An adaptive degradation control method and system based on tetracycline contaminated water body

Abstract

The present application relates to the technical field of adaptive degradation control scheme design based on tetracycline contaminated water bodies, and particularly relates to an adaptive degradation control method and system based on tetracycline contaminated water bodies. The method comprises setting multiple monitoring points in the contaminated water body, real-time monitoring of tetracycline concentration and water quality parameters such as dissolved oxygen and pH value, fusing and processing the data, and predicting the degradation condition using a tetracycline degradation model. When the water quality parameters meet the preset conditions, a feedback adjustment mechanism is triggered to automatically adjust the microbial flora dosage, nutrient supply amount and environmental conditions (temperature, pH value). The microbial flora dosage adjustment rule, nutrient supply amount adjustment rule and environmental condition adjustment mode are also given. The system comprises a monitoring module, a data analysis module and a feedback adjustment module. The present application realizes adaptive control of tetracycline contaminated water body degradation, effectively improves the degradation efficiency, and guarantees the stability of the treatment effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of adaptive degradation control scheme design based on tetracycline contaminated water bodies, and particularly relates to an adaptive degradation control method and system based on tetracycline contaminated water bodies. BACKGROUND

[0002] With the rapid development of industry, the pollution problem of antibiotics such as tetracycline in water bodies is becoming increasingly serious. A large amount of tetracycline entering water bodies not only causes damage to the ecological environment, but also may endanger human health through the food chain. At present, there are many methods for treating tetracycline contaminated water bodies, but there are still deficiencies. Traditional treatment methods often lack comprehensive consideration of various factors in the water environment, and the treatment effect is unstable. Moreover, as a green and environmentally friendly treatment method, the degradation efficiency of microorganisms is affected by many factors such as water quality environment, microbial population quantity and nutrient supply, and it is difficult to achieve precise control. Therefore, there is an urgent need for a scheme that can comprehensively consider various factors and adaptively adjust the tetracycline degradation process to improve the treatment efficiency of tetracycline contaminated water bodies and improve the water environment quality.

[0003] Therefore, the prior art still needs further development. SUMMARY

[0004] The purpose of the present application is to overcome the above technical deficiencies and provide an adaptive degradation control method and system based on tetracycline contaminated water bodies to solve the problems existing in the prior art.

[0005] To achieve the above technical purpose, according to the first aspect of the present application, the present application provides an adaptive degradation control method based on tetracycline contaminated water bodies, comprising:

[0006] S100, a plurality of monitoring points are arranged in the tetracycline contaminated water body, and the tetracycline concentration and related water quality parameters in the water body are monitored in real time, the related water quality parameters including dissolved oxygen, pH value, temperature and organic matter content;

[0007] S200, the data obtained by different types of sensors are fused and processed, the fused data are analyzed in real time, and the degradation trend and degradation rate of tetracycline are predicted through the established tetracycline degradation model;

[0008] S300, when the change of tetracycline concentration or related water quality parameters in the water body meets the preset condition, a feedback adjustment mechanism is triggered; the feedback adjustment mechanism automatically adjusts the amount of microbial population participating in tetracycline degradation, the amount of nutrient supply and the survival environment conditions of microorganisms according to the preset rules, and the survival environment conditions include temperature and pH value.

[0009] Specifically, the adjustment of the amount of the microbial flora is performed according to the tetracycline concentration and the hydrolysis condition, and in accordance with a preset feeding ratio and feeding frequency.

[0010] Specifically, the adjustment of the nutrient supply amount includes dynamically adjusting the supply concentration of carbon source, nitrogen source and phosphorus source according to the growth requirement of the microorganism.

[0011] Specifically, the adjustment of the microorganism survival environment condition is automatically performed by a temperature control device and a pH adjusting device installed in the water body according to the monitoring value of the environmental parameter.

[0012] Specifically, the tetracycline degradation model is obtained by training a machine learning algorithm based on historical experimental data, and the machine learning algorithm includes a support vector machine algorithm or a random forest algorithm.

[0013] Specifically, the fusion processing of the data obtained by different types of sensors includes:

[0014] The sensor data is subjected to data cleaning, feature extraction and data fusion algorithm processing, and the data fusion algorithm adopts a neural network algorithm.

[0015] Specifically, the method includes:

[0016] If the tetracycline concentration decreases at a rate less than a threshold value v1 from a time t1 to a time t2, and the dissolved oxygen concentration DO is lower than a threshold value O1, the amount of the microbial flora is increased by Δm;

[0017] If the pH value deviates from a target pH value range, a pH adjusting device is started, and the adjusting amount is ΔpHadj, and an alkaline substance is added to the water body.

[0018] Specifically, if the tetracycline concentration decreases at a rate greater than or equal to the threshold value v1 from the time t1 to the time t2, and the dissolved oxygen concentration DO is greater than or equal to the threshold value O1, and the pH value is within the target pH value range, the current microbial feeding amount, nutrient supply amount and environmental condition are kept unchanged.

[0019] Specifically, the method includes:

[0020] A temperature sensor is installed in the water body to monitor the water temperature T in real time, a target temperature range Ttarget is set, and when the monitored temperature T deviates from the target range, a temperature adjusting device is started, and if T>Ttarget, cooling measures are adopted, and a cooling pipeline is arranged in the water body to cool the water body by circulating cooling water.

[0021] According to a second aspect of the present application, an adaptive degradation control system based on a tetracycline contaminated water body is provided, including:

[0022] The monitoring module comprises a plurality of monitoring points arranged in the tetracycline-polluted water body, and is used for monitoring the tetracycline concentration in the water body and related water quality parameters in real time, wherein the related water quality parameters comprise dissolved oxygen, pH value, temperature and organic matter content.

[0023] The data analysis module is used for fusing the data acquired by different types of sensors, analyzing the fused data in real time, and predicting the degradation trend and degradation rate of tetracycline through the established tetracycline degradation model.

[0024] The feedback adjustment module is used for triggering a feedback adjustment mechanism when the change of the tetracycline concentration or the related water quality parameters in the water body meets a preset condition; the feedback adjustment mechanism automatically adjusts the amount of the microbial flora participating in the tetracycline degradation, the amount of the nutrient supply and the survival environment conditions of the microorganisms, wherein the survival environment conditions comprise temperature and pH value, according to a preset rule.

[0025] Advantages:

[0026] The self-adaptive degradation control method and system based on the tetracycline-polluted water body provided by the application have remarkable effects. First, the tetracycline concentration and the related water quality parameters are monitored in real time through a plurality of monitoring points, so that the water body environment conditions are comprehensively mastered, and data support is provided for subsequent accurate treatment. Second, the sensor data is fused and processed, and a tetracycline degradation model is established, so that the degradation trend and rate can be accurately predicted, and preparation for regulation and control can be made in advance. In terms of feedback adjustment, the amount of the microbial flora, the amount of the nutrient supply and the survival environment conditions are automatically adjusted according to the preset conditions, so that accurate control is realized. For example, the amount of the microbial flora is reasonably adjusted according to the changes of the tetracycline concentration and the dissolved oxygen concentration, so that resource waste or insufficient treatment is avoided. Through the temperature control device and the pH adjustment device, the suitable survival environment of the microorganisms is maintained, and the degradation efficiency is improved. Moreover, the method and system have self-adaptive capability, can be dynamically adjusted with the change of the water quality, ensure stable treatment effect, effectively solve the treatment problem of the tetracycline-polluted water body, and have broad application prospects. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is a flowchart of the self-adaptive degradation control method based on the tetracycline-polluted water body provided in the embodiments of the application;

[0028] Figure 2 is a system composition schematic diagram of the self-adaptive degradation control system based on the tetracycline-polluted water body provided in the embodiments of the application. DETAILED DESCRIPTION

[0029] In order to make the personnel in the art better understand the technical solutions of the present application, the technical solutions of the present application will be described clearly and completely below in combination with the drawings of the present application. Based on the embodiments in the present application, other similar embodiments obtained by the personnel in the art without making creative efforts shall all belong to the scope of protection of the present application. In addition, the direction words mentioned in the following embodiments, the present application is preferred to be "up", "down", "left", "right" and the like, which are only the directions of the drawings. Therefore, the direction words used are used to illustrate but not to limit the present application.

[0030] The present application will be further described below in combination with the drawings and preferred embodiments.

[0031] Please refer to Figure 1 , the present application provides a self-adaptive degradation control method based on tetracycline contaminated water body, comprising:

[0032] S100, a plurality of monitoring points are arranged in the tetracycline contaminated water body, and the concentration of tetracycline in the water body and related water quality parameters are monitored in real time, the related water quality parameters including dissolved oxygen, pH value, temperature and organic matter content.

[0033] S200, the data obtained by different types of sensors are fused and processed, the fused data are analyzed in real time, and the degradation trend and degradation rate of tetracycline are predicted through the established tetracycline degradation model.

[0034] Specifically, the data obtained by different types of sensors are fused and processed, comprising:

[0035] The sensor data are subjected to data cleaning, feature extraction and data fusion algorithm processing, wherein the data fusion algorithm adopts a neural network algorithm.

[0036] Specifically, the data cleaning, feature extraction and data fusion algorithm processing of the sensor data comprise:

[0037] (I) Data cleaning

[0038] 1. Noise removal

[0039] For the original data obtained by the sensor, a sliding average filtering algorithm is adopted to remove noise. The present application is preferred to set a sliding window with a length of 5 for the data of the dissolved oxygen sensor (the selection of the value is based on the relatively slow change of the dissolved oxygen concentration in the tetracycline contaminated water body, and it is verified by experiments that the window length of 5 can effectively remove high-frequency noise while better preserving the real change trend of the data). For the data in the window, the filtered data are calculated as follows:

[0040] ​​​

[0041] 2. Outlier removal

[0042] Use statistically based methods such as Grubbs's criterion to calculate the mean of a data series. and standard deviation For a data point , if:

[0043]

[0044] in (This value is the commonly used judgment threshold in the Grubbs criterion and can better identify outliers), then the judgment After data cleaning, relatively pure original data is obtained, laying the foundation for subsequent fusion processing.

[0045] (2) Feature extraction

[0046] 1. Time domain feature extraction

[0047] The time domain features of the data after data cleaning are extracted, mainly including mean, variance, maximum value, minimum value, etc. Taking pH sensor data as an example, the data sequence is .

[0048] mean The calculation formula is:

[0049]

[0050] variance The calculation formula is:

[0051]

[0052] Maximum , minimum These time domain characteristics can reflect the statistical properties of the data and help analyze the environmental status of water bodies.

[0053] 2. Frequency domain feature extraction

[0054] Time domain data is converted into frequency domain data through fast Fourier transform (FFT). For the monitoring data of organic matter content in tetracycline-contaminated water bodies, after FFT transformation, the main frequency components and their amplitudes in the spectrum are extracted. The present invention preferably sets the spectrum amplitude threshold T = 0.1 (this threshold is set based on the analysis of a large amount of experimental data. When the amplitude is lower than 0.1, the corresponding frequency components have little significance in representing the organic matter content). Frequency components with amplitudes greater than this threshold are considered to be the main frequency components. These characteristics are related to the chemical structure and reaction kinetics of organic matter in water bodies, which can further enrich the data features.

[0055] (3) Data fusion algorithm

[0056] A multilayer perceptron neural network was constructed. The number of input layer nodes was 4 (corresponding to the four variables of tetracycline concentration, dissolved oxygen concentration, pH value and temperature in tetracycline-contaminated water), the number of hidden layer nodes was 10 (the selection of the number of nodes was obtained after many experiments. By comparing the prediction accuracy of the model with different numbers of nodes, when the number of nodes was 10, the average prediction error of the model on the validation set was the smallest), and the number of output layer nodes was 3. The network weights were initialized using the Xavier initialization method. The weight matrix of the layer , its elements The initialization formula is:

[0057]

[0058]

[0059] in and They are Layer and The number of neurons in the layer. The activation function uses the ReLU function, that is, The training data set was obtained through a large number of experiments, including data with different tetracycline pollution levels and different initial water quality conditions. The mean square error (MSE) was used as the loss function. and the true value , the calculation formula of MSE is: is the number of training samples. The optimization algorithm selects Adam optimization algorithm, and the learning rate is set to (The learning rate setting was obtained through experimental adjustment. A smaller learning rate can ensure convergence stability, while a larger learning rate may lead to slow convergence or even failure to converge. Through multiple sets of comparative experiments, a learning rate of 0.001 can enable the model to achieve good convergence within a reasonable number of training rounds.) A neural network algorithm is used to perform nonlinear fusion of multi-source data, explore deep relationships in the data, and improve the accuracy of tetracycline-contaminated water status assessment.

[0060] Specifically, the degradation trend and rate of tetracycline are predicted by the established tetracycline degradation model, including:

[0061] (I) Data collection and preprocessing

[0062] 1. Data collection

[0063] Collect experimental data from different tetracycline pollution levels (set low pollution concentration range 0-1 mg / L, medium pollution concentration range 1-5 mg / L, high pollution concentration range 5-10 mg / L), different water quality environments (including different dissolved oxygen concentration ranges such as 0-2 mg / L, 2-8 mg / L, 8-14 mg / L, different pH value ranges (2-6, 6-8, 8-10) and temperature ranges (10-20℃, 20-30℃, 30-40℃). Data sources include laboratory simulation experiments and monitoring data of actual polluted water bodies, ensuring the diversity and representativeness of the data. 2. Data preprocessing - clean the collected data, remove duplicate data, error data (such as data that obviously do not conform to physical and chemical laws) and missing data (for missing data, use mean filling or model-based filling methods, such as using Kalman filter algorithm to predict and fill the data at missing time). Then normalize the data, use the min-max normalization method, for variable x, the normalization formula is:

[0064]

[0065] Where x_{min} and x_{max} are the minimum and maximum values of variable x in the data set. Normalization helps improve the training efficiency and generalization ability of the model.

[0066] (II) Model selection and construction

[0067] 1. Support vector machine (SVM) model construction - select SVM model with radial basis function (RBF) kernel, its decision function is:

[0068]

[0069] Where is the input tetracycline pollution related data vector, is the training sample data vector, is the Lagrange multiplier, is the RBF kernel function, the expression is:

[0070]

[0071] Where is the kernel width parameter, which is optimized by cross-validation method (dividing the data set into training set and validation set with a ratio of 7:3). (This value is determined based on different Comprehensive evaluation of the classification accuracy and mean square error of the model on the validation set under the value of The model performed well in predicting the degradation trend and degradation rate of tetracycline. Is the bias term. The SVM model is trained with training data to learn the degradation law of tetracycline.

[0072] 2. Random Forest Algorithm Construction

[0073] A random forest model is constructed, consisting of multiple decision trees. For each decision tree, the CART (Classification and Regression Trees) algorithm is used. Each node of the decision tree is split based on information gain or the Gini index (the Gini index is selected here because it performs better when processing continuous variables). The present invention preferably calculates the Gini index for each feature split node for features such as tetracycline concentration and dissolved oxygen concentration, and selects the feature with the smallest Gini index for splitting. The calculation formula for the Gini index is:

[0074]

[0075] in is a sample dataset, It is a dataset Belong to the The probability of class samples, is the number of categories. When building a random forest model, the number of decision trees is set to (This value is determined by experiments. The experiments show that when the number of decision trees is 100, the model achieves a good balance between accuracy and stability.) The maximum depth of the tree is (Experiments have shown that a depth of 10 layers can better capture complex relationships in the data while avoiding overfitting.) By studying a large amount of training data, a random forest model was constructed to predict the degradation model of tetracycline.

[0076] S300. When the changes in the tetracycline concentration or related water quality parameters in the monitored water body meet the preset conditions, the feedback regulation mechanism is triggered; the feedback regulation mechanism automatically adjusts the amount of microbial flora involved in tetracycline degradation, the amount of nutrients supplied, and the living environment conditions of the microorganisms according to the preset rules, and the living environment conditions include temperature and pH value.

[0077] Specifically, the adjustment of the amount of the microbial flora is performed according to the tetracycline concentration and the hydrolysis condition, and according to the preset feeding ratio and feeding frequency.

[0078] Specifically, the adjustment of the nutrient supply amount includes dynamically adjusting the supply concentration of carbon source, nitrogen source and phosphorus source according to the growth demand of the microorganism.

[0079] Specifically, the adjustment of the microorganism survival environment condition is automatically performed by the temperature control device and the pH adjusting device installed in the water body according to the monitoring value of the environmental parameter.

[0080] Specifically, the tetracycline degradation model is obtained by training a machine learning algorithm based on historical experimental data, and the machine learning algorithm includes a support vector machine algorithm or a random forest algorithm.

[0081] Specifically, the method comprises:

[0082] If the tetracycline concentration decreases at a rate less than a threshold v1 from t1 to t2, and the dissolved oxygen concentration DO is lower than a threshold O1, the amount of the microbial flora is increased by Δm.

[0083] If the pH value deviates from the target pH value range, the pH adjusting device is started, and the adjusting amount is ΔpHadj, and an alkaline substance is added to the water body.

[0084] Specifically, if the tetracycline concentration decreases at a rate greater than or equal to the threshold v1 from t1 to t2, and the dissolved oxygen concentration DO is greater than or equal to the threshold O1, and the pH value is within the target pH value range, the current microbial feeding amount, nutrient supply amount and environmental condition are kept unchanged.

[0085] Specifically, the method comprises:

[0086] A temperature sensor is installed in the water body to monitor the water temperature T in real time, and a target temperature range Ttarget is set, and when the monitored temperature T deviates from the target range, a temperature adjusting device is started, and if T>Ttarget, cooling measures are adopted, and a cooling pipeline is arranged in the water body to cool the water body by circulating cooling water.

[0087] In the preferred embodiment of the present application, the feedback adjustment mechanism comprises:

[0088] (I) The working process of the intelligent decision-making unit

[0089] 1. Data acquisition and analysis

[0090] The intelligent decision-making unit obtains from the data analysis module the predicted results of the tetracycline degradation model and real-time data on relevant water quality parameters, such as the current tetracycline concentration, dissolved oxygen concentration, pH value, and temperature. Preferably, the data is obtained every T = 1 hour (this interval is determined based on factors such as the relatively slow change in water quality in tetracycline-contaminated water and the measurement accuracy and energy consumption of the sensor).

[0091] Perform feature analysis on the acquired data and calculate the rate of change of each parameter (such as the rate of change of tetracycline concentration ,in is the tetracycline concentration, is time), and judge the stability of the current water environment.

[0092] 2. Rule matching and strategy generation

[0093] Match rules based on the preset rule base. The rules in the rule base include:

[0094] If the tetracycline concentration is Time has come The decreasing rate at the moment is less than the threshold ( This value is obtained through degradation experiments on water bodies with different pollution levels. When the degradation rate is lower than this value, the degradation strategy needs to be adjusted to improve the degradation efficiency), and the dissolved oxygen concentration Below threshold ( , based on the optimal dissolved oxygen concentration range for microorganisms to degrade tetracycline by aerobic respiration), the amount of microbial flora added is increased. ( , according to the experiment on the degradation ability of microorganisms on tetracycline, the amount increased each time can effectively improve the degradation efficiency without causing waste of microbial resources and deterioration of water quality).

[0095] If the pH value deviates from the pH range suitable for the growth of tetracycline-degrading microorganisms (set to 6-8) and exceeds the threshold (This value is determined based on the microbial tolerance experiment to pH changes), then start the pH adjustment device, and the adjustment amount is (Determined by the difference between the current pH value and the appropriate range, if the current pH value is 5.5, which is lower than 6, then ), add alkaline substances to the water body (such as sodium hydroxide solution, ensuring that the amount added can adjust the pH value to the appropriate range without causing other adverse effects of chemical reactions).

[0096] If the tetracycline concentration is Time has come The rate of decrease at the moment is greater than and the dissolved oxygen concentration higher than If the pH value is in the appropriate range, the current microbial inoculation amount, nutrient supply amount and environmental conditions are maintained, and the parameters of photocatalysis or physical adsorption are appropriately optimized according to the prediction of the data model and the analysis of historical data to improve the treatment efficiency.

[0097] In other cases (microbial activity is inhibited, which is determined by the accumulation of microbial metabolites exceeding a threshold value , or abnormal data (data deviation exceeds a threshold value of the normal range ), a system self-checking and maintenance program is triggered, and the current control strategy is suspended to solve the problems of data anomalies or system failures.

[0098] (II) Comprehensive analysis method of historical data and real-time data

[0099] 1. Data fusion and feature extraction

[0100] The historical data and real-time data are fused to establish a time series database. Feature extraction is performed on the fused data, in addition to the above-mentioned change rate features of tetracycline concentration, dissolved oxygen concentration and other basic parameters, long-term trend features (in the preferred embodiment of the present application, the average trend of tetracycline concentration in the past week is preferred, and the trend coefficient is obtained by linear fitting of the data) and periodic features (in the preferred embodiment of the present application, whether there is a weekly or monthly periodicity of water quality and tetracycline concentration variation is preferred, which is obtained by Fourier series analysis or autocorrelation function analysis).

[0101] 2. Machine learning algorithm assisted analysis

[0102] The hidden Markov model (HMM) is used to analyze the data, and the observation sequence of HMM is the real-time and historical water quality parameter data, and the hidden state is the potential state of tetracycline contaminated water body (in the preferred embodiment of the present application, the active state of microorganisms and different stages of tetracycline degradation are preferred). Through the training of the model (the training is performed by using the Baum-Welch algorithm, the maximum number of iterations is set to N=100, and the convergence threshold is set to 0.001, and these values are set based on the trade-off between the convergence speed and accuracy of the model in the experiment), the dynamic variation law of the water body system is understood, and more comprehensive data support is provided for the rule making of the intelligent decision unit.

[0103] In the preferred embodiment of the present application, the feedback regulation mechanism further comprises:

[0104] (I) Microbial inoculation amount adjustment

[0105] 1. Tetracycline concentration and water quality condition based dosage calculation

[0106] According to the water quality parameters such as tetracycline concentration C, dissolved oxygen concentration DO, pH value and temperature T in the tetracycline contaminated water body, a pre-established microbial dosage calculation model is used. The model is constructed based on the principles of microbial metabolic kinetics and tetracycline degradation kinetics, and has the form:

[0107]

[0108] Where {m} is the microbial dosage (unit: kg / m³), k is the proportionality constant (obtained by a large number of experiments, k = 0.01 for specific microbial flora and tetracycline pollution type), f(pH) is a function of pH, and g(T) is a function of temperature T.

[0109] The present application is preferably:

[0110]

[0111]

[0112] The determination of these functions is based on the experimental results of microbial tetracycline degradation activity under different pH and temperature conditions, and is obtained by nonlinear fitting. When the changes of tetracycline concentration, dissolved oxygen, pH value and temperature and other parameters in the water body are monitored, the dosage of microorganisms is recalculated according to the above formula to ensure that the degradation ability of microorganisms is always in the best state.

[0113] 2. Control of dosage frequency

[0114] The dosage frequency f is determined according to the change rate of tetracycline concentration and the survival and metabolic time Tm of microorganisms in the water body (determined by experiment, Tm = 24 hours for specific microbial flora). When the change rate is greater than the threshold value v2 (v2 = 0.02 mg / (L·h), determined based on the experimental requirements of microbial degradation of rapidly increasing tetracycline concentration), the dosage frequency is increased, f = 2f0 (f0 is the initial dosage frequency, initially set to 1 time / day, and the initial value determined by multiple experiments can ensure normal tetracycline degradation in the initial stage according to the actual water body conditions and microbial metabolic characteristics).

[0115] (II) Nutrient supply strategy

[0116] 1. Nutrient type determination

[0117] Based on the microbial species and the metabolic pathway for tetracycline degradation, the primary nutrients are determined to be carbon, nitrogen, and phosphorus sources. For the selected tetracycline-degrading microbial flora (preferably Pseudomonas species in this invention), glucose is selected as the carbon source because it is a commonly used and readily available carbon source in microbial metabolism, and experiments have shown that it significantly promotes the growth and metabolic activity of tetracycline-degrading microorganisms.

[0118] Urea is the nitrogen source, and potassium dihydrogen phosphate is the phosphorus source. The ratio of these nutrients is based on the carbon, nitrogen, and phosphorus requirements of the microorganisms (C:N:P = 100:5:1, a ratio determined through extensive microbial experiments to maintain normal microbial cell growth and metabolism and ensure their ability to degrade tetracycline).

[0119] 2. Dynamic adjustment of supply

[0120] The amount of nutrients supplied is dynamically adjusted according to the growth stage of the microorganism and the degradation of tetracycline. In the logarithmic growth phase of the microorganism, the demand for tetracycline degradation is large, and the amount of nutrients supplied is appropriately increased. The present invention preferably provides a carbon source supply of The calculation formula is:

[0121]

[0122] in is the number of microorganisms (obtained by counting under a microscope or detecting with a biosensor), is the initial concentration of the carbon source (set to 100 mg / L in the experiment), is the adjustment coefficient ( , obtained through experimental adjustment, can ensure the nutritional needs of microorganisms in the logarithmic growth phase). is the coefficient related to the degradation rate of tetracycline ( (determined based on nutrient consumption experiments of microorganisms during tetracycline degradation). The supply of nitrogen and phosphorus sources was adjusted proportionally based on the supply of carbon source to maintain a C:N:P ratio of 100:5:1.

[0123] (3) Temperature control

[0124] Install temperature sensors in water bodies to monitor water temperature in real time . Set the temperature target range to (This range is determined based on the optimal growth temperature range of tetracycline-degrading microorganisms. After a large number of microbial experiments, it was found that the enzyme activity of microorganisms in this temperature range is the highest and the tetracycline degradation efficiency is the best).

[0125] When the monitored temperature If the temperature deviates from the target range, the temperature control device is activated. cooling measures, such as setting cooling pipes in the water body, circulating cooling water (the cooling water temperature is set to , which can effectively reduce the water temperature, taking into account the energy consumption and the amplitude of temperature reduction, and through experiments, it is determined that it can quickly and stably reduce the temperature to the target range) to cool the water. The cooling rate is precisely controlled by controlling the flow of cooling water, which is realized by installing a flow regulating valve on the cooling water pipe. The flow regulating valve is linked with the temperature monitoring system, and when the temperature T deviates from the target range to different degrees, the opening of the flow regulating valve is automatically adjusted. The present application is preferably , the opening of the flow regulating valve is adjusted to a larger value, the cooling water is quickly circulated, and a large amount of heat is taken away to quickly reduce the water temperature; when , the opening of the flow regulating valve is appropriately reduced to maintain a relatively moderate cooling rate to avoid the impact of rapid temperature drop on microorganisms. During the cooling process, the temperature sensor continuously monitors the water temperature until , the target range , at this time, the flow regulating valve is fine-tuned according to the difference between the current temperature and the target temperature to stabilize the temperature in the appropriate interval. If , heating measures are started. Heating wires are set on the cooling pipe to increase the water temperature by converting electrical energy into heat energy. The heating power also needs to be precisely controlled according to the temperature deviation. When , the input power of the heating wire is increased to speed up the water temperature rise; when , the heating power is appropriately reduced to stably and accurately raise the temperature to the target range. At the same time, in the heating process, the heating power is continuously adjusted using the data feedback from the temperature sensor to ensure that the water temperature smoothly reaches and stabilizes in the range.

[0126] (IV) pH value control

[0127] The pH monitoring and adjusting system installs high-precision pH sensors in the water body to determine the pH value of the water body in real time. For tetracycline-degrading microorganisms, the pH target range is set to (This range is determined based on the growth and metabolism characteristics of such microorganisms. In this pH range, the enzyme activity and cell membrane stability of the microorganisms are best, which is beneficial to the degradation of tetracycline). When the monitored pH value deviates from the target range, the pH adjusting device is started. If , it indicates that the water body is alkaline and needs to be adjusted by acidification. Suitable acid solution, such as dilute hydrochloric acid (HCl) solution, is accurately injected into the water body through a metering pump. The flow of the metering pump is intelligently controlled according to the degree of pH value deviation. The present application is preferably , the flow of the metering pump is increased to quickly reduce the pH value of the water body; when When the pH value is too low, the flow of the metering pump is appropriately reduced to avoid the adverse effects of too rapid a decrease in the pH value on the growth of the microorganisms. During the adjustment, the pH sensor is continuously monitored until The target range is entered At this time, the metering pump is finely adjusted according to the small difference between the current pH value and the target value to ensure the stability of the pH value. If , it indicates that the water body is acidic and needs to be adjusted by alkalization. Sodium hydroxide (NaOH) solution is selected as the lye, and the lye is injected into the water body through the metering pump. When , the flow of the metering pump is increased to rapidly increase the pH value of the water body; when , the flow of the metering pump is reduced to maintain the stable increase of the pH value. The entire adjustment process relies on the real-time feedback of the pH sensor to ensure that the pH value of the water body is stably within the range of , thereby providing a suitable growth environment for the tetracycline-degrading microorganisms.

[0128] It can be understood that the self-adaptive degradation control method and system based on a tetracycline-polluted water body provided by the present application has remarkable effects. First, the tetracycline concentration and related water quality parameters are monitored in real time through multiple monitoring points to comprehensively grasp the water body environmental conditions and provide data support for subsequent accurate treatment. Second, the sensor data are fused and processed and a tetracycline degradation model is established to accurately predict the degradation trend and rate and make preparation in advance. In terms of feedback adjustment, the amount of microorganism inoculation, the amount of nutrient supply and the survival environment conditions are automatically adjusted according to preset conditions to realize accurate control. For example, the amount of microorganism inoculation is reasonably adjusted according to the changes in the tetracycline concentration and the dissolved oxygen concentration to avoid resource waste or insufficient treatment. Through the temperature control device and the pH adjustment device, the microorganism suitable survival environment is maintained to improve the degradation efficiency. Moreover, the method and system have self-adaptive capability and can be dynamically adjusted with the changes in the water quality to ensure the stability of the treatment effect, effectively solve the problem of tetracycline-polluted water body treatment and have broad application prospects.

[0129] Referring to Figure 2 , the present application provides another embodiment, which provides a self-adaptive degradation control system based on a tetracycline-polluted water body, comprising:

[0130] A monitoring module 100, comprising multiple monitoring points arranged in the tetracycline-polluted water body, for monitoring the tetracycline concentration in the water body and related water quality parameters in real time, the related water quality parameters including dissolved oxygen, pH value, temperature and organic matter content;

[0131] A data analysis module 200, for fusing the data acquired by different types of sensors, analyzing the fused data in real time and predicting the degradation trend and rate of tetracycline through a tetracycline degradation model established.

[0132] The feedback adjustment module 300 is configured to trigger a feedback adjustment mechanism when it is monitored that the change of the tetracycline concentration or the related water quality parameter in the water body meets a preset condition; the feedback adjustment mechanism automatically adjusts the amount of the microbial flora participating in the tetracycline degradation, the amount of the nutrient substance supply and the survival environment condition of the microorganism according to a preset rule, and the survival environment condition includes the temperature and the pH value.

[0133] It should be noted that the self-adaptive degradation control method and system based on the tetracycline-polluted water body provided by the present application has remarkable effects. First, the tetracycline concentration and the related water quality parameter are monitored in real time through multiple monitoring points, so that the water body environment condition is comprehensively mastered, and data support is provided for subsequent accurate treatment. Second, the sensor data is fused and processed, and a tetracycline degradation model is established, so that the degradation trend and rate can be accurately predicted, and preparation for regulation and control can be made in advance. In terms of feedback adjustment, the amount of the microbial flora, the amount of the nutrient substance supply and the survival environment condition are automatically adjusted according to the preset condition, so that accurate control is realized. For example, according to the change of the tetracycline concentration and the dissolved oxygen concentration, the amount of the microbial flora is reasonably adjusted, so that resource waste or insufficient treatment is avoided. Through the temperature control device and the pH adjusting device, the suitable survival environment of the microorganism is maintained, and the degradation efficiency is improved. Moreover, the method and the system have self-adaptive capability, can be dynamically adjusted according to the change of the water quality, ensure stable treatment effect, effectively solve the treatment problem of the tetracycline-polluted water body, and have broad application prospects.

[0134] In a preferred embodiment, the present application further provides an electronic device, comprising:

[0135] a memory and a processor, wherein the computer readable instructions are stored on the memory and are executed by the processor to implement the self-adaptive degradation control method based on the tetracycline-polluted water body. The computer device can be a server, a terminal or any other electronic device with necessary computing and / or processing capability. In an embodiment, the computer device can include a processor, a memory, a network interface, a communication interface and the like connected by a system bus. The processor of the computer device can be used to provide necessary computing, processing and / or control capability. The memory of the computer device can include a non-volatile storage medium and an internal memory. The operating system, the computer program and the like can be stored in or on the non-volatile storage medium. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the computer device can be used to connect and communicate with external devices through a network. The computer program is executed by the processor to execute the steps of the method of the present application.

[0136] The application can be implemented as a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, causes the steps of the method of the embodiments of the application to be performed. In one embodiment, the computer program is distributed across multiple computer devices or processors coupled via a network, such that the computer program is stored, accessed and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, can be performed by a single computer device or processor, or by two or more computer devices or processors. One or more method steps / operations can be performed by one or more computer devices or processors, and one or more other method steps / operations can be performed by one or more other computer devices or processors. One or more computer devices or processors can perform a single method step / operation, or perform two or more method steps / operations.

[0137] It will be appreciated by those skilled in the art that the method steps of the application can be instructed by a computer program to relevant hardware such as a computer device or processor, which can be stored in a non-transitory computer-readable storage medium, and which, when executed, causes the steps of the application to be performed. Depending on the circumstances, any reference herein to a memory, storage, database or other medium can include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0138] It can be understood that the self-adaptive degradation control method and system based on tetracycline contaminated water bodies provided by the application have significant effects. First, the tetracycline concentration and related water quality parameters are monitored in real time through multiple monitoring points, and the water body environmental conditions are comprehensively mastered to provide data support for subsequent accurate treatment. Second, the sensor data are fused and processed, and a tetracycline degradation model is established, which can accurately predict the degradation trend and rate, and make preparation in advance. In terms of feedback regulation, the microbial flora dosage, nutrient supply amount and survival environment conditions are automatically adjusted according to the preset conditions, so that accurate control is realized. For example, the microbial dosage is reasonably adjusted according to the changes of tetracycline concentration and dissolved oxygen concentration, so as to avoid resource waste or insufficient treatment. Through the temperature control device and the pH adjusting device, the suitable survival environment of the microorganisms is maintained, and the degradation efficiency is improved. Moreover, the method and system have self-adaptive ability, can be dynamically adjusted with the change of water quality, ensure the stability of treatment effect, effectively solve the treatment problem of tetracycline contaminated water bodies, and have broad application prospect.

[0139] The technical features described above can be combined arbitrarily. Although all possible combinations of the technical features are not described, any combination of the technical features should be considered to be covered by the present specification, as long as there is no contradiction in such a combination.

[0140] The specific embodiments of the application described above are not to be construed as limiting the scope of the present application. Any other corresponding changes and modifications of the present application according to the technical concept of the present application should be included in the scope of the present application.

Claims

1. An adaptive degradation control method based on tetracycline-contaminated water, characterized in that: The method comprises: S100, setting up multiple monitoring points in a tetracycline-contaminated water body to perform real-time monitoring of the tetracycline concentration and related water quality parameters in the water body, wherein the related water quality parameters include dissolved oxygen, pH value, temperature, and organic matter content; S200, fusing data acquired by different types of sensors, performing real-time analysis on the fused data, and predicting the degradation trend and degradation rate of tetracycline through the established tetracycline degradation model; S300. When the concentration of tetracycline or changes in related water quality parameters in the monitored water body meet preset conditions, triggering a feedback regulation mechanism; the feedback regulation mechanism automatically adjusts the amount of microbial flora involved in tetracycline degradation, the amount of nutrients supplied, and the living environment conditions of the microorganisms according to preset rules, the living environment conditions including temperature and pH value; The amount of microbial flora added is adjusted according to the tetracycline concentration and hydrolysis conditions, and is added according to a pre-set addition ratio and frequency; Adjustment of nutrient supply, including dynamic regulation of carbon, nitrogen, and phosphorus supply concentrations according to microbial growth requirements; Carbon source supply The calculation formula is: in, is the number of microorganisms, is the initial concentration of the carbon source, is the adjustment coefficient, is the coefficient related to the degradation rate of tetracycline; the supply of nitrogen and phosphorus sources was adjusted proportionally according to the supply of carbon source to maintain the ratio of C:N:P=100:5:1; The regulation of the living environment conditions of microorganisms is achieved through the temperature control device and pH adjustment device installed in the water body, which are automatically adjusted according to the monitored values ​​of environmental parameters; The method comprises: If the rate of decrease of tetracycline concentration from time t1 to time t2 is less than the threshold value v1, and the dissolved oxygen concentration DO is lower than the threshold value O1, the amount of microbial flora added Δm is increased; If the pH value deviates from the pH target range, the pH adjustment device is started with an adjustment amount of ΔpHadj to add alkaline substances to the water body.

2. The adaptive degradation control method based on tetracycline-contaminated water according to claim 1, characterized in that: The tetracycline degradation model is established based on historical experimental data and is obtained through training using a machine learning algorithm, wherein the machine learning algorithm includes a support vector machine algorithm or a random forest algorithm.

3. The adaptive degradation control method based on tetracycline-contaminated water according to claim 1, characterized in that: The fusing of data acquired by different types of sensors includes: The sensor data is processed by data cleaning, feature extraction and data fusion algorithm, among which the data fusion algorithm adopts neural network algorithm.

4. The adaptive degradation control method based on tetracycline-contaminated water according to claim 1, characterized in that: If the rate of decrease of tetracycline concentration from time t1 to time t2 is greater than or equal to the threshold value v1, and the dissolved oxygen concentration DO is greater than or equal to the threshold value O1, and the pH value is within the target pH range, the current microbial input, nutrient supply and environmental conditions are kept unchanged.

5. The adaptive degradation control method based on tetracycline-contaminated water according to claim 1, characterized in that: The method comprises: A temperature sensor is installed in the water body to monitor the water temperature T in real time. The temperature target range is set as Ttarget. When the monitored temperature T deviates from the target range, the temperature adjustment device is started. If T>Ttarget, cooling measures are adopted. A cooling pipe is set in the water body to cool the water body by circulating cooling water.

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