Whole-flow intelligent regulation and control method for sewage treatment plant based on digital twinborn driving

Abstract

The invention discloses a sewage treatment plant full-flow intelligent regulation and control method based on digital twin driving, and relates to the technical field of sewage treatment, and the method comprises the following steps: collecting to-be-treated sewage samples at at least two positions and time; obtaining basic information of the sludge by measuring physical parameters of the sludge in the to-be-treated sewage sample; carrying out feature extraction by measuring chemical components of sludge in a sewage sample to be treated; the biological activity and the treatment potential of the sludge in the sewage are revealed by analyzing the biological characteristics of the sludge in the to-be-treated sewage sample; carrying out correlation analysis on the extracted physical, chemical and biological characteristic data and data in a historical sewage treatment database; carrying out sample pretreatment on a to-be-treated sewage sample; optimizing sewage suction parameters and adjusting a sewage treatment process. The properties and treatment requirements of the sludge are known by comprehensively extracting the characteristics of the sludge, so that a basis is provided for realizing precise sewage treatment, and personalized customization of sewage treatment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and specifically to a full-process intelligent control method for a sewage treatment plant driven by digital twins. Background Art

[0002] Wastewater treatment technology is an advanced technology commonly used in the treatment of industrial and municipal wastewater. This technology uses the negative pressure generated by the wastewater to pump it from one location to another, while also achieving initial lifting and transportation. Sludge, a solid waste generated during the wastewater treatment process, has a complex composition encompassing a variety of physical, chemical, and biological properties. Sludge type feature extraction technology extracts key characteristic information from sludge through detailed analysis and testing.

[0003] The accuracy and real-time nature of sludge type feature extraction technology are key factors limiting its effectiveness. Traditional sludge analysis methods are often time-consuming and labor-intensive, and the results are susceptible to interference and influence from various factors. Furthermore, since sludge composition and properties vary over time and with treatment processes, achieving real-time and accurate sludge feature extraction is a critical challenge. Summary of the Invention

[0004] In order to solve the above technical problems, a full-process intelligent control method for sewage treatment plants driven by digital twins is provided. This technical solution solves the problems raised in the above background technology.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] The digital twin-driven full-process intelligent control method for sewage treatment plants includes:

[0007] Collect wastewater samples for treatment at at least two locations and times;

[0008] Obtaining basic information about the sludge by measuring physical parameters of the sludge in the wastewater sample to be treated, wherein the physical parameters include density, moisture content, particle size and distribution;

[0009] By measuring the chemical composition of the sludge in the sewage sample to be treated, feature extraction is performed to obtain the composition and properties of the sludge in the sewage;

[0010] The biological activity and treatment potential of the sludge in the sewage sample to be treated are revealed by analyzing the biological characteristics of the sludge in the sewage sample, including the type, number, activity and toxicity of microorganisms;

[0011] Correlation analysis was performed between the extracted physical, chemical and biological characteristic data and the data in the historical sewage treatment database;

[0012] Perform sample pretreatment on the sewage samples to be treated to remove impurities and interfering substances in the sewage samples to be treated;

[0013] Based on the correlation analysis of the characteristic data of the sewage samples to be treated and the data in the historical sewage treatment database, the sewage pumping parameters are optimized and the sewage treatment process is adjusted.

[0014] Preferably, obtaining basic information of the sludge by measuring the physical parameters of the sludge in the wastewater sample to be treated specifically includes:

[0015] Measure the total mass of the sewage sample to be treated and the sedimentation volume and residual liquid volume of the sludge after standing and stratification;

[0016] The density of sludge in the wastewater sample to be treated is calculated using the sedimentation density formula;

[0017] Using the principle of laser scattering, the size and distribution of sludge particles in the wastewater sample to be treated are calculated by measuring the angle and intensity of light scattering;

[0018] Drying the wastewater sample to be treated and measuring the mass change of the wastewater sample to be treated before and after drying;

[0019] Calculate the moisture content of the wastewater sample to be treated using the moisture content formula;

[0020] The sedimentation density formula is:

[0021]

[0022] Where, ρ n is the density of the sludge in the sewage sample to be treated, M is the total mass of the sewage sample to be treated, ρ h is the density of water, V h V is the volume of liquid remaining after the sewage sample is allowed to stand and stratify. n is the sedimentation volume of sludge after static stratification;

[0023] The water content formula is:

[0024]

[0025] Where γ is the moisture content of the sewage sample to be treated, and M' is the mass of the sewage sample to be treated after drying.

[0026] Preferably, the feature extraction is performed by measuring the chemical composition of the sludge in the sewage sample to be treated, and the composition and properties of the sludge in the sewage are obtained specifically including:

[0027] Place a test tube containing a mixed solution of sludge, potassium dichromate solution and concentrated sulfuric acid in a heating device and heat it to boiling to fully oxidize the organic matter in the sludge;

[0028] Add a preset volume of o-phenanthroline indicator to the test solution and blank solution respectively;

[0029] Titrate the ferrous sulfate standard solution until the solution changes from orange-yellow to blue-green and then to brown-red, and record the volume of ferrous sulfate standard solution consumed during the titration.

[0030] The organic matter content of the sludge in the wastewater sample to be treated was calculated using the organic content formula;

[0031] X-ray fluorescence spectrometer was used to analyze the inorganic content of sludge in the wastewater samples to be treated;

[0032] The pH value of the sludge in the wastewater sample to be treated is measured by a pH meter;

[0033] The organic content formula is:

[0034]

[0035] Where ε is the organic matter content of the sludge in the sewage sample to be treated, n is the molar mass of the carbon atom, V0 is the volume of the ferrous sulfate standard solution consumed when titrating the blank solution, V1 is the volume of the ferrous sulfate standard solution consumed when titrating the test solution, and c is the concentration of the ferrous sulfate standard solution.

[0036] Preferably, the method of revealing the biological activity and treatment potential of the sludge in the sewage by analyzing the biological characteristics of the sludge in the sewage sample to be treated specifically includes:

[0037] Extract DNA from sludge in wastewater samples to be treated;

[0038] Design and synthesize primers specific to the DNA;

[0039] Conduct PCR amplification reactions and analyze amplification products to determine the types and quantities of microorganisms;

[0040] The sludge sample in the sewage sample to be treated is placed in a sealed container, and an oxygen diluent of a preset concentration is added;

[0041] Place the sealed container at a constant temperature for a preset time and measure the change in oxygen concentration in the sealed container;

[0042] The local method formula is used to calculate the respiration rate of microorganisms in the sludge in the sewage sample to be treated and output it as the metabolic activity of the sludge;

[0043] exposing luminescent bacteria to sludge samples from wastewater samples to be treated;

[0044] Observe and record the changes in the luminous intensity of luminous bacteria;

[0045] The toxicity of the sludge in the wastewater sample to be treated is evaluated based on the percentage of luminescence intensity reduction;

[0046] The local method formula is:

[0047]

[0048] Where v is the respiration rate of microorganisms in the sludge in the sewage sample to be treated, t is the preset time, c0 is the initial oxygen concentration in the closed container, and c1 is the oxygen concentration after the closed container is placed at a constant temperature for a preset time.

[0049] Preferably, the correlation analysis of the extracted physical, chemical and biological characteristic data with the data in the historical sewage treatment database specifically includes:

[0050] Descriptive statistical methods were used to analyze the relationship between the physical, chemical and biological characteristics of sludge and the sewage treatment effect;

[0051] Descriptive statistics were used to summarize the basic characteristics of the data, including mean, median, and standard deviation;

[0052] By drawing visual charts such as scatter plots or line graphs, the relationship between the physical, chemical and biological characteristics of sludge and the sewage treatment effect can be intuitively displayed;

[0053] Extract association rules from sludge characteristic data and sewage treatment database;

[0054] Based on the extracted association rules and machine learning algorithms, a prediction model between sludge type and sewage treatment effect is constructed.

[0055] Preferably, the sample pretreatment of the wastewater sample to be treated to remove impurities and interfering substances in the wastewater sample to be treated specifically includes:

[0056] Aluminum hydroxide is used as a chemical flocculant to condense colloidal impurities in the wastewater sample to be treated into large particles, which are then subjected to static sedimentation treatment;

[0057] Use filtration devices to remove suspended solids and particulate impurities from wastewater samples to be treated;

[0058] Using sulfuric acid-nitric acid system as oxidant, the organic matter and inorganic salt interfering substances in the wastewater sample to be treated are decomposed;

[0059] Activated carbon was used as an adsorbent to remove heavy metals from wastewater samples to be treated;

[0060] Membrane separation technology is used to remove dissolved matter from the wastewater sample to be treated by utilizing the selective permeability of the semipermeable membrane.

[0061] Preferably, the optimization of sewage pumping parameters and adjustment of sewage treatment process based on correlation analysis of characteristic data of the sewage sample to be treated and data in the historical sewage treatment database specifically include:

[0062] Based on the density and moisture content of the sludge in the wastewater sample to be treated, the suction pressure and flow rate of the sewage are adjusted so that the sludge can be effectively sucked and transported to the subsequent treatment unit;

[0063] Set the sewage pumping time interval based on the sedimentation and filtration performance of the sludge in the sewage sample to be treated;

[0064] Select a biological treatment process based on the types and quantities of microorganisms in the sludge of the wastewater sample to be treated;

[0065] Based on the organic matter content and heavy metal content of the sludge in the sewage sample to be treated, chemical treatment agents are selected to remove the organic matter and heavy metals. The chemical treatment agents include coagulants and oxidants.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] Comprehensive extraction of sludge characteristics is achieved, which helps to more accurately understand the properties and treatment requirements of sludge, thus providing a basis for precise sewage treatment. Correlation analysis helps to select or optimize sewage treatment processes according to the actual characteristics of sludge, and realize personalized customization of sewage treatment. Correlation analysis helps to select or optimize sewage treatment processes according to the actual characteristics of sludge, and realize personalized customization of sewage treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flow chart of the full-process intelligent control method of a sewage treatment plant driven by digital twins according to the present invention;

[0069] Figure 2 This is a flow chart of a method for obtaining basic information of sludge by measuring physical parameters of sludge in a wastewater sample to be treated according to the present invention;

[0070] Figure 3 This is a flow chart of a method for extracting features by measuring the chemical composition of sludge in a wastewater sample to be treated according to the present invention;

[0071] Figure 4 A flow chart of the method for revealing the biological activity and treatment potential of sludge in sewage by analyzing the biological characteristics of sludge in a sewage sample to be treated according to the present invention;

[0072] Figure 5 A flow chart of a method for performing correlation analysis on the extracted physical, chemical and biological characteristic data and the data in the historical sewage treatment database according to the present invention;

[0073] Figure 6 This is a flow chart of a method for pre-treating a wastewater sample to be treated according to the present invention;

[0074] Figure 7 This is a flow chart of the method for optimizing sewage pumping parameters and adjusting sewage treatment process according to the present invention. DETAILED DESCRIPTION

[0075] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0076] Reference Figure 1 As shown in the figure, the full-process intelligent control method of sewage treatment plants driven by digital twins includes:

[0077] Collect wastewater samples for treatment at at least two locations and times;

[0078] Obtaining basic information about the sludge by measuring physical parameters of the sludge in the wastewater sample to be treated, wherein the physical parameters include density, moisture content, particle size and distribution;

[0079] By measuring the chemical composition of the sludge in the sewage sample to be treated, feature extraction is performed to obtain the composition and properties of the sludge in the sewage;

[0080] The biological activity and treatment potential of the sludge in the sewage sample to be treated are revealed by analyzing the biological characteristics of the sludge in the sewage sample, including the type, number, activity and toxicity of microorganisms;

[0081] Correlation analysis was performed between the extracted physical, chemical and biological characteristic data and the data in the historical sewage treatment database;

[0082] Perform sample pretreatment on the sewage samples to be treated to remove impurities and interfering substances in the sewage samples to be treated;

[0083] Based on the correlation analysis of the characteristic data of the sewage samples to be treated and the data in the historical sewage treatment database, the sewage pumping parameters are optimized and the sewage treatment process is adjusted.

[0084] Reference Figure 2 As shown in FIG, the basic information of the sludge obtained by measuring the physical parameters of the sludge in the sewage sample to be treated includes:

[0085] Measure the total mass of the sewage sample to be treated and the sedimentation volume and residual liquid volume of the sludge after standing and stratification;

[0086] The density of sludge in the wastewater sample to be treated is calculated using the sedimentation density formula;

[0087] Using the principle of laser scattering, the size and distribution of sludge particles in the wastewater sample to be treated are calculated by measuring the angle and intensity of light scattering;

[0088] Drying the wastewater sample to be treated and measuring the mass change of the wastewater sample to be treated before and after drying;

[0089] Calculate the moisture content of the wastewater sample to be treated using the moisture content formula;

[0090] The sedimentation density formula is:

[0091]

[0092] Where, ρ n is the density of the sludge in the sewage sample to be treated, M is the total mass of the sewage sample to be treated, ρ h is the density of water, V h V is the volume of liquid remaining after the sewage sample is allowed to stand and stratify. n is the sedimentation volume of sludge after static stratification;

[0093] The water content formula is:

[0094]

[0095] Where γ is the moisture content of the sewage sample to be treated, and M' is the mass of the sewage sample to be treated after drying.

[0096] According to the characteristics of the sewage sample and the experimental requirements, select a suitable drying method, such as natural air drying, oven drying, etc., for drying treatment. Place the sewage sample in a drying device or an appropriate container and dry it according to the selected method until the sample reaches a constant weight, that is, the mass no longer changes.

[0097] Reference Figure 3 As shown in the figure, feature extraction is performed by measuring the chemical composition of the sludge in the sewage sample to be treated, and the composition and properties of the sludge in the sewage are obtained, including:

[0098] Place a test tube containing a mixed solution of sludge, potassium dichromate solution and concentrated sulfuric acid in a heating device and heat it to boiling to fully oxidize the organic matter in the sludge;

[0099] Add a preset volume of o-phenanthroline indicator to the test solution and blank solution respectively;

[0100] Titrate the ferrous sulfate standard solution until the solution changes from orange-yellow to blue-green and then to brown-red, and record the volume of ferrous sulfate standard solution consumed during the titration.

[0101] The organic matter content of the sludge in the wastewater sample to be treated was calculated using the organic content formula;

[0102] X-ray fluorescence spectrometer was used to analyze the inorganic content of sludge in the wastewater samples to be treated;

[0103] The pH value of the sludge in the wastewater sample to be treated is measured by a pH meter;

[0104] The organic content formula is:

[0105]

[0106] Where ε is the organic matter content of the sludge in the sewage sample to be treated, n is the molar mass of the carbon atom, V0 is the volume of the ferrous sulfate standard solution consumed when titrating the blank solution, V1 is the volume of the ferrous sulfate standard solution consumed when titrating the test solution, and c is the concentration of the ferrous sulfate standard solution.

[0107] Potassium dichromate solution and concentrated sulfuric acid are mixed in a certain proportion to form a strong oxidant system, which is used to oxidize organic matter in sludge. The o-phenanthroline indicator is used to indicate the titration end point, that is, when the iron ion concentration in the solution reaches a certain value, the color of the solution will change. The test solution is a solution containing sludge oxidation products, while the blank solution is the same oxidant system without sludge, which is used for calibration and elimination of background interference. The color changes from orange-yellow to blue-green and then to brown-red are typical color changes of the reaction between iron ions and o-phenanthroline indicator.

[0108] Reference Figure 4 As shown in the figure, the biological characteristics of the sludge in the sewage samples to be treated are analyzed to reveal the biological activity and treatment potential of the sludge in the sewage, including:

[0109] Extract DNA from sludge in wastewater samples to be treated;

[0110] Design and synthesize primers specific to the DNA;

[0111] Conduct PCR amplification reactions and analyze amplification products to determine the types and quantities of microorganisms;

[0112] The sludge sample in the sewage sample to be treated is placed in a sealed container, and an oxygen diluent of a preset concentration is added;

[0113] Place the sealed container at a constant temperature for a preset time and measure the change in oxygen concentration in the sealed container;

[0114] The local method formula is used to calculate the respiration rate of microorganisms in the sludge in the sewage sample to be treated and output it as the metabolic activity of the sludge;

[0115] exposing luminescent bacteria to sludge samples from wastewater samples to be treated;

[0116] Observe and record the changes in the luminous intensity of luminous bacteria;

[0117] The toxicity of the sludge in the wastewater sample to be treated is evaluated based on the percentage of luminescence intensity reduction;

[0118] The local method formula is:

[0119]

[0120] Where v is the respiration rate of microorganisms in the sludge in the sewage sample to be treated, t is the preset time, c0 is the initial oxygen concentration in the closed container, and c1 is the oxygen concentration after the closed container is placed at a constant temperature for a preset time.

[0121] DNA extraction is a basic step in understanding the diversity of microorganisms in sludge. The design and synthesis of primers are based on known microbial sequences and are used to specifically amplify target DNA fragments. After the PCR amplification reaction, the types of microorganisms present in the sludge can be identified by sequencing or comparing the amplified products, and the number of microorganisms can be roughly estimated. The local method is a commonly used method to measure the respiration rate of microorganisms, which is estimated by calculating the reduction in oxygen concentration per unit time. Metabolic activity is an important indicator reflecting the biological activity of sludge and is closely related to the treatment potential of sludge. Luminescent bacteria are commonly used biological indicators that are sensitive to toxic substances. When toxic substances are present in the sludge, the luminescence intensity of luminescent bacteria will weaken. The toxicity of the sludge can be evaluated by measuring the change in luminescence intensity.

[0122] Reference Figure 5 As shown in the figure, the extracted physical, chemical and biological characteristic data are correlated with the data in the historical sewage treatment database, specifically including:

[0123] Descriptive statistical methods were used to analyze the relationship between the physical, chemical and biological characteristics of sludge and the sewage treatment effect;

[0124] Descriptive statistics were used to summarize the basic characteristics of the data, including mean, median, and standard deviation;

[0125] By drawing visual charts such as scatter plots or line graphs, the relationship between the physical, chemical and biological characteristics of sludge and the sewage treatment effect can be intuitively displayed;

[0126] Extract association rules from sludge characteristic data and sewage treatment database;

[0127] Based on the extracted association rules and machine learning algorithms, a prediction model between sludge type and sewage treatment effect is constructed.

[0128] Association rules are extracted from sludge characteristic data and sewage treatment database to reveal the intrinsic connection between sludge characteristics and sewage treatment effects. Data mining technology is used to extract association rules from a large amount of data to find the strong correlation between sludge characteristics and sewage treatment effects. The extracted association rules can be used to explain how sludge characteristics affect sewage treatment effects, providing a basis for subsequent prediction model construction.

[0129] Reference Figure 6 As shown in the figure, the sample pretreatment of the wastewater sample to be treated to remove impurities and interfering substances in the wastewater sample to be treated specifically includes:

[0130] Aluminum hydroxide is used as a chemical flocculant to condense colloidal impurities in the wastewater sample to be treated into large particles, which are then subjected to static sedimentation treatment;

[0131] Use filtration devices to remove suspended solids and particulate impurities from wastewater samples to be treated;

[0132] Using sulfuric acid-nitric acid system as oxidant, the organic matter and inorganic salt interfering substances in the wastewater sample to be treated are decomposed;

[0133] Activated carbon was used as an adsorbent to remove heavy metals from wastewater samples to be treated;

[0134] Membrane separation technology is used to remove dissolved matter from the wastewater sample to be treated by utilizing the selective permeability of the semipermeable membrane.

[0135] Select the appropriate type and dosage of aluminum hydroxide to ensure that the colloidal impurities in the sewage can be effectively flocculated. After adding aluminum hydroxide, stir and mix thoroughly to ensure that the flocculant is evenly dispersed in the sewage. According to the sewage characteristics and treatment requirements, set a reasonable standing time to allow the colloidal impurities to fully settle. The strong oxidizing properties of sulfuric acid and nitric acid can destroy the structure of organic matter and convert it into small molecules or carbon dioxide and water; at the same time, inorganic salt interfering substances may also be oxidized or converted into a form that is easier to handle.

[0136] Reference Figure 7 As shown, based on the correlation analysis of the characteristic data of the sewage sample to be treated and the data in the historical sewage treatment database, the sewage pumping parameters are optimized and the sewage treatment process is adjusted, specifically including:

[0137] Based on the density and moisture content of the sludge in the wastewater sample to be treated, the suction pressure and flow rate of the sewage are adjusted so that the sludge can be effectively sucked and transported to the subsequent treatment unit;

[0138] Set the sewage pumping time interval based on the sedimentation and filtration performance of the sludge in the sewage sample to be treated;

[0139] Select a biological treatment process based on the types and quantities of microorganisms in the sludge of the wastewater sample to be treated;

[0140] Based on the organic matter content and heavy metal content of the sludge in the sewage sample to be treated, chemical treatment agents are selected to remove the organic matter and heavy metals. The chemical treatment agents include coagulants and oxidants.

[0141] The density and moisture content of sludge directly affect its fluidity and pumpability. By adjusting the suction pressure and flow rate of sewage, it can be ensured that the sludge will not be over-compressed and blocked during transportation, nor will it be effectively pumped due to insufficient pressure. The sedimentation and filtration properties of sludge determine its treatment efficiency in subsequent treatment units. By reasonably setting the sewage suction time interval, it can be ensured that the sludge reaches the optimal sedimentation and filtration state before being transported to the subsequent treatment units. The type and number of microorganisms in the sludge are crucial to the selection of biological treatment processes. By analyzing the microbial composition of the sludge, the most suitable biological treatment process can be determined, such as aerobic treatment, anaerobic treatment or a combination of the two. In view of the organic matter and heavy metal content in the sludge, appropriate chemical treatment agents need to be selected for removal. Coagulants can be used to remove suspended matter and colloidal substances, while oxidants can be used to remove organic matter and certain heavy metals.

[0142] Furthermore, this solution also proposes a computer-readable storage medium on which a computer-readable program is stored. When the computer-readable program is called, it executes the above-mentioned full-process intelligent control method of the sewage treatment plant driven by digital twins.

[0143] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state disk (SSD).

[0144] To sum up, the advantages of the present invention are: it realizes the comprehensive extraction of sludge characteristics, and this comprehensive feature extraction helps to more accurately understand the properties and treatment requirements of the sludge, thereby providing a basis for realizing precise sewage treatment. Correlation analysis helps to select or optimize the sewage treatment process according to the actual characteristics of the sludge, and realize personalized customization of sewage treatment. Correlation analysis helps to select or optimize the sewage treatment process according to the actual characteristics of the sludge, and realize personalized customization of sewage treatment.

[0145] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the invention as claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital twin-driven full-process intelligent control method for sewage treatment plants, characterized by: include: Collect wastewater samples for treatment at at least two locations and times; Obtaining basic information about the sludge by measuring physical parameters of the sludge in the wastewater sample to be treated, wherein the physical parameters include density, moisture content, particle size and distribution; By measuring the chemical composition of the sludge in the sewage sample to be treated, feature extraction is performed to obtain the composition and properties of the sludge in the sewage; The biological activity and treatment potential of the sludge in the sewage sample to be treated are revealed by analyzing the biological characteristics of the sludge in the sewage sample, including the type, number, activity and toxicity of microorganisms; Correlation analysis was performed between the extracted physical, chemical and biological characteristic data and the data in the historical sewage treatment database; Perform sample pretreatment on the sewage samples to be treated to remove impurities and interfering substances in the sewage samples to be treated; Based on the correlation analysis of the characteristic data of the sewage samples to be treated and the data in the historical sewage treatment database, the sewage pumping parameters are optimized and the sewage treatment process is adjusted.

2. The digital twin-driven full-process intelligent control method for sewage treatment plants according to claim 1 is characterized in that: The method of obtaining basic information of the sludge by measuring the physical parameters of the sludge in the sewage sample to be treated specifically includes: Measure the total mass of the sewage sample to be treated and the sedimentation volume and residual liquid volume of the sludge after standing and stratification; The density of sludge in the wastewater sample to be treated is calculated using the sedimentation density formula; Using the principle of laser scattering, the size and distribution of sludge particles in the wastewater sample to be treated are calculated by measuring the angle and intensity of light scattering; Drying the wastewater sample to be treated and measuring the mass change of the wastewater sample to be treated before and after drying; Calculate the moisture content of the wastewater sample to be treated using the moisture content formula; The sedimentation density formula is: Where, ρ n is the density of the sludge in the sewage sample to be treated, M is the total mass of the sewage sample to be treated, ρ h is the density of water, V h V is the volume of liquid remaining after the sewage sample is allowed to stand and stratify. n is the sedimentation volume of sludge after static stratification; The water content formula is: Where γ is the moisture content of the sewage sample to be treated, and M' is the mass of the sewage sample to be treated after drying.

3. The full-process intelligent control method for a sewage treatment plant based on digital twin drive according to claim 2 is characterized in that: The feature extraction is performed by measuring the chemical composition of the sludge in the sewage sample to be treated, and the composition and properties of the sludge in the sewage are obtained, which specifically include: Place a test tube containing a mixed solution of sludge, potassium dichromate solution and concentrated sulfuric acid in a heating device and heat it to boiling to fully oxidize the organic matter in the sludge; Add a preset volume of o-phenanthroline indicator to the test solution and blank solution respectively; Titrate the ferrous sulfate standard solution until the solution changes from orange-yellow to blue-green and then to brown-red, and record the volume of ferrous sulfate standard solution consumed during the titration. The organic matter content of the sludge in the wastewater sample to be treated was calculated using the organic content formula; X-ray fluorescence spectrometer was used to analyze the inorganic content of sludge in the wastewater samples to be treated; The pH value of the sludge in the wastewater sample to be treated is measured by a pH meter; The organic content formula is: Where ε is the organic matter content of the sludge in the sewage sample to be treated, n is the molar mass of the carbon atom, V0 is the volume of the ferrous sulfate standard solution consumed when titrating the blank solution, V1 is the volume of the ferrous sulfate standard solution consumed when titrating the test solution, and c is the concentration of the ferrous sulfate standard solution.

4. The full-process intelligent control method for a sewage treatment plant based on digital twin drive according to claim 3 is characterized in that: The method of revealing the biological activity and treatment potential of the sludge in the sewage by analyzing the biological characteristics of the sludge in the sewage sample to be treated specifically includes: Extract DNA from sludge in wastewater samples to be treated; Design and synthesize primers specific to the DNA; Conduct PCR amplification reactions and analyze amplification products to determine the types and quantities of microorganisms; The sludge sample in the sewage sample to be treated is placed in a sealed container, and an oxygen diluent of a preset concentration is added; Place the sealed container at a constant temperature for a preset time and measure the change in oxygen concentration in the sealed container; The local method formula is used to calculate the respiration rate of microorganisms in the sludge in the sewage sample to be treated and output it as the metabolic activity of the sludge; exposing luminescent bacteria to sludge samples from wastewater samples to be treated; Observe and record the changes in the luminous intensity of luminous bacteria; The toxicity of the sludge in the wastewater sample to be treated is evaluated based on the percentage of luminescence intensity reduction; The local method formula is: Where v is the respiration rate of microorganisms in the sludge in the sewage sample to be treated, t is the preset time, c0 is the initial oxygen concentration in the closed container, and c1 is the oxygen concentration after the closed container is placed at a constant temperature for a preset time.

5. The full-process intelligent control method for a sewage treatment plant based on digital twin drive according to claim 4 is characterized in that: The correlation analysis of the extracted physical, chemical and biological characteristic data with the data in the historical sewage treatment database specifically includes: Descriptive statistical methods were used to analyze the relationship between the physical, chemical and biological characteristics of sludge and the sewage treatment effect; Descriptive statistics were used to summarize the basic characteristics of the data, including mean, median, and standard deviation; By drawing visual charts such as scatter plots or line graphs, the relationship between the physical, chemical and biological characteristics of sludge and the sewage treatment effect can be intuitively displayed; Extract association rules from sludge characteristic data and sewage treatment database; Based on the extracted association rules and machine learning algorithms, a prediction model between sludge type and sewage treatment effect is constructed.

6. The digital twin-driven full-process intelligent control method for sewage treatment plants according to claim 5 is characterized in that: The sample pretreatment of the wastewater sample to be treated to remove impurities and interfering substances in the wastewater sample to be treated specifically includes: Aluminum hydroxide is used as a chemical flocculant to condense colloidal impurities in the wastewater sample to be treated into large particles, which are then subjected to static sedimentation treatment; Use filtration devices to remove suspended solids and particulate impurities from wastewater samples to be treated; Using sulfuric acid-nitric acid system as oxidant, the organic matter and inorganic salt interfering substances in the wastewater sample to be treated are decomposed; Activated carbon was used as an adsorbent to remove heavy metals from wastewater samples to be treated; Membrane separation technology is used to remove dissolved matter from the wastewater sample to be treated by utilizing the selective permeability of the semipermeable membrane.

7. The digital twin-driven full-process intelligent control method for sewage treatment plants according to claim 6 is characterized in that: The optimization of sewage pumping parameters and adjustment of sewage treatment process based on correlation analysis of characteristic data of the sewage sample to be treated and data in the historical sewage treatment database specifically include: Based on the density and moisture content of the sludge in the wastewater sample to be treated, the suction pressure and flow rate of the sewage are adjusted so that the sludge can be effectively sucked and transported to the subsequent treatment unit; Set the sewage pumping time interval based on the sedimentation and filtration performance of the sludge in the sewage sample to be treated; Select a biological treatment process based on the types and quantities of microorganisms in the sludge of the wastewater sample to be treated; Based on the organic matter content and heavy metal content of the sludge in the sewage sample to be treated, chemical treatment agents are selected to remove the organic matter and heavy metals. The chemical treatment agents include coagulants and oxidants.

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