A method and system for large-scale selection of polyacrylamide based on data processing

Abstract

The present application relates to the technical field of polyacrylamide, and in particular to a method and system for large-scale selection of polyacrylamide based on data processing, wherein the overall polyacrylamide charge density of polyacrylamide capillary flocs is obtained based on drug consumption restrictions, and multiple polyacrylamide charge density structures under the overall polyacrylamide charge density are used; based on the sewage treatment capacity diagnostic parameters of polyacrylamide capillary flocs, a sewage treatment capacity diagnostic system of polyacrylamide capillary flocs is generated; combined with the diagnostic formula and parameter importance, the diagnostic scores of multiple polyacrylamide charge density structures under the sewage treatment capacity diagnostic system of polyacrylamide capillary flocs are analyzed; based on the diagnostic scores, the optimal selection results among multiple polyacrylamide charge density structures are obtained. The present invention can show the differences in sewage treatment capacity parameters between different polyacrylamide product designs, and guide the optimization design direction of polyacrylamide products by supplementing the shortcomings.

Description

Technical Field

[0001] The present application relates to the technical field of polyacrylamide, and in particular to a method and system for large-scale selection of polyacrylamide based on data processing. Background Art

[0002] Polyacrylamide is a water-soluble high molecular polymer, insoluble in most organic solvents, has good flocculation properties, can reduce the friction resistance between liquids, and can be divided into four types according to ion indicators: non-ionic, anionic, cationic and amphoteric. The traditional system for selecting polyacrylamide samples has the disadvantages of low efficiency and inaccurate selection in the process of selecting polyacrylamide samples. There are also problems such as unreasonable design, complicated operation, and potential safety hazards. Summary of the invention

[0003] The main purpose of the present application is to provide a large-scale polyacrylamide selection scheme based on data processing to solve the problem of optimal selection and optimization of local polyacrylamide charge density of polyacrylamide in the prior art.

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] Based on the first aspect of the present invention, the present invention claims a method for large-scale selection of polyacrylamide based on data processing, characterized in that it comprises:

[0006] Based on the drug consumption limitation, the overall polyacrylamide charge density of the polyacrylamide capillary flocs is obtained, and a plurality of polyacrylamide charge density structures under the overall polyacrylamide charge density are obtained;

[0007] Based on the sewage treatment capacity diagnostic parameters of polyacrylamide capillary flocs, a sewage treatment capacity diagnostic system of polyacrylamide capillary flocs is generated;

[0008] Combining the diagnostic formula and the parameter importance, analyzing the diagnostic scores of the multiple polyacrylamide charge density structures under the sewage treatment capacity diagnostic system of polyacrylamide capillary flocs;

[0009] According to the diagnostic score, the most preferred result among the multiple polyacrylamide charge density structures is obtained.

[0010] Furthermore, the plurality of polyacrylamide charge density structures at least include:

[0011] The first polyacrylamide charge density structure, the second polyacrylamide charge density structure, and the ordered polyacrylamide charge density structure;

[0012] The first polyacrylamide charge density structure is that the charge density of polyacrylamide in each layer of the capillary flocs changes linearly from dense to sparse from the outside to the inside;

[0013] The second polyacrylamide charge density structure is that the charge density of polyacrylamide in each layer of the capillary flocs changes linearly from sparse to dense from the outside to the inside;

[0014] The ordered polyacrylamide charge density structure is that the polyacrylamide charge density of each layer of the capillary floc is evenly distributed from the outside to the inside.

[0015] Furthermore, the sewage treatment capacity diagnostic parameters of the polyacrylamide capillary flocs include at least:

[0016] Characteristic sewage treatment capacity parameters, physical sewage treatment capacity parameters, clean sewage treatment capacity parameters, and charge sewage treatment capacity parameters;

[0017] The characteristic sewage treatment capacity parameters include the floc size of the polyacrylamide capillary flocs and the amount of polyacrylamide used;

[0018] The physical sewage treatment capacity parameters include the capillary water absorption performance and water filtration index of the polyacrylamide capillary flocs;

[0019] The clean sewage treatment capacity parameters include the sludge compactness of the polyacrylamide capillary flocs and the supernatant turbidity;

[0020] The charge sewage treatment capacity parameter includes the initial solid content of the sewage or sludge of the polyacrylamide capillary flocs.

[0021] Furthermore, the method further comprises:

[0022] The floc size is the core sewage treatment capacity parameter of the capillary floc, and the difference in sewage treatment capacity of multiple polyacrylamide charge density structures under the same sludge source is compared using a particle size detection instrument and an equal sludge source method.

[0023] Furthermore, the method further comprises:

[0024] The capillary water absorption performance reflects the production process characteristics of the polyacrylamide capillary flocs, and its sewage treatment capacity depends on the strength of the molecular chain itself and the strength after immersion flocculation and sedimentation treatment. The capillary water absorption performance is tested by a capillary water absorption instrument;

[0025] The test method for the water filtration indicator is obtained by analyzing the initial size of the polyacrylamide capillary flocs of the multiple polyacrylamide charge density structures without water filtration quality and the size difference after multiple water filtration.

[0026] Furthermore, the method further comprises:

[0027] The initial solid content of the sewage or sludge includes the uniformity of the size of the polyacrylamide capillary flocs before and after the sludge or sewage treatment, the turbidity of the supernatant, the capillary water absorption performance, and the range of change of the floc size.

[0028] Furthermore, the combination of the diagnostic formula and the parameter importance to analyze the diagnostic scores of the multiple polyacrylamide charge density structures under the sewage treatment capacity diagnostic system of polyacrylamide capillary flocs also includes:

[0029] According to the diagnostic formula of each sewage treatment capacity diagnostic parameter under the polyacrylamide capillary flocs, the diagnostic value of each sewage treatment capacity diagnostic parameter is analyzed and obtained;

[0030] Assign parameter importance to each sewage treatment capacity diagnostic parameter value according to the importance of each parameter sewage treatment capacity to the polyacrylamide sewage treatment capacity;

[0031] The diagnostic scores of the multiple polyacrylamide charge density structures under the sewage treatment capacity diagnostic system of polyacrylamide capillary flocs are analyzed.

[0032] Based on the second aspect of the present invention, the present invention claims a data processing-based polyacrylamide scale selection system, characterized in that it includes:

[0033] one or more processors;

[0034] A memory having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the polyacrylamide large-scale selection method based on data processing.

[0035] The present application relates to the technical field of polyacrylamide, and in particular to a method and system for large-scale selection of polyacrylamide based on data processing, wherein the overall polyacrylamide charge density of polyacrylamide capillary flocs is obtained based on drug consumption restrictions, and multiple polyacrylamide charge density structures under the overall polyacrylamide charge density are used; based on the sewage treatment capacity diagnostic parameters of the polyacrylamide capillary flocs, a sewage treatment capacity diagnostic system of the polyacrylamide capillary flocs is generated; the diagnostic scores of multiple polyacrylamide charge density structures under the sewage treatment capacity diagnostic system of the polyacrylamide capillary flocs are analyzed in combination with the diagnostic formula and parameter importance; based on the diagnostic scores, the optimal selection results among multiple polyacrylamide charge density structures are obtained. The present invention can not only screen out the best polyacrylamide product design, but also show the differences in sewage treatment capacity parameters between different polyacrylamide product designs, and guide the optimization design direction of polyacrylamide products by supplementing the shortcomings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1A work flow chart of a method for large-scale selection of polyacrylamide based on data processing as claimed in the first embodiment of the present invention;

[0037] Figure 2 A second working flow chart of a method for large-scale selection of polyacrylamide based on data processing as claimed in the first embodiment of the present invention;

[0038] Figure 3 This is a structural diagram of a polyacrylamide large-scale selection system based on data processing as claimed in the first embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0040] The terms "first", "second" and "third" in this application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of the present application (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0041] Reference to "embodiments" herein means that a particular feature, structure, or indication described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0042] Based on the first embodiment of the present invention, the present invention claims a method for large-scale selection of polyacrylamide based on data processing, referring to Figure 1 ,include:

[0043] S1, based on the drug consumption limit, the overall polyacrylamide charge density of the polyacrylamide capillary flocs is obtained, and multiple polyacrylamide charge density structures under the overall polyacrylamide charge density are obtained;

[0044] S2, generating a sewage treatment capacity diagnosis system of polyacrylamide capillary flocs according to the sewage treatment capacity diagnosis parameters of the polyacrylamide capillary flocs;

[0045] S3, combining the diagnostic formula and parameter importance, analyzing the diagnostic scores of multiple polyacrylamide charge density structures under the sewage treatment capacity diagnostic system of polyacrylamide capillary flocs;

[0046] S4, based on the diagnostic scores, the most preferred result among multiple polyacrylamide charge density structures is obtained.

[0047] Further, the plurality of polyacrylamide charge density structures at least include:

[0048] The first polyacrylamide charge density structure, the second polyacrylamide charge density structure, and the ordered polyacrylamide charge density structure;

[0049] The first polyacrylamide charge density structure is that the charge density of polyacrylamide in each layer of the capillary floc changes linearly from dense to sparse from the outside to the inside;

[0050] The second polyacrylamide charge density structure is that the charge density of polyacrylamide in each layer of the capillary floc changes linearly from sparse to dense from the outside to the inside;

[0051] The ordered polyacrylamide charge density structure is that the charge density of polyacrylamide in each layer of the capillary floc is evenly distributed from the outside to the inside.

[0052] Furthermore, the diagnostic parameters of the sewage treatment capacity of the polyacrylamide capillary flocs include at least:

[0053] Characteristic sewage treatment capacity parameters, physical sewage treatment capacity parameters, clean sewage treatment capacity parameters, and charge sewage treatment capacity parameters;

[0054] Characteristic wastewater treatment capacity parameters include the floc size of polyacrylamide capillary flocs and the amount of polyacrylamide used;

[0055] Physical wastewater treatment capacity parameters include the capillary water absorption performance and water filtration index of polyacrylamide capillary flocs;

[0056] Clean wastewater treatment capacity parameters include sludge compactness of polyacrylamide capillary flocs and supernatant turbidity;

[0057] Charged sewage treatment capacity parameters include the initial solid content of sewage or sludge of polyacrylamide capillary flocs.

[0058] In this embodiment, for a capillary floc, the mechanical sewage treatment capacity under different polyacrylamide charge density combinations is quite different. Due to the difference in structure, the overall sewage treatment capacity is different.

[0059] Furthermore, the method further comprises:

[0060] Floc size is the core parameter of capillary flocs for sewage treatment capacity. The particle size detection instrument and the equal sludge source method are used to compare the differences in sewage treatment capacity of multiple polyacrylamide charge density structures under the same sludge source.

[0061] Furthermore, the method further comprises:

[0062] Capillary water absorption performance reflects the production and manufacturing process characteristics of polyacrylamide capillary flocs. Its sewage treatment capacity depends on the strength of the molecular chain itself and the strength after immersion flocculation and sedimentation treatment. The capillary water absorption performance is tested by a capillary water absorption instrument.

[0063] The test method for the water filtration indication is obtained by analyzing the initial size of polyacrylamide capillary flocs of multiple polyacrylamide charge density structures without water filtration quality and the size difference after multiple water filtration.

[0064] Among them, in this embodiment, in addition to the initial filtration and sewage treatment capacity, the repeated filtration and rebound sewage treatment capacity of the capillary flocs is of great significance to the consistency of the polyacrylamide assembly process and sewage treatment capacity.

[0065] The water filtration indicator test method uses repeated application of water filtration quality on the outside such as;

[0066] Record the first water filtration rate γ1, the second water filtration rate γ2, the third water filtration rate γ3, etc. The water filtration rate is analyzed according to formula (1):

[0067]

[0068] Where: is the initial average size of polyacrylamide flocs (μm); s wm It is the size (μm) under the preset water filtration quality.

[0069] In conventional water filtration tests, the first water filtration rate γ1 is generally used for analysis. However, since the initial size of polyacrylamide flocs changes significantly after repeated water filtration, in this embodiment, the initial size of polyacrylamide flocs without water filtration quality is analyzed according to formula (2): and the size after multiple filtrations Difference, the ability μ to use the Nth filtration rate (N>=2) as a diagnostic indicator of filtration.

[0070]

[0071] Furthermore, the method further comprises:

[0072] The initial solid content of sewage or sludge includes the uniformity of the size of polyacrylamide capillary flocs before and after sludge or sewage treatment, the turbidity of the supernatant, the capillary water absorption performance, and the range of change in floc size.

[0073] In this embodiment, the charge M of the sewage or sludge to be treated is determined, the particle size distribution range N of the sewage or sludge to be treated is determined, the initial solid content of the sewage or sludge to be treated is determined, and the initial turbidity of the sewage or sludge to be treated is determined.

[0074] Calculate the theoretical dosage X0 of polyacrylamide based on the solid content of sewage or sludge.

[0075] The water filterability of polyacrylamide was verified based on the capillary water absorption test and the dynamic water filterability test.

[0076] The applicability of the flocculation effect of polyacrylamide was verified based on the flocculation sedimentation test.

[0077] The applicability of the polyacrylamide flocculation effect was verified by the changes in indicators such as COD and turbidity of sewage and sludge before and after treatment.

[0078] The distribution of charge and particle size range requires the use of advanced foreign charge detection instruments and particle

[0079] The initial solid content of sewage and sludge needs to be tested with a moisture meter.

[0080] The initial turbidity of water and sludge needs to be detected by a turbidity meter. The specific values ​​detected are initial charge M0, initial particle size distribution N0, initial solid content N0, and initial turbidity Z0. Record the initial data of the sewage and sludge to be treated for later data comparison.

[0081] Further, see Figure 2 , step S3, further comprising:

[0082] According to the diagnostic formula of each sewage treatment capacity diagnostic parameter under the polyacrylamide capillary flocs, the diagnostic value of each sewage treatment capacity diagnostic parameter is analyzed and obtained;

[0083] Assign parameter importance to each sewage treatment capacity diagnostic parameter value according to the importance of each parameter sewage treatment capacity to the polyacrylamide sewage treatment capacity;

[0084] Analyze the diagnostic scores of multiple polyacrylamide charge density structures under the wastewater treatment capacity diagnostic system of polyacrylamide capillary flocs.

[0085] In this embodiment, the theoretical unit consumption of polyacrylamide needs to be calculated according to the following formula:

[0086] Polyacrylamide dosage L*polyacrylamide concentration (‰)*1000 / sludge treatment volume L1*(1-original sludge solid content);

[0087] Based on the above data, we compare the polyacrylamide models that need to be selected on a large scale with the conventional models currently used using capillary water absorption tests, dynamic water filtration tests, and flocculation sedimentation tests, and select the most suitable polyacrylamide model to reduce customers' drug consumption and costs.

[0088] The capillary water absorption experiment is to use the polyacrylamide model that needs to be selected for scale and the conventional polyacrylamide model to react with the sewage or sludge that needs to be treated, and then use a capillary water absorption instrument to detect the capillary water absorption time T.

[0089] Select the most suitable model X according to the time value.

[0090] The specific data are shown in Table 1:

[0091] Table 1 Capillary water absorption time table of polyacrylamide model

[0092] Polyacrylamide Model A B C D E F G H Conventional models Capillary absorption time S T1 T2 T3 T4 T5 T6 T7 T8 T0

[0093] The dynamic water filtration experiment is to use the polyacrylamide model that needs to be selected for large-scale selection and the conventional polyacrylamide model to react with the sewage and sludge that need to be treated, and use the water filtration rate Y per unit time to determine the applicability of the polyacrylamide.

[0094] According to the value of dynamic water filtration quality, the most suitable model X2 is selected, and the specific data are shown in Table 2.

[0095] Table 2 Dynamic water filtration quality table of polyacrylamide models

[0096]

[0097]

[0098] The flocculation and sedimentation experiment is to use the polyacrylamide model that needs to be selected for scale and the conventional polyacrylamide model to react with the sewage and sludge that need to be treated, and then select the most suitable model X3 based on the sludge floc size, shear resistance, turbidity Z1 of the treated sewage or sludge, COD value of the treated sewage or sludge supernatant and other data. The specific data are shown in Table 3.

[0099] Table 3 Polyacrylamide model flocculation sedimentation test table

[0100] Polyacrylamide Model A B C D E F G H Conventional models Dynamic water filtration quality g P1 P2 P3 P4 P5 P6 P7 P8 P0 Turbidity of the treated liquid Z1 Z2 Z3 Z4 Z5 Z6 Z7 Z8 Z0 COD of treated liquid Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q0

[0101] Based on the models selected from the above experiments, the most cost-effective model is calculated in combination with the market prices of different models to reduce drug consumption and costs for customers.

[0102] Calculation of unit sewage or sludge cost: unit cost = drug consumption * unit price.

[0103] In addition to relying on the comprehensive evaluation parameter system, the best selection result from the structural selection results of different polyacrylamide charge density distributions also needs to rely on the support of diagnostic formulas and parameter importance. Based on the test results and the generated scoring formula / importance, the structural selection result products of different polyacrylamide charge density distributions are comprehensively scored to obtain the best solution. The maximum score for a single parameter is 100 points, which is multiplied by the importance factor to obtain the final score.

[0104] Size uniformity is diagnosed by the size variation coefficient x1 (size standard deviation divided by average size), and the maximum size variation coefficient Tmax in the structure selection results of different polyacrylamide charge density distribution is selected as the lower limit of the score, and the minimum size variation coefficient Tmin in the structure selection results of different polyacrylamide charge density distribution is selected as the upper limit of the score. That is, the maximum value (max) and minimum value (min) that exist in each multiple distribution structure of fixed polyacrylamide charge density. However, when the maximum value meets the technical requirements of first-class products, full marks can be directly obtained.

[0105] Based on the second embodiment of the present invention, the present invention claims a polyacrylamide scale selection system based on data processing, referring to Figure 3 ,include:

[0106] one or more processors;

[0107] A memory stores one or more programs, and when the one or more programs are executed by one or more processors, the one or more processors implement a polyacrylamide scale selection method based on data processing.

[0108] Based on a data processing-based large-scale polyacrylamide selection method according to the third embodiment of the present invention, three experimental data of the first polyacrylamide charge density structure, the second polyacrylamide charge density structure and the ordered polyacrylamide charge density structure with the same polyacrylamide charge density but different structural designs are selected to provide a complete diagnostic process.

[0109] This embodiment can not only screen out the best product design, but also identify the differences in sewage treatment capacity parameters between different product designs, and guide the direction of product optimization design by making up for the shortcomings.

[0110] In the several embodiments provided in the present application, it should be understood that the disclosed systems, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, physical or other forms.

[0111] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the content of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

[0112] The specific implementation methods of the invention are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for large-scale selection of polyacrylamide based on data processing, characterized in that: include: Based on the drug consumption limitation, the overall polyacrylamide charge density of the polyacrylamide capillary flocs is obtained, and a plurality of polyacrylamide charge density structures under the overall polyacrylamide charge density are obtained; Based on the sewage treatment capacity diagnostic parameters of polyacrylamide capillary flocs, a sewage treatment capacity diagnostic system of polyacrylamide capillary flocs is generated; Combining the diagnostic formula and the parameter importance, analyzing the diagnostic scores of the multiple polyacrylamide charge density structures under the sewage treatment capacity diagnostic system of polyacrylamide capillary flocs; According to the diagnostic score, obtaining the most preferred result among the multiple polyacrylamide charge density structures; The plurality of polyacrylamide charge density structures at least include: The first polyacrylamide charge density structure, the second polyacrylamide charge density structure, and the ordered polyacrylamide charge density structure; The first polyacrylamide charge density structure is that the charge density of polyacrylamide in each layer of the capillary flocs changes linearly from dense to sparse from the outside to the inside; The second polyacrylamide charge density structure is that the charge density of polyacrylamide in each layer of the capillary flocs changes linearly from sparse to dense from the outside to the inside; The ordered polyacrylamide charge density structure is that the polyacrylamide charge density of each layer of the capillary floc is evenly distributed from the outside to the inside; The sewage treatment capacity diagnostic parameters of the polyacrylamide capillary flocs at least include: characteristic sewage treatment capacity parameters, physical sewage treatment capacity parameters, clean sewage treatment capacity parameters, and charge sewage treatment capacity parameters; The characteristic sewage treatment capacity parameters include the floc size of the polyacrylamide capillary flocs and the amount of polyacrylamide used; The physical sewage treatment capacity parameters include the capillary water absorption performance and water filtration index of the polyacrylamide capillary flocs; The clean sewage treatment capacity parameters include the sludge compactness of the polyacrylamide capillary flocs and the supernatant turbidity; The charge sewage treatment capacity parameter includes the initial solid content of the sewage or sludge of the polyacrylamide capillary flocs; The method further comprises: The floc size is the core sewage treatment capacity parameter of the capillary floc, and the difference in sewage treatment capacity of multiple polyacrylamide charge density structures under the same sludge source is compared by using a particle size detection instrument and the equal sludge source method; The method further comprises: The capillary water absorption performance reflects the production process characteristics of the polyacrylamide capillary flocs, and its sewage treatment capacity depends on the strength of the molecular chain itself and the strength after immersion flocculation and sedimentation treatment. The capillary water absorption performance is tested by a capillary water absorption instrument; The test method of the water filtration indicator is obtained by analyzing the difference in initial size of polyacrylamide capillary flocs of the multiple polyacrylamide charge density structures without water filtration quality and after multiple water filtrations; The method further comprises: The initial solid content of the sewage or sludge includes the uniformity of the size of the polyacrylamide capillary flocs before and after the sludge or sewage treatment, the turbidity of the supernatant, the capillary water absorption performance, and the range of change of the floc size; The combining of the diagnostic formula and the parameter importance to analyze the diagnostic scores of the multiple polyacrylamide charge density structures under the sewage treatment capacity diagnostic system of polyacrylamide capillary flocs also includes: According to the diagnostic formula of each sewage treatment capacity diagnostic parameter under the polyacrylamide capillary flocs, the diagnostic value of each sewage treatment capacity diagnostic parameter is analyzed and obtained; Assign parameter importance to each sewage treatment capacity diagnostic parameter value according to the importance of each parameter sewage treatment capacity to the polyacrylamide sewage treatment capacity; The diagnostic scores of the multiple polyacrylamide charge density structures under the sewage treatment capacity diagnostic system of polyacrylamide capillary flocs are analyzed.

2. A polyacrylamide scale selection system based on data processing, characterized in that: include: A molecular chain strategy module, which obtains the overall polyacrylamide charge density of the polyacrylamide capillary flocs based on drug consumption limitation, and obtains multiple polyacrylamide charge density structures under the overall polyacrylamide charge density; The capacity diagnosis module generates a sewage treatment capacity diagnosis system of polyacrylamide capillary flocs based on the sewage treatment capacity diagnosis parameters of polyacrylamide capillary flocs; A sewage diagnosis module, combining a diagnosis formula and parameter importance, analyzes the diagnosis scores of the multiple polyacrylamide charge density structures under the sewage treatment capacity diagnosis system of polyacrylamide capillary flocs; A selection module, based on the diagnosis score, obtains the best selection result among the multiple polyacrylamide charge density structures; The polyacrylamide scale selection system based on data processing is used to execute the polyacrylamide scale selection method based on data processing as described in claim 1.

3. A polyacrylamide scale selection system based on data processing, characterized in that: include: one or more processors; A memory having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement a large-scale polyacrylamide selection method based on data processing according to claim 1.

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