Intelligent agent adding control method based on metal surface treatment wastewater treatment

Abstract

The invention discloses an intelligent agent adding control method based on metal surface treatment wastewater treatment, and relates to the technical field of industrial wastewater treatment.The intelligent agent adding control method comprises the steps that the pH value and the heavy metal ion concentration in metal surface treatment wastewater are monitored and obtained, and fluctuation characteristic data are generated; carrying out normalization processing on the fluctuation characteristic data, calculating fluctuation intensity, and generating an agent adding demand quantity in the current period based on the fluctuation intensity; collecting the actual medicament adding amount of the current period, and carrying out difference analysis on the actual medicament adding amount and the medicament adding demand quantity to generate a medicament adding deviation value; the pH value and the heavy metal ion concentration of the treating fluid after metal treatment are obtained, and based on the pH value, the heavy metal ion concentration, the pH value of the treating fluid and the heavy metal ion concentration of the treating fluid, a wastewater treatment response factor and a reaction label are generated through treatment; according to the method, a dynamic regulation and control mechanism is constructed on the basis of fluctuation characteristic data, and intelligent calculation and closed-loop correction control over the drug adding demand quantity under the multi-period disturbance condition are achieved.

Description

Technical Field

[0001] This invention relates to the field of industrial wastewater treatment technology, and specifically to a method for intelligent dosing control of reagents in the treatment of metal surface treatment wastewater. Background Technology

[0002] The metal surface treatment industry widely employs processes such as chemical cleaning, electroplating, and phosphating. The wastewater discharged from these processes often contains large amounts of acidic and alkaline substances and heavy metal ions. Improper control of the treatment process can easily lead to waste of chemicals, fluctuations in compliance with standards, and even secondary pollution. The dynamic changes in pH value and heavy metal ion concentration in wastewater exhibit significant temporal fluctuations and are influenced by factors such as raw water composition, reaction efficiency, and environmental disturbances. This results in poor adaptability and untimely control of chemical dosing strategies. To meet environmental emission standards, chemical dosing systems urgently need to possess real-time identification capabilities for water quality changes and intelligent control capabilities based on response efficiency.

[0003] Most existing methods for controlling chemical dosing employ quantitative control models based on target thresholds, using preset upper and lower limits to regulate pH or heavy metal ion concentrations in a closed loop. While these methods are simple in structure and easy to deploy in engineering, they often rely on fixed control rules, making it difficult to dynamically reflect changes in water quality characteristics and to reasonably assess the response efficiency of current chemicals. This leads to problems of insufficient or excessive chemical dosing when wastewater pollution levels change rapidly or when there are disturbances in the treatment process, affecting overall treatment efficiency and economy. Furthermore, existing methods generally lack feedback correction mechanisms for multi-cycle water quality responses, and the control strategy cannot be dynamically adjusted according to the treatment effect, resulting in insufficient long-term operational stability of the system. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for intelligent dosing control of reagents in the treatment of metal surface treatment wastewater.

[0005] A method for intelligent dosing control of reagents in the treatment of metal surface treatment wastewater, the method comprising: S11, monitor and obtain the pH value and heavy metal ion concentration in metal surface treatment wastewater, and generate fluctuation characteristic data; S12, normalize the fluctuation characteristic data and calculate the fluctuation intensity, and generate the drug dosage requirement in the current period based on the fluctuation intensity; S13, collect the actual amount of pesticide added in the current cycle, and perform a difference analysis with the required amount of pesticide added to generate the pesticide addition deviation value; S14, obtain the pH value and heavy metal ion concentration of the treated solution after metal treatment, and based on the pH value and heavy metal ion concentration, process and generate wastewater treatment response factors and reaction labels. S15: Based on the reaction label, wastewater treatment response factor, and reagent dosing deviation value, generate reagent correction control value, and adjust the reagent dosing requirement for the next cycle according to the reagent correction control value.

[0006] Furthermore, the steps for generating fluctuation characteristic data are as follows: S111, obtain pH value, heavy metal ion concentration and their corresponding timestamps, and construct a water quality monitoring dataset sorted by time. The water quality monitoring dataset includes a pH value set and a heavy metal ion concentration set. S112, Based on the water quality monitoring dataset, calculate the difference between any two adjacent data in the dataset to construct a difference set, which includes a pH value difference set and a heavy metal ion concentration difference set; S113, generate fluctuation characteristic data based on the difference set, the fluctuation characteristic data including average pH deviation, pH peak change, average concentration deviation and concentration peak change; S114, fluctuation characteristic data are constructed based on average pH deviation, pH peak change, average concentration deviation, and concentration peak change.

[0007] Furthermore, the steps for normalizing the fluctuation characteristic data and calculating the fluctuation intensity are as follows: S121. Based on the fluctuation characteristic data and the established normalized reference maximum data, normalized characteristic data is generated. The normalized reference maximum data includes the maximum reference value of pH average deviation, the maximum reference value of pH peak change, the maximum reference value of concentration average deviation, and the maximum reference value of concentration peak change. S122, calculate and obtain the fluctuation intensity based on the normalized feature data and the preset weight set.

[0008] Furthermore, the steps for generating the drug dosage demand for the current period based on the fluctuation intensity are as follows: S123, obtain the average pH deviation, average concentration deviation, pH peak change and concentration peak change from step S113, retrieve the predetermined reagent requirement benchmark value, and calculate the dynamic adjustment coefficient. S124, calculate the required amount of reagent to be added based on the fluctuation intensity and dynamic analysis coefficient.

[0009] Furthermore, the steps for generating wastewater treatment response factors are as follows: S141, obtain the initial pH value and heavy metal ion concentration of the metal surface treatment wastewater, and calculate the pH difference and concentration difference before and after the treatment liquid by combining the pH value and heavy metal ion concentration of the treatment liquid. S142, based on the difference in pH and concentration before and after, calculates and generates the wastewater treatment response factor.

[0010] Furthermore, the reaction tags include positive reaction tags and negative reaction tags, and the generation logic of the reaction tags is as follows: Based on the wastewater treatment response factor, if the wastewater treatment response factor is greater than 0, a positive response label is generated; if the wastewater treatment response factor is less than or equal to 0, a negative response label is generated.

[0011] Furthermore, the drug correction control values ​​include positive reaction label correction control values ​​and negative reaction label correction control values.

[0012] Furthermore, the logic for generating the positive reaction label correction control value is as follows: When the reaction label is positive, a positive reaction label correction control value is generated based on the wastewater treatment response factor, reagent dosing deviation, and a predetermined positive control factor. The calculation formula is as follows: In the formula, For the first The positive reaction label correction control value for each cycle, It is a positive regulatory factor. For the first The drug dosage deviation value for each cycle, For the first The water treatment response factor corresponding to the positive reaction label generated in each cycle.

[0013] Furthermore, the logic for generating the negative reaction label correction control value is as follows: When the reaction label is negative, a negative reaction label correction control value is generated based on the wastewater treatment response factor, reagent dosage deviation value, and predetermined negative control factor. The calculation formula is as follows: In the formula, For the first The negative reaction label correction control value for each cycle. As a negative regulator, a negative regulator Greater than the positive regulatory factor , For the first The water treatment response factor corresponding to the negative reaction label generated in each cycle.

[0014] Furthermore, the calculation formula for adjusting the pesticide dosage requirement for the next cycle based on the pesticide correction control value is as follows: When the reaction label is a positive reaction label: In the formula, For the first generation based on positive reaction label The actual dosage of the drug in each cycle For the first The actual dosage of the drug in each cycle This is a preset positive adjustment correction value; When the reaction label is a negative reaction label In the formula, For the first generation based on negative reaction tags The actual dosage of the agent in each cycle, where dc is the predetermined negative reaction dosage compensation value.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention normalizes fluctuation characteristic data such as average pH value deviation, peak value change, and average deviation and peak value change of heavy metal ion concentration, and generates fluctuation intensity by combining a set of reference maximum values ​​and weight sets. It establishes a mapping relationship between the required amount of reagent dosage and the water quality state, realizes a unified dimensional expression of reagent demand under different wastewater disturbance characteristics, and thus improves the adaptability of the treatment system under multi-dimensional disturbance conditions such as sampling cycle and pool volume changes, thereby enhancing the calculation accuracy and consistency of reagent dosage. Furthermore, this invention constructs a reaction label based on the positive and negative differences of wastewater treatment response factors, and generates a branch correction control value based on the reagent addition deviation value in conjunction with positive control factors and negative compensation factors. In the actual addition process, a single-cycle closed-loop adjustment mechanism is formed to achieve a targeted response to the situation of over-addition or under-addition of reagents, thereby improving the recovery rate when dealing with sudden fluctuations or operating disturbances, and thus enhancing the closed-loop control capability of target pH value and heavy metal ion concentration. Furthermore, this invention constructs a dynamic control link under multi-cycle evolution based on water quality monitoring results and dosing deviation feedback in each cycle, so that the reagent demand, correction control value and final actual dosage form an asymptotic convergence trend in the closed-loop system, thereby achieving a dose-efficiency balance of reagent use, thereby reducing the waste of reagents and the risk of secondary pollution caused by misdosing or redundancy during operation, thus improving the operating efficiency and environmental response capability of the wastewater treatment system under multi-scenario operating conditions. In summary, this invention constructs a dynamic control mechanism based on the fluctuation characteristics of pH value and heavy metal ion concentration in metal surface treatment wastewater, realizing intelligent calculation and closed-loop correction control of reagent dosage under multi-period disturbance conditions, thereby improving the accuracy of reagent dosage and response stability in the wastewater treatment process. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 The flowchart is provided in Embodiment 1 of the present invention for a method for intelligent dosing control of reagents for treating wastewater from metal surface treatment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 As shown in the figure, this embodiment discloses a method for intelligent dosing control of reagents in the treatment of metal surface treatment wastewater. The method includes: S11, monitor and obtain the pH value and heavy metal ion concentration in metal surface treatment wastewater, and generate fluctuation characteristic data; Specifically, the steps for generating fluctuation characteristic data are as follows: S111, obtain pH value, heavy metal ion concentration and their corresponding timestamps, and construct a water quality monitoring dataset sorted by time. The water quality monitoring dataset includes a pH value set and a heavy metal ion concentration set. The set of pH values ​​is represented as follows: in, A set of pH values, The first in the pH value set A pH value, This represents the numerical values ​​for all pH values; The set of heavy metal ion concentrations is represented as follows: in, This is a collection of heavy metal ion concentrations. The first in the set of heavy metal ion concentrations The concentration of each heavy metal ion This represents the numerical values ​​of the concentrations of all heavy metal ions. It should be noted that: pH value and heavy metal ion concentration are collected based on pH sensors and ion-selective electrodes deployed at the inlet of the wastewater treatment tank and in the reaction tank; S112, Based on the water quality monitoring dataset, calculate the difference between any two adjacent data in the dataset to construct a difference set, which includes a pH value difference set and a heavy metal ion concentration difference set; The formula for calculating the pH difference is: In the formula, For the first The pH value and the first The pH difference between multiple pH values ​​is used to construct a pH difference set based on multiple sets of pH differences; The formula for calculating the difference in heavy metal ion concentration is: In the formula, For the first The concentration of each heavy metal ion and the first The difference in heavy metal ion concentrations. A set of heavy metal ion concentration difference values ​​is constructed based on multiple sets of heavy metal ion concentration difference values; S113, generate fluctuation characteristic data based on the difference set, the fluctuation characteristic data including average pH deviation, pH peak change, average concentration deviation and concentration peak change; The formula for calculating the average pH deviation is as follows: In the formula, This represents the average pH deviation. It should be noted that: Used to characterize the overall magnitude of acid-base changes within a sampling period; The formula for calculating the change in pH peak value is expressed as follows: in, This represents the change in pH peak value; It should be noted that: Used to characterize the maximum fluctuation range of acid-base changes within a sampling period; The formula for calculating the average concentration deviation is as follows: In the formula, This represents the average concentration deviation. It should be noted that: Used to characterize the overall magnitude of concentration fluctuations; The formula for calculating the change in peak concentration is expressed as: in, This represents the change in peak concentration. It should be noted that: Used to characterize the maximum fluctuation range of concentration fluctuations; S114, fluctuation characteristic data are constructed based on average pH deviation, pH peak change, average concentration deviation, and concentration peak change; Represented as: in, This is data exhibiting fluctuation characteristics; What needs to be understood is: fluctuation characteristic data Used to characterize the water quality fluctuation state during wastewater treatment in the current cycle; In step S11, fluctuation feature data is constructed to characterize the changing trends of acid and alkali and heavy metal ion concentrations in metal surface treatment wastewater within a sampling period, thereby realizing the quantitative expression of the wastewater quality disturbance state and improving the accuracy of subsequent control logic in identifying disturbance amplitude and fluctuation rate.

[0020] S12, normalize the fluctuation characteristic data and calculate the fluctuation intensity, and generate the drug dosage requirement in the current period based on the fluctuation intensity; Specifically, the steps for normalizing the fluctuation characteristic data and calculating the fluctuation intensity are as follows: S121. Based on the fluctuation characteristic data and the established normalized reference maximum data, normalized characteristic data is generated. The normalized reference maximum data includes the maximum reference value of pH average deviation, the maximum reference value of pH peak change, the maximum reference value of concentration average deviation, and the maximum reference value of concentration peak change. Represented as: in, For fluctuation characteristic data In the normalized feature data after normalization, among which, The maximum reference value for average pH deviation The reference value for the maximum change in pH peak value. This is the maximum reference value for the average concentration deviation. This is the reference value for the maximum change in concentration peak value; It should be noted that the normalized reference maximum data is set based on historical metal wastewater treatment data; S122, calculate and obtain the fluctuation intensity based on the normalized feature data and the preset weight set; The preset weight set is represented as follows: in, , , and They are respectively , , , Weighting factors; It should be noted that: , , and Pre-set parameters are determined by recording historical experimental results. Among them, the weight set Each item in The data is obtained by fitting a large amount of historical fluctuation response data from the laboratory. The current implementation uses fixed values, but it supports subsequent integration with a dynamic learning model for adaptive adjustment. The formula for calculating wave intensity is: The intensity of the fluctuation; It should be noted that: The larger the size, the more unstable the water quality, and the greater the amount of chemicals required. Specifically, the steps for generating the drug dosage requirement for the current period based on the fluctuation intensity are as follows: S123, obtain the average pH deviation, average concentration deviation, pH peak change and concentration peak change from step S113, retrieve the predetermined reagent requirement benchmark value, and calculate the dynamic adjustment coefficient. Represented as: In the formula, This represents the change in pH peak value. This represents the change in peak concentration. For dynamic adjustment coefficients, Given a baseline value for drug demand, This is a preset perturbation factor; It should be noted that the reagent requirement benchmark value is set based on the capacity of the wastewater tank for metal treatment. The larger the capacity, the larger the reagent requirement benchmark value is set. The reagent requirement benchmark value is the minimum reagent dosage for the corresponding wastewater tank. The unit is the unit corresponding to the dosage of the drug. This was estimated based on the minimum reagent requirement when water quality was relatively stable during historical sampling periods. Values ​​with dimensions; perturbation factor Dimensionless and The units are the same. The values ​​are all less than 0.001 and are only used to prevent... and The occurrence of a denominator of 0 does not affect the result. Dimensions.

[0021] S124, calculate the required amount of reagent to be added based on the fluctuation intensity and dynamic analysis coefficient; Represented as: in, This refers to the required dosage of the drug. It should be noted that: due to The settings, For values ​​with dimensions; In step S12, the fluctuation characteristic data is normalized and the fluctuation intensity is constructed by combining the historical maximum reference value and weight set. On this basis, a dynamic adjustment coefficient is introduced to generate a dimensional reagent dosing requirement, so as to achieve accurate matching of reagent dosing requirements under diverse disturbance conditions and pool volume conditions, thereby improving the adaptability of reagent dosing strategy in unstable wastewater environment, thus enhancing the quantitative control capability of the entire system and the stability of basic input data.

[0022] S13, collect the actual amount of pesticide added in the current cycle, and perform a difference analysis with the required amount of pesticide added to generate the pesticide addition deviation value; The formula for difference analysis is expressed as: in, This represents the actual dosage of the agent used in the current cycle. This refers to the deviation value of drug dosage. It should be noted that the actual amount of reagent added is obtained by a weight-type metering device connected to the liquid outlet of the dosing equipment, and the actual amount of reagent added in each treatment cycle is recorded in real time. Step S13 constructs a chemical dosing deviation value by performing a difference analysis between the actual chemical dosing amount and the chemical dosing demand amount generated based on the fluctuation intensity and dynamic adjustment coefficient, thereby realizing the measurement of the difference between the chemical input behavior and the actual water quality fluctuation response in each cycle.

[0023] S14, obtain the pH value and heavy metal ion concentration of the treated solution after metal treatment, and based on the pH value and heavy metal ion concentration, process and generate wastewater treatment response factors and reaction labels. It should be noted that the equipment for collecting the pH value and heavy metal ion concentration of the treated liquid is still the pH sensor and ion selective electrode deployed at the inlet of the wastewater treatment tank and inside the reaction tank. Specifically, the steps for generating wastewater treatment response factors are as follows: S141, obtain the initial pH value and heavy metal ion concentration of the metal surface treatment wastewater, and calculate the pH difference and concentration difference before and after the treatment liquid by combining the pH value and heavy metal ion concentration of the treatment liquid. Represented as: In the formula, For the first The pH difference before and after each cycle, The initial pH value of the metal surface treatment wastewater. pH value of the treatment solution; In the formula, For the first The concentration difference before and after each cycle, This represents the initial heavy metal ion concentration in the metal surface treatment wastewater. The concentration of heavy metal ions in the treatment solution; It should be noted that the initial pH value and heavy metal ion concentration of the metal surface treatment wastewater are those obtained in step 111. and ; This can be understood as the pH value and heavy metal ion concentration collected at the initial moment of the treatment of metal surface treatment wastewater; S142, based on the difference in pH and concentration before and after, calculate and generate the wastewater treatment response factor; Represented as: In the formula, For the first Water treatment response factor for each cycle, and The predetermined zero-adjustment value; It should be noted that pH value is dimensionless. and , The units are the same. The output value is dimensionless; Specifically, the reaction tags include positive reaction tags and negative reaction tags; the generation logic of the reaction tags is as follows: Based on the wastewater treatment response factor, if the wastewater treatment response factor is greater than 0, a positive response label is generated; if the wastewater treatment response factor is less than or equal to 0, a negative response label is generated. It should be noted that the zero-prevention adjustment value is set based on historical experimental data; The absolute value of the wastewater treatment response factor is used to characterize the treatment response intensity per unit initial pollution level, that is, the treatment efficiency of the current cycle of the agent on pH value and heavy metal ion concentration under the same pollution starting point. This treatment efficiency is the positive treatment efficiency under the positive reaction label and the negative treatment efficiency under the negative reaction label. A positive reaction label indicates that the treatment process is effective and the wastewater treatment is improving as expected; a negative reaction label indicates that the treatment process has deviated from the treatment objective, and may even have worsened the pollution level due to interference or improper dosing of agents.

[0024] Step S14 generates wastewater treatment response factors and combines positive and negative values ​​of the response factors to generate response labels, thereby enabling a positive or negative assessment of the treatment effect of the current cycle of the reagents. This improves the system's ability to identify the efficiency of the reagents in the treatment process, thus introducing a regulatory decision basis based on the actual response trend for the dosing control logic.

[0025] S15, based on the reaction label, wastewater treatment response factor and reagent dosing deviation value, generate reagent correction control value, and adjust the reagent dosing requirement for the next cycle according to the reagent correction control value; Specifically, the drug correction control values ​​include positive reaction label correction control values ​​and negative reaction label correction control values; Specifically, the logic for generating positive reaction label correction control values ​​is as follows: When the reaction label is positive, a positive reaction label correction control value is generated based on the wastewater treatment response factor, reagent dosing deviation, and a predetermined positive control factor. The calculation formula is as follows: In the formula, For the first The positive reaction label correction control value for each cycle, It is a positive regulatory factor. For the first The drug dosage deviation value for each cycle, For the first The water treatment response factor corresponding to the positive reaction label generated in each cycle; It should be noted that: The calculation formula in step S13 is used to obtain the result; Specifically, the logic for generating negative reaction label correction control values ​​is as follows: When the reaction label is negative, a negative reaction label correction control value is generated based on the wastewater treatment response factor, reagent dosage deviation value, and predetermined negative control factor. The calculation formula is as follows: In the formula, For the first The negative reaction label correction control value for each cycle. As a negative regulator, a negative regulator Greater than the positive regulatory factor , For the first The water treatment response factor corresponding to the negative reaction label generated in each cycle; It should be noted that: and All are obtained through step S14; negative regulatory factors and positive regulatory factors The specific values ​​were determined through gradual optimization and screening based on feedback from researchers in different treatment scenarios (e.g., drastic fluctuations in heavy metal concentration vs. stable conditions).

[0026] Specifically, the calculation formula for adjusting the pesticide dosage requirement for the next cycle based on the pesticide correction control value is as follows: When the reaction label is a positive reaction label: In the formula, For the first generation based on positive reaction label The actual dosage of the drug in each cycle For the first The actual dosage of the drug in each cycle This is a preset positive adjustment correction value; It should be noted that: It is mainly used to reduce the actual dosage of a drug when too much is added, and to compensate for insufficient dosage when too little is added. The dosage was set based on historical experiments. Units and The units are the same; When the reaction label is a negative reaction label In the formula, For the first generation based on negative reaction tags The actual dosage of the agent in each cycle, dc is the predetermined negative reaction dosage compensation value, preferably dc=1.12834; It should be noted that the value of dc is determined based on experimental statistical optimization, and dc is a dimensionless value. or During the adjustment process, if the peak dosage of the reagent exceeds the preset value, the peak dosage will be used as the adjustment amount for the next cycle, and no further increase in dosage will be made. Compared to the positive reaction label, the negative reaction label requires a large amount of dosage compensation. The positive adjustment correction value and the negative reaction dosage compensation value are determined based on experimental data to balance response speed and stability.

[0027] Step S15, based on the reaction label, response factor, and reagent dosage deviation value, combined with positive control factor and negative compensation coefficient, constructs a corrected control value and adjusts the actual reagent dosage for the next cycle, so that the dosage forms a convergence trend in multi-cycle operation, realizing dynamic closed-loop correction of reagent dosage under fluctuating response, thereby reducing the risk of over-dosing and insufficient treatment, and thus improving reagent utilization efficiency and wastewater treatment stability.

[0028] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired or wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0029] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0030] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0031] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0032] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0033] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for intelligent dosing control of reagents in the treatment of wastewater from metal surface treatment, characterized in that, The method includes: S11, monitor and obtain the pH value and heavy metal ion concentration in metal surface treatment wastewater, and generate fluctuation characteristic data; S12, normalize the fluctuation characteristic data and calculate the fluctuation intensity, and generate the drug dosage requirement in the current period based on the fluctuation intensity; S13, collect the actual amount of pesticide added in the current cycle, and perform a difference analysis with the required amount of pesticide added to generate the pesticide addition deviation value; S14, obtain the pH value and heavy metal ion concentration of the treated solution after metal treatment, and based on the pH value and heavy metal ion concentration, process and generate wastewater treatment response factors and reaction labels. S15: Based on the reaction label, wastewater treatment response factor, and reagent dosing deviation value, generate reagent correction control value, and adjust the reagent dosing requirement for the next cycle according to the reagent correction control value.

2. The intelligent dosing control method for treating wastewater from metal surface treatment according to claim 1, characterized in that, The steps for generating fluctuation characteristic data are as follows: S111, obtain pH value, heavy metal ion concentration and their corresponding timestamps, and construct a water quality monitoring dataset sorted by time. The water quality monitoring dataset includes a pH value set and a heavy metal ion concentration set. S112, Based on the water quality monitoring dataset, calculate the difference between any two adjacent data in the dataset to construct a difference set, which includes a pH value difference set and a heavy metal ion concentration difference set; S113, generate fluctuation characteristic data based on the difference set, the fluctuation characteristic data including average pH deviation, pH peak change, average concentration deviation and concentration peak change; S114, fluctuation characteristic data are constructed based on average pH deviation, pH peak change, average concentration deviation, and concentration peak change.

3. The intelligent dosing control method for treating wastewater from metal surface treatment according to claim 2, characterized in that, The steps for normalizing the fluctuation characteristic data and calculating the fluctuation intensity are as follows: S121. Based on the fluctuation characteristic data and the established normalized reference maximum data, normalized characteristic data is generated. The normalized reference maximum data includes the maximum reference value of pH average deviation, the maximum reference value of pH peak change, the maximum reference value of concentration average deviation, and the maximum reference value of concentration peak change. S122, calculate and obtain the fluctuation intensity based on the normalized feature data and the preset weight set.

4. The intelligent dosing control method for treating wastewater from metal surface treatment according to claim 3, characterized in that, The steps for generating the drug dosage demand for the current period based on the fluctuation intensity are as follows: S123, obtain the average pH deviation, average concentration deviation, pH peak change and concentration peak change from step S113, retrieve the predetermined reagent requirement benchmark value, and calculate the dynamic adjustment coefficient. S124, calculate the required amount of reagent to be added based on the fluctuation intensity and dynamic analysis coefficient.

5. The intelligent dosing control method for treating wastewater from metal surface treatment according to claim 4, characterized in that, The steps for generating wastewater treatment response factors are as follows: S141, obtain the initial pH value and heavy metal ion concentration of the metal surface treatment wastewater, and calculate the pH difference and concentration difference before and after the treatment liquid by combining the pH value and heavy metal ion concentration of the treatment liquid. S142, based on the difference in pH and concentration before and after, calculates and generates the wastewater treatment response factor.

6. The intelligent dosing control method for treating wastewater from metal surface treatment according to claim 5, characterized in that, The reaction tags include positive reaction tags and negative reaction tags, and the generation logic of the reaction tags is as follows: Based on the wastewater treatment response factor, if the wastewater treatment response factor is greater than 0, a positive response label is generated; if the wastewater treatment response factor is less than or equal to 0, a negative response label is generated.

7. The intelligent dosing control method for treating wastewater from metal surface treatment according to claim 6, characterized in that, The drug correction control values ​​include positive reaction label correction control values ​​and negative reaction label correction control values.

8. The intelligent dosing control method for treating wastewater from metal surface treatment according to claim 7, characterized in that, The logic for generating positive reaction label correction control values ​​is as follows: When the reaction label is positive, a positive reaction label correction control value is generated based on the wastewater treatment response factor, reagent dosing deviation, and a predetermined positive control factor. The calculation formula is as follows: In the formula, For the first The positive reaction label correction control value for each cycle, It is a positive regulatory factor. For the first The dosage deviation value for each cycle, For the first The water treatment response factor corresponding to the positive reaction label generated in each cycle.

9. The intelligent dosing control method for treating wastewater from metal surface treatment according to claim 8, characterized in that, The logic for generating negative reaction label correction control values ​​is as follows: When the reaction label is negative, a negative reaction label correction control value is generated based on the wastewater treatment response factor, reagent dosage deviation value, and predetermined negative control factor. The calculation formula is as follows: In the formula, For the first The negative reaction label correction control value for each cycle. As a negative regulator, a negative regulator Greater than the positive regulatory factor , For the first The water treatment response factor corresponding to the negative reaction label generated in each cycle.

10. The intelligent dosing control method for treating wastewater from metal surface treatment according to claim 9, characterized in that, The formula for adjusting the pesticide dosage requirement for the next cycle based on the pesticide correction control value is as follows: When the reaction label is a positive reaction label: In the formula, For the first generation based on positive reaction label The actual dosage of the drug in each cycle For the first The actual dosage of the drug in each cycle This is a preset positive adjustment correction value; When the reaction label is a negative reaction label In the formula, For the first generation based on negative reaction tags The actual dosage of the agent in each cycle, where dc is the predetermined negative reaction dosage compensation value.

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