Flocculation thickening process regulation and control method based on multi-scale characteristics

Abstract

The invention discloses a flocculation thickening process regulation and control method based on multi-scale characteristics, and relates to the technical field of flocculation thickening process regulation and control. According to the flocculation thickening process regulation and control method based on the multi-scale characteristics, the treatment state data of the sewage treatment pool are obtained in real time, the real-time regulation and control state index of the sewage treatment pool is analyzed, judgment and analysis are conducted on the real-time regulation and control state index and the preset regulation and control state interval, and regulation and control measures are adopted for the sewage treatment pool based on the judgment and analysis result; according to the method, by integrating multi-scale data in a sewage treatment tank and collecting and fusing microscopic parameters, mesoscopic parameters and macroscopic parameters of particles in real time, a real-time regulation and control state index which comprehensively reflects the dynamic state of the flocculation and thickening process is constructed; the problems of misjudgment and untimely adjustment caused by only depending on single or local parameter monitoring in the prior art are avoided, so that the refined monitoring of the sewage treatment state is realized, the stability of the effluent quality and the solid-liquid separation efficiency are improved, and the medicament and energy consumption cost is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of flocculation thickening process control, and specifically provides a method for controlling the flocculation thickening process based on multi-scale features. Background Art

[0002] At present, the flocculation thickening process in sewage treatment processes, as an important link in solid-liquid separation, is widely used in urban sewage, industrial wastewater, and mine tailings. Its main purpose is to add flocculants to promote the aggregation of suspended particles into larger flocs, thereby achieving rapid sedimentation and efficient separation. However, traditional flocculation thickening processes mainly rely on the monitoring of single or a few physical parameters, such as suspended solid concentration, pH value, fluid viscosity, and stirring speed, often ignoring the complex multi-scale physical phenomena existing in the particle aggregation and sedimentation processes.

[0003] On the microscopic scale, factors such as the effective diameter, surface potential, surface roughness, and morphological fractal dimension of particles play a decisive role in the collision and aggregation of particles; on the mesoscopic scale, hydrodynamic parameters such as local turbulence intensity and shear stress directly affect the stability and sedimentation efficiency of flocs after particle aggregation; while on the macroscopic scale, the average flow velocity of the fluid, liquid density, and overall hydraulic retention time determine the final effect of solid-liquid separation in the entire reaction tank.

[0004] Moreover, the limitations of the existing technologies at least include the following problems. First, in the current flocculation thickening processes, only partial or single-dimensional monitoring and analysis of some parameters of sewage treatment are often carried out, lacking the overall integration of multi-scale features such as microscopic, mesoscopic, macroscopic, and charge synergy, which easily leads to the disconnection between key links such as chemical dosing, flow rate control, or electric field regulation and the actual treatment status. Second, due to the lack of a multi-dimensional comprehensive regulation mechanism for real-time data, the phenomenon of mismatch between treatment efficiency and energy consumption, chemical consumption, etc. often occurs, which easily leads to an increase in operating costs and an extension of the regulation cycle, and is not conducive to flexibly adjusting the efficiency and stability of the flocculation thickening process under different working conditions. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technologies, the present invention provides a method for controlling the flocculation thickening process based on multi-scale features, which solves the problems that the existing flocculation thickening processes lack the overall integration of multi-scale features and real-time multi-dimensional regulation mechanisms, easily leading to mismatches in links such as treatment efficiency, energy consumption, and chemical consumption, and being difficult to flexibly adjust.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for regulating the flocculation and thickening process based on multi-scale features, comprising the following steps: obtaining in real time the treatment status data of the sewage treatment tank and performing preprocessing, where the treatment status data of the sewage treatment tank includes the effective particle diameter value, particle surface potential value, particle surface roughness, particle density value, real-time fluid viscosity value, real-time fluid density value, real-time fluid average flow velocity value, local electric field strength value, and fluid pH value of the sewage treatment tank; respectively performing comprehensive analysis on the preprocessed treatment status data of the sewage treatment tank to obtain the real-time microscopic scale index, real-time mesoscopic scale index, real-time macroscopic scale index, real-time comprehensive flow pattern characteristic index, and real-time charge synergy effect index of the sewage treatment tank, and performing comprehensive analysis to obtain the real-time regulation status index of the sewage treatment tank; judging and analyzing the real-time regulation status index of the sewage treatment tank with a preset regulation status interval, and taking regulation measures for the sewage treatment tank based on the judgment and analysis results; where the specific formula for calculating the real-time regulation status index of the sewage treatment tank is as follows: Among them, TkZ is the real-time regulation status index of the sewage treatment tank, WgZ is the real-time microscopic scale index of the sewage treatment tank, μ1 is the microscopic weight coefficient stored in the database, λ1 is the microscopic influence coefficient stored in the database, ZgZ is the real-time mesoscopic scale index of the sewage treatment tank, μ2 is the mesoscopic weight coefficient stored in the database, λ2 is the mesoscopic influence coefficient stored in the database, HgZ is the real-time macroscopic scale index of the sewage treatment tank, μ3 is the macroscopic weight coefficient stored in the database, λ3 is the macroscopic influence coefficient stored in the database, LtZ is the real-time comprehensive flow pattern characteristic index of the sewage treatment tank, μ4 is the flow pattern weight coefficient stored in the database, λ4 is the flow pattern influence coefficient stored in the database, DxZ is the real-time charge synergy effect index of the sewage treatment tank, μ5 is the charge synergy weight coefficient stored in the database, λ5 is the charge synergy influence coefficient stored in the database, and ε is the regulation factor stored in the database.

[0007] Further, the specific steps for obtaining the real-time microscopic scale index of the sewage treatment tank are as follows: obtaining the particle morphology fractal dimension and particle reference potential value of the sewage treatment tank; respectively combining the effective particle diameter value, particle surface potential value, particle surface roughness, and real-time fluid viscosity value of the sewage treatment tank with the particle morphology fractal dimension and particle reference potential value of the sewage treatment tank for comprehensive analysis to obtain the real-time microscopic scale index of the sewage treatment tank.

[0008] Further, the specific steps for obtaining the fractal dimension of the particle morphology in the sewage treatment tank are as follows: Obtain the particle sample image data and perform preprocessing; perform image segmentation on the preprocessed particle sample image data; perform morphological processing on the particle sample image data after image segmentation; perform target region extraction on the particle sample image data after morphological processing, and analyze the fractal dimension of the particle morphology in the sewage treatment tank based on a preset fractal dimension algorithm.

[0009] Further, the specific formula for calculating the real-time microscopic scale index of the sewage treatment tank is as follows: Where, WgZ is the real-time microscopic scale index of the sewage treatment tank, KyZ is the effective particle diameter value of the sewage treatment tank, KxF is the fractal dimension of the particle morphology in the sewage treatment tank, NdZ is the real-time fluid viscosity value of the sewage treatment tank, BdW is the particle surface potential value of the sewage treatment tank, BdC is the particle reference potential value of the sewage treatment tank, BcC is the particle surface roughness of the sewage treatment tank, and α is the microscopic adjustment factor stored in the database.

[0010] Further, the specific steps for obtaining the real-time mesoscopic scale index of the sewage treatment tank are as follows: Obtain the local turbulence amplitude value of the sewage treatment tank; read the effective particle diameter value and the real-time fluid viscosity value of the sewage treatment tank, and perform comprehensive analysis in combination with the particle density value, the real-time fluid density value, the real-time fluid average flow velocity value, and the local turbulence amplitude value of the sewage treatment tank to obtain the real-time mesoscopic scale index of the sewage treatment tank.

[0011] Further, the specific formula for calculating the real-time mesoscopic scale index of the sewage treatment tank is as follows: Where, ZgZ is the real-time mesoscopic scale index of the sewage treatment tank, KmD is the particle density value of the sewage treatment tank, LmD is the real-time fluid density value of the sewage treatment tank, g is the acceleration due to gravity, KyZ is the effective particle diameter value of the sewage treatment tank, NdZ is the real-time fluid viscosity value of the sewage treatment tank, β is the viscosity adjustment factor stored in the database, FdZ is the local turbulence amplitude value of the sewage treatment tank, LsZ is the real-time fluid average flow velocity value of the sewage treatment tank, and χ is the flow velocity adjustment factor stored in the database.

[0012] Further, the specific steps for obtaining the real-time macroscopic scale index of the sewage treatment tank are as follows: Perform fluctuation analysis on the particle surface potential value and the particle reference potential value of the sewage treatment tank to obtain the particle potential fluctuation value of the sewage treatment tank; obtain the local turbulence energy dissipation rate value, the local shear stress value, the fluid conductivity value, and the local electric field intensity reference value of the sewage treatment tank, and perform comprehensive analysis in combination with the local electric field intensity value and the particle potential fluctuation value of the sewage treatment tank to obtain the real-time macroscopic scale index of the sewage treatment tank.

[0013] Further, the specific steps to obtain the real-time comprehensive flow state characteristic index of the sewage treatment tank are as follows: Read the local turbulent energy dissipation rate value, local shear stress value, real-time fluid viscosity value, and real-time fluid average flow velocity value of the sewage treatment tank for comprehensive analysis to obtain the real-time comprehensive flow state characteristic index of the sewage treatment tank.

[0014] Further, the specific steps to obtain the real-time charge synergy effect index of the sewage treatment tank are as follows: Obtain the fluid pH reference value of the sewage treatment tank; Read the particle potential fluctuation value and fluid pH value of the sewage treatment tank, and conduct comprehensive analysis in combination with the fluid pH reference value of the sewage treatment tank to obtain the real-time charge synergy effect index of the sewage treatment tank.

[0015] Further, the preset regulation state intervals include the normal regulation state interval, warning regulation state interval, and abnormal regulation state interval. The specific steps to judge and analyze the real-time regulation state index of the sewage treatment tank with the preset regulation state intervals and take regulation measures for the sewage treatment tank based on the judgment and analysis results are as follows: Judge and analyze the real-time regulation state index of the sewage treatment tank with the preset regulation state intervals; If the real-time regulation state index of the sewage treatment tank is within the preset normal regulation state interval, no regulation measures are taken; If the real-time regulation state index of the sewage treatment tank is within the preset warning regulation state interval, warning regulation measures are taken; If the real-time regulation state index of the sewage treatment tank is within the preset abnormal regulation state interval, abnormal regulation measures are taken.

[0016] The present invention has the following beneficial effects:

[0017] (1) The flocculation and thickening process regulation method based on multi-scale features integrates multi-scale data in the sewage treatment tank, and real-time collects and fuses microscopic parameters of particles, such as effective particle diameter, particle surface potential and roughness, mesoscopic parameters, such as local turbulence amplitude, fluid viscosity, particle density, and macroscopic parameters, such as fluid density, average flow velocity, conductivity, pH value, and local electric field strength, to construct a real-time regulation state index that comprehensively reflects the dynamic state of the flocculation and thickening process. By adopting technical means such as image analysis, online sensor data fusion, and preprocessing, it can finely capture the tiny changes in each stage of sewage treatment, avoiding the problems of misjudgment and untimely adjustment caused by relying solely on single or local parameter monitoring in the past, thus realizing the refined monitoring of the sewage treatment state, improving the stability of the effluent quality and the solid-liquid separation efficiency, and effectively reducing the chemical agent and energy consumption costs, laying a solid data support and theoretical foundation for the long-term stable operation of the system.

[0018] (2) The method for regulating the flocculation and thickening process based on multi-scale features uses the influence coefficients and weight coefficients of microscopic, mesoscopic, macroscopic, flow state, and charge synergy pre-stored in the database. After calibration through historical data and sensitivity analysis, the contributions of parameters in each dimension to the overall flocculation and thickening effect are accurately quantified, and then a real-time regulation state index is formed through weighted superposition. This comprehensive index can comprehensively reflect the current operating conditions of the sewage treatment tank, provide a scientific and quantitative basis for regulation decisions, and thus can adaptively optimize operating parameters under different working conditions, achieve precise adjustment of complex and variable water quality environments, reduce the system operation risk, improve the solid-liquid separation efficiency and the stability of the effluent water quality, promote the automation and intelligent management of the sewage treatment process, and effectively enhance the overall environmental safety and economic benefits.

[0019] (3) The method for regulating the flocculation and thickening process based on multi-scale features preset three regulation state intervals of normal, warning, and abnormal, and combined with the multi-scale weight coefficients and influence coefficients stored in the database to automatically compare and judge the real-time regulation state index, realizing intelligent closed-loop feedback control. When the real-time regulation state index deviates from the normal interval, the system can quickly identify specific abnormal links, such as uneven flow state, excessive shear stress, or insufficient charge synergy effect, etc., and automatically issue adjustment instructions to optimize and adjust key process parameters such as the dosage of flocculant, stirring rate, inlet and outlet water flow rates, and auxiliary electric field, so as to correct operation deviations in a timely manner, shorten the response time, reduce energy consumption and chemical agent waste during the treatment process, significantly improve the sewage treatment efficiency and economic benefits, and ensure the safe and reliable operation of the process.

[0020] Of course, it is not necessary for any product implementing the present invention to simultaneously achieve all the above-mentioned advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of a method for regulating the flocculation and thickening process based on multi-scale features of the present invention.

[0022] Figure 2 It is a specific step flow chart for obtaining the real-time microscopic scale index of the sewage treatment tank in a method for regulating the flocculation and thickening process based on multi-scale features of the present invention.

[0023] Figure 3 It is a specific step flow chart for obtaining the real-time mesoscopic scale index of the sewage treatment tank in a method for regulating the flocculation and thickening process based on multi-scale features of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Please refer to Figure 1, an embodiment of the present invention provides a technical solution: a method for regulating the flocculation and thickening process based on multi-scale features, including the following steps: obtaining the treatment status data of the sewage treatment tank in real time and performing preprocessing. The treatment status data of the sewage treatment tank includes the effective particle diameter value, particle surface potential value, particle surface roughness, particle density value, real-time fluid viscosity value, real-time fluid density value, real-time fluid average flow velocity value, local electric field strength value, and fluid pH value of the sewage treatment tank; respectively performing comprehensive analysis on the preprocessed treatment status data of the sewage treatment tank to obtain the real-time microscopic scale index, real-time mesoscopic scale index, real-time macroscopic scale index, real-time comprehensive flow pattern feature index, and real-time charge synergy effect index of the sewage treatment tank, and performing comprehensive analysis to obtain the real-time regulation status index of the sewage treatment tank; judging and analyzing the real-time regulation status index of the sewage treatment tank with a preset regulation status interval, and taking regulation measures on the sewage treatment tank based on the judgment and analysis results.

[0025] Among them, the specific formula for calculating the real-time regulation status index of the sewage treatment tank is as follows: Among them, TkZ is the real-time regulation status index of the sewage treatment tank, WgZ is the real-time microscopic scale index of the sewage treatment tank, μ1 is the microscopic weight coefficient stored in the database, λ1 is the microscopic influence coefficient stored in the database, ZgZ is the real-time mesoscopic scale index of the sewage treatment tank, μ2 is the mesoscopic weight coefficient stored in the database, λ2 is the mesoscopic influence coefficient stored in the database, HgZ is the real-time macroscopic scale index of the sewage treatment tank, μ3 is the macroscopic weight coefficient stored in the database, λ3 is the macroscopic influence coefficient stored in the database, LtZ is the real-time comprehensive flow pattern feature index of the sewage treatment tank, μ4 is the flow pattern weight coefficient stored in the database, λ4 is the flow pattern influence coefficient stored in the database, DxZ is the real-time charge synergy effect index of the sewage treatment tank, μ5 is the charge synergy weight coefficient stored in the database, λ5 is the charge synergy influence coefficient stored in the database, and ε is the regulation factor stored in the database, which is used to prevent the denominator from being 0.

[0026] It should be explained that the specific acquisition steps of the microscopic weight coefficient μ1, mesoscopic weight coefficient μ2, macroscopic weight coefficient μ3, flow pattern weight coefficient μ4, and charge synergy weight coefficient μ5 stored in the database are as follows: first, obtain them through regression or machine learning training with a large amount of historical operation data and experimental data. The specific method is to collect key indicators such as microscopic (such as particle diameter, potential), mesoscopic (such as concentration, sedimentation rate), macroscopic (such as flow velocity, viscosity), as well as flow pattern and charge synergy from the system, and use multivariate analysis or model predictive control (MPC) algorithms to iterate repeatedly to evaluate the contribution of each indicator to the target (such as sedimentation efficiency, flocculation effect). Finally, solidify these contribution degrees into the corresponding weight coefficients and store them in the database for direct call during online calculation.

[0027] The specific steps for obtaining the microscopic influence coefficient λ1, mesoscopic influence coefficient λ2, macroscopic influence coefficient λ3, flow state influence coefficient λ4, and charge synergy influence coefficient λ5 stored in the database are as follows: After performing sensitivity analysis on the process indicators and overall treatment effects at each stage or dimension, the following steps are carried out: First, perform correlation or regression analysis on the microscopic, mesoscopic, macroscopic, flow state, and charge data under different working conditions and the treatment results (such as mud-water separation degree, effluent turbidity, etc.), extract the influence degree of each type of parameter on the final result, and then normalize and solidify these influence degrees into influence coefficients and store them in the database for weighting or correction during subsequent judgment and regulation.

[0028] Specifically, as Figure 2 shown, the specific steps for obtaining the real-time microscopic scale index of the sewage treatment tank are as follows: Obtain the particle morphology fractal dimension and particle reference potential value of the sewage treatment tank; comprehensively analyze the effective particle diameter value, particle surface potential value, particle surface roughness, and real-time fluid viscosity value of the sewage treatment tank in combination with the particle morphology fractal dimension and particle reference potential value of the sewage treatment tank to obtain the real-time microscopic scale index of the sewage treatment tank.

[0029] Among them, the effective particle diameter value is an index representing the actual size of particles (or primary flocs) participating in aggregation and sedimentation in the liquid. The larger the size, the higher the probability of particle collision and aggregation, which is more beneficial for subsequent sedimentation. It can be measured and obtained by a laser particle size analyzer or a dynamic light scattering instrument. Its role in the flocculation and thickening process is that the larger the particle diameter, the more obvious the "spark effect" of microscopic aggregation usually is, and more stable flocs can be formed in subsequent reactions.

[0030] The fluid viscosity value refers to the viscosity of the liquid medium, representing the resistance of the fluid to the movement of particles. The larger the viscosity, the slower the particle movement speed, and the collision frequency will also be affected. It can be measured and obtained by using a rotational viscometer, a capillary rheometer, or an on-line viscosity measuring device. Its role in the flocculation and thickening process is to affect the aggregation efficiency and sedimentation speed of flocs in the liquid and is an important parameter determining stirring and flocculant dispersion.

[0031] The particle surface potential value is an index indicating the charge amount on the particle surface, reflecting the electrostatic repulsion or attraction between particles. In most water treatments, particles usually carry a negative charge, and a positively charged flocculant needs to be added for electro-neutralization. It can be measured and obtained by using a Zeta potential meter. Its role in the flocculation and thickening process is that if the particle surface potential is too high, the mutual repulsion between particles is strong, and the aggregation efficiency decreases; if the potential is neutralized or moderately adjusted, it is easier to form flocs.

[0032] The particle surface potential value represents the particle surface potential value expected in theory or by process, which is used to compare with the actual measured value to evaluate the deviation of the particle surface potential. It is usually set according to past experience, experimental data or process requirements.

[0033] The particle surface roughness refers to the degree of unevenness on the particle surface at the microscale. The rougher the surface, the more likely it is to increase the mechanical interlocking between particles and enhance the aggregation effect. However, excessive roughness will also lead to an increase in fluid resistance. It can be measured and obtained by an atomic force microscope (AFM) or a scanning probe microscope (SPM). Its role in the flocculation and thickening process is as follows: when the particle surface roughness is relatively high, it is easier to form stable aggregates after collision. However, if the roughness is too high, it may also introduce more attached impurities.

[0034] The specific formula for calculating the real-time microscale index of the sewage treatment tank is as follows: Among them, WgZ is the real-time microscale index of the sewage treatment tank, KyZ is the effective particle diameter value of the sewage treatment tank, KxF is the particle morphology fractal dimension of the sewage treatment tank, NdZ is the real-time fluid viscosity value of the sewage treatment tank, BdW is the particle surface potential value of the sewage treatment tank, BdC is the reference potential value of the particles in the sewage treatment tank, BcC is the particle surface roughness of the sewage treatment tank, and α is the micro-adjustment factor stored in the database, which is used to prevent the denominator from being zero.

[0035] Among them, the specific implementation example of calculating the real-time microscale index of the sewage treatment tank is as follows. The following data are available:

[0036] The effective particle diameter value of the sewage treatment tank is approximately: 150.235 μm.

[0037] The particle morphology fractal dimension of the sewage treatment tank is: 2.327 (dimensionless).

[0038] The real-time fluid viscosity value of the sewage treatment tank is: 1.190 mPa·s.

[0039] The particle surface potential value of the sewage treatment tank is: -31.578 mV.

[0040] The reference potential value of the particles in the sewage treatment tank is: -29.634 mV.

[0041] The particle surface roughness of the sewage treatment tank is: 73.492 nm.

[0042] The micro-adjustment factor stored in the database is: 0.857 (dimensionless).

[0043] Substitute the above data into the specific formula for calculating the real-time microscale index of the sewage treatment tank respectively, and we get:

[0044] Real-time microscale index of sewage treatment tank = (150.235 × 2.327) / (1.190 × ((│-31.578 - (-29.634)│) + 73.492 + 0.857)) ≈ 3.850。

[0045] In this implementation plan, by comprehensively utilizing key parameters such as the effective diameter of particles, morphological fractal dimension, fluid viscosity, particle surface potential, reference potential, and particle surface roughness, the accurate characterization of the aggregation and sedimentation mechanisms of particles in sewage treatment is achieved. First of all, the effective particle diameter can reflect the actual size of particles participating in aggregation. The larger the size, the more obvious the collision aggregation effect; the morphological fractal dimension reveals the complexity of particle structure and determines the looseness or tightness of flocs; the real-time fluid viscosity determines the resistance of particle movement in the liquid and has a direct impact on floc formation and sedimentation speed; the comparative analysis of particle surface potential and reference potential can evaluate the charge regulation effect to ensure that the flocculant can fully neutralize the negative charge of particles; the detection of particle surface roughness helps to judge the contribution of mechanical occlusion effect to aggregation stability. By comprehensively analyzing these parameters and introducing the micro-adjustment factor stored in the database to prevent abnormal situations where the denominator is 0, this method can not only reflect the micro-dynamic changes in the sewage treatment tank in real time, but also provide a reliable quantitative basis for the subsequent accurate regulation of the flocculation and thickening process, thereby achieving more efficient and stable solid-liquid separation, reducing energy consumption and chemical dosage, and ultimately improving the treatment effect and economic benefits.

[0046] Specifically, the specific steps to obtain the fractal dimension of the particle morphology in the sewage treatment tank are as follows: Obtain the particle sample image data and perform preprocessing, including removing noise using filtering (such as mean filtering, median filtering, Gaussian filtering, etc.); perform brightness / contrast adjustment, histogram equalization, etc. when necessary to enhance the details of the particles or flocs; perform image segmentation on the preprocessed particle sample image data, that is, select a suitable threshold segmentation method (global threshold, local threshold, Otsu method, etc.) or other segmentation algorithms to separate the target particles / flocs from the background and generate a binary image; perform morphological processing on the particle sample image data after image segmentation, that is, perform morphological operations such as opening and closing as needed to remove isolated noise points, fill small holes, and retain the complete area of the main particles / flocs; perform target area extraction on the particle sample image data after morphological processing and analyze the fractal dimension of the particle morphology in the sewage treatment tank based on a preset fractal dimension algorithm, that is, perform connected component analysis or contour extraction on the segmented binary image, identify and separate the areas of single or multiple particles / flocs, remove the parts irrelevant to the research, and on the extracted target areas, select a suitable fractal dimension algorithm (such as box dimension method, perimeter-area method, boundary tracking method, etc.), determine the specific calculation method according to the requirements and software support, and execute the selected algorithm in the software to calculate the fractal dimension of each target area one by one; if the box dimension method is used, it is necessary to set the range of box sizes and perform fitting in logarithmic coordinates, and the obtained slope is the fractal dimension.

[0047] Among them, the fractal dimension of particle morphology is used to reflect the internal structure complexity of particles or flocs. The higher the fractal dimension, the more branched and loose the floc structure; a lower fractal dimension indicates that the flocs are denser. Its role in the flocculation and thickening process is as follows: when the morphological fractal dimension is higher, the surface area of the flocs is larger, and the adhesion force between particles may also be more significant; however, excessive branching may also lead to a decrease in sedimentation performance.

[0048] In this implementation scheme, by acquiring particle sample images and performing preprocessing steps such as filtering, brightness and contrast adjustment, and histogram equalization, noise is effectively removed and image details are enhanced, making subsequent segmentation more accurate. Secondly, a suitable threshold segmentation method is used to distinguish target particles or flocs from the background, and then morphological processing (such as opening and closing operations) is performed to eliminate isolated noise and fill holes, ensuring that the extracted target area has high integrity and representativeness. Finally, through connected component analysis and contour extraction, combined with fractal algorithms such as the box dimension method and the perimeter-area method, the fractal dimension of the particles is calculated. This value intuitively reflects the complexity of the internal structure of the particles, which can be used not only to evaluate the flocculation aggregation effect but also to indicate its sedimentation performance (when the fractal dimension is relatively high, the floc surface area is large and the adhesion force is significant, but excessive branching may lead to a decline in sedimentation performance). In summary, this method can not only provide high-precision particle morphology fractal dimension information, providing key data support for the microscopic regulation of the flocculation and thickening process, but also assist in optimizing chemical dosing and stirring strategies, ultimately achieving an improvement in the efficiency and stability of sewage treatment.

[0049] Specifically, as Figure 3 shown, the specific steps to obtain the real-time mesoscale index of the sewage treatment tank are as follows: Obtain the local turbulence amplitude value of the sewage treatment tank; read the effective particle diameter value, real-time fluid viscosity value of the sewage treatment tank, and comprehensively analyze them in combination with the particle density value, real-time fluid density value, real-time fluid average flow velocity value, and local turbulence amplitude value of the sewage treatment tank to obtain the real-time mesoscale index of the sewage treatment tank.

[0050] Among them, the particle density value represents the density of the particles or flocs themselves, which is usually greater than the density of the surrounding liquid. Therefore, it can sink or settle under the gravitational field. In the flocculation and thickening process, if the particle density is relatively high, the particle sedimentation speed is usually faster, which helps to achieve efficient solid-liquid separation. It can be measured in the laboratory by a densitometer or the weighing / volume method; for solid particles, a pycnometer or true density tester can also be used. Its role in the flocculation and thickening process is that the difference between the particle density and the fluid density is one of the key factors determining the driving force for particle sinking (the difference between gravity and buoyancy). When the density difference is larger, the particle sedimentation power is more obvious and the sedimentation rate is higher.

[0051] The real-time fluid density value is the density of the fluid (usually water or wastewater) used in the flocculation tank or sedimentation tank. In the water treatment scenario, it is usually close to 1 g / cm 3 (or 1000 kg / m 3 ), but if it contains more dissolved substances or the temperature is higher, the density will change slightly. It can be measured by using a liquid density sensor. Its role in the flocculation and thickening process is that the greater the liquid density, the smaller the density difference between the particles and the liquid, and the weaker the sedimentation speed; if the liquid density is lower, the sedimentation is faster.

[0052] The local turbulence amplitude value indicates the pulsation amplitude or turbulence intensity of the liquid flow velocity in the local area. The larger the value, the more violent the flow velocity fluctuation. In the process of flocculation and thickening, moderate turbulence helps particles to collide and aggregate, but excessive turbulence may break up the flocs. The fluctuation range of the local flow velocity can be measured by Doppler flowmeter or high-speed photography (for example: install the Doppler flowmeter in the target area and ensure that the measuring point is representative; set the sampling frequency and sampling time so that the sensor can continuously record the instantaneous flow velocity data of the liquid in the area at a high frequency, and import the recorded instantaneous flow velocity sequence into the data processing software; calculate the average flow velocity, which is usually obtained by time averaging the collected flow velocity data; calculate the turbulence fluctuation component, square the difference between the flow velocity at each moment and the average flow velocity and take the average, then take the square root to obtain the flow velocity standard deviation, which is the local turbulence amplitude). Its role in the flocculation and thickening process is: turbulence fluctuation can help flocs to collide and aggregate to a certain extent; but for sedimentation, if the turbulence is too strong, the flocs may be disturbed and broken or re-suspended.

[0053] The real-time average fluid flow rate value indicates the average flow rate of the liquid in the entire sedimentation tank or reaction tank. It is a macroscopic description of the overall hydraulic conditions. The greater the average flow rate, the faster the fluid passes through the tank body, the shorter the residence time of the particles, and the more difficult the sedimentation will be. It can be measured by a flow meter, radar flow meter or other online measuring equipment to measure the inlet and outlet flow of the sedimentation tank and combine it with the cross-sectional area calculation, or averaged after measuring at multiple points in the tank. Its role in the flocculation and thickening process is: too high an average flow rate means insufficient residence time of the particles, affecting the sedimentation efficiency; too low a flow rate may cause siltation, which needs to be balanced in the process design.

[0054] The specific formula for calculating the real-time mesoscale index of a sewage treatment pond is as follows: Among them, ZgZ is the real-time mesoscale index of the sewage treatment pool, KmD is the particle density value of the sewage treatment pool, LmD is the real-time fluid density value of the sewage treatment pool, and g is the gravitational acceleration, which is 9.81 m / s in this embodiment. 2 , KyZ is the effective particle diameter value of the sewage treatment pool, NdZ is the real-time fluid viscosity value of the sewage treatment pool, β is the viscosity adjustment factor stored in the database, and in this embodiment, the value is 18, FdZ is the local turbulence amplitude value of the sewage treatment pool, LsZ is the real-time average fluid flow rate value of the sewage treatment pool, and χ is the flow rate adjustment factor stored in the database, which is used to prevent the denominator from being zero.

[0055] In this implementation scheme, by comprehensively analyzing the parameters such as particle density, fluid density, gravity, effective particle diameter, fluid viscosity, local turbulence amplitude and real-time fluid average flow rate in the sewage treatment pool, the mesoscale index is calculated in real time, which provides an accurate quantitative basis for evaluating the fluid dynamic characteristics in the flocculation and thickening process. Specifically, the local turbulence amplitude is first obtained by using an online sensor, and then combined with the average flow rate and liquid density measured by the flow meter, and the particle diameter obtained by the laser particle size analyzer or dynamic light scattering instrument, supplemented by the fluid viscosity measured in the laboratory and the preset viscosity and flow rate adjustment factors for comprehensive correction. This method can accurately reflect the influence of turbulence and shear effects in the fluid on the particle collision, aggregation and sedimentation process, and effectively prevent the problem of siltation caused by insufficient particle residence time due to excessively high flow rate or too low flow rate, thereby improving the solid-liquid separation efficiency. In addition, the construction of the real-time mesoscale index provides scientific data support for the automatic control system, so that the sewage treatment pool can be adaptively regulated under different working conditions to achieve economical, stable and efficient operation, greatly improving the overall sewage treatment effect and environmental benefits.

[0056] Specifically, the specific steps for obtaining the real-time macro-scale index of the sewage treatment pool are as follows: perform fluctuation analysis on the particle surface potential value and particle parameter potential value of the sewage treatment pool (i.e., the absolute value of the difference) to obtain the particle potential fluctuation value of the sewage treatment pool; obtain the local turbulent energy dissipation rate value, local shear stress value, fluid conductivity value, and local electric field strength parameter value of the sewage treatment pool, and conduct a comprehensive analysis in combination with the local electric field strength value and particle potential fluctuation value of the sewage treatment pool to obtain the real-time macro-scale index of the sewage treatment pool.

[0057] Among them, the local turbulent energy dissipation rate value indicates the rate at which turbulent energy in the local flow field is dissipated (converted into heat energy). The larger the value, the stronger the water disturbance, which may cause damage or resuspension of the flocs. It can be measured by high-frequency laser Doppler velocimetry in the target area to measure the flow velocity pulsation, and the energy dissipation rate can be calculated in combination with turbulence theory. It can also be collected in three dimensions by an acoustic Doppler flowmeter High-frequency flow velocity data is calculated using a turbulence subscale model.

[0058] The local shear stress value represents the "shear force" exerted by the local flow on the liquid and particles, which is usually caused by the flow velocity gradient or stirring. If its value is too large, it is easy to destroy the formed flocs. It can be measured and obtained by a shear stress sensor.

[0059] The water conductivity value reflects the ion concentration and electrolyte content in the water, and indirectly affects the electrical environment (particle surface charge distribution, flocculant reaction, etc.). It can be measured and obtained through an online conductivity sensor, and the unit is usually S / m or μS / cm.

[0060] The local electric field strength value. If an external electrode is set in the pool or the flocculant generates a local potential difference, a local electric field will be formed. The electric field can change the charge distribution on the particle surface or accelerate the flocculation process, and can be measured and obtained through an on-line electric field sensor.

[0061] The reference value of the local electric field strength parameter, which is used to normalize or compare the actually measured electric field strength; it can be obtained based on historical data or experimental calibration.

[0062] Among them, the specific formula for calculating the real-time macroscopic scale index of the sewage treatment pool is as follows: Among them, HgZ is the real-time macroscopic scale index of the sewage treatment pool, HsL is the value of the local turbulent energy dissipation rate of the sewage treatment pool, JyL is the value of the local shear stress of the sewage treatment pool, ω is the shear stress adjustment factor stored in the database, which is used to prevent the denominator from being zero, DdL is the fluid conductivity value of the sewage treatment pool, DcQ is the local electric field strength value of the sewage treatment pool, DcC is the reference value of the local electric field strength of the sewage treatment pool, DwB is the particle potential fluctuation value of the sewage treatment pool, and ξ is the potential fluctuation coefficient stored in the database, and its value is not zero.

[0063] It should be explained that the potential fluctuation coefficient stored in the database is a reference value for normalizing the potential fluctuation, and is usually set based on experiments or process requirements.

[0064] In this implementation plan, by measuring the local turbulent energy dissipation rate, shear stress, conductivity, and local electric field strength in the sewage treatment pool in real time, and then combining the normalization process of the particle potential fluctuation, the comprehensive evaluation of the hydrodynamic and electrical environment in the entire pool body is realized. Specifically, the local turbulent energy dissipation rate is obtained by using a high-frequency laser Doppler or acoustic Doppler instrument, and then the local shear stress is measured by a shear stress sensor. The on-line conductivity meter and electric field sensor respectively collect the water body conductivity and local electric field strength data, and compare them with the reference value to calculate the potential fluctuation. Finally, the real-time macroscopic scale index is obtained through the normalization process using the shear stress adjustment factor and potential fluctuation coefficient stored in the database. This method can comprehensively reflect the influence of water body disturbance, ionic environment, and electric field action on flocculation sedimentation, making up for the deficiency of traditional processes that only focus on a single parameter. The real-time feedback information enables the control system to optimize the hydrodynamic and electrical control strategies in a timely manner according to the actual operating state, thereby improving the solid-liquid separation efficiency and effluent stability of sewage treatment, reducing the operating energy consumption and chemical agent waste at the same time, and providing strong technical support for the economy and safety of the entire treatment process.

[0065] Specifically, the specific steps to obtain the real-time comprehensive flow state characteristic index of the sewage treatment tank are as follows: Read the local turbulent energy dissipation rate value, local shear stress value, real-time fluid viscosity value, and real-time fluid average flow velocity value of the sewage treatment tank for comprehensive analysis to obtain the real-time comprehensive flow state characteristic index of the sewage treatment tank.

[0066] Among them, the specific formula for calculating the real-time comprehensive flow state characteristic index of the sewage treatment tank is as follows: Among them, LtZ is the real-time comprehensive flow state characteristic index of the sewage treatment tank, LsZ is the local turbulent energy dissipation rate value of the sewage treatment tank, NdZ is the real-time fluid viscosity value of the sewage treatment tank, HsL is the local turbulent energy dissipation rate value of the sewage treatment tank, JyL is the local shear stress value of the sewage treatment tank, and ω is the shear stress adjustment factor stored in the database, which is used to prevent the denominator from being zero.

[0067] In this implementation plan, by reading parameters such as the local turbulent energy dissipation rate, real-time fluid viscosity, and local shear stress in the sewage treatment tank, and combining the shear stress adjustment factor stored in the database for comprehensive analysis, a real-time comprehensive flow state characteristic index is constructed, which has multiple advantages. First, by real-time online monitoring of the local turbulent energy dissipation rate, the degree of fluid disturbance can be accurately captured, which has a direct impact on particle collision and aggregation during the flocculation and thickening process; second, the real-time fluid viscosity reflects the flow resistance of the liquid in the sewage and can reveal the regulatory effect of fluid motion on particle sedimentation efficiency; third, the local shear stress, as a key physical quantity in fluid motion, directly reflects the mechanical force of the water body in a local area, and its numerical value is closely related to the integrity and sedimentation rate of flocs. The real-time comprehensive flow state characteristic index obtained by fusing and calculating these parameters can comprehensively reflect the flow state and mixing effect of the water body in the sewage treatment tank, providing accurate hydrodynamic data support for the automatic control system. In this way, when the system detects uneven flow state or excessive turbulence, the control system can timely adjust the inlet and outlet water flow rates, agitator rotation speeds, and other key process parameters to ensure that the flocculation and thickening process is in the best working state. At the same time, using the preset shear stress adjustment factor in the database effectively prevents the abnormal situation of the denominator being zero at the data minimum value, ensuring the stability and reliability of the entire calculation process.

[0068] Specifically, the specific steps to obtain the real-time charge synergy effect index of the sewage treatment tank are as follows: Obtain the fluid pH reference value of the sewage treatment tank; Read the particle potential fluctuation value and fluid pH value of the sewage treatment tank, and combine the fluid pH reference value of the sewage treatment tank for comprehensive analysis to obtain the real-time charge synergy effect index of the sewage treatment tank.

[0069] Among them, the specific formula for calculating the real-time charge synergy effect index of the sewage treatment tank is as follows: Among them, DxZ is the real-time charge synergy effect index of the sewage treatment pool, DwB is the particle potential fluctuation value of the sewage treatment pool, PhZ is the fluid pH value of the sewage treatment pool, PhC is the fluid pH parameter value of the sewage treatment pool, η is the pH adjustment coefficient stored in the database, ξ is the potential fluctuation coefficient stored in the database, and its value is not 0.

[0070] It needs to be explained that the specific steps for obtaining the pH adjustment coefficient η stored in the database are: it is obtained after regression or sensitivity analysis of a large amount of historical operation data and experimental results: the specific approach is to first collect the water treatment effects under different pH values ​​(such as flocculation efficiency, sedimentation rate) and their corresponding information such as added chemicals, flow rate, temperature, etc., and use multivariate regression or machine learning models to determine the impact of pH deviation from the target value on the overall treatment effect, and then normalize and solidify this impact into an adjustment coefficient that can be directly used for online calculation and automatic control, and finally store it in the database for quick call during real-time operation.

[0071] In this implementation scheme, by introducing the fluid pH parameter value and the particle potential fluctuation value, the real-time measured fluid pH value and the preset target pH value are comprehensively analyzed to calculate the real-time charge synergy effect index of the sewage treatment pool, which provides accurate data support for evaluating the electrical regulation state. The pH adjustment coefficient stored in the database is based on a large amount of historical operation data and experimental results, and is obtained through regression analysis or machine learning models, that is, the water treatment effect (such as flocculation efficiency, sedimentation rate) and its corresponding agent addition, flow rate, temperature and other information under different pH conditions are first collected, and the sensitivity of pH deviation to the overall treatment effect is determined by multivariate regression analysis, and then this sensitivity is normalized to form an adjustment coefficient that can be directly used for real-time control. It can not only dynamically reflect the deviation between the fluid pH and the target pH in the sewage treatment pool, but also The instability of charge distribution in the system is revealed through particle potential fluctuations, thereby providing a basis for fine-tuning charge synergistic regulation. Overall, the construction of this real-time charge synergistic effect index uses the pH adjustment coefficient and potential fluctuation coefficient pre-calibrated in the database to achieve a seamless connection from historical data to real-time control, so that the system can capture slight changes in the electrical state in time during operation, and then automatically adjust the flocculant dosage or auxiliary electric field parameters to ensure that the flocculation process is stable and efficient, thereby reducing energy consumption, improving effluent quality and shortening the regulation cycle. This data-driven intelligent regulation method provides a fine, real-time and highly robust control strategy for the sewage treatment process, which significantly improves the treatment instability problem caused by fluctuations in environmental conditions in traditional processes, and has important engineering application value and economic benefits.

[0072] Specifically, the preset regulation state intervals include the normal regulation state interval, the warning regulation state interval, and the abnormal regulation state interval. The specific steps for judging and analyzing the real-time regulation state index of the sewage treatment tank with the preset regulation state intervals and taking regulation measures for the sewage treatment tank based on the judgment and analysis results are as follows: Judge and analyze the real-time regulation state index of the sewage treatment tank with the preset regulation state intervals; if the real-time regulation state index of the sewage treatment tank is within the preset normal regulation state interval, no regulation measures are taken; if the real-time regulation state index of the sewage treatment tank is within the preset warning regulation state interval, warning regulation measures are taken; if the real-time regulation state index of the sewage treatment tank is within the preset abnormal regulation state interval, abnormal regulation measures are taken.

[0073] Among them, the warning regulation measures include:

[0074] Adjust the dosage of flocculant: Appropriately increase or decrease it to improve the surface potential and aggregation effect of particles;

[0075] Adjust the rotation speed of the agitator: Reduce the stirring intensity to reduce the local shear stress and protect the formed flocs;

[0076] Adjust the inflow and outflow water velocities: Optimize the water residence time and improve the sedimentation conditions.

[0077] The abnormal regulation measures include:

[0078] Quickly increase or decrease the flocculant: Immediately adjust the flocculant dosage according to the feedback of the charge coordination index;

[0079] Enable the auxiliary electric field or ultrasonic device: Improve the charge distribution and promote rapid aggregation;

[0080] Adjust the fluid temperature or pH value: Make the process conditions return to the optimal state by heating / cooling or adding regulators.

[0081] In this implementation scheme, it is possible to achieve dynamic optimization and precise regulation of the entire process of the sewage treatment tank. Specifically, by comparing the real-time regulation state index with the three preset state intervals of normal, warning, and abnormal, the system can automatically identify whether the current working condition is in an ideal state; if it is in the normal interval, the existing operating parameters are maintained without intervention to ensure the stable operation of the process; if it is in the warning interval, the system will automatically take warning regulation measures, such as appropriately adjusting the dosage of the flocculant to improve the particle potential and aggregation effect, reducing the rotation speed of the agitator to reduce the local shear stress and protect the flocs, and adjusting the inlet and outlet water flow rates to optimize the water residence time, so as to correct the operation deviation; when the state index falls into the abnormal interval, the system will quickly execute abnormal regulation measures, such as quickly adjusting the flocculant dosage, enabling auxiliary electric field or ultrasonic equipment to improve the charge distribution, and adjusting the fluid temperature or pH value to quickly restore the process conditions to the best state. It not only realizes real-time monitoring and multi-level early warning to ensure that the treatment tank can maintain the best operating state at each stage, but also significantly shortens the response time through the automated closed-loop feedback system, reduces the errors and resource waste caused by manual intervention, thereby improving the solid-liquid separation efficiency, effluent quality and system economic benefits, and at the same time providing a strong technical guarantee for the safety and stability of the sewage treatment process.

[0082] In summary, this application has at least the following effects:

[0083] By integrating multi-scale data in the sewage treatment tank, real-time collecting and fusing the microscopic parameters of particles, such as the effective particle diameter, particle surface potential and roughness, the mesoscopic parameters, such as the local turbulence amplitude, fluid viscosity, particle density, and the macroscopic parameters, such as fluid density, average flow rate, conductivity, pH value and local electric field strength, a real-time regulation state index that comprehensively reflects the dynamic state of the flocculation and thickening process is constructed. By using technical means such as image analysis, online sensor data fusion and preprocessing, it is possible to carefully capture the minute changes in each stage of sewage treatment, avoiding the problems of misjudgment and untimely adjustment caused by relying solely on single or local parameter monitoring in the past, thus realizing the refined monitoring of the sewage treatment state, improving the stability of the effluent quality and the solid-liquid separation efficiency, and at the same time effectively reducing the costs of chemicals and energy consumption, laying a solid data support and theoretical foundation for the long-term stable operation of the system.

[0084] Using the influence coefficients and weight coefficients of micro, meso, macro, flow state and charge synergy pre-stored in the database, through the calibration of historical data and sensitivity analysis, the contributions of each dimension parameter to the overall flocculation thickening effect are accurately quantified, and then a real-time regulation state index is formed by weighted superposition. This comprehensive index can comprehensively reflect the current operating conditions of the sewage treatment tank, provide a scientific and quantitative basis for regulation decisions, so that the operating parameters can be adaptively optimized under different working conditions, realize the precise regulation of complex and variable water quality environments, reduce the system operation risk, improve the solid-liquid separation efficiency and the stability of the effluent water quality, promote the automation and intelligent management of the sewage treatment process, and effectively improve the overall environmental safety and economic benefits.

[0085] By presetting three regulation state intervals of normal, warning and abnormal, and combining the multi-scale weight coefficients and influence coefficients stored in the database, the real-time regulation state index is automatically compared and judged, realizing intelligent closed-loop feedback control. When the real-time regulation state index deviates from the normal interval, the system can quickly identify the specific abnormal links, such as uneven flow state, excessive shear stress or insufficient charge synergy, etc., and automatically issue adjustment instructions to optimize and adjust the key process parameters such as the dosage of flocculant, stirring rate, inlet and outlet water flow rates, and auxiliary electric field, so as to correct the operation deviation in time, shorten the response time, reduce the energy consumption and chemical agent waste in the treatment process, significantly improve the sewage treatment efficiency and economic benefits, and ensure the safe and reliable operation of the process.

[0086] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0087] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for regulating the flocculation and thickening process based on multi-scale features, characterized in that, It includes the following steps: Obtain the treatment status data of the sewage treatment tank in real time and perform preprocessing. The treatment status data of the sewage treatment tank includes the effective particle diameter value, particle surface potential value, particle surface roughness, particle density value, real-time fluid viscosity value, real-time fluid density value, real-time fluid average flow velocity value, local electric field strength value, and fluid pH value of the sewage treatment tank; Respectively perform comprehensive analysis on the preprocessed treatment status data of the sewage treatment tank to obtain the real-time microscopic scale index, real-time mesoscopic scale index, real-time macroscopic scale index, real-time comprehensive flow regime characteristic index, and real-time charge synergistic effect index of the sewage treatment tank, and perform comprehensive analysis to obtain the real-time regulation status index of the sewage treatment tank; Judge and analyze the real-time regulation status index of the sewage treatment tank with the preset regulation status interval, and take regulation measures for the sewage treatment tank based on the judgment and analysis results; Among them, the specific formula for calculating the real-time regulation status index of the sewage treatment tank is as follows: Among them, TkZ, WgZ, ZgZ, HgZ, LtZ, DxZ are the real-time regulation status index, real-time microscopic scale index, real-time mesoscopic scale index, real-time macroscopic scale index, real-time comprehensive flow regime characteristic index, and real-time charge synergistic effect index of the sewage treatment tank in turn, and μ1, μ2, μ3, μ4, μ5 are the microscopic weight coefficient, mesoscopic weight coefficient, macroscopic weight coefficient, flow regime weight coefficient, and charge synergistic weight coefficient stored in the database in turn, and λ1, λ2, λ3, λ4, λ5 are the microscopic influence coefficient, mesoscopic influence coefficient, macroscopic influence coefficient, flow regime influence coefficient, and charge synergistic influence coefficient stored in the database in turn, and ε is the regulation factor stored in the database.

2. The method for regulating the flocculation and thickening process based on multi-scale features according to claim 1, wherein The specific steps for obtaining the real-time microscopic scale index of the sewage treatment tank are as follows: Obtain the particle morphology fractal dimension and particle reference potential value of the sewage treatment tank; Respectively combine the effective particle diameter value, particle surface potential value, particle surface roughness, and real-time fluid viscosity value of the sewage treatment tank with the particle morphology fractal dimension and particle reference potential value of the sewage treatment tank for comprehensive analysis to obtain the real-time microscopic scale index of the sewage treatment tank.

3. The method for regulating the flocculation and thickening process based on multi-scale features according to claim 1, wherein The specific steps for obtaining the particle morphology fractal dimension of the sewage treatment tank are as follows: Obtain the particle sample image data and perform preprocessing; Perform image segmentation processing on the preprocessed particle sample image data; Perform morphological processing on the particle sample image data after image segmentation processing; Perform target area extraction processing on the particle sample image data after morphological processing, and analyze the particle morphology fractal dimension of the sewage treatment tank based on the preset fractal dimension algorithm.

4. The method for regulating the flocculation and thickening process based on multi-scale features according to claim 1, wherein The specific formula for calculating the real-time microscopic scale index of the sewage treatment tank is as follows: Among them, WgZ, KyZ, KxF, NdZ, BdW, BdC, BcC are the real-time microscopic scale index, effective particle diameter value, particle morphology fractal dimension, real-time fluid viscosity value, particle surface potential value, particle reference potential value, and particle surface roughness of the sewage treatment tank in turn, and α is the microscopic adjustment factor stored in the database.

5. The method for regulating the flocculation and thickening process based on multi-scale features according to claim 1, wherein The specific steps for obtaining the real-time mesoscopic scale index of the sewage treatment tank are as follows: Obtain the local turbulence amplitude value of the sewage treatment tank; Read the effective particle diameter value and real-time fluid viscosity value of the sewage treatment tank, and conduct a comprehensive analysis in combination with the particle density value, real-time fluid density value, real-time fluid average flow velocity value, and local turbulence amplitude value of the sewage treatment tank to obtain the real-time mesoscopic scale index of the sewage treatment tank.

6. The method for regulating the flocculation and thickening process based on multi-scale features according to claim 1, characterized in that The specific formula for calculating the real-time mesoscopic scale index of the sewage treatment tank is as follows: Among them, ZgZ, KmD, LmD, KyZ, NdZ, FdZ, LsZ are the real-time mesoscopic scale index, particle density value, real-time fluid density value, effective particle diameter value, real-time fluid viscosity value, local turbulence amplitude value, and real-time fluid average flow velocity value of the sewage treatment tank in sequence, g is the acceleration due to gravity, and β and χ are the viscosity adjustment factor and flow velocity adjustment factor stored in the database in sequence.

7. The method for regulating the flocculation and thickening process based on multi-scale features according to claim 1, characterized in that The specific steps for obtaining the real-time macroscopic scale index of the sewage treatment tank are as follows: Conduct a fluctuation analysis on the particle surface potential value and particle reference potential value of the sewage treatment tank to obtain the particle potential fluctuation value of the sewage treatment tank; Obtain the local turbulence energy dissipation rate value, local shear stress value, fluid conductivity value, and local electric field strength reference value of the sewage treatment tank, and conduct a comprehensive analysis in combination with the local electric field strength value and particle potential fluctuation value of the sewage treatment tank to obtain the real-time macroscopic scale index of the sewage treatment tank.

8. The method for regulating the flocculation and thickening process based on multi-scale features according to claim 1, characterized in that The specific steps for obtaining the real-time comprehensive flow regime characteristic index of the sewage treatment tank are: Read the local turbulence energy dissipation rate value, local shear stress value, real-time fluid viscosity value, and real-time fluid average flow velocity value of the sewage treatment tank and conduct a comprehensive analysis to obtain the real-time comprehensive flow regime characteristic index of the sewage treatment tank.

9. The method for regulating the flocculation and thickening process based on multi-scale features according to claim 1, wherein The specific steps for obtaining the real-time charge synergy effect index of the sewage treatment tank are as follows: Obtain the fluid pH reference value of the sewage treatment tank; Read the particle potential fluctuation value and fluid pH value of the sewage treatment tank, and conduct a comprehensive analysis in combination with the fluid pH reference value of the sewage treatment tank to obtain the real-time charge synergy effect index of the sewage treatment tank.

10. The method for regulating the flocculation and thickening process based on multi-scale features according to claim 1, wherein The preset regulation state intervals include the normal regulation state interval, warning regulation state interval, and abnormal regulation state interval. The specific steps for judging and analyzing the real-time regulation state index of the sewage treatment tank with the preset regulation state intervals and taking regulation measures for the sewage treatment tank based on the judgment and analysis results are as follows: Judge and analyze the real-time regulation state index of the sewage treatment tank with the preset regulation state intervals; If the real-time regulation state index of the sewage treatment tank is within the preset normal regulation state interval, no regulation measures are taken; If the real-time regulation state index of the sewage treatment tank is within the preset warning regulation state interval, warning regulation measures are taken; If the real-time regulation state index of the sewage treatment tank is within the preset abnormal regulation state interval, abnormal regulation measures are taken.

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