive flocculation dose is determined by summing the feedback flocculation dose and the basic flocculation dose.

[0152] When the flocculation change particle size is not greater than the required increase particle size, it indicates that the current water quality can be treated well. At this time, the flocculant dosage and the basic flocculant dosage are added together to obtain the flocculant dosage for water quality treatment per unit time, which is the comprehensive flocculant dosage. Subsequently, flocculant can be added according to this comprehensive flocculant dosage to achieve better water purification.

[0153] Step S6032: If the flocculation change particle size is greater than the required increase particle size, the basic flocculation dose is updated by summing the feedback flocculation dose and the basic flocculation dose. The current secondary flocculation particle size is updated to the new detection flocculation particle size. The similar detection turbidity is determined again based on the new basic flocculation dose and the basic flocculation dose, until the comprehensive flocculation dose is determined.

[0154] When the flocculation particle size changes are greater than the required increase in particle size, it indicates that the floc particle size is still growing significantly. This means that the water turbidity does not meet the requirements and further treatment is needed. Therefore, by updating the basic flocculation dosage and the basic flocculation dosage, similar turbidity tests can be re-analyzed to determine the comprehensive flocculation dosage that meets the current water purification requirements.

[0155] If the flocculation change particle size is not greater than the required increase particle size, intelligent water purification methods based on water quality analysis also include:

[0156] Step S700: Calculate the percentage of particle size change based on the flocculation change particle size and the increase in demand particle size.

[0157] The percentage of particle size change is the value obtained by dividing the flocculation change particle size by the demand growth particle size.

[0158] Step S701: Calculate and determine the effective flocculant dosage based on the proportion of particle size change and the feedback flocculant dosage.

[0159] The effective flocculation dose is the value obtained by multiplying the percentage of particle size change by the feedback flocculation dose.

[0160] Step S702: Calculate and update the feedback flocculation dose based on the effective flocculation dose and the preset risk-bearing dose.

[0161] The risk-bearing dose is the amount of flocculant added by staff to reduce the amount of water that cannot be effectively purified. The flocculant dosage can be updated by adding the risk-bearing dose to the effective flocculant dosage, which facilitates the subsequent addition of flocculant.

[0162] Reference Figure 2 Based on the same inventive concept, embodiments of the present invention provide an intelligent water purification system based on water quality analysis, comprising:

[0163] The acquisition module is used to acquire the raw water turbidity and raw water unit flow rate;

[0164] The processing module, connected to the acquisition and judgment modules, is used for information storage and processing;

[0165] The judgment module, connected to the acquisition and processing modules, is used for judging information.

[0166] The processing module determines the basic flocculation dosage corresponding to the raw water turbidity and the raw water unit flow rate based on the preset basic matching relationship, and adds the basic flocculation dosage of flocculant to the preset reaction zone;

[0167] The acquisition module acquires the size of the detected flocculent particles and the turbidity of the subsequent water in the reaction zone.

[0168] The processing module constructs a historical interval on a preset timeline, with the current time point as the endpoint and a width of a preset historical duration.

[0169] The processing module determines the similarity parameters for each time point in the historical interval based on the current raw water turbidity, raw water unit flow rate, and detected flocculent particle size. It defines the time point corresponding to the largest similarity parameter as the similar time point and obtains the similar turbidity based on the similar time point.

[0170] The processing module calculates the difference between the turbidity detected in the downstream water and the turbidity detected in similar water to determine the detection deviation turbidity.

[0171] The judgment module determines whether the detected turbidity is within the preset permissible deviation range;

[0172] If the judgment module determines that the detected turbidity is within the permissible deviation range, the processing module outputs a normal operation signal;

[0173] If the judgment module determines that the detected turbidity is not within the permissible deviation range, the processing module outputs a device abnormality signal.

[0174] The similarity comparison analysis module is used to perform similarity detection and turbidity analysis when the similarity parameter values ​​are not large at each time point.

[0175] The appropriate deviation turbidity determination module is used to effectively determine the appropriate deviation turbidity;

[0176] The multi-similarity analysis module is used to perform similarity detection and turbidity analysis when the similarity parameter values ​​are large at multiple time points.

[0177] The turbidity similarity range filtering module is used to filter multiple turbidity similarity ranges that meet the requirements.

[0178] The device anomaly analysis module performs data analysis when the turbidity sensor is damaged and cannot detect water turbidity.

[0179] The feedback flocculation dosage update module is used to update and determine the appropriate feedback flocculation dosage.

[0180] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

Claims

1. A smart water purification method based on water quality analysis, characterized in that, include: Obtain the raw water turbidity and raw water unit flow rate; Based on the preset basic matching relationship, the basic flocculation dosage corresponding to the raw water turbidity and raw water unit flow rate is determined, and the flocculant of the basic flocculation dosage is added to the preset reaction zone; The particle size of the flocculated particles and the turbidity of the subsequent water were measured in the reaction zone. Construct a historical interval on a preset timeline, with the current time point as the endpoint and a width equal to the preset historical duration. Based on the current raw water turbidity, raw water unit flow rate, and detected flocculent particle size, the similarity parameters for each time point in the historical interval are determined, and the time point corresponding to the largest similarity parameter is defined as the similar time point. The similar turbidity is then obtained based on the similar time point. The difference between the turbidity of the subsequent water test and the turbidity of similar tests is calculated to determine the detection deviation turbidity. Determine whether the detected turbidity is within the preset permissible deviation range; If the detected turbidity is within the permissible deviation range, a normal operation signal will be output. If the detected turbidity is outside the permissible deviation range, an abnormal signal will be output to the device.

2. The intelligent water purification method based on water quality analysis according to claim 1, characterized in that, Once the parameters for similar conditions are determined, intelligent water purification methods based on water quality analysis also include: Determine whether the maximum similarity parameter is greater than the preset near similarity parameter; If the maximum similarity parameter is greater than the near similarity parameter, then the time point corresponding to the maximum similarity parameter is defined as the similar time point. If the maximum case similarity parameter is not greater than the near similarity parameter, then the time point corresponding to the maximum case similarity parameter is defined as the limit time point, and the limit detection turbidity is obtained based on the limit time point. The combination of deviation parameters is determined by calculating the raw water turbidity, raw water unit flow rate, and detected flocculant particle size at the current time point and the extreme time point. Within the historical interval, the raw water turbidity, raw water unit flow rate and detected floc size are used to determine the comparison parameter combination, and the two time points where the comparison parameter combination is consistent with the deviation parameter combination are defined as mutual binding points. The difference between the turbidity of the water at the time points that are mutually dependent is used to calculate the dependent deviation turbidity, and the appropriate deviation turbidity is calculated based on all dependent deviation turbidities. The similarity detection turbidity is determined by calculation based on the appropriate deviation turbidity and the limit detection turbidity.

3. The intelligent water purification method based on water quality analysis according to claim 2, characterized in that, The steps for determining the appropriate bias turbidity based on all bond bias turbidities include: Define the bond point that corresponds to the current time point among the two bond points as the control point, and define the situation similarity parameter determined at the control point as the control similarity parameter; The control interval is determined based on the control point and the current time point, and the control confidence coefficient is calculated based on the control interval and control similarity parameters. The weight of each tether bias turbidity is determined by calculation based on all control confidence coefficients. The appropriate deviation turbidity is determined by calculating based on all the turbidity of the synergy deviations and their corresponding weights.

4. The intelligent water purification method based on water quality analysis according to claim 2, characterized in that, If the maximum similarity parameter is greater than the near-similarity parameter, intelligent water purification methods based on water quality analysis also include: Determine if there is only one time point where the similarity parameter is greater than the nearest similarity parameter; If there is only one time point where the similarity parameter is greater than the near similarity parameter, then the time point where the similarity parameter is greater than the near similarity parameter is defined as the similar time point. If there is more than one time point where the similarity parameter is greater than the near similarity parameter, then the time points where the similarity parameter is greater than the near similarity parameter are combined to construct a set of similar times. Historical turbidity is obtained from time points in a similar time set, and a similar turbidity range is constructed based on the historical turbidity and preset similar turbidity. The number of internal times is determined by counting the time points corresponding to historical turbidity measurements within a similar turbidity range. The number of internal time values ​​with the largest value is determined according to the preset sorting rules, and the turbidity range corresponding to this number of internal time values ​​is defined as the effective turbidity range. The similar detection turbidity is determined by calculating based on the historical detection turbidity within the effective turbidity range.

5. The intelligent water purification method based on water quality analysis according to claim 4, characterized in that, Once the internal time quantity is determined, intelligent water purification methods based on water quality analysis also include: Determine whether there exist at least two internal time ranges with the same number of times and similar maximum turbidity; If there are no at least two turbidity ranges with the same number of internal time and the largest similar turbidity range, then the turbidity range corresponding to the largest number of internal time is defined as the effective turbidity range. If there are at least two similar turbidity ranges with the same number of internal time and the largest number of internal time, then the similar turbidity range corresponding to the largest number of internal time is defined as the turbidity candidate range. Representative similar parameters are calculated based on historical data showing that turbidity was within the turbidity candidate range at specific times. Based on the sorting rules, the parameter with the largest value is determined as the representative similar parameter, and the turbidity candidate range corresponding to this representative similar parameter is defined as the effective turbidity range.

6. The intelligent water purification method based on water quality analysis according to claim 1, characterized in that, Following the output of an abnormal signal from the device, intelligent water purification methods based on water quality analysis also include: The feedback flocculation dose corresponding to the similar detection turbidity is determined according to the preset feedback matching relationship, and the flocculant of the feedback flocculation dose is added to the reaction zone; Secondary flocculation particle size is obtained in the reaction zone, and the difference between the secondary flocculation particle size and the detected flocculation particle size is calculated to determine the flocculation change particle size. The required growth particle size is determined based on the preset particle size matching relationship to determine the corresponding feedback flocculation dosage. Determine whether the flocculation change particle size is greater than the demand growth particle size; If the flocculation change particle size is not greater than the required increase particle size, the comprehensive flocculation dose is determined by summing the feedback flocculation dose and the basic flocculation dose. If the flocculation change particle size is greater than the required growth particle size, the basic flocculation dose is updated by summing the feedback flocculation dose and the basic flocculation dose. The current secondary flocculation particle size is updated to the new detection flocculation particle size. The similar detection turbidity is determined again based on the new basic flocculation dose and the basic flocculation dose, until the comprehensive flocculation dose is determined.

7. The intelligent water purification method based on water quality analysis according to claim 6, characterized in that, If the flocculation change particle size is not greater than the required increase particle size, intelligent water purification methods based on water quality analysis also include: The percentage of particle size change is determined by calculation based on the changes in flocculation particle size and the increase in demand particle size. The effective flocculant dosage is determined by calculation based on the proportion of particle size change and the feedback flocculant dosage. The feedback flocculation dose is updated based on the effective flocculation dose and the preset risk-bearing dose.

8. An intelligent water purification system based on water quality analysis, characterized in that, include: The acquisition module is used to acquire the raw water turbidity and raw water unit flow rate; The processing module, connected to the acquisition and judgment modules, is used for information storage and processing; The judgment module, connected to the acquisition and processing modules, is used for judging information. The processing module determines the basic flocculation dosage corresponding to the raw water turbidity and the raw water unit flow rate based on the preset basic matching relationship, and adds the basic flocculation dosage of flocculant to the preset reaction zone; The acquisition module acquires the size of the detected flocculent particles and the turbidity of the subsequent water in the reaction zone. The processing module constructs a historical interval on a preset timeline, with the current time point as the endpoint and a width of a preset historical duration. The processing module determines the similarity parameters for each time point in the historical interval based on the current raw water turbidity, raw water unit flow rate, and detected flocculent particle size. It defines the time point corresponding to the largest similarity parameter as the similar time point and obtains the similar turbidity based on the similar time point. The processing module calculates the difference between the turbidity detected in the downstream water and the turbidity detected in similar water to determine the detection deviation turbidity. The judgment module determines whether the detected turbidity is within the preset permissible deviation range; If the judgment module determines that the detected turbidity is within the permissible deviation range, the processing module outputs a normal operation signal; If the judgment module determines that the turbidity of the detection deviation is not within the permissible deviation range, the processing module outputs a device abnormality signal.

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