Intelligent water purification method and system based on water quality analysis

Abstract

The invention relates to an intelligent water purification method and system based on water quality analysis, and relates to the technical field of water treatment.The method comprises the steps that raw water detection turbidity and raw water unit flow are obtained; determining a basic flocculating agent dosage according to the detected turbidity of the raw water and the unit flow of the raw water, and adding a flocculating agent with the basic flocculating agent dosage into the reaction zone; obtaining a detection flocculation particle size and post-water detection turbidity in the reaction zone; constructing a historical interval, determining condition similar parameters of each time point in the historical interval according to the current raw water detection turbidity, the raw water unit flow and the detected flocculation particle size, defining the time point corresponding to the maximum condition similar parameter as a similar time point, and obtaining similar detection turbidity according to the similar time point; determining a detection deviation turbidity according to the post-water detection turbidity and the similar detection turbidity; and outputting a device abnormal signal when the detected deviation turbidity is not within the allowable deviation range. The water purification device has the function of improving the treatment effect of water purification operation.

Description

TECHNICAL FIELD

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

[0002] In the water purification process, water quality analysis is a key link to ensure efficient purification. Different purification strategies need to be adopted for different water quality conditions to achieve precise dosing and optimized process parameters. Especially when dealing with high turbidity raw water, coagulants such as polyaluminum chloride (PAC) or polyacrylamide (PAM) are usually added to make colloidal particles in water unstable and form flocs, which are then separated by sedimentation or filtration to achieve purification.

[0003] Currently, intelligent dosing control systems usually use a combination of feedforward and feedback to optimize the amount of flocculant added. Specifically, a turbidity sensor is installed at the entrance of the flocculation tank to monitor the turbidity of raw water in real time, and the initial dosage is calculated based on an empirical model. At the same time, a turbidity sensor is added in the reaction zone of the flocculation tank to detect the water quality after flocculation, and the PAC / PAM dosage is dynamically adjusted through a PID control algorithm to ensure that the effluent turbidity meets the standard.

[0004] In the above related technology, due to the complex internal environment of the flocculation tank, such as floc adhesion, mechanical stirring impact, chemical corrosion, etc., the turbidity sensor in the reaction zone may have measurement deviation or complete failure, but the sensor itself has no self-checking function, which causes the system to dynamically optimize the flocculant based on incorrect data, resulting in poor water purification treatment effect, and there is still room for improvement. SUMMARY

[0005] In order to improve the treatment effect of water purification operation, the present application provides an intelligent water purification method and system based on water quality analysis.

[0006] In the first aspect, the present application provides an intelligent water purification method based on water quality analysis, which adopts the following technical solution: An intelligent water purification method based on water quality analysis, comprising: Obtaining raw water detection turbidity and raw water unit flow rate; According to the preset basic matching relationship, the basic flocculant amount corresponding to the raw water detection turbidity and the raw water unit flow rate is determined, and the basic flocculant amount of flocculant is added to the preset reaction zone; Obtaining detection flocculation particle size and post-water detection turbidity in the reaction zone; On the preset time axis, a historical interval with the current time point as the back end point and a preset historical length as the width is constructed; determine a similar parameter at each time point according to the current raw water turbidity, raw water unit flow rate and detected flocculation particle size in the historical interval, and define the time point corresponding to the maximum similar parameter as a similar time point, and obtain a similar detection turbidity according to the similar time point; perform difference calculation according to the post-water detection turbidity and the similar detection turbidity to determine a detection deviation turbidity; determine whether the detection deviation turbidity is within a preset allowable deviation range; if the detection deviation turbidity is within the allowable deviation range, output a normal operation signal; if the detection deviation turbidity is not within the allowable deviation range, output a device abnormality signal.

[0007] Optionally, after the similar parameter is determined, the intelligent water purification method based on water quality analysis further comprises: determine whether the maximum similar parameter is greater than a preset close similar parameter; if the maximum similar parameter is greater than the close similar parameter, define the time point corresponding to the maximum similar parameter as a similar time point; if the maximum similar parameter is not greater than the close similar parameter, define the time point corresponding to the maximum similar parameter as an extreme time point, and obtain an extreme detection turbidity according to the extreme time point; perform calculation according to the raw water detection turbidity, raw water unit flow rate and detected flocculation particle size at the current time point and the extreme time point respectively to determine a deviation parameter combination; determine a comparison parameter combination according to the raw water detection turbidity, raw water unit flow rate and detected flocculation particle size at any two time points in the historical interval, and define the two time points with the same comparison parameter combination as each other as a binding point; perform difference calculation according to the post-water detection turbidity at the time points that are the binding points to each other to determine a binding deviation turbidity, and perform calculation according to all the binding deviation turbidities to determine a suitable deviation turbidity; perform calculation according to the suitable deviation turbidity and the extreme detection turbidity to determine a similar detection turbidity.

[0008] Optionally, the step of performing calculation according to all the binding deviation turbidities to determine a suitable deviation turbidity comprises: define a control point as the binding point corresponding to the current time point among the two binding points, and define a control similar parameter as the similar parameter determined at the control point; determine a control interval according to the control point and the current time point, and perform calculation according to the control interval and the control similar parameter to determine a control confidence coefficient; perform calculation according to all the control confidence coefficients to determine a binding deviation weight of each binding deviation turbidity; The proper deviation turbidity is determined according to all the constraint deviation turbidities and corresponding constraint deviation weights.

[0009] Optionally, if the maximum case similarity parameter is greater than the proximity similarity parameter, the intelligent water purification method based on water quality analysis further comprises: determining whether there is only one time point at which the case similarity parameter is greater than the proximity similarity parameter; if there is only one time point at which the case similarity parameter is greater than the proximity similarity parameter, defining the time point at which the case similarity parameter is greater than the proximity similarity parameter as a similarity time point; if there is not only one time point at which the case similarity parameter is greater than the proximity similarity parameter, combining the time points at which the case similarity parameter is greater than the proximity similarity parameter to construct a similarity time set; acquiring historical detection turbidities according to the time points in the similarity time set, and constructing a turbidity proximity range according to the historical detection turbidities and a preset proximity turbidity; counting the time points corresponding to the historical detection turbidities within the turbidity proximity range to determine an internal time quantity; determining the internal time quantity with the largest numerical value according to a preset sorting rule, defining the turbidity proximity range corresponding to the internal time quantity as a turbidity effective range, and calculating the similarity detection turbidity according to the historical detection turbidities within the turbidity effective range.

[0010] Optionally, after the internal time quantity is determined, the intelligent water purification method based on water quality analysis further comprises: determining whether there are at least two turbidity proximity ranges with the same maximum internal time quantity; if there are not at least two turbidity proximity ranges with the same maximum internal time quantity, defining the turbidity proximity range corresponding to the maximum internal time quantity as the turbidity effective range; if there are at least two turbidity proximity ranges with the same maximum internal time quantity, defining the turbidity proximity range corresponding to the maximum internal time quantity as a turbidity candidate range; calculating a representative similarity parameter according to the case similarity parameters of the time points at which the historical detection turbidities are within the turbidity candidate range; determining the representative similarity parameter with the largest numerical value according to the sorting rule, and defining the turbidity candidate range corresponding to the representative similarity parameter as the turbidity effective range.

[0011] Optionally, after the device abnormal signal is output, the intelligent water purification method based on water quality analysis further comprises: determining a feedback flocculant amount corresponding to the similarity detection turbidity according to a preset feedback matching relationship, and adding the flocculant of the feedback flocculant amount to the reaction zone; obtaining the secondary flocculation particle size in the reaction zone, and calculating the difference between the secondary flocculation particle size and the detected flocculation particle size to determine the flocculation change particle size; determining the demand growth particle size corresponding to the feedback flocculant dosage according to the preset particle size matching relationship; determining 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 demand growth particle size, summing the feedback flocculant dosage and the basic flocculant dosage to determine the comprehensive flocculant dosage; if the flocculation change particle size is greater than the demand growth particle size, summing the feedback flocculant dosage and the basic flocculant dosage to update the basic flocculant dosage, updating the current secondary flocculation particle size to the new detected flocculation particle size, and determining the similar detected turbidity again according to the new basic flocculant dosage and the basic flocculant dosage until the comprehensive flocculant dosage is determined.

[0012] Optionally, if the flocculation change particle size is not greater than the demand growth particle size, the intelligent water purification method based on water quality analysis further comprises: calculating the particle size change proportion according to the flocculation change particle size and the demand growth particle size; calculating the effective flocculant dosage according to the particle size change proportion and the feedback flocculant dosage; updating the feedback flocculant dosage according to the effective flocculant dosage and the preset risk bearing dosage.

[0013] In a second aspect, the present application provides an intelligent water purification system based on water quality analysis, which adopts the following technical solution: An intelligent water purification system based on water quality analysis, comprising: an obtaining module, configured to obtain raw water detected turbidity and raw water unit flow; a processing module, connected with the obtaining module and the judging module, configured to store and process information; a judging module, connected with the obtaining module and the processing module, configured to judge information; the processing module determines the basic flocculant dosage corresponding to the raw water detected turbidity and the raw water unit flow according to the preset basic matching relationship, and adds the flocculant of the basic flocculant dosage to the preset reaction zone; the obtaining module obtains the detected flocculation particle size and the post-water detected turbidity in the reaction zone; the processing module constructs a historical interval with the current time point as the rear end point and the preset historical length as the width on the preset time axis; 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 detected turbidity is not within the permissible deviation range, the processing module outputs a device abnormality signal.

[0014] In summary, this application includes at least one of the following beneficial technical effects: 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. 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; 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

[0015] Fig. 1 This is a flowchart of an intelligent water purification method based on water quality analysis.

[0016] Fig. 2 This is a flowchart of the modules for an intelligent water purification method based on water quality analysis. Detailed Implementation

[0017] To make the purpose, technical solution, and advantages of this application clearer, the following is combined with Figs. 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.

[0018] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0019] This application discloses an intelligent water purification method based on water quality analysis, referring to... Fig. 1 The method flow of the intelligent water purification method based on water quality analysis includes the following steps: Step S100: Obtain raw water detection turbidity and raw water unit flow.

[0020] The raw water detection turbidity is the water quality turbidity value obtained by the turbidity sensor installed at the water inlet of the flocculation tank, and the raw water unit flow is the water flow entering the flocculation tank per unit time.

[0021] Step S101: Determine the basic coagulant dosage corresponding to the raw water detection turbidity and the raw water unit flow according to the preset basic matching relationship, and add the coagulant of the basic coagulant dosage to the preset reaction zone.

[0022] The basic coagulant dosage is the dosage of the coagulant used for the preliminary flocculation treatment of raw water. Different raw water detection turbidity and raw water unit flow indicate different amounts of suspended solids to be treated, and the corresponding basic coagulant dosage is also different. The basic matching relationship between the three can be determined by the staff through multiple tests in advance. By adding the coagulant of the basic coagulant dosage to the reaction zone, the suspended solids in the raw water in the reaction zone can be flocculated and treated.

[0023] Step S102: Obtain detection flocculation particle size and post-water detection turbidity in the reaction zone.

[0024] The detection flocculation particle size is the average particle size of the flocculation of the suspended solids, which can be obtained by a flocculation imager. The post-water detection turbidity is the turbidity value of the water in the flocculation tank detected by the turbidity sensor installed in the reaction zone.

[0025] Step S103: Construct a historical interval with the current time point as the rear end point and the width as the preset historical length on the preset time axis.

[0026] The time axis is a coordinate axis formed by combining each time point. The coordinate axis points from the time points that have passed to the time points that have not yet arrived. The time points that have 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 length is the total length of the historical water quality purification operation set by the staff. By constructing the historical interval, the data in the historical length can be easily obtained and analyzed.

[0027] Step S104: Determine the situation similarity parameter of each time point according to the current raw water detection turbidity, raw water unit flow and detection flocculation particle size in the historical interval, and define the time point corresponding to the maximum situation similarity parameter as the similar time point, and obtain the similar detection turbidity according to the similar time point.

[0028] The situation similarity parameter is a value reflecting the similarity of the water quality purification of the time points in the history and the current time point. The greater the value, the more similar the water quality purification process of the two. The calculation formula is , wherein a case similarity parameter, an absolute value of a difference between the turbidity of the raw water at the two time points, an absolute value of a difference between the unit flow of the raw water at the two time points, an absolute value of a difference between the detected flocculation particle size at the two time points, a parameter value preset by the staff to reflect the influence of the deviation degree of the detected turbidity of the raw water on the similarity degree of the water purification treatment, a parameter value preset by the staff to reflect the influence of the deviation degree of the unit flow of the raw water on the similarity degree of the water purification treatment, a parameter value preset by the staff to reflect the influence of the deviation degree of the detected flocculation particle size on the similarity degree of the water purification treatment; the time point corresponding to the largest case similarity parameter is defined as a similar time point to mark the time point most similar to the current water purification treatment case, and the similar detected turbidity is the turbidity value of the water in the reaction zone detected at the similar time point.

[0029] Step S105: difference calculation is performed according to the post-water detected turbidity and the similar detected turbidity to determine a detected deviation turbidity.

[0030] The detected deviation turbidity is the difference between the post-water detected turbidity and the similar detected turbidity.

[0031] Step S106: it is judged whether the detected deviation turbidity is within a preset permitted deviation range.

[0032] The permitted deviation range is a range in which the staff sets the difference value to be allowed when it is determined that the turbidity cases of two detections are relatively similar, and the purpose of the judgment is to know whether the currently detected turbidity data is abnormal.

[0033] Step S1061: if the detected deviation turbidity is within the permitted deviation range, a normal operation signal is output.

[0034] When the detected deviation turbidity is within the permitted deviation range, it means that the currently detected turbidity data is relatively normal, and at this time, a normal operation signal is output to mark this case.

[0035] Step S1062: if the detected deviation turbidity is not within the permitted deviation range, a device abnormal signal is output.

[0036] When the detected deviation turbidity is not within the permitted deviation range, it means that the currently detected turbidity data is abnormal, that is, the turbidity sensor has data deviation or damage, at this time, the dosage of the flocculant cannot be dynamically adjusted according to the data of the turbidity sensor, therefore, a device abnormal signal is output to identify this case, so as to reduce the misadjustment and improve the treatment effect of the water purification operation.

[0037] After the similar situation parameters are determined, the intelligent water purification method based on water quality analysis further comprises: Step S200: determining whether the maximum similar situation parameter is greater than a preset similar parameter.

[0038] The similar parameter is a minimum similar situation parameter required when the staff sets a high degree of similar purification situation, and the purpose of the determination is to know whether the most similar water purification situation meets the similar analogy requirement.

[0039] Step S2001: if the maximum similar situation parameter is greater than the similar parameter, defining the time point corresponding to the maximum similar situation parameter as a similar time point.

[0040] When the maximum similar situation parameter is greater than the similar parameter, it means that the most similar water purification situation meets the similar analogy requirement, and at this time the corresponding time point can be defined as a similar time point.

[0041] Step S2002: if the maximum similar situation parameter is not greater than the similar parameter, defining the time point corresponding to the maximum similar situation parameter as a limit time point, and obtaining a limit detection turbidity according to the limit time point.

[0042] When the maximum similar situation parameter is not greater than the similar parameter, it means that the most similar water purification situation cannot meet the analogy requirement of the current water purification situation, i.e., it has no reference significance and needs to be further analyzed; the limit time point is defined to identify different time points, and the limit detection turbidity is the detected water turbidity at the limit time point.

[0043] Step S201: calculating according to the raw water detection turbidity, raw water unit flow and detection flocculation particle size of the current time point and the limit time point to determine a deviation parameter combination.

[0044] The deviation parameter combination is a combination of the differences between various parameters of the current time point and the limit time point, for example, the raw water detection turbidity of the current time point is 100 NTU, the raw water unit flow is 10 , the detection flocculation particle size is 0.5 mm, the raw water detection turbidity of the limit time point is 80 NTU, the raw water unit flow is 9 , and the detection flocculation particle size is 0.4 mm, and the obtained deviation parameter combination is {20, 1, 0.1}.

[0045] Step S202: determining a comparison parameter combination according to the raw water detection turbidity, raw water unit flow and detection flocculation particle size of any two time points in the historical interval, and defining the two time points with the same comparison parameter combination and deviation parameter combination as each other as a constraint point.

[0046] The combination of comparison parameters is the combination of the differences of various parameters between two time points. The determination method is consistent with the above-mentioned deviation parameter combination. When the comparison parameter combination is consistent with the deviation parameter combination, it indicates that the water purification deviation situation at the two time points corresponding to the current comparison parameter combination is consistent with the water purification deviation situation at the limit time point and the current time point in the current situation. At this time, reference analysis can be performed, and therefore, the constraint point is defined to distinguish different time points.

[0047] Step S203: Difference calculation is performed according to the turbidity of the post-water detected at the time points that are mutual constraint points to determine the constraint deviation turbidity, and calculation is performed according to all the constraint deviation turbidities to determine the appropriate deviation turbidity.

[0048] The constraint deviation turbidity is the difference of the turbidity of the post-water detected at the time points that are mutual constraint points, and the appropriate deviation turbidity is the water quality turbidity deviation parameter that needs to be met in the theoretical situation under the deviation situation of the current various parameters. The appropriate deviation turbidity can be determined by calculating the mean value of all the constraint deviation turbidities, or can be determined through steps S300-S303.

[0049] Step S204: Calculation is performed according to the appropriate deviation turbidity and the limit detection turbidity to determine the similar detection turbidity.

[0050] The similar detection turbidity is the value obtained by adding the appropriate deviation turbidity to the limit detection turbidity.

[0051] The step of calculating the appropriate deviation turbidity according to all the constraint deviation turbidities includes: Step S300: The constraint point corresponding to the current time point among the two constraint points is defined as a control point, and the similar parameter determined at the control point is defined as a control similar parameter.

[0052] The constraint point corresponding to the current time point is the time point between the two constraint points, and the relationship is consistent with the relationship between the current time point and the limit time point. Defining the control point can achieve the determination of the specific reference point, and defining the control similar parameter can achieve the differentiation of the similar parameters in different situations.

[0053] Step S301: The control interval is determined according to the control point and the current time point, and the control confidence coefficient is calculated according to the control interval and the control similar parameter.

[0054] The control interval is the time interval between the control point and the current time point, and the control confidence coefficient is a parameter value reflecting the data reliability of the currently determined control point. The larger the value is, the stronger the data reliability is, that is, the more meaningful the data reference is. The calculation formula of the control confidence coefficient is wherein is the control confidence coefficient, a contrast similar parameter, a contrast interval, a preset calculation weight parameter reflecting the influence of the contrast similar parameter on the data reliability, a preset calculation weight parameter reflecting the influence of the contrast interval on the data reliability.

[0055] Step S302: Calculate according to all the contrast confidence coefficients to determine the bondage deviation weight of each bondage deviation turbidity.

[0056] The bondage deviation weight is the parameter value obtained by the single contrast confidence coefficient in the sum of all the contrast confidence coefficients.

[0057] Step S303: Calculate according to all the bondage deviation turbidity and the corresponding bondage deviation weight to determine the appropriate deviation turbidity.

[0058] The appropriate deviation turbidity of the more reliable data can be obtained by multiplying all the bondage deviation turbidity by the corresponding bondage deviation weight and adding them all up, thereby improving the accuracy of data analysis.

[0059] If the maximum case similar parameter is greater than the proximity similar parameter, the intelligent water purification method based on water quality analysis further comprises: Step S400: Determine whether there is only one time point at which the case similar parameter is greater than the proximity similar parameter.

[0060] The purpose of the determination is to know whether there is only one time point that meets the requirements.

[0061] Step S4001: If there is only one time point at which the case similar parameter is greater than the proximity similar parameter, define the time point at which the case similar parameter is greater than the proximity similar parameter as the similar time point.

[0062] When there is only one time point at which the case similar parameter is greater than the proximity similar parameter, the time point can be directly defined as the similar time point for analysis.

[0063] Step S4002: If there is not only one time point at which the case similar parameter is greater than the proximity similar parameter, combine the time points at which the case similar parameter is greater than the proximity similar parameter to construct a similar time set.

[0064] When there is not only one time point at which the case similar parameter is greater than the proximity similar parameter, it means that there are multiple time points with high similarity, and the accurate similar detection turbidity can be further analyzed at this time, so the similar time set is constructed to extract data from the time points that meet the requirements.

[0065] Step S401: Obtain historical detection turbidity according to time points in the similar time set, and construct a turbidity similar range according to the historical detection turbidity and a preset similar turbidity.

[0066] The historical detection turbidity is the post-water detection turbidity obtained at the time points in the similar time set, the similar turbidity is the maximum difference in turbidity allowed by the staff when identifying that two turbidity values are relatively similar, the turbidity similar range is a numerical interval formed by taking the two values obtained by adding and subtracting the similar turbidity to the historical detection turbidity as endpoints, and the values in the turbidity similar range are relatively close to the current historical detection turbidity, wherein one historical detection turbidity corresponds to one turbidity similar range.

[0067] Step S402: Count the time points corresponding to the historical detection turbidity within the turbidity similar range to determine the internal time quantity.

[0068] The internal time quantity is the number of time points in which the historical detection turbidity within a single turbidity similar range is located.

[0069] Step S403: Determine the internal time quantity with the largest value according to a preset sorting rule, define the turbidity similar range corresponding to the internal time quantity as the turbidity effective range, and calculate the similar detection turbidity according to the historical detection turbidity within the turbidity effective range.

[0070] The sorting rule is a method set by the staff to sort the values, such as the bubble method, which can determine the internal time quantity with the largest value, that is, the historical detection turbidity within the current turbidity similar range is more reasonable, so it is defined as the turbidity effective range, and the similar detection turbidity with higher data reliability can be obtained by mean calculation according to all historical detection turbidities within the turbidity effective range.

[0071] After the internal time quantity is determined, the intelligent water purification method based on water quality analysis further comprises: Step S500: Determine whether there are at least two turbidity similar ranges with the same maximum internal time quantity.

[0072] The purpose of the determination is to know whether there are multiple turbidity similar ranges that meet the requirements, so as to determine the unique turbidity effective range.

[0073] Step S5001: If there are not at least two turbidity similar ranges with the same maximum internal time quantity, define the turbidity similar range corresponding to the maximum internal time quantity as the turbidity effective range.

[0074] When there are at least two internal time ranges with the same number of time and the maximum turbidity similar range, it indicates that there is only one turbidity similar range that meets the requirements, and the turbidity effective range is defined as the turbidity similar range.

[0075] Step S5002: If there are at least two internal time ranges with the same number of time and the maximum turbidity similar range, the turbidity similar range corresponding to the maximum number of internal time is defined as the turbidity candidate range.

[0076] When there are at least two internal time ranges with the same number of time and the maximum turbidity similar range, it indicates that there are multiple turbidity similar ranges that meet the requirements, and further analysis is needed, so the turbidity candidate range is defined to distinguish different turbidity similar ranges.

[0077] Step S501: Calculate the representative similarity parameter according to the condition similarity parameter of the historical detection turbidity at the time point in the turbidity candidate range.

[0078] The representative similarity parameter is the average value of all historical detection turbidity at the time point in the turbidity candidate range.

[0079] Step S502: Determine the representative similarity parameter with the maximum value according to the sorting rule, and define the turbidity candidate range corresponding to the representative similarity parameter as the turbidity effective range.

[0080] The representative similarity parameter with the maximum value can be determined by the sorting rule, which means that the data reference reliability in the turbidity candidate range corresponding to the representative similarity parameter is higher, so the turbidity effective range can be defined.

[0081] After the device abnormal signal is output, the intelligent water purification method based on water quality analysis further includes: Step S600: Determine the feedback flocculant amount corresponding to the similar detection turbidity according to the preset feedback matching relationship, and add the feedback flocculant amount of flocculant to the reaction zone.

[0082] The feedback flocculant amount is the amount of flocculant needed to be added again when the water quality turbidity after flocculation treatment is detected to be the similar detection turbidity, and the feedback flocculant amount corresponding to different similar detection turbidity is different. The feedback matching relationship between them is determined by the staff in advance through multiple times, and the further purification treatment of water quality turbidity can be realized by continuously adding flocculant.

[0083] Step S601: Obtain the secondary flocculation particle size in the reaction zone, and calculate the difference between the secondary flocculation particle size and the detection flocculation particle size to determine the flocculation change particle size.

[0084] The secondary flocculation particle size is an average particle size of flocculation in the flocculation tank detected after adding the flocculant again, and the flocculation change particle size is a parameter value obtained by subtracting the detected flocculation particle size from the secondary flocculation particle size.

[0085] Step S602: determining a demand growth particle size corresponding to the feedback flocculant amount according to the preset particle size matching relationship.

[0086] The demand growth particle size is a particle size that can be increased in the theoretical case after adding the feedback flocculant amount when the water quality turbidity does not meet the requirement and can continue to be treated, and the demand growth particle size corresponding to different feedback flocculant amounts is determined by the staff in advance through experiments.

[0087] Step S603: determining whether the flocculation change particle size is greater than the demand growth particle size.

[0088] The purpose of the determination is to know whether the current change of the flocculation meets the requirement of the normal flocculation reaction, that is, whether the water quality turbidity is treated to meet the requirement through the change of the flocculation particle size.

[0089] Step S6031: if the flocculation change particle size is not greater than the demand growth particle size, performing summation calculation according to the feedback flocculant amount and the basic flocculant amount to determine a comprehensive flocculant amount.

[0090] When the flocculation change particle size is not greater than the demand growth particle size, it is indicated that the current water quality can be better treated, and the comprehensive flocculant amount of the water quality treatment per unit time can be obtained by adding the feedback flocculant amount and the basic flocculant amount, that is, the comprehensive flocculant amount, and the subsequent water quality purification can be better performed according to the comprehensive flocculant amount.

[0091] Step S6032: if the flocculation change particle size is greater than the demand growth particle size, performing summation calculation according to the feedback flocculant amount and the basic flocculant amount to update the basic flocculant amount, updating the secondary flocculation particle size as a new detected flocculation particle size, and determining similar detected turbidity again according to the new basic flocculant amount and the basic flocculant amount until the comprehensive flocculant amount is determined.

[0092] When the flocculation change particle size is greater than the demand growth particle size, it is indicated that the flocculation particle size still has a relatively obvious growth, that is, the water quality turbidity does not meet the requirement at this time and needs to be further treated, and therefore the comprehensive flocculant amount meeting the current water quality purification requirement can be determined by updating the basic flocculant amount and the basic flocculant amount to reanalyze the similar detected turbidity.

[0093] If the flocculation change particle size is not greater than the demand growth particle size, the intelligent water purification method based on water quality analysis further includes: Step S700: performing calculation according to the flocculation change particle size and the demand growth particle size to determine a particle size change proportion.

[0094] The particle size change ratio is a value obtained by dividing the flocculation change particle size by the required growth particle size.

[0095] Step S701: According to the particle size change ratio and the feedback flocculant amount, the effective flocculant amount is calculated.

[0096] The effective flocculant amount is a value obtained by multiplying the particle size change ratio by the feedback flocculant amount.

[0097] Step S702: According to the effective flocculant amount and the preset risk bearing amount, the feedback flocculant amount is updated.

[0098] The risk bearing amount is the flocculant amount added by the staff to reduce the water quality that cannot be effectively purified. The feedback flocculant amount is updated by adding the effective flocculant amount and the risk bearing amount, so as to facilitate subsequent flocculant addition.

[0099] Reference Fig. 2 Based on the same inventive concept, the embodiment of the present application provides an intelligent water purification system based on water quality analysis, comprising: The acquisition module is used to acquire the raw water detection turbidity and the raw water unit flow; The processing module is connected with the acquisition module and the judgment module, and is used for information storage and processing; The judgment module is connected with the acquisition module and the processing module, and is used for information judgment; The processing module determines the basic flocculant amount corresponding to the raw water detection turbidity and the raw water unit flow according to the preset basic matching relationship, and adds the flocculant of the basic flocculant amount to the preset reaction zone; The acquisition module acquires the detection flocculation particle size and the post-water detection turbidity in the reaction zone; The processing module constructs a historical interval with the current time point as the rear end point and the width as the preset historical length on the preset time axis; The processing module determines the situation similarity parameter of each time point according to the current raw water detection turbidity, the raw water unit flow and the detection flocculation particle size in the historical interval, and defines the time point corresponding to the maximum situation similarity parameter as the similar time point, and acquires the similar detection turbidity according to the similar time point; The processing module determines the detection deviation turbidity by difference calculation according to the post-water detection turbidity and the similar detection turbidity; The judgment module judges whether the detection deviation turbidity is within the preset allowable deviation range; If the judgment module judges that the detection deviation turbidity is within the allowable deviation range, the processing module outputs a normal operation signal; If the judgment module judges that the detected deviation turbidity is not within the permitted deviation range, the processing module outputs a device abnormality signal; The similar poor analysis module is configured to perform similar detection turbidity analysis on a case where the similar parameter values at each time point are not large. The suitable deviation turbidity determination module is configured to effectively determine the suitable deviation turbidity. The multiple similar analysis module is configured to perform similar detection turbidity analysis on a case where the similar parameter values at multiple time points are large. The turbidity proximity range screening module is configured to screen multiple turbidity proximity ranges that meet the requirements. The device abnormality condition analysis module is configured to perform data analysis on a case where the turbidity sensor is damaged and cannot detect the water turbidity. The feedback flocculant amount updating module is configured to update the suitable feedback flocculant amount.

[0100] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is taken as an example for illustration, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is 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 refer to the corresponding process in the foregoing method embodiments, which will not be described 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 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 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.

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 detected turbidity is not within the permissible deviation range, the processing module outputs a device abnormality signal.

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