Intelligent feeding method and system for sewage treatment carbon source and storage medium

Abstract

The invention provides an intelligent carbon source adding method and system for sewage treatment and a storage medium, and relates to the technical field of intelligent planning. The carbon source adding process is accurately regulated and controlled by comprehensively analyzing the action effect of target heavy metal and target bactericide and utilizing regression and optimization technologies; meanwhile, an intelligent neutralizing agent and carbon source adjustment strategy is provided by combining actual error judgment, so that the maximization of denitrification efficiency and the optimal configuration of system resources are realized; the precision and stability of carbon source feeding are improved, and a technical foundation is laid for long-term sustainable operation of sewage treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent planning, and in particular to an intelligent carbon source dosing method, system and storage medium for sewage treatment. Background Art

[0002] Denitrifying biofilters are widely used in the treatment of nitrogen-containing wastewater. Denitrifying bacteria convert nitrate nitrogen into nitrogen gas and release it to reduce the harm of nitrogen pollution in wastewater to the ecological environment. However, the pollutants in wastewater are complex, especially heavy metals and fungicides, which can have adverse effects on microbial activity and directly weaken the denitrification effect. Therefore, it is an important research direction for wastewater treatment to maintain the functional bacteria of the system at an efficient activity level by precisely controlling the amount of carbon source added during the denitrification process. Traditional carbon source addition methods are usually based on empirical rules and lack sufficient scientific basis. Their addition strategies often have problems such as waste of resources, insufficient treatment or imbalance of microbial metabolism. In recent years, with the development of sensor monitoring technology, machine learning algorithms and refined control technology, a data modeling method based on the operating conditions of the sewage system has been constructed, which has provided the possibility of achieving precise control of carbon source addition.

[0003] In the prior art, the publication number is CN117892970A, and the name is a carbon source intelligent dosing method, system and storage medium, which involves the field of intelligent planning. The method includes: based on the historical water quality parameters and historical operating parameters in the historical data, a multi-parameter prediction model for carbon source dosing is constructed; real-time detection of real-time water quality parameters and real-time operating parameters in the sewage treatment process is performed to obtain a real-time detection data set; the real-time detection data set is input into the multi-parameter prediction model to obtain an optimized carbon source dosage; when the difference between the optimized carbon source dosage and the current actual dosage is within the allowable error range, the current carbon source dosage is maintained; when the difference between the optimized carbon source dosage and the current actual dosage exceeds the allowable error range, the current carbon source dosage is gradually adjusted in a step-by-step manner. The implementation of this method dynamically adjusts the carbon source amount according to the actual working conditions, rather than simply fixed addition, which improves the flexibility of the dosing control and achieves a better treatment effect.

[0004] Although there are some advanced carbon source dosing control methods, the existing technologies are still insufficient in dealing with complex influencing factors (such as the inhibitory effects of heavy metals and fungicides on microorganisms); on the one hand, most technologies ignore the dynamic effects of heavy metals or fungicides on microbial activity and fail to fully capture the complex relationship between them and carbon source demand; on the other hand, the existing methods for dynamic carbon source dosing control mainly rely on linear or empirical derivation, and fail to combine multi-dimensional dynamic constraints (such as dosing dosage and the neutralization effect of heavy metals and fungicides, etc.), resulting in low carbon source utilization, and may even cause secondary pollution or decreased microbial activity due to excessive or insufficient dosing; the existing technologies lack effective solutions; The above information disclosed in the above Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a method, system and storage medium for intelligently adding carbon sources to sewage treatment to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: The intelligent method of adding carbon source to sewage treatment includes the following specific steps: Step S1: Determine the target heavy metal and target fungicide that are strongly correlated with the microbial activity evaluation index, obtain the microbial activity evaluation index, target heavy metal concentration, and target fungicide concentration of the current denitrification biofilter at different times during the monitoring period, wherein the microbial activity evaluation index is determined based on the microbial respiration rate and the microbial denitrification rate, and summarize the target heavy metal concentration, target fungicide concentration, and microbial activity evaluation index to form a historical data set; Step S2: Based on the historical data set, the target heavy metal concentration and the target fungicide concentration are used as independent variables, and the microbial activity evaluation index is used as the dependent variable, and a microbial activity prediction model is constructed using multiple linear regression; Step S3: Based on the constructed microbial activity prediction model and in combination with the target heavy metal concentration and target fungicide concentration obtained in real time, the predicted value of the microbial activity evaluation index at the current moment is calculated; Based on the calculated predicted values ​​of microbial activity evaluation indicators, the carbon source addition requirement in the sewage treatment process is determined based on the nonlinear optimization equation; Step S4: Compare the calculated carbon source dosage requirement with the actual carbon source dosage currently added to determine whether the error is within a preset allowable threshold, and then obtain an error judgment result for providing an adjustment addition strategy for the actual carbon source dosage.

[0007] A wastewater treatment carbon source intelligent dosing system, the system is used to execute the wastewater treatment carbon source intelligent dosing method, comprising: Range value determination module: used to determine the target heavy metals and target fungicides that are strongly correlated with the microbial activity evaluation index, obtain the microbial activity evaluation index, target heavy metal concentration, and target fungicide concentration of the current denitrification biofilter at different times during the monitoring period, the microbial activity evaluation index is determined based on the microbial respiration rate and the microbial denitrification rate, and the target heavy metal concentration, target fungicide concentration, and microbial activity evaluation index are summarized to form a historical data set; Prediction model building module: It is used to build a microbial activity prediction model based on historical data sets, with target heavy metal concentration and target fungicide concentration as independent variables and microbial activity evaluation index as dependent variable, using multiple linear regression; Calculation and analysis module: used to calculate the predicted value of the microbial activity evaluation index at the current moment based on the constructed microbial activity prediction model and in combination with the target heavy metal concentration and target fungicide concentration obtained in real time; Based on the calculated predicted values ​​of microbial activity evaluation indicators, the carbon source addition requirement in the sewage treatment process is determined based on the nonlinear optimization equation; Judgment and adjustment module: used to compare the calculated carbon source addition demand with the actual carbon source addition amount currently put in to determine whether the error is within the preset allowable threshold, and then obtain the error judgment result used to provide an adjustment addition strategy for the actual carbon source addition amount.

[0008] A readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the intelligent carbon source addition method for sewage treatment are implemented.

[0009] Compared with the prior art, the beneficial effects of the present invention are: by comprehensively analyzing the effects of target heavy metals and target fungicides, and utilizing regression and optimization techniques, the carbon source addition process is accurately regulated, and at the same time, combined with actual error judgment, an intelligent neutralizer and carbon source adjustment strategy is provided to maximize the denitrification efficiency and optimize the configuration of system resources; it improves the accuracy and stability of carbon source addition, laying a technical foundation for the long-term sustainable operation of sewage treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic diagram of the process of the intelligent carbon source addition method for sewage treatment of the present invention; Figure 2 The present invention is a system module block logic diagram of the intelligent carbon source addition method for sewage treatment using the present invention. DETAILED DESCRIPTION

[0011] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0012] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0013] Embodiment 1: See also Figure 1 , the present invention provides a technical solution: The intelligent method of adding carbon source to sewage treatment includes the following specific steps: Step S1: Determine the target heavy metal and target fungicide that are strongly correlated with the microbial activity evaluation index, obtain the microbial activity evaluation index, target heavy metal concentration, and target fungicide concentration of the current denitrification biofilter at different times during the monitoring period, wherein the microbial activity evaluation index is determined based on the microbial respiration rate and the microbial denitrification rate, and summarize the target heavy metal concentration, target fungicide concentration, and microbial activity evaluation index to form a historical data set; Further explanation: First, the target heavy metals and target fungicides are determined as follows: For target heavy metals, including: lead (Pb), cadmium (Cd), chromium (Cr), mercury (Hg), copper (Cu); For target fungicides, these include: chlorine, sodium hypochlorite, ozone, and a variety of antibiotics (such as penicillin, tetracycline, etc.); The above-mentioned target heavy metals and target fungicides are screened and determined by the following methods; In the current denitrifying biofilter, a target heavy metal with the greatest impact on microbial activity is selected from lead (Pb), cadmium (Cd), chromium (Cr), mercury (Hg), and copper (Cu); Select a target fungicide that has the greatest impact on microbial activity from chlorine, sodium hypochlorite, ozone, and a variety of antibiotics (such as penicillin, tetracycline, etc.); Expert experience and literature research: Learn from the experience of experts in related fields or academic literature to understand which heavy metals and fungicides usually have a significant impact on microbial activity in previous studies or cases.

[0014] Visual observation of microbial activity: Based on the records of changes in microbial activity, it is possible to determine which heavy metals or fungicides have obvious inhibitory effects on microbial activity when their concentrations change. This “observation-judgment” approach can quickly identify problematic substances.

[0015] Configure an atomic absorption spectrometer for online monitoring of target heavy metal concentrations in wastewater.

[0016] A liquid chromatography-mass spectrometry system was set up for online monitoring of the target fungicide concentration.

[0017] Start the atomic absorption spectrometer to measure and record the target heavy metal concentration in the sewage in real time. ; Start the liquid chromatography-mass spectrometry system to measure and record the target fungicide concentration data in the sewage in real time, and record the target fungicide concentration as ; The monitoring data is transmitted through a dedicated interface and Automatically transmitted to the monitoring center database; Determining the target heavy metals and target fungicides that are strongly correlated with the microbial activity evaluation index includes: obtaining the microbial activity evaluation index of the current denitrification biofilter at different times during the monitoring period, as well as the concentrations of various heavy metals and fungicides, and using these acquired data as a data set, based on the calculation results of the Pearson correlation coefficient, screening out the heavy metal concentrations and fungicide concentrations that are strongly correlated with the microbial activity evaluation index, and determining them as the target heavy metal concentrations and target fungicide concentrations; Based on the calculation results of the Pearson correlation coefficient, it specifically includes: determining that the collected data set contains data on three variables: microbial activity evaluation index, heavy metal concentration, and fungicide concentration; Calculate the Pearson correlation coefficient: The Pearson correlation coefficient was calculated for each pair of variables (microbial activity evaluation index and heavy metal concentration, microbial activity evaluation index and fungicide concentration). The Pearson correlation coefficient (r) is a statistic used to measure the strength and direction of the linear relationship between two variables, with a value ranging from -1 to 1, as follows: r = 1 indicates a perfect positive correlation; r = -1 indicates a perfect negative correlation; r = 0 means no correlation; Screening variables with strong correlation: Set a strong correlation threshold value |r|>0.5 to determine which heavy metal concentrations and fungicide concentrations have significant correlation with the microbial activity evaluation index. Based on the calculated Pearson correlation coefficient, screen the heavy metal concentrations and fungicide concentrations that meet the threshold conditions and have the largest |r| as the target heavy metal concentrations and target fungicide concentrations.

[0018] The microbial activity evaluation index is defined as , the calculation formula is as follows: ; in, is the respiration rate of microorganisms in the denitrifying biofilter at the i-th moment in the monitoring period, is the denitrification rate of microorganisms in the denitrification biofilter at the i-th moment in the monitoring period, and are the weight values ​​of the corresponding parameters, and , and The values ​​are all in the interval (0,1).

[0019] It should be noted that when or The larger the value, the higher the respiration rate and denitrification rate, which means the greater the microbial activity.

[0020] The calculation formula is as follows: ; in, The denitrifying biofilter is The amount of oxygen consumed per unit time under the combination, The xh represents "consumption"; t is the time (hours) used to consume oxygen; is the total amount of microorganisms in activated sludge; is the target heavy metal concentration at the current moment; is the target fungicide concentration at the current moment; The calculation formula is as follows: ; in, The denitrifying biofilter is The amount of nitrogen removed per unit time under the combination.

[0021] It is further explained that the target heavy metal concentration range values ​​and the target fungicide concentration range values ​​are divided into multiple impact levels, and the impact level judgment results are comprehensively obtained. The higher the impact level, the greater the impact on the microbial activity evaluation index; In the current denitrifying biofilter, a first neutralizer and a second neutralizer are respectively determined for reducing a target heavy metal concentration and a target fungicide concentration; Setting the expected reduction ratio of the target heavy metal concentration by the first neutralizer at a preset addition amount; and the expected reduction ratio of the target fungicide concentration by the second neutralizer at a preset addition amount; The first neutralizing agent is defined to be capable of reacting with the target heavy metal to form an insoluble precipitate; For example, calcium-based compounds such as lime are used for precipitation treatment of lead, copper, etc.

[0022] Sulfide sodium sulfide, etc.: used to form sulfide precipitation with target heavy metals (mercury, cadmium); The second neutralizing agent is defined as being capable of reducing or oxidizing the target fungicide, rendering the target fungicide inactive; For example: Sodium sulfite or sodium thiosulfate: used to neutralize the residual chlorine in the target fungicide.

[0023] The expected reduction ratio of the target heavy metal concentration by the first neutralizer at the preset addition amount is set to A1%; The expected reduction ratio of the target fungicide concentration by the second neutralizer at the preset addition amount is set to A2%; Impact level determination results include: The threshold interval of target heavy metal concentration is set as ; They are the lower and upper limits of the threshold interval corresponding to the target heavy metal concentration; Will be below the threshold The target heavy metal concentration is classified into the heavy metal low impact level ( ); Will be in the threshold range The target heavy metal concentration is divided into the impact level of heavy metals ( ); Will be above the threshold The target heavy metal concentration is classified into the heavy metal high impact level ( ); in, , and Marks representing the low impact level of heavy metals, the medium impact level of heavy metals, and the high impact level of heavy metals; The threshold interval of the target fungicide concentration is set as ; are the lower and upper limits of the threshold interval corresponding to the target fungicide concentration; Will be below the threshold The target fungicide concentration is divided into low-impact fungicide levels ( ); Will be in the threshold range The target fungicide concentration is divided into the impact level of fungicide ( ); Will be above the threshold The target fungicide concentration is divided into high-impact fungicide levels ( ); in, , and The marks represent the low impact level of fungicides, the medium impact level of fungicides, and the high impact level of fungicides respectively.

[0024] Step S2: Based on the historical data set, the target heavy metal concentration and the target fungicide concentration are used as independent variables, and the microbial activity evaluation index is used as the dependent variable, and a microbial activity prediction model is constructed using multiple linear regression; Further explanation: The calculation formula for defining the microbial activity prediction model is as follows: ; in, It represents the predicted value of the microbial activity evaluation index of microorganisms in the denitrification biofilter at the i-th moment; and is the weight coefficient of the corresponding parameter, and , and The values ​​are all in the interval (0,1); The bias of the microbial activity prediction model is determined based on historical data sets and experimental data. , and bias The specific value of is the target heavy metal concentration at the i-th moment; is the target fungicide concentration at the i-th moment.

[0025] It should be noted that: This embodiment sets Although the target heavy metal concentration will affect the microbial activity, the target fungicide concentration will have a greater impact on the microbial activity. Therefore, in the distribution of weight ratio, ; and Example settings are 0.4 and 0.6; Step S3: Based on the constructed microbial activity prediction model and in combination with the target heavy metal concentration and target fungicide concentration obtained in real time, the predicted value of the microbial activity evaluation index at the current moment is calculated; Based on the calculated predicted values ​​of microbial activity evaluation indicators, the carbon source addition requirement in the sewage treatment process is determined based on the nonlinear optimization equation; Further explanation: Obtain the carbon source addition demand corresponding to the microbial activity evaluation index at different times in the historical data set; The carbon source addition requirement and the microbial activity evaluation index were correlated and analyzed, and the following correlation calculation formula was obtained: ; in, Indicates the microorganisms in the denitrification biofilter corresponding to the i-th moment in the monitoring period Carbon source addition requirement under is the proportionality coefficient; is the system adjustment coefficient; it is used to fine-tune the stability of process regulation, k1 and The value of is obtained by fitting the historical running data, which is specifically determined by using the linear regression function in Python or Matlab.

[0026] Determined by expert systems using experimental data or by sewage treatment workers based on historical data and standard manuals; The current moment Prediction value of microbial activity evaluation index Input to middle; is the microorganism in the denitrifying biofilter at the current moment The carbon source addition requirement under this condition.

[0027] Step S4: Compare the calculated carbon source dosage requirement with the actual carbon source dosage currently added to determine whether the error is within a preset allowable threshold, and then obtain an error judgment result for providing an adjustment addition strategy for the actual carbon source dosage; Further explanation: Set the target heavy metal concentration of microorganisms in the denitrification biofilter at the current moment and target fungicide concentration The preset allowable threshold corresponding to the combination is ; and set the actual carbon source dosage at the current moment to In this embodiment, Set to ; Ruodang When the error judgment result indicates that the error is outside the preset allowable threshold, there are two specific situations: The first case: If When the carbon source dosage is too large relative to the carbon source dosage requirement, excessive carbon source addition will lead to rapid microbial reproduction, exceeding the management capacity of the system, causing the expansion of biofilm or activated sludge, and easily leading to failure of the sewage treatment system. The second case: If When , it means that the actual carbon source dosage is too small relative to the carbon source dosage requirement. Insufficient carbon source addition will limit the growth and metabolic activities of microorganisms, resulting in low efficiency in removing pollutants. like When , the error judgment result indicates that the error is within the preset allowable threshold, indicating that the actual carbon source addition amount is close to the carbon source addition demand amount, which meets the carbon source addition requirement.

[0028] The impact level judgment results and the error judgment results are combined for analysis, and an adjustment and addition strategy is formulated for the actual carbon source dosage, the first neutralizer and the second neutralizer.

[0029] Further explanation: For the actual carbon source dosage adjustment addition strategy explanation: when and When the carbon source is too high, it means there is excess carbon source. It is necessary to adjust the temperature, pH and nutrients in the denitrifying biofilter to improve the efficiency of carbon source utilization by microorganisms until until; Temperature regulation: The metabolic rate of microorganisms is closely related to temperature. Within a suitable temperature range (such as 20°C-30°C), the enzyme activity of denitrifying bacteria increases, which can significantly accelerate the denitrification process and convert excess carbon sources into carbon dioxide and water, thereby reducing the carbon source concentration. pH optimization: Microorganisms can maintain optimal growth and metabolic activities within a specific pH range (such as 6.5-8.5). By maintaining a suitable pH, the optimal activity of denitrifying bacterial enzymes can be ensured, their utilization efficiency of carbon sources can be improved, the consumption of carbon sources can be promoted, and the excessive load in the system can be reduced.

[0030] Nutrient ratio adjustment: A reasonable carbon-nitrogen ratio (C:N ratio) is crucial to the denitrification process. Increasing the amount of nitrogen source added can improve the utilization rate of carbon sources by microorganisms, allowing microorganisms to effectively utilize excess carbon sources during denitrification metabolism. The ideal C:N ratio is usually 5:1 to 10:1, ensuring that denitrifying bacteria can efficiently convert when the carbon source is relatively sufficient.

[0031] when and When the actual carbon source dosage is increased, the actual carbon source dosage after the increase is defined as ; in is the actual amount of carbon source added after the increase, is the increase in carbon source addition each time, ; is the number of carbon source additions until until; Above The setting is used to indicate that the carbon source is added gradually in batches to avoid adding too much carbon source at one time, which will cause the microorganisms in the denitrifying biofilter to fail to adapt quickly. Based on the expected reduction ratio of the first neutralizer to the second neutralizer, the following adjustment addition strategy description is made for the first neutralizer and the second neutralizer: If the “target heavy metal concentration value” and / or “target fungicide concentration value” are at the low impact level or , adding the "first neutralizer" and / or "second neutralizer" lower than the preset addition amount into the current denitrification biofilter; If the "target heavy metal concentration value" and / or "target fungicide concentration value" are at the medium impact level or , adding a preset amount of "first neutralizer" and / or "second neutralizer" into the current denitrification biofilter; If the “target heavy metal concentration value” and / or “target fungicide concentration value” are at the high impact level or , adding a "first neutralizer" and / or "second neutralizer" higher than a preset addition amount into the current denitrification biofilter; The adjustment target of the preset addition amount is set as follows: until the "target heavy metal concentration value" and "target fungicide concentration value" after neutralization are respectively at the corresponding low impact level. , until.

[0032] It should be noted in this embodiment that the corresponding neutralizer addition amount "higher than the preset addition amount" or "lower than the preset addition amount" needs to be determined based on the expected decrease ratio A1% of the first neutralizer and the expected decrease ratio A2% of the second neutralizer.

[0033] Embodiment 2: See also Figure 2 , a sewage treatment carbon source intelligent dosing system, the system is used to execute the sewage treatment carbon source intelligent dosing method, comprising: Range value determination module: used to determine the target heavy metals and target fungicides that are strongly correlated with the microbial activity evaluation index, obtain the microbial activity evaluation index, target heavy metal concentration, and target fungicide concentration of the current denitrification biofilter at different times during the monitoring period, the microbial activity evaluation index is determined based on the microbial respiration rate and the microbial denitrification rate, and the target heavy metal concentration, target fungicide concentration, and microbial activity evaluation index are summarized to form a historical data set; Prediction model building module: It is used to build a microbial activity prediction model based on historical data sets, with target heavy metal concentration and target fungicide concentration as independent variables and microbial activity evaluation index as dependent variable, using multiple linear regression; Calculation and analysis module: used to calculate the predicted value of the microbial activity evaluation index at the current moment based on the constructed microbial activity prediction model and in combination with the target heavy metal concentration and target fungicide concentration obtained in real time; Based on the calculated predicted values ​​of microbial activity evaluation indicators, the carbon source addition requirement in the sewage treatment process is determined based on the nonlinear optimization equation; Judgment and adjustment module: used to compare the calculated carbon source addition demand with the actual carbon source addition amount currently put in to determine whether the error is within the preset allowable threshold, and then obtain the error judgment result used to provide an adjustment addition strategy for the actual carbon source addition amount.

[0034] A readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the intelligent carbon source addition method for sewage treatment are implemented.

[0035] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0036] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0037] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0038] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for intelligently adding carbon sources to sewage treatment, characterized in that: The specific steps include: Step S1: Determine the target heavy metal and target fungicide that are strongly correlated with the microbial activity evaluation index, obtain the microbial activity evaluation index, target heavy metal concentration, and target fungicide concentration of the current denitrification biofilter at different times during the monitoring period, wherein the microbial activity evaluation index is determined based on the microbial respiration rate and the microbial denitrification rate, and summarize the target heavy metal concentration, target fungicide concentration, and microbial activity evaluation index to form a historical data set; Step S2: Based on the historical data set, the target heavy metal concentration and the target fungicide concentration are used as independent variables, and the microbial activity evaluation index is used as the dependent variable, and a microbial activity prediction model is constructed using multiple linear regression; Step S3: Based on the constructed microbial activity prediction model and in combination with the target heavy metal concentration and target fungicide concentration obtained in real time, the predicted value of the microbial activity evaluation index at the current moment is calculated; Based on the calculated predicted values ​​of microbial activity evaluation indicators, the carbon source addition requirement in the sewage treatment process is determined based on the nonlinear optimization equation; Step S4: Compare the calculated carbon source dosage requirement with the actual carbon source dosage currently added to determine whether the error is within a preset allowable threshold, and then obtain an error judgment result for providing an adjustment addition strategy for the actual carbon source dosage.

2. The method for intelligently adding carbon sources for sewage treatment according to claim 1, characterized in that: The determining of the target heavy metal and target fungicide that are strongly correlated with the microbial activity evaluation index comprises: obtaining the microbial activity evaluation index of the current denitrification biofilter at different times within the monitoring time period, and the concentrations of various heavy metals and fungicides, and based on the calculation result of the Pearson correlation coefficient, screening out the heavy metal concentration and fungicide concentration that are strongly correlated with the microbial activity evaluation index, and determining them as the target heavy metal concentration and target fungicide concentration; The microbial activity evaluation index is defined as , the calculation formula is as follows: ； in, is the respiration rate of microorganisms in the denitrifying biofilter at the i-th moment in the monitoring period, is the denitrification rate of microorganisms in the denitrification biofilter at the i-th moment in the monitoring period, and are the weight values ​​of the corresponding parameters, and , and The values ​​are all in the interval (0,1).

3. The method for intelligently adding carbon sources for sewage treatment according to claim 2, characterized in that: The calculation formula for defining the microbial activity prediction model is as follows: ； in, It represents the predicted value of the microbial activity evaluation index of microorganisms in the denitrification biofilter at the i-th moment; and is the weight coefficient of the corresponding parameter, and , and The values ​​are all in the interval (0,1); Bias in the prediction model for microbial activity; is the target heavy metal concentration at the i-th moment; is the target fungicide concentration at the i-th moment.

4. The method for intelligently adding carbon sources for sewage treatment according to claim 3, characterized in that: The threshold interval of target heavy metal concentration is set as ; They are the lower and upper limits of the threshold interval corresponding to the target heavy metal concentration; Will be below the threshold The target heavy metal concentration is classified into the heavy metal low impact level ( ); Will be in the threshold range The target heavy metal concentration is divided into the impact level of heavy metals ( ); Will be above the threshold The target heavy metal concentration is classified into the heavy metal high impact level ( ); in, , and Marks representing the low impact level of heavy metals, the medium impact level of heavy metals, and the high impact level of heavy metals; The threshold interval of the target fungicide concentration is set as ; are the lower and upper limits of the threshold interval corresponding to the target fungicide concentration; Will be below the threshold The target fungicide concentration is divided into low-impact fungicide levels ( ); Will be in the threshold range The target fungicide concentration is divided into the impact level of fungicide ( ); Will be above the threshold The target fungicide concentration is divided into high-impact fungicide levels ( ); in, , and The marks represent the low impact level of fungicides, the medium impact level of fungicides, and the high impact level of fungicides respectively.

5. The method for intelligently adding carbon sources for sewage treatment according to claim 4, characterized in that: Obtain the carbon source addition requirements corresponding to the microbial activity evaluation indicators at different times in the historical data set; The carbon source addition requirement and the microbial activity evaluation index were correlated and analyzed, and the following correlation calculation formula was obtained: ； in, Indicates the microorganisms in the denitrification biofilter corresponding to the i-th moment in the monitoring period Carbon source addition requirement under is the proportionality coefficient; is the system adjustment coefficient; The current moment Prediction value of microbial activity evaluation index Input to middle; is the microorganism in the denitrifying biofilter at the current moment The carbon source addition requirement under this condition.

6. The method for intelligently adding carbon sources for sewage treatment according to claim 5, characterized in that: The calculated carbon source dosage requirement is compared with the actual carbon source dosage currently put in to determine whether the error is within the preset allowable threshold. The specific logic includes: Set the target heavy metal concentration of microorganisms in the denitrification biofilter at the current moment and target fungicide concentration The preset allowable threshold corresponding to the combination is ; and set the actual carbon source dosage at the current moment to ; Ruodang When the error judgment result indicates that the error is outside the preset allowable threshold, there are two specific situations: The first case: If When , it means that the actual carbon source dosage is too large relative to the carbon source dosage requirement; The second situation: If When , it means that the actual carbon source dosage is too small relative to the carbon source dosage requirement; Ruodang When , the error judgment result indicates that the error is within the preset allowable threshold, indicating that the actual carbon source addition amount is close to the carbon source addition demand amount, which meets the carbon source addition requirement.

7. The method for intelligently adding carbon sources for sewage treatment according to claim 6, characterized in that: Explanation of the adjustment strategy for the actual carbon source dosage: when and When the carbon source is too high, it means there is excess carbon source. It is necessary to adjust the temperature, pH and nutrients in the denitrifying biofilter to improve the efficiency of carbon source utilization by microorganisms until until; when and , the actual carbon source dosage needs to be increased, and the actual carbon source dosage after the increase is defined as ; in is the actual amount of carbon source added after the increase, is the increase in carbon source addition each time, ; is the number of carbon source additions until until.

8. The method for intelligently adding carbon sources for sewage treatment according to claim 7, characterized in that: In the current denitrifying biofilter, a first neutralizer and a second neutralizer are respectively determined for reducing a target heavy metal concentration and a target fungicide concentration; Setting the expected reduction ratio of the target heavy metal concentration by the first neutralizer at a preset addition amount; and the expected reduction ratio of the target fungicide concentration by the second neutralizer at a preset addition amount; The first neutralizing agent is defined to be capable of reacting with the target heavy metal to form an insoluble precipitate; The second neutralizing agent is defined as being capable of reducing or oxidizing the target fungicide, rendering the target fungicide inactive; The expected reduction ratio of the target heavy metal concentration by the first neutralizer at the preset addition amount is set to A1%; The expected reduction ratio of the target fungicide concentration by the second neutralizer at the preset addition amount is set to A2%; Based on the impact level judgment results, the following adjustment and addition strategy descriptions are made for the first neutralizer and the second neutralizer: If the "target heavy metal concentration value" and / or "target fungicide concentration value" are at the low impact level or , adding "first neutralizer" and / or "second neutralizer" lower than the preset addition amount into the current denitrification biofilter; If the "target heavy metal concentration value" and / or "target fungicide concentration value" are at the medium impact level or , adding a preset amount of "first neutralizer" and / or "second neutralizer" into the current denitrification biofilter; If the "target heavy metal concentration value" and / or "target fungicide concentration value" are at the high impact level or , adding a "first neutralizer" and / or "second neutralizer" higher than the preset addition amount into the current denitrification biofilter; The adjustment target of the preset addition amount is set as follows: until the "target heavy metal concentration value" and "target fungicide concentration value" after neutralization are respectively at the corresponding low impact level. , until.

9. A carbon source intelligent dosing system for sewage treatment, characterized by: The system is used to implement the intelligent carbon source addition method for sewage treatment according to any one of claims 1 to 8, comprising: Range value determination module: used to determine the target heavy metals and target fungicides that are strongly correlated with the microbial activity evaluation index, obtain the microbial activity evaluation index, target heavy metal concentration, and target fungicide concentration of the current denitrification biofilter at different times during the monitoring period, the microbial activity evaluation index is determined based on the microbial respiration rate and the microbial denitrification rate, and the target heavy metal concentration, target fungicide concentration, and microbial activity evaluation index are summarized to form a historical data set; Prediction model building module: It is used to build a microbial activity prediction model based on historical data sets, with target heavy metal concentration and target fungicide concentration as independent variables and microbial activity evaluation index as dependent variable, using multiple linear regression; Calculation and analysis module: used to calculate the predicted value of the microbial activity evaluation index at the current moment based on the constructed microbial activity prediction model and in combination with the target heavy metal concentration and target fungicide concentration obtained in real time; Based on the calculated predicted values ​​of microbial activity evaluation indicators, the carbon source addition requirement in the sewage treatment process is determined based on the nonlinear optimization equation; Judgment and adjustment module: used to compare the calculated carbon source addition demand with the actual carbon source addition amount currently put in to determine whether the error is within the preset allowable threshold, and then obtain the error judgment result used to provide an adjustment addition strategy for the actual carbon source addition amount.

10. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of the intelligent carbon source addition method for sewage treatment as described in any one of claims 1 to 8 are implemented.

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