Configuration method and device of compound substrate for sewage treatment and server

Abstract

The invention provides a preparation method and device of a composite matrix for sewage treatment and a server, and relates to the technical field of water treatment material.The preparation method comprises the steps that center combination treatment is conducted on a ternary composite matrix for sewage treatment, and a test formula set is generated; obtaining test data corresponding to the test formula set, and taking the removal rate corresponding to each matrix as a response value to obtain target test data; substituting the target test data into a quadratic polynomial regression model of the ternary composite matrix to carry out regression analysis processing to obtain a model predicted value, and carrying out model verification updating processing on the quadratic polynomial regression model by utilizing the model predicted value and the target test data to determine a target quadratic polynomial regression model; and through the target quadratic polynomial regression model, optimizing the ratio of the ternary composite matrix, and determining the target mass ratio of the ternary composite matrix. According to the invention, the pollutant removal rate of synergistic decontamination of various pollutants in the sewage treatment process can be obviously improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water treatment material preparation, and in particular to a configuration method and device of a composite substrate for sewage treatment and a server. BACKGROUND

[0002] Developing efficient and economical water treatment technology has become a research focus in the field of environmental engineering. Currently, composite substrate materials are usually used in water pollution treatment, which can integrate the advantages of multiple single materials and exhibit significant application potential in the fields of sewage treatment and substrate improvement. However, the existing composite substrate materials mainly rely on manual experience or simple literature research to determine the proportion during preparation, lacking systematic multi-objective optimization research, thus it is difficult to ensure the stability and consistency of product performance. In addition, the above scheme ignores the complex interaction of each component during actual cooperative work, resulting in low utilization efficiency of the substrate after proportioning, and further affecting the removal rate of water pollutants. SUMMARY

[0003] Therefore, the purpose of the present application is to provide a configuration method and device of a composite substrate for sewage treatment and a server, which can significantly improve the pollutant removal rate of multiple pollutants during sewage treatment.

[0004] In a first aspect, an embodiment of the present application provides a configuration method of a composite substrate for sewage treatment, which comprises: performing center combination processing on a ternary composite substrate for sewage treatment to generate a test formula set, wherein the ternary composite substrate comprises biochar, zeolite and volcanic rock, and the test formula set comprises composite substrates with different mass proportions; obtaining test data of sewage treatment tests corresponding to the test formula set, and taking the removal rates of each substrate in the test data as response values to obtain target test data; performing regression analysis processing on the target test data in a quadratic polynomial regression model of the ternary composite substrate to obtain model prediction values, and performing model verification and updating processing on the quadratic polynomial regression model by using the model prediction values and the target test data to determine a target quadratic polynomial regression model; and performing optimization processing on the mass proportion of the ternary composite substrate by using the target quadratic polynomial regression model to determine a target mass proportion of the ternary composite substrate.

[0005] In one embodiment, the step of performing center combination processing on a ternary composite substrate to generate a test formula set comprises: substituting the ternary composite substrate into a mathematical framework of center combination design, determining the coding values corresponding to the ternary composite substrate according to the number of factors of the ternary composite substrate and the preset coding level of the mathematical framework, wherein the preset coding level comprises factor points, axis points and center points; and converting the coding values into actual values to determine the test formula set according to the actual values.

[0006] In an embodiment, after the step of taking the removal rate of each matrix in the test data as a response value, the method comprises: performing data analysis processing on the test data to determine the average value and the standard deviation of the test; sending the average value and the standard deviation to a preset coefficient of variation calculation model to obtain the coefficient of variation, and determining the test data as target test data when the coefficient of variation is less than a preset coefficient of variation threshold.

[0007] In an embodiment, the step of substituting the target test data into the quadratic polynomial regression model of the ternary composite matrix for regression analysis processing to obtain the model predicted value comprises: substituting the target test data into the quadratic polynomial regression model of the ternary composite matrix for regression analysis processing to determine the model coefficients corresponding to each constant term in the quadratic polynomial regression model; constructing a regression equation of the target water quality index removal rate according to the model coefficients, and performing model prediction processing using the regression equation of the target water quality index removal rate to obtain the model predicted value, wherein the regression equation of the target water quality index removal rate comprises: a total nitrogen removal rate regression equation, a chemical oxygen demand removal rate regression equation, and a total phosphorus removal rate regression equation.

[0008] In an embodiment, before the step of constructing a regression equation of the target water quality index removal rate according to the model coefficients, the method comprises: calculating the determination coefficient, the adjusted determination coefficient, the F verification value, and the P value in the quadratic polynomial regression model respectively, and constructing the regression equation of the target water quality index removal rate according to the model coefficients when the calculation results of the determination coefficient, the adjusted determination coefficient, the F verification value, and the P value are all verified.

[0009] In an embodiment, the step of using the model predicted value and the target test data to perform model verification and update processing on the quadratic polynomial regression model to determine a target quadratic polynomial regression model comprises: determining a relative error value by performing error analysis processing on the model predicted value and the target test data; if the relative error value meets a preset relative error threshold, determining the current quadratic polynomial regression model as the target quadratic polynomial regression model; if the relative error value does not meet the preset relative error threshold, performing closed-loop iteration processing on the current quadratic polynomial regression model.

[0010] In an embodiment, the step of optimizing the ratio of the ternary composite matrix by using the target quadratic polynomial regression model to determine a target mass ratio of the ternary composite matrix comprises: performing model solving processing on the target quadratic polynomial regression model based on the constraint conditions of the total nitrogen removal rate, the chemical oxygen demand removal rate, and the total phosphorus removal rate, maximizing the expected function value within the range that meets the constraint conditions, and obtaining the target mass ratio of the ternary composite matrix.

[0011] In a second aspect, the embodiments of the present application also provide a device for configuring a composite substrate for sewage treatment, which comprises: a test formula generation module, configured to perform central composite processing on a ternary composite substrate for sewage treatment to generate a test formula set, wherein the ternary composite substrate comprises biochar, zeolite and volcanic rock, and the test formula set comprises composite substrates with different mass ratios; a data acquisition module, configured to acquire test data of sewage treatment tests corresponding to the test formula set, and take the removal rates of each substrate in the test data as response values to obtain target test data; a model optimization module, configured to perform regression analysis on the target test data in a quadratic polynomial regression model of the ternary composite substrate to obtain model prediction values, and perform model verification and update processing on the quadratic polynomial regression model by using the model prediction values and the target test data to determine a target quadratic polynomial regression model; and a composite substrate ratio module, configured to perform optimization processing on the ratio of the ternary composite substrate by using the target quadratic polynomial regression model to determine a target mass ratio of the ternary composite substrate.

[0012] In a third aspect, the embodiments of the present application also provide a server, comprising a processor and a memory, wherein the memory stores computer executable instructions capable of being executed by the processor, and the processor executes the computer executable instructions to implement the method of any one of the first aspect.

[0013] In a fourth aspect, the embodiments of the present application also provide a computer readable storage medium, which stores computer executable instructions, and the computer executable instructions, when invoked and executed by a processor, cause the processor to implement the method of any one of the first aspect.

[0014] The embodiments of the present application have the following beneficial effects: The method, device and server for configuring a composite substrate for sewage treatment provided by the embodiments of the present application can significantly improve the pollutant removal rate of collaborative pollutant removal in the sewage treatment process.

[0015] Other features and advantages of the present application will be set forth in the descriptions that follow, and in part will be apparent from the description, or can be learned by practice of the application. The purposes and other advantages of the present application will be realized and attained by the structures particularly pointed out in the description, claims and drawings.

[0016] In order to make the above objectives, features and advantages of the present application more apparent, the following will describe preferred embodiments in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0018] Figure 1 A structural schematic diagram of a configuration system of a composite substrate for sewage treatment provided by the embodiment of the present application; Figure 2 A flowchart of a configuration method of a composite substrate for sewage treatment provided by the embodiment of the present application; Figure 3 A flowchart of a closed-loop iteration method provided by the embodiment of the present application; Figure 4 A structural schematic diagram of a configuration device of a composite substrate for sewage treatment provided by the embodiment of the present application; Figure 5 A structural schematic diagram of a server provided by the embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the present application will be described clearly and completely with reference to the embodiments. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0020] At present, with the increasingly serious water pollution problem, the development of efficient and economical water treatment technology has become the research focus in the field of environmental engineering. Among them, the substrate improvement technology, as one of the core measures of water ecological restoration, has attracted more and more attention, especially under complex substrate conditions such as sandy soil, pebble bottom, newly excavated hard bottom, and hardened bottom. The substrate conditions directly limit the planting and survival of submerged plants and directly affect the restoration effect of water ecological system. Composite substrate materials show significant application potential in wastewater treatment and substrate improvement due to their ability to integrate the advantages of multiple single materials.

[0021] In terms of preparation process, the preparation process of existing composite substrates mainly includes key links such as raw material pretreatment and ingredient mixing. Raw material pretreatment usually includes basic operations such as crushing, screening, and washing. Some studies also involve simple activation modification treatment. The ingredient mixing step mainly relies on manual experience or simple literature research to determine the proportion, and lacks systematic multi-objective optimization research. The mixing process mainly uses traditional equipment such as V-shaped mixers, double-cone mixers, or horizontal screw belt mixers. The operating personnel usually set the mixing time according to the equipment instructions or experience, and use visual uniformity as the mixing endpoint judgment standard. Although this process mode is simple and easy to implement, it is difficult to ensure the stability and consistency of product performance. For example, in the preparation method of substrate improver for aquaculture, multiple powders and liquid materials need to be mixed and granulated to ensure the performance of the final product. From the perspective of materials science, existing composite substrates mainly use physical mixing to combine materials with different functional characteristics, and they often only focus on the removal effect of a single or two pollutants, and lack of research on the synergistic removal mechanism of multiple pollutants such as COD, total nitrogen, and total phosphorus.

[0022] The existing technology lacks scientificity and systematicness in proportion optimization. The proportioning scheme based on experience or simple literature combination often ignores the complex interactions (including positive synergy and negative antagonism) of each component when working together. For example, a certain proportion may be beneficial to COD adsorption, but it may inhibit the activity of certain microorganisms and affect denitrification; or two materials compete for adsorption sites. Without finding the global optimal proportion for a specific target (such as maximizing the removal rates of COD, TN, and TP simultaneously) through methods such as multivariate statistical optimization, the potential of existing composite substrates has not been fully tapped, and their comprehensive treatment efficiency is often lower than the theoretical optimal value. This non-optimized proportion also leads to low material utilization efficiency, so more substrate is needed to achieve the desired treatment effect, increasing the cost.

[0023] Based on this, the configuration method of the composite substrate for sewage treatment provided by the present application significantly improves the synergistic removal efficiency of the water treatment substrate for various pollutants compared with the prior art: the response surface method is combined with the central composite design to comprehensively investigate the proportion of each component and the interaction thereof, an accurate mathematical model is established, and the global optimal proportioning scheme is determined, thereby significantly improving the pollutant removal efficiency, improving the effluent water quality, simplifying the subsequent treatment process, while ensuring the efficiency and repeatability of the treatment effect, and improving the stability and reliability of the entire water treatment process.

[0024] In addition, the present application also saves resources and protects the environment through optimized proportioning and precise preparation technology: the most efficient proportioning scheme is found through scientific optimization, the utilization efficiency of raw materials is improved, the substrate consumption per unit of treated water is reduced, the high-precision batching system and efficient mixing process significantly reduce the generation of waste and defective products, and material loss is reduced. At the same time, the efficient composite substrate prepared by the present application can more completely remove pollutants in water, reducing the pollution load on downstream water environments, and having significant environmental benefits.

[0025] Further, the present application also organically integrates advanced statistical optimization methods, precise automation control technology and innovative stirring process in the research and production of the composite water treatment substrate, thereby surpassing the traditional mode of combining experience and trial and error, and providing a systematic, data-driven and quantifiable solution. The integrated online monitoring and feedback system further improves the advanced nature of the technology, enabling real-time evaluation and self-adjustment optimization potential, conforming to the development trend of intelligent and fine water treatment technology, and having the potential to lead the development of the industry.

[0026] In order to facilitate the understanding of the present embodiment, first, the configuration method of a composite substrate for sewage treatment disclosed in the present embodiment is introduced in detail. The method is applied to the proportioning optimization model of the configuration system of the composite substrate for sewage treatment. In order to facilitate the understanding of the configuration system of the composite substrate for sewage treatment, the present embodiment provides a structural diagram of the configuration system of the composite substrate for sewage treatment, as shown in Figure 1 The system is composed of a ternary composite substrate bin (biochar bin, zeolite bin, and volcanic rock bin), a double-helix feeder, a belt conveyor, a stirring system, a PLC controller, a torque sensor, a rotational speed sensor, an ultrasonic level sensor, a weight sensor, an inlet water quality sensor, an outlet water quality sensor, a data acquisition and processing unit, an intelligent analysis unit, and a water pipe.

[0027] The batching system in the wastewater treatment composite substrate preparation system employs a combination of high-precision weight sensing and intelligent control to achieve accurate proportioning of the ternary composite substrate. The system monitors the weight changes of each substrate material in real time using weight sensors. After signal conditioning, the weight signals are input to the PLC controller, which calculates the target feed amount for each substrate based on a proportioning optimization model. :

[0028] in, The target feed amount (kg) Let be the proportioning coefficient of the i-th matrix. Total ingredient quantity (kg) Allowable deviation (%). For example, biochar. zeolite volcanic rock .

[0029] The PLC controller employs a PID closed-loop control algorithm to adjust the speed of the screw feeder based on real-time feedback from the weight sensor. :

[0030] in, The rotational speed (r / min) of the screw feeder. For the deviation in feed rate, , , These are the proportional, integral, and differential coefficients, respectively.

[0031] In addition, the system is equipped with a material level detection device, which monitors the material height h in the silo in real time through an ultrasonic sensor. When the material level is lower than the warning value, a replenishment signal is triggered.

[0032] Each substrate is metered by a screw feeder and then conveyed to the mixing system by a belt conveyor. The conveyor belt speed v and the feed rate Q satisfy the following:

[0033] Where v is the conveyor belt speed (m / s), Q is the feed rate (kg / s), and A is the cross-sectional area of ​​the conveyor belt (m / s). ), Bulk density of the material ( ).

[0034] Preferably, the stirring system of the present invention employs a three-stage variable-speed stirring process, combined with a double-propeller blade structure, to achieve efficient and uniform mixing of the composite matrix. The stirring process is divided into three stages: The first stage is the high-speed premixing stage, with a stirring speed of [missing information]. For 300-400 r / min, duration For 3-5 min. The main purpose of this stage is to quickly break up the material and prevent clumping. The paddle angle is set to 45° to obtain a larger axial conveying force.

[0035] The second stage is a medium-speed homogenization stage, the stirring speed is reduced to 150-200 r / min, duration is 8-12 min. This stage focuses on achieving full mixing of the material, the paddle angle is adjusted to 35° to enhance the radial mixing effect. The stirring power satisfies:

[0036] wherein, is the stirring power coefficient, is the material density, is the rotational speed (r / s), is the stirring paddle diameter (m).

[0037] The third stage is a low-speed fine mixing stage, the stirring speed is further reduced to 80-100 r / min, duration is 5-8 min. The purpose of this stage is to eliminate local unevenness, the paddle angle is increased to 55° to strengthen the axial circulating flow. The mixing uniformity is calculated by sampling detection:

[0038] wherein, is the sample standard deviation, is the sample mean, ≤ 3% is considered to be uniformly mixed.

[0039] The stirring system adopts a double helix paddle structure, and the paddle angle can be adjusted within the range of 30°~60° to meet the needs of different stirring stages. The paddle is made of wear-resistant alloy steel and the surface is treated by plasma spraying to improve wear resistance and service life.

[0040] Further, the sensor system in the configuration system of the composite substrate for sewage treatment includes: an inlet water quality sensor, an outlet water quality sensor, a data acquisition and processing unit, and an intelligent analysis unit. Through multi-parameter online monitoring technology, real-time analysis of COD removal rate, total nitrogen removal rate, and total phosphorus removal rate, the system is composed of inlet and outlet water quality monitoring units, data acquisition and processing units, and intelligent analysis units.

[0041] The water quality monitoring unit adopts a multi-parameter water quality sensor, which can simultaneously monitor COD, total nitrogen, total phosphorus, etc. The data acquisition and processing unit adopts a high-performance processor, and the sampling frequency can reach 100Hz.

[0042] The intelligent analysis unit analyzes water quality data in real time. COD removal rate According to the measured value of the COD concentration of the influent and effluent water:

[0043] Total nitrogen removal rate According to the measured value of the total nitrogen concentration of the influent and effluent water:

[0044] Total phosphorus removal rate According to the measured value of the total phosphorus concentration of the influent and effluent water:

[0045] The influent and effluent sensors are connected by a DN50 stainless steel water flow pipe, the inner wall of the pipe is polished, and the surface roughness Ra is ≤0.4μm, so as to reduce water flow resistance and pollutant adsorption. Through the calculation results of these measured data, real-time evaluation and dynamic monitoring of system performance are realized.

[0046] In practical application, the water quality parameters can be set as: influent water 350 (mg / L), effluent water 45 (mg / L), influent total nitrogen 45 (mg / L), effluent total nitrogen 12 (mg / L), influent total phosphorus 8 (mg / L), and effluent total phosphorus 0.8 (mg / L). In addition, sensors (such as turbidity, conductivity, pH, ORP, etc.) that are easier to realize and have lower cost are used to monitor the alternative indicators related to the target pollutant concentration online.

[0047] Based on Figure 1 The configuration system of the composite substrate for sewage treatment is shown in the structural schematic diagram, and the configuration method of the composite substrate for sewage treatment is introduced in detail, and the flow schematic diagram of the configuration method of the composite substrate for sewage treatment is shown in the flow schematic diagram. Figure 2 The configuration method of the composite substrate for sewage treatment mainly includes the following steps S202 to S206: Step S202, the ternary composite substrate for sewage treatment is subjected to central combination treatment, and a test formula set is generated, wherein the ternary composite substrate includes: biochar, zeolite and volcanic rock, and the test formula set includes: composite substrates with different mass ratios.

[0048] In an embodiment, it is first necessary to comprehensively collect and analyze the water quality parameters of the influent and effluent, which mainly include the concentration data of key indicators such as COD, total nitrogen and total phosphorus. The concentration values of various pollutant indicators are determined by collecting water samples at the influent end and the effluent end, and the treatment efficiency is calculated according to the concentration difference between the influent and the effluent. The collection and analysis of water quality parameters provide an important data basis for subsequent optimization of the ratio. Then, the response surface method (RSM) is used to systematically optimize the ratio of the ternary composite substrate, with COD removal rate ( ), total nitrogen removal rate ( ) and total phosphorus removal rate ( ) as response values. RSM is a statistical tool that uses a small number of experiments to fit a mathematical surface and quickly find the optimal formula. It can be thought of as a mountain of all substrate ratios. RSM first drills 20 wells in the most likely location of the optimal ratio, fits the topographic map of the mountain with these data, and then directly locates the optimal ratio.

[0049] Step S204, obtaining the test data of the sewage treatment test corresponding to the test formula set, and taking the removal rates of each substrate in the test data as response values to obtain target test data.

[0050] In an embodiment, the configuration system of the composite substrate for sewage treatment is sequentially executed with the above-mentioned test formula set to perform water treatment experiments of ternary composite substrates with different ratios. During the experiment, the consistency of the experimental conditions is strictly controlled, including water temperature, pH value, hydraulic retention time and other key parameters. For each experimental formula, the mixing ratio of the ternary composite substrate is first accurately prepared according to the designed code value to ensure the accuracy of the ratio. Then, the prepared composite substrate is loaded into the experimental device for standardized water treatment experiments. During the experiment, the influent and effluent samples are systematically collected and recorded, and the water quality indicators such as chemical oxygen demand (COD), total nitrogen and total phosphorus are measured to calculate the removal rates of each indicator. Through this standardized experimental process, a complete experimental data set (i.e., the target test data) is obtained, thereby laying a solid data foundation for subsequent response surface analysis.

[0051] Step S206, substituting the target test data into the quadratic polynomial regression model of the ternary composite substrate for regression analysis to obtain model prediction values, and using the model prediction values and the target test data to update the model verification of the quadratic polynomial regression model to determine the target quadratic polynomial regression model.

[0052] In an embodiment, based on the experimental data of the central composite design, quadratic polynomial regression models of COD removal rate, total nitrogen removal rate and total phosphorus removal rate are respectively constructed, and the experimental data are subjected to multiple quadratic regression analysis through the models to obtain the regression model coefficients and their significance levels, thereby verifying the models.

[0053] In step S208, the ratio of the ternary composite substrate is optimized through the target quadratic polynomial regression model to determine the target mass ratio of the ternary composite substrate.

[0054] In an embodiment, the target mass ratio needs to be sent to the configuration system of the composite substrate for sewage treatment, so that the system determines the target feeding amount and configures the substrate with the target feeding amount to perform three-stage variable speed stirring to obtain the ternary composite substrate, thereby treating sewage.

[0055] The above-mentioned configuration method of the composite substrate for sewage treatment provided by the embodiment of the present application can efficiently remove chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) in water, and the technical scheme points include: using the optimization method of response surface method (RSM) combined with central composite design (CCD) to systematically determine the optimal mass ratio (i.e., target mass ratio) of three components of biochar, zeolite and volcanic rock with COD, TN and TP removal rates as response values, and then sending the target mass ratio to the preparation system (including a stock bin, a sensor, a PLC controller, a screw feeder, a conveyor and a stirring system with a specific stirring structure) of the configuration system of the composite substrate for sewage treatment. When the system is working, the PLC controller calculates the target feeding amount of each component according to the optimized ratio, reads the real-time data of the sensor, and accurately controls the speed of the feeder by using a PID closed-loop control algorithm to realize high-precision batching. Subsequently, the material is conveyed to the stirring system to perform a three-stage variable speed stirring process (including high-speed premixing , medium-speed homogenization and low-speed fine mixing stage) to ensure that the final ternary composite substrate is mixed extremely uniformly. The present application combines scientific optimization of the ratio with precise automatic preparation to purify water quality in the fields of municipal sewage and industrial wastewater, and significantly improves the comprehensive performance of the water treatment substrate.

[0056] The embodiment of the present application also provides a specific embodiment of optimizing the substrate ratio, which is described as follows (1) to (6): (1) Experimental Design (CCD): The ternary composite matrix can be substituted into the mathematical framework of the central composite design. Based on the number of factors of the ternary composite matrix and the preset coding level of the mathematical framework, the corresponding coding value of the ternary composite matrix is ​​determined. Finally, the coding value is converted into the actual value so as to determine the experimental formula set based on the actual value. The preset coding level includes: factor point, axis point and center point.

[0057] Specifically, a central composite design (CCD) scheme is used to construct the experimental design, with a ternary composite matrix as the independent variable, such as biochar ( ), zeolite ( ) and volcanic rocks ( Central composite design, a commonly used response surface methodology, consists of 2ᵏ factor points (k being the number of factors), 2k pivot points, and several central points. It effectively estimates the coefficients of a quadratic response surface model. To comprehensively study the influence of each factor on the response value, this experiment set five coding levels: -1.682, -1, 0, 1, and 1.682. The coding levels are dimensionless values ​​obtained by standardizing the actual factor levels. -1.682 and 1.682 represent the lowest and highest levels, respectively (rotational parameter α = 1.682), 0 represents the central point level, and -1 and 1 represent intermediate levels. Based on this design, 20 experimental formulations were generated, including 8 factor points, 6 pivot points, and 6 central points. The conversion relationship between the coding values ​​and the actual values ​​is as follows:

[0058] in, For encoded values, This is the actual value. The actual value at the center point refers to the encoded value. When the value is 0, the independent variable The corresponding actual values, in this experiment, are the actual values ​​of the center point of the ternary composite matrix (biochar, zeolite, and volcanic rock). They are 0.650, 0.200, and 0.150 respectively; The step size represents the actual value change, indicating the step size when the encoded value changes. When the independent variable changes by 1 unit, The change in actual value. In this experiment, the step size of the change in the actual value of biochar. The step size for the actual value variation of zeolite and volcanic rock is 0.090. All values ​​were 0.060. Biochar, due to its high stability and large adsorption capacity, is often used in composite matrix formulations with a relatively high proportion, and its value range is relatively wide. Zeolite and volcanic rock, on the other hand, are usually used as functional fillers, accounting for a lower proportion in composite systems, and their physicochemical properties (such as pore structure and ion exchange capacity) determine that their optimal concentration is more concentrated. The settings need to be combined with its engineering practice background to ensure that the experimental design conforms to the actual application scenario.

[0059] The specific experimental designs for the 20 groups are shown in Table 1 below: Table 1:

[0060] This central composite design scheme includes three types of experimental points: eight factor points located at the vertices of the cube, six axis points located on the coordinate axes, and six center points located at the center of the experimental design. Among them, factor points ( , , The ±1 value is mainly used to study the main effect of a single factor and the interaction between two factors; the axis point is located at ±1.682 from the center point, and each factor is taken at ±1.682 when other factors are at the 0 level, to estimate the nonlinear quadratic effect of the factor; the center point ( The experimental error and model fitness were evaluated through six repeated trials. The multiple repetitions at the center point are significant: repeated trials allow for the estimation of purely random errors, crucial for assessing the model's fitness and reliability; they also assess the stability and reproducibility of the experimental process; large differences in repeated trial results indicate potential instability. Furthermore, repeated trials at the center point can improve model accuracy, particularly in estimating the curvature of the quadratic response surface model, as detailed in Table 2 below. Table 2

[0061] (2) Experimental execution: After obtaining the results of the central composite design (CCD) experiment, it is also necessary to perform data analysis and processing on the experimental data to determine the mean and standard deviation of the experiment. Then, the mean and standard deviation are sent to the preset coefficient of variation calculation model to obtain the coefficient of variation. When the coefficient of variation is less than the preset coefficient of variation threshold, the experimental data is determined as the target experimental data. For details, please refer to (A) to (B) below: (A) Calculate the average value of repeated trials at the center point ( ) and standard deviation ( ):

[0062] in, the average value of the central point repeated tests, the response value (COD removal rate, total nitrogen removal rate or total phosphorus removal rate) of the group test, from 15 to 20 represents 6 central point tests.

[0063]

[0064] wherein, the standard deviation, the square deviation of each test value from the average value, and 5 is the degree of freedom (repetition number minus 1).

[0065] (B) Calculate the coefficient of variation (CV) to evaluate the reproducibility of the experiment:

[0066] wherein, CV is the coefficient of variation, the standard deviation, the average value. Generally, CV≤5% can be considered to have good reproducibility.

[0067] Such analysis can help to evaluate the reliability of the experimental method and the quality of the data, has good rotation and orthogonality, can obtain the maximum amount of information in the least number of tests, and effectively evaluate the linear effect, interaction effect and curvature effect of each factor.

[0068] In one embodiment, the central composite design (CCD) experimental results are shown in Table 3 as follows: Table 3:

[0069] wherein, in the above experimental results, the sum of the proportions of the ternary composite matrix (biochar, zeolite, volcanic rock) is not strictly equal to 1. This is because the present application uses the standard central composite design (CCD) method to study the effect of the change of the content of each component on the treatment effect, rather than using the mixture experimental design. The focus of the present application is to investigate the influence of the change of the content of each component on the water purification effect near the central ratio point (0.650:0.200:0.150). This design method, although the total sum of some test points deviates from 1, can better reflect the influence of the independent change of the content of each component on the treatment effect.

[0070] (3) Regression analysis: the target test data can be substituted into the quadratic polynomial regression model of ternary composite matrix for regression analysis processing to determine the model coefficients corresponding to each constant term in the quadratic polynomial regression model, and then a regression equation of the target water quality index removal rate is constructed according to the model coefficients, and the regression equation of the target water quality index removal rate is used for model prediction processing to obtain the model predicted value, wherein the regression equation of the target water quality index removal rate includes: a total nitrogen removal rate regression equation, a chemical oxygen demand removal rate regression equation and a total phosphorus removal rate regression equation.

[0071] In one embodiment, the quadratic polynomial regression model is as follows:

[0072] wherein, represents a response value, is a constant term, is a first-order term coefficient, is a second-order term coefficient, is an interaction term coefficient.

[0073] Further, in order to evaluate the significance level and fitting degree of the model, and to ensure that the model can accurately reflect the influence law of each process parameter on the treatment effect, the determination coefficient (R2) of the quadratic polynomial regression model, ), the adjusted determination coefficient (R2adj) of the quadratic polynomial regression model, ), the F verification value and the P value are calculated, and when the calculation results of the determination coefficient, the adjusted determination coefficient, the F verification value and the P value are verified, the regression equation of the target water quality index removal rate is constructed according to the model coefficients. For details, see (A) to (D) as follows: (A) The calculation formula of the determination coefficient is as follows:

[0074] wherein, represents the residual sum of squares, is the total sum of squares. The determination coefficient represents the proportion of the variation of the response variable that can be explained by the model, and the closer the value is to 1, the better the model fitting degree.

[0075] (B) The adjusted determination coefficient can more accurately evaluate the fitting degree of the model, especially in the case of more independent variables, on the basis of the determination coefficient. The calculation formula is as follows:

[0076] wherein, is the sample size, is the number of independent variables in the model.

[0077] (C) F test value is used to test the overall significance of the regression model, and the calculation formula is as follows:

[0078] Wherein, is the regression sum of squares, is the residual sum of squares, is the regression sum of squares. The larger the F value, the higher the overall significance of the model.

[0079] (D) P value is the probability value corresponding to F test value, which is used to judge the significance of F test. The smaller the P value, the more significant the overall regression effect of the model. Generally, when the P value is less than 0.05, the model is considered to have statistical significance.

[0080] In an embodiment, by performing multiple quadratic regression analysis on experimental data, the present application obtains the regression model coefficients of COD removal rate and their significance levels, and each coefficient is shown in Table 4 as follows: Table 4

[0081] From the above table, it can be seen that the determination coefficient of the model reaches 0.967, the adjusted determination coefficient is 0.952, the value is 42.80, the value is 0.0001. These statistical indicators show that the model has significance and excellent goodness of fit. In addition, the regression model of total nitrogen removal rate established by the present application is:

[0082] The COD removal rate regression model is:

[0083] At the same time, the total phosphorus removal rate regression model expression is:

[0084] (4) Model verification: the relative error value can be determined by error analysis and processing of the predicted value of the model and the target test data; if the relative error value meets the preset relative error threshold, the current quadratic polynomial regression model is determined as the target quadratic polynomial regression model; if the relative error value does not meet the preset relative error threshold, the current quadratic polynomial regression model is subjected to closed loop iteration processing.

[0085] In an embodiment, the verification experiment aims to evaluate the predictive ability of the mathematical model constructed by response surface method on COD, total nitrogen and total phosphorus removal rates. By comparing the relative error between the model predicted value and the experimentally determined value, the reliability of the model and the applicability within the test range, i.e. the ternary compound substrate ratio range, can be evaluated. If the verification experiment result is ideal, it indicates that the model can provide a basis for subsequent optimization, predict the pollutant removal rate under different ratios, and ultimately guide the preparation of efficient water treatment substrate. To more specifically illustrate the verification result, the actual determination value and the model predicted value of the verification experiment need to be analyzed, and the relative error is calculated. When the relative error of most verification points is within the acceptable range, it can be considered that the model verification is successful. In order to comprehensively verify the accuracy and reliability of the model, the present application adopts the Box-Behnken experimental design method to design 15 groups of test points including boundary points and center points, so as to fully evaluate the predictive ability of the model within the entire test range. The calculation formula of the relative error is as follows:

[0086] wherein, is the model predicted value, is the experimentally determined value. After completing the verification experiment, the present application compares and analyzes the experimentally determined value and the model predicted value, and calculates the relative error to quantitatively evaluate the prediction accuracy and reliability of the model.

[0087] In an embodiment, the target quadratic polynomial regression model can be solved based on the constraint conditions of total nitrogen removal rate, chemical oxygen demand removal rate and total phosphorus removal rate, the expected function value is maximized within the range satisfying the constraint conditions, and the target mass ratio of the ternary compound substrate is obtained, that is, under the constraint conditions of COD removal rate , total nitrogen removal rate and total phosphorus removal rate , and combined with the response surface analysis result and verification test data, the optimal mass ratio of the ternary compound substrate is finally determined.

[0088] In order to verify the accuracy and reliability of the above model, the present application designs a series of verification experiments, including 12 boundary points and 3 center points. The specific test design scheme is shown in Table 5 as follows: Table 5

[0089] The specific results of the verification experiment are shown in Table 6 as follows: Table 6

[0090] (5) Response surface visualization: In order to intuitively understand the influence of each factor on the removal rate, the present application uses the response surface analysis method for visualization. By drawing a two-dimensional contour map, the interactive influence of any two factors on the removal rate is clearly shown under the fixed level of the third factor. At the same time, the three-dimensional response surface graph presents the overall trend of the response value with the change of each factor. This visualization method helps to intuitively grasp the optimal proportion region. Based on the comprehensive analysis of the contour map and the response surface graph, it is shown that when the proportion of biochar is at a high level and the proportion of zeolite and volcanic rock is at a medium level, the system shows the best pollutant removal effect. This visualization analysis result provides an important theoretical basis for subsequent optimization of the proportioning scheme.

[0091] Based on the response surface analysis result, the present application sets the maximum comprehensive removal efficiency as the optimization goal, and considers the constraint conditions that the COD removal rate is not less than 85%, the total nitrogen removal rate is not less than 80%, and the total phosphorus removal rate is not less than 80%. Through numerical optimization method, the optimal ternary composite substrate proportioning scheme is obtained. In this embodiment, the optimal proportions of biochar, zeolite and volcanic rock are 72.0%, 18.0% and 10.0% respectively. Under this proportioning condition, the model predicts that the removal rates of COD, total nitrogen and total phosphorus can reach 91.5%, 85.2% and 88.7% respectively, and the expected function value is 0.93. This optimization result not only meets the minimum removal rate requirements of each index, but also shows a significant advantage in the comprehensive treatment effect.

[0092] (6) Closed loop iteration: see Figure 3The flowchart illustrates a closed-loop iterative method. To comprehensively evaluate the feasibility and expected effects of the optimization results and apply them to actual system operation, this invention integrates result evaluation, system application, and performance monitoring. First, the actual feasibility of the optimal ratio is evaluated, its performance improvement relative to the current ratio is calculated, and the expected improvement effects of various indicators are analyzed in depth, ultimately generating a detailed evaluation report. Subsequently, the optimized ratio scheme is practically applied to the actual system. Specific operations include transmitting the optimal ratio parameters to the system dashboard, updating the feeder's ratio settings, preparing the formula for the next batch of production, and finally executing the ratio adjustment to realize the application of the ratio scheme. After the system applies the new ratio scheme, the system's operating status is continuously monitored, water quality treatment data under the new ratio is collected, and the deviation between the actual removal rate and the predicted value is calculated. This comprehensively evaluates the optimization effect and determines whether a next round of optimization is needed, thus achieving performance monitoring and feedback data output. Response surface optimization itself is a closed-loop iterative process. Through continuous data collection, analysis, and optimization, the treatment efficiency of the ternary composite matrix can be continuously improved. Each round of optimization is based on the actual operating data of the previous round, ensuring that the system performance gradually approaches the optimal level. This data-driven optimization method can effectively adapt to changes in water quality and dynamic adjustments in system operating status, thereby ensuring the stability and reliability of the treatment effect.

[0093] In summary, the present invention has the following advantages: (1) By integrating online monitoring and closed-loop feedback, a closed-loop system integrating real-time online water quality monitoring, data processing and analysis, and application of ratio optimization results was constructed to verify the optimization effect and realize continuous performance monitoring and dynamic adjustment of the system. The system monitors key indicators such as COD, TN, and TP of influent and effluent in real time and calculates the actual removal rate in real time. This provides rapid verification of the formulation effect. Simultaneously, the system compares and analyzes the actual performance data obtained from online monitoring with the predicted values ​​of the RSM model, calculating the relative error. This mechanism evaluates the optimization effect and feeds the evaluation results back to the ingredient batching system in real time, thus forming a complete closed-loop optimization mechanism that supports the system to continuously iterate and optimize based on actual operating results.

[0094] (2) A ternary composite matrix ratio optimization method based on response surface methodology (RSM) and central composite design (CCD) was used to systematically design experiments and mathematically model the mass ratio of biochar, zeolite, and volcanic rock to scientifically determine the optimal ratio. Applying RSM and CCD to the ratio optimization of ternary composite matrices breaks through the limitations of traditional trial-and-error methods or single-factor experiments. By establishing a scientific experimental design and statistical analysis system, this method can efficiently and accurately identify the complex interactions between components and determine the globally optimal ratio with the goal of maximizing the removal rates of COD, total nitrogen, and total phosphorus simultaneously, significantly improving the comprehensive treatment efficiency of the composite matrix.

[0095] (3) High-precision intelligent batching and three-stage variable speed uniform mixing system: The present invention developed an automated batching and mixing system that integrates high-precision weight sensing, PLC intelligent control and special mixing process for the precise preparation of optimized ternary composite matrix. The system uses a weight sensor and combines the PLC's PID closed-loop control algorithm to achieve precise ratio control based on the target feed rate by precisely adjusting the feeder speed.

[0096] Regarding the preparation method of the composite substrate for wastewater treatment provided in the foregoing embodiments, this embodiment of the invention provides a preparation device for the composite substrate for wastewater treatment, see [link to previous embodiment]. Figure 4 The diagram shows a structural schematic of a composite substrate preparation device for wastewater treatment, which includes the following parts: The experimental formula generation module 402 performs central combination treatment on the ternary composite matrix for wastewater treatment to generate an experimental formula set, wherein the ternary composite matrix includes: biochar, zeolite and volcanic rock, and the experimental formula set includes: composite matrix with different mass ratios; The data acquisition module 404 acquires the test data of the wastewater treatment test corresponding to the test formula set, and uses the removal rate of each matrix in the test data as the response value to obtain the target test data; The model optimization module 406 substitutes the target experimental data into the quadratic polynomial regression model of the ternary composite matrix for regression analysis to obtain the model prediction value. Then, using the model prediction value and the target experimental data, the quadratic polynomial regression model is validated and updated to determine the target quadratic polynomial regression model. The composite matrix ratio module 408 optimizes the ratio of the ternary composite matrix using a target quadratic polynomial regression model to determine the target quality ratio of the ternary composite matrix.

[0097] The wastewater treatment composite matrix configuration device provided in this application embodiment can significantly improve the pollutant removal rate for the synergistic removal of multiple pollutants during wastewater treatment.

[0098] In an embodiment, when the step of generating the test formula set by performing the central composite design on the ternary composite substrate for sewage treatment is performed, the test formula generation module 402 is further configured to: substitute the ternary composite substrate into a mathematical framework of the central composite design, determine an encoding value corresponding to the ternary composite substrate according to a number of factors of the ternary composite substrate and a preset encoding level of the mathematical framework, wherein the preset encoding level includes a factor point, an axis point and a center point; and convert the encoding value into an actual value to determine the test formula set according to the actual value.

[0099] In an embodiment, after the step of taking the removal rates of the respective substrates in the test data as response values is performed, the data acquisition module 404 is further configured to: perform data analysis processing on the test data to determine a mean value and a standard deviation of the test; send the mean value and the standard deviation to a preset coefficient of variation calculation model to obtain a coefficient of variation, and determine the test data as target test data when the coefficient of variation is less than a preset coefficient of variation threshold.

[0100] In an embodiment, when the step of obtaining the model predicted value by performing regression analysis processing on the target test data in the quadratic polynomial regression model of the ternary composite substrate is performed, the model optimization module 406 is further configured to: perform regression analysis processing on the target test data in the quadratic polynomial regression model of the ternary composite substrate to determine model coefficients corresponding to respective constant terms in the quadratic polynomial regression model; construct a regression equation of the target water quality index removal rate according to the model coefficients, and perform model prediction processing by using the regression equation of the target water quality index removal rate to obtain the model predicted value, wherein the regression equation of the target water quality index removal rate includes a total nitrogen removal rate regression equation, a chemical oxygen demand removal rate regression equation and a total phosphorus removal rate regression equation.

[0101] In an embodiment, before the step of constructing the regression equation of the target water quality index removal rate according to the model coefficients is performed, the model optimization module 406 is further configured to: respectively calculate a determination coefficient, an adjusted determination coefficient, an F verification value and a P value in the quadratic polynomial regression model, and when the calculation results of the determination coefficient, the adjusted determination coefficient, the F verification value and the P value are all verified, construct the regression equation of the target water quality index removal rate according to the model coefficients.

[0102] In one embodiment, when the step of determining the target quadratic polynomial regression model by performing model validation update processing on the quadratic polynomial regression model using the model prediction value and the target test data, the model optimization module 406 is further configured to: determine a relative error value by performing error analysis processing on the model prediction value and the target test data; if the relative error value meets a preset relative error threshold, determine the current quadratic polynomial regression model as the target quadratic polynomial regression model; and if the relative error value does not meet the preset relative error threshold, perform closed-loop iteration processing on the current quadratic polynomial regression model.

[0103] In one embodiment, when the step of determining the target mass ratio of the ternary composite substrate by performing optimization processing on the ratio of the ternary composite substrate by the target quadratic polynomial regression model, the composite substrate ratio module 408 is further configured to: based on the constraint conditions of the total nitrogen removal rate, the chemical oxygen demand removal rate and the total phosphorus removal rate, perform model solving processing on the target quadratic polynomial regression model, maximize the expected function value within the range that meets the constraint conditions, and obtain the target mass ratio of the ternary composite substrate.

[0104] The device provided by the embodiments of the present application has the same implementation principle and technical effects as the foregoing method embodiments. For brevity, the part not mentioned in the device embodiment part can be referred to the corresponding content in the foregoing method embodiments.

[0105] The embodiments of the present application provide a server, specifically, the server includes a processor and a storage device; the storage device stores a computer program, and the computer program performs the method of any one of the above embodiments when executed by the processor.

[0106] Figure 5 A structural diagram of a server provided by the embodiments of the present application is provided, and the server 100 includes a processor 50, a memory 51, a bus 52 and a communication interface 53, the processor 50, the communication interface 53 and the memory 51 are connected through the bus 52; the processor 50 is configured to execute executable modules stored in the memory 51, such as a computer program.

[0107] The memory 51 can include a high-speed random access memory (RAM) and can also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 53 (which can be wired or wireless), and the Internet, a wide area network, a local area network, a metropolitan area network, etc. can be used.

[0108] The bus 52 can be an ISA bus, a PCI bus, an EISA bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 5 Only one bidirectional arrow is used to represent the bus, but this does not mean that there is only one bus or only one type of bus.

[0109] The memory 51 is configured to store a program, and the processor 50 executes the program after receiving an execution instruction. The method executed by the device for defining the flow process according to any one of the embodiments of the application can be applied to the processor 50 or implemented by the processor 50.

[0110] The processor 50 can be an integrated circuit chip with processing capability. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 50 or an instruction in the form of software. The processor 50 described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), and the like; or can be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. Each method, step and logic block diagram disclosed in the embodiments of the application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the application can be directly embodied as a hardware code processor for execution, or a combination of hardware and software modules in the code processor for execution. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, or other mature storage media in the art. The storage medium is located in the memory 51, and the processor 50 reads the information in the memory 51 and combines the hardware to complete the steps of the above method.

[0111] The computer program product of the readable storage medium provided by the embodiments of the application includes a computer readable storage medium storing program codes, and the program codes include instructions for executing the method described in the foregoing method embodiments. For specific implementation, reference can be made to the foregoing method embodiments, which will not be described here.

[0112] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0113] Finally, it should be noted that: the above-described embodiments are only specific embodiments of the present application, which are used to illustrate the technical solutions of the present application, but not to limit them. The protection scope of the present application is not limited to this. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily think of changes to the technical solutions recorded in the foregoing embodiments within the technical range disclosed by the present application, or make equivalent replacements to some technical features. The modifications, changes or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for preparing a composite substrate for wastewater treatment, characterized in that, The method includes: A central combination treatment was carried out on a ternary composite matrix for wastewater treatment to generate a set of experimental formulations. The ternary composite matrix includes biochar, zeolite and volcanic rock, and the set of experimental formulations includes composite matrices with different mass ratios. Obtain the test data of the wastewater treatment test corresponding to the set of test formulations, and use the removal rate of each matrix in the test data as the response value to obtain the target test data; The target experimental data is substituted into the quadratic polynomial regression model of the ternary composite matrix for regression analysis to obtain the model prediction value. The model prediction value and the target experimental data are then used to perform model validation and update processing on the quadratic polynomial regression model to determine the target quadratic polynomial regression model. The proportions of the ternary composite matrix are optimized using the target quadratic polynomial regression model to determine the target quality proportions of the ternary composite matrix.

2. The method for preparing the composite substrate for wastewater treatment according to claim 1, characterized in that, The step of centrally combining and processing the ternary composite matrix for wastewater treatment to generate a set of experimental formulations includes: Substitute the ternary composite matrix into the mathematical framework of the central combination design, and determine the corresponding coding value of the ternary composite matrix according to the number of factors of the ternary composite matrix and the preset coding level of the mathematical framework. The preset coding level includes: factor points, axis points and center points. The encoded values ​​are converted into actual values ​​to determine the set of experimental formulations based on the actual values.

3. The method for preparing the composite substrate for wastewater treatment according to claim 1, characterized in that, After the step of using the removal rate corresponding to each matrix in the experimental data as the response value, the following is included: The experimental data were analyzed and processed to determine the mean and standard deviation of the experiment. The average value and the standard deviation are sent to a preset coefficient of variation calculation model to obtain the coefficient of variation. When the coefficient of variation is less than a preset coefficient of variation threshold, the experimental data is determined as the target experimental data.

4. The method for preparing the composite substrate for wastewater treatment according to claim 1, characterized in that, The step of substituting the target experimental data into the quadratic polynomial regression model of the ternary composite matrix for regression analysis to obtain the model prediction value includes: The target experimental data are substituted into the quadratic polynomial regression model of the ternary composite matrix for regression analysis to determine the model coefficients corresponding to each constant term in the quadratic polynomial regression model. The regression equations for the removal rates of the target water quality indicators are constructed based on the model coefficients, and the model prediction is performed using the regression equations for the removal rates of the target water quality indicators to obtain the model prediction values. The regression equations for the removal rates of the target water quality indicators include: total nitrogen removal rate regression equation, chemical oxygen demand removal rate regression equation, and total phosphorus removal rate regression equation.

5. The method for preparing the composite substrate for wastewater treatment according to claim 4, characterized in that, Before the step of constructing a regression equation for the removal rate of the target water quality index based on the model coefficients, the following steps are included: The coefficient of determination, adjusted coefficient of determination, F-validation value, and P-value in the quadratic polynomial regression model are calculated respectively. When the calculation results of the coefficient of determination, the adjusted coefficient of determination, the F-validation value, and the P-value are all verified, the regression equation for the removal rate of the target water quality index is constructed based on the model coefficients.

6. The method for preparing the composite substrate for wastewater treatment according to claim 1, characterized in that, The steps for determining the target quadratic polynomial regression model by using the model's predicted values ​​and the target experimental data to perform model validation and update processing on the quadratic polynomial regression model include: The relative error value is determined by performing error analysis on the model predictions and the target experimental data. If the relative error value meets the preset relative error threshold, then the current quadratic polynomial regression model is determined as the target quadratic polynomial regression model; If the relative error value does not meet the preset relative error threshold, the current quadratic polynomial regression model will undergo closed-loop iteration.

7. The method for preparing the composite substrate for wastewater treatment according to claim 1, characterized in that, The step of optimizing the proportions of the ternary composite matrix using the target quadratic polynomial regression model to determine the target quality proportions of the ternary composite matrix includes: Based on the constraints of total nitrogen removal rate, chemical oxygen demand removal rate, and total phosphorus removal rate, the target quadratic polynomial regression model is solved to maximize the expected function value within the range that satisfies the constraints, thereby obtaining the target mass ratio of the ternary composite matrix.

8. A device for preparing a composite substrate for wastewater treatment, characterized in that, The device includes: The experimental formulation generation module performs central combination treatment on the ternary composite matrix for wastewater treatment to generate a set of experimental formulations. The ternary composite matrix includes biochar, zeolite and volcanic rock, and the set of experimental formulations includes composite matrices with different mass ratios. The data acquisition module acquires the test data of the wastewater treatment test corresponding to the test formula set, and uses the removal rate of each matrix in the test data as the response value to obtain the target test data; The model optimization module substitutes the target experimental data into the quadratic polynomial regression model of the ternary composite matrix for regression analysis to obtain the model prediction value. Then, using the model prediction value and the target experimental data, the quadratic polynomial regression model is validated and updated to determine the target quadratic polynomial regression model. The composite matrix proportioning module optimizes the proportioning of the ternary composite matrix using the target quadratic polynomial regression model to determine the target quality proportion of the ternary composite matrix.

9. A server, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.

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