An intelligent control system and method for treating printing and dyeing industrial wastewater based on the Internet of Things

Abstract

The present invention discloses an intelligent control system and method for treating printing and dyeing industrial wastewater based on the Internet of Things, which relates to the field of intelligent control technology for treating printing and dyeing industrial wastewater. The main solution is as follows: By collecting real-time pH value, temperature value, turbidity value, water flow rate, sample processing data and basic operation parameter data of equipment of the water quality, calculating the influence coefficients of each water quality parameter based on historical data, and then obtaining the wastewater treatment parameters, calculating the wastewater treatment efficiency index in combination with real-time water quality parameters, setting a set of wastewater treatment efficiency thresholds, comparing the efficiency index with the thresholds, judging whether to trigger an over-standard warning and generating an adjustment instruction, so as to effectively reduce the operation risk.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control for the treatment of printing and dyeing industrial wastewater, and specifically to an intelligent control system and method for printing and dyeing industrial wastewater treatment based on the Internet of Things. Background Art

[0002] A large amount of wastewater is generated during the production process of the printing and dyeing industry. It has characteristics such as complex composition, high pollutant concentration, and large fluctuations in water quality and quantity, making it difficult to treat.

[0003] Existing printing and dyeing industrial wastewater treatment technologies mostly adopt a combination of traditional treatment processes. Its principle is based on the understanding of the physical characteristics, chemical properties, and biodegradability of various pollutants, and gradually removes pollutants such as chromaticity, organic matter, and suspended solids in the wastewater through different process links. For example, sedimentation filtration is used to remove larger particle suspended solids, chemical flocculation is used to coagulate fine particles, and biological treatment is used to decompose organic pollutants.

[0004] Traditional technologies cannot accurately judge the wastewater treatment efficiency level through sample processing data such as pH value, real-time temperature value, turbidity value, and water flow rate at different treatment stages, which leads to the wastewater treatment equipment operating at an inefficient state, and further causes the problem of low wastewater treatment efficiency. Summary of the Invention

[0005] (1) Technical Problems to be Solved

[0006] Aiming at the deficiencies of the existing technology, the present invention provides an intelligent control system and method for printing and dyeing industrial wastewater treatment based on the Internet of Things. By collecting real-time water quality data and sample processing data, and calculating various influence coefficients and wastewater treatment efficiency indexes, it avoids the problem of low wastewater treatment efficiency caused by the inability to know the wastewater treatment efficiency.

[0007] (2) Technical Solutions

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent control method for printing and dyeing industrial wastewater treatment based on the Internet of Things, including:

[0009] Collecting wastewater treatment process data and processed sample data;

[0010] Calculating the pH value influence coefficient K pH 、temperature value influence coefficient K T and turbidity value influence coefficient C respectively according to the wastewater treatment process data; calculating the water flow rate influence coefficient F according to the sample water flow rate Q at different wastewater treatment stages;

[0011] According to the pH value influence coefficient K pH 、temperature value influence coefficient K T, the turbidity value influence coefficient C and the water flow influence coefficient F are used to calculate the wastewater treatment parameter M;

[0012] According to the treated sample treatment data and the wastewater treatment parameter M, calculate the wastewater treatment efficiency index BP;

[0013] Set the wastewater treatment efficiency threshold set JV. According to the comparison result between the wastewater treatment efficiency index BP and the wastewater treatment efficiency threshold set JV, judge the wastewater treatment efficiency level, and judge whether to trigger an alarm according to the judgment result, and generate corresponding adjustment instructions.

[0014] In the preferred scheme of the above intelligent control method for printing and dyeing industrial wastewater treatment based on the Internet of Things: calculate the pH value influence coefficient K pH The specific steps are as follows:

[0015] The wastewater treatment process data includes the sample pollutant concentration C at different wastewater treatment stages i , the average value of the sample pollutant concentration C0, the sample pH value P i and the average value of the sample pH value P0;

[0016] According to the sample pollutant concentration C i , the average value of the sample pollutant concentration C0, the sample pH value P i and the average value of the sample pH value P0, calculate the pH value influence coefficient K pH , and the specific formula is as follows

[0017]

[0018] where C i represents the pollutant concentration of the i-th sample, P i represents the pH value of the i-th sample, and i is the sample serial number, with a value range of [1, 5].

[0019] In the preferred scheme of the above intelligent control method for printing and dyeing industrial wastewater treatment based on the Internet of Things: calculate the temperature value influence coefficient K T The specific steps are as follows:

[0020] The wastewater treatment process data also includes the energy consumption value A at different wastewater treatment stages i , the sample temperature value T i and the average sample temperature T avg ;

[0021] According to the energy consumption value A i , the sample temperature value T i and the average sample temperature T avg , calculate the temperature value influence coefficient K T , and the specific formula is as follows:

[0022]

[0023] Among them, A i represents the energy consumption value of the i-th wastewater treatment stage, and T i represents the sample wastewater temperature value of the i-th wastewater treatment stage.

[0024] 5. In the preferred scheme of the above intelligent control method for printing and dyeing industrial wastewater treatment based on the Internet of Things: The specific steps for calculating the average sample temperature T avg are as follows:

[0025]

[0026] In the preferred scheme of the above intelligent control method for printing and dyeing industrial wastewater treatment based on the Internet of Things: The specific steps for calculating the turbidity value influence coefficient C are as follows:

[0027] The wastewater treatment process data also includes the sample turbidity value B i ;

[0028] Set the turbidity influence reference value A;

[0029] According to the sample turbidity value B i , the turbidity influence reference value A, the sample temperature value T i , the average sample temperature T avg and the temperature value influence coefficient K T , calculate the turbidity value influence coefficient C, and the specific formula is as follows:

[0030]

[0031] Among them, B i represents the turbidity value of the i-th wastewater treatment stage.

[0032] In the preferred scheme of the above intelligent control method for printing and dyeing industrial wastewater treatment based on the Internet of Things: The specific steps for calculating the water flow influence coefficient F are as follows:

[0033] Set the maximum water flow treatment value Q max and the standard reference flow Q0;

[0034] According to the sample water flow Q i , the maximum water flow treatment value Q max , the standard reference flow Q0 and the turbidity value influence coefficient C, calculate the water flow influence coefficient F, and the specific formula is as follows:

[0035]

[0036] Among them, Q iRepresents the sample water flow rate at the i-th wastewater treatment stage.

[0037] In a preferred embodiment of the above intelligent control method for printing and dyeing industrial wastewater treatment based on the Internet of Things: The specific steps for calculating the wastewater treatment parameter M are as follows:

[0038] According to the pH value influence coefficient K pH 、the temperature value influence coefficient K T 、the turbidity value influence coefficient C, and the water flow rate influence coefficient F, calculate the wastewater treatment parameter M. The specific formula is as follows:

[0039] M = K pH × w1 + K T × w2 + C × w3 + F × w4

[0040] Wherein, w1 represents the weight coefficient of the pH value influence coefficient K pH with a value range of 0.1 ≤ w1 ≤ 0.3, w2 represents the weight coefficient of the temperature value influence coefficient K T with a value range of 0.2 ≤ w2 ≤ 0.4, w3 represents the weight coefficient of the turbidity value influence coefficient C with a value range of 0.3 ≤ w3 ≤ 0.5, w4 represents the weight coefficient of the water flow rate influence coefficient F with a value range of 0.1 < w4 ≤ 0.3, and w1 + w2 + w3 + w4 = 1.

[0041] 9. In a preferred embodiment of the above intelligent control method for printing and dyeing industrial wastewater treatment based on the Internet of Things: The specific steps for calculating the wastewater treatment efficiency index BP are as follows:

[0042] The processed sample data after treatment includes the real-time pH value PY, the real-time temperature value TY, the real-time turbidity value VY, and the real-time water flow rate FY;

[0043] According to the real-time pH value PY, the real-time temperature value TY, the real-time turbidity value VY, the real-time water flow rate FY, and the wastewater treatment parameter M, calculate the wastewater treatment efficiency index BP. The specific formula is as follows:

[0044]

[0045] Wherein, u1 represents the weight coefficient of the real-time pH value PY with a value range of 0.2 ≤ u1 ≤ 0.4, u2 represents the weight coefficient of the real-time temperature value TY with a value range of 0.1 < u2 ≤ 0.3, u3 represents the weight coefficient of the real-time turbidity value VY with a value range of 0.1 ≤ u3 ≤ 0.3, u4 represents the weight coefficient of the real-time water flow rate FY with a value range of 0.3 ≤ u4 ≤ 0.5, and u1 + u2 + u3 + u4 = 1.

[0046] In a preferred embodiment of the above intelligent control method for dyeing industrial wastewater treatment based on the Internet of Things, the specific steps for determining whether to trigger the wastewater treatment efficiency warning are as follows:

[0047] The wastewater treatment efficiency threshold set JV includes a primary threshold Jv0, a secondary threshold Jv1, and a tertiary threshold Jv2, where Jv0 > Jv1 > Jv2;

[0048] When the wastewater treatment efficiency index BP ≥ Jv0, the wastewater treatment efficiency warning is not triggered;

[0049] When Jv0 > wastewater treatment efficiency index BP ≥ Jv1, a primary wastewater treatment efficiency warning is triggered, and a primary adjustment instruction is generated;

[0050] When Jv1 > wastewater treatment efficiency index BP ≥ Jv2, a secondary wastewater treatment efficiency warning is triggered, and an intermediate adjustment instruction is generated;

[0051] When Jv2 > wastewater treatment efficiency index BP, a tertiary wastewater treatment efficiency warning is triggered, and an advanced adjustment instruction is generated.

[0052] The present invention also discloses an intelligent control system for dyeing industrial wastewater treatment based on the Internet of Things, including:

[0053] A data acquisition module for collecting wastewater treatment process data and treated sample data of the wastewater;

[0054] A data analysis module for calculating the pH value influence coefficient K pH and the temperature value influence coefficient K T as well as the turbidity value influence coefficient C according to the wastewater treatment process data; calculating the water flow influence coefficient F according to the sample water flow Q i at different wastewater treatment stages;

[0055] A data analysis module for calculating the wastewater treatment parameter M according to the pH value influence coefficient K pH , the temperature value influence coefficient K T , the turbidity value influence coefficient C, and the water flow influence coefficient F;

[0056] Calculating the wastewater treatment efficiency index BP according to the treated sample data and the wastewater treatment parameter M;

[0057] A warning generation module for setting the wastewater treatment efficiency threshold set JV, determining the wastewater treatment efficiency level according to the comparison result between the wastewater treatment efficiency index BP and the wastewater treatment efficiency threshold set JV, and determining whether to trigger a warning according to the determination result and generating a corresponding adjustment instruction.

[0058] (III) Advantageous Effects

[0059] The present invention provides an intelligent control system and method for dyeing industrial wastewater treatment based on the Internet of Things, having the following beneficial effects:

[0060] (1) By collecting the wastewater treatment process data and the data of the treated samples after treatment, it is possible to comprehensively understand the state of the wastewater after treatment, provide basic data for accurately analyzing various influencing factors in the wastewater treatment process subsequently, and ensure the accuracy of the treatment effect evaluation.

[0061] (2) Calculating the pH value, temperature value, turbidity value, and water flow influence coefficient based on the wastewater treatment process data can quantify the influence of these factors on wastewater treatment, help to deeply analyze the role of each factor in the treatment process, and provide a targeted basis for optimizing the treatment process.

[0062] (3) Calculating the wastewater treatment parameters based on the above-mentioned various influence coefficients, comprehensively considering the synergistic effects of the pH value, temperature value, turbidity value, and water flow factors on wastewater treatment, can more comprehensively reflect the wastewater treatment status of the wastewater treatment system, facilitate the discovery of potential problems, and improve the stability of the operation of the treatment system.

[0063] (4) Calculating the wastewater treatment efficiency index by combining the treated data and the wastewater treatment parameters can determine the triggered wastewater treatment efficiency level, thereby taking corresponding adjustment instructions to avoid the environmental risks caused by the treated wastewater not meeting the standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic flow chart of an intelligent control method for dyeing industrial wastewater treatment based on the Internet of Things according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0066] Please refer to Figure 1 , the present invention provides an intelligent control method for dyeing industrial wastewater treatment based on the Internet of Things, including:

[0067] Collecting the wastewater treatment process data and the data of the treated samples.

[0068] Calculating the pH value influence coefficient K pH 、temperature value influence coefficient K T and turbidity value influence coefficient C respectively according to the wastewater treatment process data; according to the sample water flow Q i, calculate the water flow influence coefficient F.

[0069] It should be noted that the wastewater treatment stage includes five wastewater treatment stages: the regulating tank treatment stage, the sedimentation treatment stage, the biological treatment stage, the advanced treatment stage, and the disinfection treatment stage. Samples are obtained for each stage to form wastewater treatment process data.

[0070] Calculate the pH value influence coefficient K pH The specific steps are as follows:

[0071] The wastewater treatment process data includes the sample pollutant concentration C of different wastewater treatment stages i , the average value of the sample pollutant concentration C0, the sample pH value P i and the average value of the sample pH value P0.

[0072] It should be noted that the sample pollutant concentration C is collected by a pollution concentration collector i , and the sample pH value P is tested according to a pH test paper i .

[0073] According to the sample pollutant concentration C i , calculate the average value of the sample pollutant concentration C0. The specific formula is as follows:

[0074]

[0075] According to the sample pH value P i , calculate the average value of the sample pH value P0. The specific formula is as follows:

[0076]

[0077] According to the sample pollutant concentration C i , the average value of the sample pollutant concentration C0, the sample pH value P i and the average value of the sample pH value P0, calculate the pH value influence coefficient K pH , and the specific formula is as follows

[0078]

[0079] Among them, C i represents the pollutant concentration of the i-th sample, P i represents the pH value of the i-th sample, and i is the sample serial number, with a value range of [1, 5].

[0080] It should be noted that The sum after multiplying the two represents the comprehensive relationship between the pollutant concentration deviation and the pH value deviation. The sum of the squared deviations for all samples is mainly used for normalization to ensure that the calculated values are within a reasonable range and that the calculation results mainly reflect the impact of pH value on pollutant concentration without being overly interfered by the number of samples. Through the above calculations of the numerator and denominator, the value of K pH can reflect the degree of influence of pH value on pollutant concentration.

[0081] The formula can quantify the degree of influence of pH value on the pollutant concentration of samples, which is helpful for accurate analysis. The calculated coefficient can provide a basis for the treatment of printing and dyeing industrial wastewater, reasonably adjust the pH value during the treatment process, and optimize the treatment effect. Supported by data, the decision-making in the wastewater treatment process can be made more scientific, avoiding blind operations, improving the treatment efficiency and quality, and ensuring that the treated wastewater meets the environmental protection standards.

[0082] The specific steps for calculating the temperature value influence coefficient K T are as follows:

[0083] The sample processing data also includes the equipment energy consumption value A i , the sample temperature value T i , and the average sample temperature T avg .

[0084] It should be noted that the sample temperature value T i is obtained by installing a temperature sensor; the energy consumption value A i at different treatment stages is obtained by an energy consumption detector.

[0085] The specific steps for calculating the average sample temperature T avg are as follows:

[0086]

[0087] Based on the equipment energy consumption value A i , the sample temperature value T i , and the average sample temperature T avg , the temperature value influence coefficient K T is calculated according to the following specific formula:

[0088] where A i represents the energy consumption value of the i-th sample, and T i represents the wastewater temperature value of the i-th sample.

[0089] It should be noted that for the entire numerator part , by multiplying the energy consumption value of each sample by the temperature deviation value and summing up all samples, a value that comprehensively considers the energy consumption and temperature deviation is obtained. This value reflects the comprehensive effect of energy consumption considering the temperature deviation from the average temperature; for the entire denominator part By summing the squares of the temperature deviation values of all samples, a comprehensive value reflecting the degree of temperature deviation of all samples from the average temperature is obtained. This value is mainly used for normalization to ensure that the calculated temperature value influence coefficient is within a reasonable range. Divide the sum of the numerators by the sum of the denominators to obtain the temperature value influence coefficient K T , and this coefficient can reflect the degree of influence of temperature on energy consumption in the wastewater treatment process.

[0090] Through this formula, the degree of influence of temperature on energy consumption in the wastewater treatment process can be quantified, K T The larger the value of K, the more significant the influence of temperature on energy consumption; it helps to better control the temperature parameters in the wastewater treatment process, thereby optimizing the treatment process, improving the treatment efficiency, and reducing energy consumption; based on the calculation results of this formula, decision-makers can make more scientific decisions according to actual data, such as adjusting the temperature control strategy of the treatment equipment or improving the treatment process, etc.

[0091] The specific steps for calculating the turbidity value influence coefficient C are as follows:

[0092] The sample processing data also includes the sample turbidity value B at different wastewater treatment stages i .

[0093] Set the turbidity influence reference value A.

[0094] It should be noted that the turbidity value b is obtained through a turbidity detector i , and the relevant standards and specifications for wastewater treatment in the printing and dyeing industry are used to obtain the turbidity influence reference value A.

[0095] According to the sample turbidity value b i , the turbidity influence reference value A, the sample temperature value T i , the average sample temperature T avg and the temperature value influence coefficient K T , calculate the turbidity value influence coefficient C. The specific formula is as follows:

[0096]

[0097] where B i represents the turbidity value at the i-th wastewater treatment stage.

[0098] It should be noted that The ratio reflects the relative size of the currently set turbidity influence reference relative to the historical turbidity situation, (T i -T avg ) This difference reflects the degree of deviation of the current sample temperature from the average temperature, It reflects the regulatory effect of temperature on turbidity. The entire formula calculates the turbidity value influence coefficient C by comprehensively considering the proportional relationship between the fouling influence reference value and the historical fouling value, as well as the degree of temperature influence on turbidity.

[0099] By considering the ratio of the historical fouling value B i and the fouling influence reference value A, it can reflect the relationship between the current fouling degree and the preset reference, which helps to evaluate the rationality of the current turbidity situation. The temperature value influence coefficient K T and the temperature difference T i -T avg are introduced in the exponential term, considering the influence of temperature on turbidity, which can more accurately quantify the influence degree of turbidity on wastewater treatment. This helps to reasonably adjust the treatment parameters, optimize the treatment effect, and improve the treatment efficiency during the wastewater treatment process.

[0100] The specific steps for calculating the water flow rate influence coefficient F are as follows:

[0101] Set the maximum water flow rate treatment value Q max and the standard reference flow rate Q0.

[0102] It should be noted that the water flow rate Q is obtained through the flowmeter at the water inlet end i ; the maximum water flow rate treatment value Q is set according to the design specifications of the printing and dyeing industrial wastewater treatment equipment max ; the standard reference flow rate Q0 is set by taking the average value of the historical flow rate values.

[0103] According to the sample water flow rate Q i 、the maximum water flow rate treatment value Q max 、the standard reference flow rate Q0 and the turbidity value influence coefficient C, calculate the water flow rate influence coefficient F. The specific formula is as follows:

[0104]

[0105] Among them, Q i represents the i-th sample water flow rate.

[0106] (Q i -Q0) 2 is used to measure the relationship between the deviation degree of the actual flow rate from the reference flow rate relative to the maximum treatment capacity of the equipment. With the natural constant e as the base and the exponent -C(Q i -Q0), when the product of C and (Q i -Q0) is larger, the value of this exponential part is smaller; when the product is smaller, the value of this exponential part is larger. This formula comprehensively considers the relationship between the flow rate deviation relative to the maximum treatment capacity and the degree of turbidity influence on the flow rate, and obtains the water flow rate influence coefficient F, which can reflect the influence degree of the water flow rate on the wastewater treatment process.

[0107] The water flow rate Q is considered in the formula i 、the standard reference flow rate Q0 and the maximum processing value Q of the water flow rate max , which can reflect the deviation between the actual water flow rate and the ideal reference flow rate, as well as the situation of this deviation relative to the processing capacity of the equipment, helps to evaluate the operating state of the equipment. By introducing the turbidity value influence coefficient C, the influence of turbidity on the water flow rate is comprehensively considered, making the calculated water flow rate influence coefficient more accurate. This helps to reasonably regulate the water flow rate in wastewater treatment, optimize the treatment process, and improve the treatment efficiency.

[0108] According to the pH value influence coefficient K pH 、the temperature value influence coefficient K T 、the turbidity value influence coefficient C and the water flow rate influence coefficient F, calculate the wastewater treatment parameter M.

[0109] According to the treated sample processing data and the wastewater treatment parameter M, calculate the wastewater treatment effectiveness index BP.

[0110] The specific steps for calculating the wastewater treatment parameter M are as follows:

[0111] According to the pH value influence coefficient K pH 、the temperature value influence coefficient K T 、the turbidity value influence coefficient C and the water flow rate influence coefficient F, calculate the wastewater treatment parameter M. The specific formula is as follows:

[0112] M = K pH × w1 + K T × w2 + C × w3 + F × w4

[0113] Among them, w1 represents the weight coefficient of the pH value influence coefficient K pH , and the value range is 0.1 ≤ w1 ≤ 0.3. w2 represents the weight coefficient of the temperature value influence coefficient K T , and the value range is 0.2 ≤ w2 ≤ 0.4. w3 represents the weight coefficient of the turbidity value influence coefficient C, and the value range is 0.3 ≤ w3 ≤ 0.5. w4 represents the weight coefficient of the water flow rate influence coefficient F, and the value range is 0.1 < w4 ≤ 0.3, and w1 + w2 + w3 + w4 = 1.

[0114] It should be noted that by calculating M, the operating influence situation under the combined action of various factors in the wastewater treatment process can be evaluated, providing a basis for optimizing the treatment process.

[0115] The influence of four key factors, namely pH value, temperature value, turbidity value and water flow rate, on wastewater treatment is comprehensively considered. Through the corresponding influence coefficients and weight coefficients of each factor, the operation situation during the treatment process can be comprehensively evaluated. The setting of the weight coefficients enables each factor to participate in the calculation reasonably according to its importance, ensuring that the calculation results can accurately reflect the actual influence degree of each factor. This helps to targetedly regulate the key factors during the wastewater treatment process, optimize the treatment process, and improve the treatment efficiency and quality.

[0116] The specific steps for calculating the wastewater treatment efficacy index BP are as follows:

[0117] The processed sample data after treatment includes the real-time pH value PY, real-time temperature value TY, real-time turbidity value VY, and real-time water flow rate FT.

[0118] According to the real-time pH value PY, real-time temperature value TY, real-time turbidity value VT, real-time water flow rate FT, and wastewater treatment parameter M, the wastewater treatment efficacy index BP is calculated. The specific formula is as follows:

[0119]

[0120] Among them, u1 represents the weight coefficient of the real-time pH value PY, with a value range of 0.2 ≤ u1 ≤ 0.4; u2 represents the weight coefficient of the real-time temperature value TY, with a value range of 0.1 < u2 ≤ 0.3; u3 represents the weight coefficient of the real-time turbidity value VT, with a value range of 0.1 ≤ u3 ≤ 0.3; u4 represents the weight coefficient of the real-time water flow rate FT, with a value range of 0.3 ≤ u4 ≤ 0.5, and u1 + u2 + u3 + u4 = 1.

[0121] It should be noted that this index BP comprehensively considers the real-time treatment data and wastewater treatment parameters, and can reflect the efficacy situation of the current wastewater treatment process. By calculating BP, it can be evaluated whether the wastewater treatment process is operating efficiently, and corresponding adjustments can be made according to the results.

[0122] Comprehensively considering the four key factors of the real-time pH value PY, real-time temperature value TY, real-time turbidity value VT, and real-time water flow rate FY, through weighted summation with the corresponding weight coefficients of each factor, the real-time state of wastewater treatment is comprehensively evaluated. By introducing the wastewater treatment parameter M and combining the real-time data with the basic operation situation, the calculation results can accurately reflect the efficacy of wastewater treatment, which helps to timely discover problems in the treatment process, optimize the treatment process, and ensure that the treated wastewater meets the discharge standards.

[0123] Set the wastewater treatment efficacy threshold set JV. According to the comparison result between the wastewater treatment efficacy index BP and the wastewater treatment efficacy threshold set JV, judge the wastewater treatment efficacy level, and judge whether to trigger an early warning according to the judgment result, and generate corresponding adjustment instructions.

[0124] The specific steps to determine whether to trigger the warning of wastewater treatment efficiency are as follows:

[0125] The set of wastewater treatment efficiency thresholds JV includes a primary threshold Jv0, a secondary threshold Jv1, and a tertiary threshold Jv2, where Jv0 < Jv1 < Jv2.

[0126] It should be noted that historical data of wastewater treatment is obtained, the wastewater treatment efficiency index BP under different periods of wastewater treatment status is calculated, and the average value of the wastewater treatment efficiency index in the previous cycle is set as the primary threshold Jv0; Jv1 is set as the 85th percentile of the historical BP average value, and Jv2 is set as the 75th percentile of the average value of the wastewater treatment efficiency index BP.

[0127] When the wastewater treatment efficiency index BP ≥ Jv0, the warning of wastewater treatment efficiency is not triggered.

[0128] When Jv0 > wastewater treatment efficiency index BP ≥ Jv1, a primary warning of wastewater treatment efficiency is triggered, and a primary adjustment instruction is generated.

[0129] When Jv1 > wastewater treatment efficiency index BP ≥ Jv2, a secondary warning of wastewater treatment efficiency is triggered, and an intermediate adjustment instruction is generated.

[0130] When Jv2 > wastewater treatment efficiency index BP, a tertiary warning of wastewater treatment efficiency is triggered, and a high-level adjustment instruction is generated.

[0131] It should be noted that when Jv0 > wastewater treatment efficiency index BP ≥ Jv1, this indicates the primary adjustment instruction for wastewater treatment:

[0132] Regulation tank treatment stage: Check the influent flow rate and water quality to ensure uniform and stable inflow, and adjust the dosage of pretreatment chemicals.

[0133] Sedimentation treatment stage: Check the hydraulic retention time, adjust the influent flow valve to ensure sufficient mixing of the sedimentation agent.

[0134] Biological treatment stage: Detect dissolved oxygen, adjust the aeration parameters, and supplement microbial nutrients.

[0135] Advanced treatment stage: Check the saturation of activated carbon or membrane flux, and replace or clean it in a timely manner.

[0136] Disinfection treatment stage: Adjust the dosage of disinfectant and check the intensity of disinfection equipment.

[0137] When Jv1 > wastewater treatment efficiency index BP ≥ Jv2, this means that the degree of decline in wastewater treatment efficiency is relatively serious and more in-depth intervention is required.

[0138] Intermediate adjustment instruction:

[0139] Regulation Pond Treatment Stage: Thoroughly clean the regulation pond and improve the agitation device.

[0140] Sedimentation Treatment Stage: Inspect the structure of the sedimentation tank, re-calculate the sedimentation agent, and install on-line monitoring equipment.

[0141] Biological Treatment Stage: Analyze the microbial community, inspect the environmental control equipment, and maintain the aeration system.

[0142] Advanced Treatment Stage: Conduct a comprehensive overhaul of the equipment and re-evaluate the service life and working efficiency of the equipment.

[0143] Disinfection Treatment Stage: Replace the disinfection method or disinfectant and strengthen water quality monitoring.

[0144] When Jv2 > the wastewater treatment efficiency index BP, this indicates that the wastewater treatment efficiency seriously fails to meet the standards, may have a greater impact on the environment, and the situation is very urgent.

[0145] Advanced Adjustment Instructions:

[0146] Regulation Pond Treatment Stage: Suspend the entry of wastewater and maintain and transform the regulation pond.

[0147] Sedimentation Treatment Stage: Reconstruct the sedimentation treatment system, select an efficient sedimentation tank, and replace with a new type of sedimentation agent.

[0148] Biological Treatment Stage: Demolish and reconstruct the biological treatment facilities, select an advanced process, and establish a microbial culture system.

[0149] Advanced Treatment Stage: Replace or upgrade the equipment and establish a whole-process traceability system.

[0150] Disinfection Treatment Stage: Adopt multiple disinfections and strengthen long-term tracking and monitoring.

[0151] Through the hierarchical early warning mechanism, it is possible to accurately judge the degree of decline in wastewater treatment efficiency. The first-level early warning can promptly detect the slight problems that begin to appear in the treatment efficiency, the second-level early warning can detect a more serious decline in efficiency, and the third-level early warning is for the situation of serious non-compliance, making the problem discovery more targeted. Corresponding adjustment instructions are generated for different levels of early warning, and the problems are gradually solved from the primary to the advanced level. This helps to optimize the wastewater treatment process, avoid environmental pollution caused by non-compliant treatment, and ensure the efficient operation of the treatment system.

[0152] On the other hand, the present invention also discloses an intelligent control system for printing and dyeing industrial wastewater treatment based on the Internet of Things, including:

[0153] A data acquisition module for collecting wastewater treatment process data and treated sample data.

[0154] A data analysis module for respectively calculating the pH value influence coefficient K according to the wastewater treatment process data pH, temperature value influence coefficient K T and turbidity value influence coefficient C; calculate the water flow influence coefficient F according to the historical water flow Q at different wastewater treatment stages.

[0155] A data analysis module for calculating the wastewater treatment parameter M according to the pH value influence coefficient K pH , temperature value influence coefficient K T , turbidity value influence coefficient C and water flow influence coefficient F.

[0156] Calculate the wastewater treatment efficiency index BP according to the processed sample treatment data and the wastewater treatment parameter M.

[0157] An early warning generation module for setting a set of wastewater treatment efficiency thresholds JV, judging the wastewater treatment efficiency level according to the comparison result between the wastewater treatment efficiency index BP and the set of wastewater treatment efficiency thresholds JV, and judging whether to trigger an early warning according to the judgment result and generating a corresponding adjustment instruction.

[0158] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution.

[0159] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0160] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application.

Claims

1. An intelligent control method for printing and dyeing industrial wastewater treatment based on the Internet of Things, characterized in that: Including: Collecting the wastewater treatment process data and the treated sample data; Calculate the pH value influence coefficient K, the temperature value influence coefficient K, and the turbidity value influence coefficient C respectively according to the wastewater treatment process data; calculate the water flow influence coefficient F according to the sample water flow Q at different stages in the wastewater treatment process. pH and the temperature value influence coefficient K T and the turbidity value influence coefficient C; according to the sample water flow Q i at different stages in the wastewater treatment process, calculate the water flow influence coefficient F; According to the pH value influence coefficient K pH , the temperature value influence coefficient K T , the turbidity value influence coefficient C, and the water flow influence coefficient F, calculate the wastewater treatment parameter M; Calculating the wastewater treatment efficiency index BP according to the treated sample processing data and the wastewater treatment parameter M. The specific steps for calculating the wastewater treatment efficiency index BP are as follows: The treated sample processing data includes the real-time pH value PY, the real-time temperature value TY, the real-time turbidity value VY, and the real-time water flow FY; Calculating the wastewater treatment efficiency index BP according to the real-time pH value PY, the real-time temperature value TY, the real-time turbidity value VY, the real-time water flow FY, and the wastewater treatment parameter M. The specific formula is as follows: Wherein, u1 represents the weight coefficient of the real-time pH value PY, u2 represents the weight coefficient of the real-time temperature value TY, u3 represents the weight coefficient of the real-time turbidity value VY, and u4 represents the weight coefficient of the real-time water flow FY; Setting the wastewater treatment efficiency threshold set JV, judging the wastewater treatment efficiency level according to the comparison result between the wastewater treatment efficiency index BP and the wastewater treatment efficiency threshold set JV, and judging whether to trigger an alarm according to the judgment result, and generating a corresponding adjustment instruction.

2. The intelligent control method for treating printing and dyeing industrial wastewater based on the Internet of Things according to claim 1, wherein: Calculate the pH value influence coefficient K pH The specific steps are as follows: The wastewater treatment process data includes the sample pollutant concentration C at different wastewater treatment stages i , the average value C0 of the sample pollutant concentration, and the sample pH value P i and the average value P0 of the sample pH value; According to the sample pollutant concentration C i , the average value of the sample pollutant concentration C0, the sample pH value P i and the average value of the sample pH value P0, calculate the pH value influence coefficient K pH , and the specific formula is as follows Among them, C i represents the sample pollutant concentration at the i-th wastewater treatment stage, and P i represents the sample pH value at the i-th wastewater treatment stage. i is the serial number of different treatment stages in the wastewater treatment process, and its value range is [1, 5].

3. The intelligent control method for treating printing and dyeing industrial wastewater based on the Internet of Things according to claim 1, characterized in that: Calculate the influence coefficient K of the temperature value T The specific steps are as follows: The wastewater treatment process data also includes the energy consumption value A at different wastewater treatment stages i , the sample temperature value T i and the average sample temperature T avg ; According to the energy consumption value A i , the sample temperature value T i , and the average sample temperature T avg , calculate the temperature value influence coefficient K T . The specific formula is as follows: Among them, A i represents the energy consumption value of the i-th wastewater treatment stage, and T i represents the sample wastewater temperature value of the i-th wastewater treatment stage.

4. The intelligent control method for treating printing and dyeing industrial wastewater based on the Internet of Things according to claim 3, wherein: Calculate the average temperature T of the sample avg The specific steps are as follows:

5. The intelligent control method for treating printing and dyeing industrial wastewater based on the Internet of Things according to claim 3, characterized in that: The specific steps for calculating the turbidity value influence coefficient C are as follows: The wastewater treatment process data also includes the sample turbidity value B at different wastewater treatment stages i ; Setting the turbidity influence reference value A; According to the sample turbidity value B i , the turbidity influence reference value A, the sample temperature value T i , the average sample temperature T avg and the temperature value influence coefficient K T , calculate the turbidity value influence coefficient C. The specific formula is as follows: Among them, B i represents the turbidity value of the i-th wastewater treatment stage.

6. The intelligent control method for treating printing and dyeing industrial wastewater based on the Internet of Things according to claim 1, characterized in that: The specific steps for calculating the water flow influence coefficient F are as follows: Set the maximum processing value Q of the water flow rate max and the standard reference flow rate Q0; According to the sample water flow rate Q i , the maximum processed value of water flow rate Q max , the standard reference flow rate Q0, and the turbidity value influence coefficient C, calculate the water flow rate influence coefficient F, and the specific formula is as follows: Among them, Q i represents the sample water flow rate of the i-th wastewater treatment stage.

7. An intelligent control method for dyeing industrial wastewater treatment based on the Internet of Things according to claim 6, characterized in that: The specific steps for calculating the wastewater treatment parameter M are as follows: According to the pH value influence coefficient K pH , the temperature value influence coefficient K T , the turbidity value influence coefficient C, and the water flow rate influence coefficient F, the specific formula for calculating the wastewater treatment parameter M is as follows: M = K pH × w1 + K T × w2 + C × w3 + F × w4 Among them, w1 represents the weight coefficient of the pH value influence coefficient K pH with a value range of 0.1 ≤ w1 ≤ 0.

3. w2 represents the weight coefficient of the temperature value influence coefficient K T with a value range of 0.2 ≤ w2 ≤ 0.

4. w3 represents the weight coefficient of the turbidity value influence coefficient C, with a value range of 0.3 ≤ w3 ≤ 0.

5. w4 represents the weight coefficient of the water flow rate influence coefficient F, with a value range of 0.1 < w4 ≤ 0.3, and w1 + w2 + w3 + w4 = 1.

8. An intelligent control method for printing and dyeing industrial wastewater treatment based on the Internet of Things according to claim 7, characterized in that: u1 takes a value of 0.2 ≤ u1 ≤ 0.4, u2 takes a value of 0.1 < u2 ≤ 0.3, u3 takes a value of 0.1 ≤ u3 ≤ 0.3, u4 takes a value of 0.3 ≤ u4 ≤ 0.5, and u1 + u2 + u3 + u4 = 1.

9. The intelligent control method for treating printing and dyeing industrial wastewater based on the Internet of Things according to claim 1, characterized in that: The specific steps for judging whether to trigger the wastewater treatment efficiency alarm and generating a corresponding adjustment instruction are as follows: The wastewater treatment efficiency threshold set JV includes a first-level threshold Jv0, a second-level threshold Jv1, and a third-level threshold Jv2, wherein, Jv0 > Jv1 > Jv2; When the wastewater treatment efficiency index BP ≥ Jv0, the wastewater treatment efficiency alarm is not triggered; When Jv0 > the wastewater treatment efficiency index BP ≥ Jv1, triggering a first-level wastewater treatment efficiency alarm and generating a primary adjustment instruction; When Jv1 > the wastewater treatment efficiency index BP ≥ Jv2, triggering a second-level wastewater treatment efficiency alarm and generating an intermediate adjustment instruction; When Jv2 > the wastewater treatment efficiency index BP, triggering a third-level wastewater treatment efficiency alarm and generating a high-level adjustment instruction.

10. An intelligent control system for dyeing industrial wastewater treatment based on the Internet of Things, characterized in that: A data acquisition module for collecting the wastewater treatment process data and the treated sample data of the wastewater; A data analysis module, configured to calculate a pH value influence coefficient K, a temperature value influence coefficient K, and a turbidity value influence coefficient C respectively according to wastewater treatment process data; and calculate a water flow influence coefficient F according to a sample water flow Q at different wastewater treatment stages. pH , a temperature value influence coefficient K T and a turbidity value influence coefficient C; and calculate a water flow influence coefficient F according to a sample water flow Q i at different wastewater treatment stages. A data analysis module for calculating wastewater treatment parameter M based on the pH value influence coefficient K pH , the temperature value influence coefficient K T , the turbidity value influence coefficient C, and the water flow influence coefficient F Calculating the wastewater treatment efficiency index BP according to the treated sample processing data and the wastewater treatment parameter M. The specific formula is as follows: Wherein, u1 represents the weight coefficient of the real-time pH value PY, u2 represents the weight coefficient of the real-time temperature value TY, u3 represents the weight coefficient of the real-time turbidity value VY, and u4 represents the weight coefficient of the real-time water flow FY; An alarm generation module for setting the wastewater treatment efficiency threshold set JV, judging the wastewater treatment efficiency level according to the comparison result between the wastewater treatment efficiency index BP and the wastewater treatment efficiency threshold set JV, and judging whether to trigger an alarm according to the judgment result, and generating a corresponding adjustment instruction.

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