Mechanism model-based wastewater treatment control system and method

Abstract

The present application relates to sewage treatment technical field, specifically to sewage treatment control system and method based on mechanism model, including server;The server includes real-time water monitoring module, abnormality judging module, comparison and selection cycle module, control module, effluent monitoring module, effluent judging module, scheme updating module, wherein the effluent judging module is used for judging whether the effluent quality monitoring data meets the preset water quality value according to the real-time monitoring effluent quality monitoring data;If yes, then judge the corresponding sewage treatment mechanism model as feasible model, if no, then select one of the model parameters in the model parameter group of the sewage treatment mechanism model of the last moment in turn to correct and adjust, and re-output corresponding effluent quality re-simulation data, until the effluent quality re-simulation data and effluent quality monitoring data are within the preset simulation error, at this time, then judge the corresponding sewage treatment mechanism model as feasible model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sewage treatment, and particularly relates to a sewage treatment control system and method based on a mechanism model. BACKGROUND

[0002] With the acceleration of urbanization and the rapid development of industrial production, water resource shortage and water pollution problems are becoming increasingly serious. Sewage treatment is an important link to protect water resources security and maintain ecological environment.

[0003] Traditional sewage treatment systems usually rely on fixed operating parameters and empirical rules, which can achieve good results in relatively stable treatment conditions. However, in actual operation, the influent water quality fluctuates greatly, and the traditional method is difficult to cope with such changes, resulting in unstable effluent water quality, which may even not meet the standards, or in order to meet the standards, the aeration amount and the amount of chemicals are increased, resulting in increased power and chemical costs.

[0004] With the development of information technology, some sewage treatment facilities have begun to introduce automatic control technology to improve treatment efficiency and stability. However, the existing automatic control system still has the following main problems:

[0005] When the influent or effluent of the sewage treatment system appears abnormal, the operator often needs to manually adjust the process parameters according to experience, which not only consumes time and effort, but also the consistency and accuracy of the adjustment results are difficult to guarantee.

[0006] Most of the existing control systems use fixed process schemes, lack the ability to continuously optimize process parameters, and cannot find the optimal treatment scheme for different influent conditions, resulting in poor reliability and adaptability of the corresponding control system when the influent or effluent of the sewage treatment system appears abnormal.

[0007] Therefore, there is an urgent need for a sewage treatment control system and method based on a mechanism model, which can automatically adjust the process parameters according to the changes in the influent conditions of the sewage treatment system and automatically take measures when the effluent appears abnormal, to realize the automatic adjustment of process parameters and response strategies in the sewage system, thereby improving the reliability and adaptability of the entire sewage treatment. SUMMARY

[0008] One of the purposes of the present application is to provide a sewage treatment control system and method based on a mechanism model, which can automatically adjust the process parameters according to the changes in the influent conditions of the sewage treatment system and automatically take measures when the effluent appears abnormal, to realize the automatic adjustment of process parameters and response strategies in the sewage system, thereby improving the reliability and adaptability of the entire sewage treatment.

[0009] In order to achieve the above object, a sewage treatment control system based on a mechanism model is provided, comprising a server;

[0010] The server comprises:

[0011] A real-time influent monitoring module is configured to monitor influent data of the sewage treatment system in real time to form real-time influent data;

[0012] An abnormality judging module is configured to judge whether the real-time influent data at the current time is abnormal according to the real-time influent data. If the result is no, the real-time influent data at the current time is accurate data, and the intermediate control parameters of the sewage treatment system at the previous time are retrieved as the process control scheme corresponding to the current time of the sewage treatment system.

[0013] A comparison and selection cycle module is configured to simulate the sewage treatment of the sewage treatment system based on the sewage treatment mechanism model at the previous time according to the real-time influent data at the current time and the process control scheme corresponding to the current time of the sewage treatment system, output corresponding effluent quality simulation data, and judge whether the corresponding effluent quality meets the preset requirements according to the effluent quality simulation data. If yes, the process control scheme corresponding to the current time of the sewage treatment system is a feasible scheme. If no, each intermediate control parameter in the process control scheme corresponding to the current time of the sewage treatment system is adjusted and re-input into the sewage treatment mechanism model at the previous time.

[0014] A control module is configured to calculate the control parameters of the control equipment corresponding to each intermediate control parameter in the process control scheme according to each intermediate control parameter in the process control scheme corresponding to the current time when the result is that the process control scheme corresponding to the current time of the sewage treatment system is a feasible scheme, and control each control equipment in the sewage treatment system.

[0015] An effluent monitoring module is configured to monitor effluent quality monitoring data of the sewage treatment system in real time when each control equipment in the sewage treatment system is controlled.

[0016] An effluent judging module is configured to judge whether the effluent quality monitoring data meets the preset water quality value according to the real-time monitoring effluent quality monitoring data. If yes, the corresponding sewage treatment mechanism model is a feasible model. If no, one of the model parameters in the model parameter group in the sewage treatment mechanism model at the previous time is selected for correction and adjustment in sequence, and corresponding effluent quality re-simulation data is re-output until the effluent quality re-simulation data and the effluent quality monitoring data are within the preset simulation error, at which time the corresponding sewage treatment mechanism model is a feasible model.

[0017] The model updating module is configured to update the wastewater treatment mechanism model at the previous moment according to the adjusted model parameters, when the judgment result is that the corresponding wastewater treatment mechanism model is a feasible model.

[0018] The technical principle and effect of the present scheme are as follows: in the present scheme, the influent data of the wastewater treatment system is first monitored in real time, and the real-time influent data is used to determine whether the influent data at the current moment is abnormal. When there is no abnormality, the intermediate control parameters of the wastewater treatment system corresponding to the previous moment are retrieved as the process control scheme of the corresponding wastewater treatment system at the current moment. Then, the wastewater treatment mechanism model at the previous moment is used to simulate the wastewater treatment corresponding to the wastewater treatment system, by using the process control scheme at the current moment and the real-time influent data, to obtain the effluent quality simulation data. Then, it is determined whether the effluent quality simulation data meets the standard by judging whether the effluent quality simulation data meets the preset requirements. Once it does not meet the standard, the intermediate control parameters of the corresponding process control scheme are adjusted, and then re-input into the wastewater treatment mechanism model at the previous moment for simulation again. Through repeated adjustment, the process control scheme that meets the requirements is obtained.

[0019] Then, the control parameters of the control equipment corresponding to the intermediate control parameters are calculated by using the intermediate control parameters in the process control scheme corresponding to the wastewater treatment system at the current moment, so as to realize the control of the control equipment in the wastewater treatment system, thereby realizing the treatment of the wastewater in the wastewater treatment system at the current moment.

[0020] In the process of treating the wastewater, the effluent quality monitoring data of the wastewater treatment system is monitored in real time, and the quality of the effluent quality of the wastewater treatment system is monitored by using the effluent quality monitoring data. Once it is determined that the effluent quality monitoring data does not meet the preset water quality value, one of the model parameters in the model parameter group in the wastewater treatment mechanism model at the previous moment is selected for correction and adjustment, and the corresponding effluent quality simulation data is re-output until the effluent quality simulation data and the effluent quality monitoring data are within the preset simulation error. At this time, it is determined that the corresponding wastewater treatment mechanism model is a feasible model. Thus, it is determined whether the wastewater treatment mechanism model at the previous moment meets the requirements at the current moment, and the wastewater treatment mechanism model at the previous moment is updated and corrected, so that the wastewater treatment system corresponding to the wastewater treatment system can better treat the influent data at the current moment, so that the effluent quality data of the wastewater treatment system meets the preset water quality value.

[0021] In the present scheme, through the real-time water inlet monitoring module and the abnormality judgment module, the system can quickly identify the change of water inlet data, realize the identification of the accuracy of the water inlet data. Then the process control scheme of the last moment and the wastewater treatment mechanism model of the last moment are used to simulate the effluent water quality in the first time, so as to judge whether the corresponding process control scheme of the last moment meets the current wastewater treatment requirement, and when it does not meet the requirement, adjust each intermediate control parameter in the process control scheme of the last moment corresponding to the wastewater treatment system, through the preliminary adjustment of the intermediate control parameter, the each intermediate control parameter in the corresponding process control scheme is closer to the current effluent requirement, realizing the preliminary satisfaction of the process control scheme. Through the preliminary adjustment of this step, the system reduces unnecessary chemical use, reduces operating cost, improves processing efficiency, reduces the residence time of wastewater in the treatment unit, and speeds up the overall processing speed.

[0022] Then the process control scheme that meets the requirement preliminarily is used to control each control device of the corresponding wastewater treatment system to realize the corresponding preliminary wastewater treatment, and the effluent water quality monitoring data is monitored in real time to further judge whether the process control scheme that meets the requirement preliminarily actually meets the requirement. Once the effluent water quality monitoring data does not meet the preset water quality value, one of the model parameters in the model parameter group in the wastewater treatment mechanism model of the last moment is selected for correction and adjustment, and the corresponding effluent water quality re-simulation data is output again until the effluent water quality re-simulation data and the effluent water quality monitoring data are within the preset simulation error, at this time it is judged that the corresponding wastewater treatment mechanism model is a feasible model; Then according to the adjusted model parameters, the wastewater treatment mechanism model of the last moment is updated when the judgment result is that the corresponding wastewater treatment mechanism model is a feasible model.

[0023] On the basis of the first adjustment, the system adjusts the model parameters of the wastewater treatment mechanism model at the previous time again. Through this adjustment, the system ultimately makes the effluent quality fully meet the preset standard, ensuring stable effluent quality. For example, the system may further optimize the dissolved oxygen concentration to minimize the pollutant content in the effluent. Through two consecutive parameter adjustments, the system not only achieves the expected effluent quality standard, but also exhibits higher stability and reliability under different working conditions. The system can automatically adapt to changes in the influent water quality and continuously optimize through a closed-loop feedback mechanism, ensuring long-term stable operation, i.e., it can quickly and accurately automatically adjust the corresponding process parameters when the influent or effluent of the wastewater treatment system appears abnormal, thereby improving the reliability and adaptability of the entire wastewater treatment. That is, it realizes automatic adjustment of process parameters and automatic response measures when the effluent is abnormal according to changes in the influent of the wastewater treatment system, thereby improving the reliability and adaptability of the entire wastewater treatment.

[0024] Further, the abnormality judgment module is also configured to, when the judgment result is yes, correct the real-time influent data monitored at the current time, judge that the corrected real-time influent data is accurate data, and call the intermediate control parameters of the wastewater treatment system at the previous time as the process control scheme corresponding to the current time of the wastewater treatment system.

[0025] Further, the abnormality judgment module is also configured to, when the judgment result is yes, correct the real-time influent data monitored at the current time, judge that the corrected real-time influent data is accurate data, and call the intermediate control parameters of the wastewater treatment system at the previous time as the process control scheme corresponding to the current time of the wastewater treatment system.

[0026] Beneficial effects: In this scheme, when the monitored data is determined to be abnormal, the system will automatically correct it to ensure that subsequent processing is based on accurate data, avoiding decision-making errors caused by false data. The corrected data will be re-evaluated for accuracy, ensuring that only verified data is used in actual operations, improving the reliability of the overall system.

[0027] By data cleaning of the real-time influent data monitored in real time, it is ensured that the real-time influent data can meet the format required by the corresponding model, thereby improving the reliability of the data.

[0028] Further, the server further comprises:

[0029] The aerobic tank control module is configured to monitor the DO value of the aerobic tank in the sewage treatment system when controlling various control devices in the sewage treatment system, and determine whether the corresponding DO value meets a preset DO value. If not, the aerobic amount parameter in each intermediate control parameter in the process control scheme calculated by the control module is corrected based on a preset aerobic amount correction model.

[0030] Beneficial effects: By correcting the aerobic amount parameter based on the preset aerobic amount correction model, the system can more accurately control the working state of the aeration equipment. This not only avoids energy waste caused by excessive aeration, but also reduces the decline in treatment efficiency caused by insufficient aeration, and can ensure that the treatment process in the aerobic tank is always in the best state, thereby improving the stability of the effluent water quality.

[0031] Further, the effluent judgment module comprises:

[0032] The first retrieval module is configured to retrieve, after obtaining the corresponding effluent water quality monitoring data at the current time, historical effluent water quality monitoring data corresponding to the previous time and the time before the previous time, and historical influent data corresponding to the effluent water quality monitoring data at the current time and the historical effluent water quality monitoring data from the historical database;

[0033] The error calculation module is configured to calculate the prediction error value of the corresponding sewage treatment mechanism model at the current time according to the effluent water quality monitoring data at the current time, the historical effluent water quality monitoring data corresponding to the previous time and the time before the previous time, and the effluent water quality simulation data corresponding to each time, and determine whether the corresponding prediction error value is greater than a preset error threshold. If yes, it is determined that the sewage treatment mechanism model at the previous time needs to be adjusted, otherwise it does not need to be adjusted;

[0034] The sorting module is configured to, when the determination result is that the sewage treatment mechanism model at the previous time needs to be adjusted, identify the adjustment sensitivity of each model parameter in the model parameter group corresponding to the sewage treatment mechanism model at the previous time, and sort the model parameters in descending order of adjustment sensitivity to form a sensitivity sorting table;

[0035] The selection module is configured to select the model parameter with the highest adjustment sensitivity according to the sensitivity sorting table;

[0036] The correction adjustment module is used for correcting and adjusting the model parameters based on a preset adjustment amount after selecting the corresponding intermediate control parameters, updating the sewage treatment mechanism model corresponding to the previous moment after the correction is completed, and inputting the historical influent data corresponding to the effluent monitoring data of the current moment and the process control scheme corresponding to the current moment of the sewage treatment system into the updated sewage treatment mechanism model corresponding to the previous moment to output corresponding effluent quality re-simulation data.

[0037] The iterative loop module is used for judging whether the simulation error between the effluent quality re-simulation data and the effluent quality monitoring data is less than a preset simulation error, and if yes, the corresponding sewage treatment mechanism model is a feasible model.

[0038] If no, the next model parameter with the highest adjustment sensitivity is selected, and the correction adjustment module is repeatedly executed until the effluent quality re-simulation data and the effluent quality monitoring data are within the preset simulation error, at which time it is judged that the corresponding sewage treatment mechanism model is a feasible model.

[0039] Beneficial effects: In the present scheme, the prediction error value of the corresponding sewage treatment mechanism model under the corresponding previous moment is calculated by combining the effluent quality monitoring data corresponding to the current moment, the previous moment and the moment before the previous moment, and the effluent quality simulation data and the historical influent data corresponding to each moment, so as to realize reliable and accurate evaluation of the feasibility of the corresponding sewage treatment mechanism model under the previous moment.

[0040] The adjustment sensitivity of the model parameters is identified and sorted in descending order of sensitivity. This method ensures that the model parameters with the greatest impact on effluent quality are adjusted first, thereby more efficiently optimizing the sewage treatment mechanism model. The selection module and the correction adjustment module gradually correct and adjust the model parameters according to the sensitivity ranking table. This step-by-step optimization method can avoid system instability caused by adjusting too many parameters at once, while ensuring that each adjustment brings significant improvement. The iterative loop module ensures that the system will stop adjusting only when the effluent quality re-simulation data meets the preset water quality value. This closed-loop control mechanism ensures that the effluent quality always meets the standard, reducing fluctuations.

[0041] Further, the intermediate control parameters include air quantity parameters, DO values, oxygen demand parameters, internal reflux ratio parameters, external reflux ratio parameters, sludge discharge parameters and dosing parameters of the sewage treatment system.

[0042] The present application also provides a sewage treatment control method based on a mechanism model, which uses the above-mentioned sewage treatment control system based on a mechanism model. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 Fig. 1 is a logic diagram of a sewage treatment control system based on a mechanism model according to an embodiment of the present application. DETAILED DESCRIPTION

[0044] The present application will be further described in detail by the following specific embodiments:

[0045] Embodiment One

[0046] The sewage treatment control system based on a mechanism model comprises a server, as shown in Fig. 1. Figure 1

[0047] The server comprises:

[0048] a real-time influent monitoring module for monitoring the influent data of the sewage treatment system in real time to form real-time influent data;

[0049] an abnormality judging module for judging whether the real-time influent data corresponding to the current time is abnormal according to the real-time influent data, and if the result is no, judging that the real-time influent data corresponding to the current time is accurate data and calling the intermediate control parameters of the sewage treatment system corresponding to the previous time as the process control scheme corresponding to the current time of the sewage treatment system;

[0050] The abnormality judging module is further used for correcting the real-time influent data monitored at the current time if the result is yes, judging that the corrected real-time influent data is accurate data, and calling the intermediate control parameters of the sewage treatment system corresponding to the previous time as the process control scheme corresponding to the current time of the sewage treatment system. In this embodiment, the intermediate control parameters of the sewage treatment system corresponding to the previous time are corrected by combining the real-time influent data at the current time and through the parameter adjustment experience of the operator to realize the correction of the corresponding parameters. For example, if a certain content in the real-time influent data is relatively high, the operator can know according to experience that the size of the corresponding intermediate control parameter can be adjusted to reduce the corresponding content. Of course, the system can also be automatically adjusted, for example, according to the change trend of the previous two groups of data, 1 / 2 correction, such as the previous two groups are 20, 24, and the abnormal data of this group is corrected to 24+1 / 2*(24-20)=26, and the decreasing trend is the same. In this embodiment, the intermediate control parameters comprise the air quantity parameter, the DO value, the oxygen demand parameter, the internal reflux ratio parameter, the external reflux ratio parameter, the sludge discharge quantity parameter, the flow parameter, and the dosing quantity parameter of the sewage treatment system.

[0051] ​The data cleaning module is further included for cleaning the monitored real-time influent data when it is determined that the monitored real-time influent data is accurate data. The influent monitoring data is ensured to be in a format required by an algorithm to enter a model, and this step includes parameters of an influent component distribution ratio (percentage of each component). The online monitoring of the influent mainly monitors COD / TN / TP / SS / NH4 of a sewage treatment plant. The model needs to use the data after cleaning, and the ratio needs to be dynamically calibrated.

[0052] The comparison and selection cycle module is configured to simulate the sewage treatment of the sewage treatment system based on the real-time influent data corresponding to the current time and the process control scheme corresponding to the current time of the sewage treatment system, and output corresponding simulated effluent quality data. The simulated effluent quality data is used to determine whether the corresponding simulated effluent quality meets the preset requirements. If yes, the process control scheme corresponding to the current time of the sewage treatment system is determined to be a feasible scheme. If no, each intermediate control parameter in the process control scheme corresponding to the current time of the sewage treatment system is adjusted and re-input into the sewage treatment mechanism model of the previous time. In this embodiment, the adjustment of each intermediate control parameter in the process control scheme corresponding to the current time of the sewage treatment system is performed by using a preset comparison and selection parameter adjustment model. The comparison and selection parameter adjustment model is constructed by using an existing random forest algorithm.

[0053] The control module is configured to, when the determination result is that the process control scheme corresponding to the current time of the sewage treatment system is a feasible scheme, calculate control parameters of control equipment corresponding to each intermediate control parameter in the process control scheme according to each intermediate control parameter in the process control scheme corresponding to the current time, and control each control equipment in the sewage treatment system.

[0054] The effluent monitoring module is configured to monitor real-time effluent quality monitoring data of the sewage treatment system when each control equipment in the sewage treatment system is controlled.

[0055] The effluent determination module is configured to determine whether the effluent quality monitoring data meets a preset water quality value according to the real-time monitored effluent quality monitoring data. If yes, the corresponding sewage treatment mechanism model is determined to be a feasible model. If no, one of the model parameters in the model parameter group in the sewage treatment mechanism model of the previous time is selected for correction and adjustment, and corresponding re-simulated effluent quality data is output until the re-simulated effluent quality data and the effluent quality monitoring data are within a preset simulation error. At this time, the corresponding sewage treatment mechanism model is determined to be a feasible model.

[0056] The effluent determination module includes:

[0057] The first calling module is configured to call historical effluent water quality monitoring data corresponding to the previous time and the time before the previous time from the historical database after obtaining the effluent water quality monitoring data corresponding to the current time, and call historical influent water data corresponding to the current effluent water quality monitoring data and the historical effluent water quality monitoring data;

[0058] The error calculation module is configured to calculate a prediction error value of the corresponding sewage treatment mechanism model at the current time according to the effluent water quality monitoring data at the current time, the historical effluent water quality monitoring data corresponding to the previous time and the time before the previous time, and the effluent water quality simulation data corresponding to each time, and determine whether the corresponding prediction error value is greater than a preset error threshold value, if yes, it is determined that the sewage treatment mechanism model at the previous time needs to be adjusted, otherwise it does not need to be adjusted;

[0059] In the embodiment, the prediction error value calculation formula is:

[0060]

[0061] In the formula, W is the prediction error value, y t is the effluent water quality monitoring data at the current time t, is the effluent water quality simulation data at the current time t, w1, w2, and w3 are corresponding dynamic weight values, and the greater the corresponding effluent water quality error at the current time, the greater the corresponding weight value.

[0062] The sorting module is configured to identify the adjustment sensitivity of each model parameter in the model parameter group corresponding to the sewage treatment mechanism model at the previous time when the determination result is that the sewage treatment mechanism model at the previous time needs to be adjusted, and sort the adjustment sensitivity in descending order to form a sensitivity sorting table.

[0063] The selection module is configured to select the model parameter with the highest adjustment sensitivity according to the sensitivity sorting table. In the embodiment, the corresponding model parameters include kinetic parameters and stoichiometric parameters of the mechanism model. These parameters change with the environmental temperature, season, and microbial growth conditions, and need to be calibrated.

[0064] The correction and adjustment module is configured to correct and adjust the model parameter based on a preset adjustment amount after selecting the corresponding intermediate control parameter, update the sewage treatment mechanism model corresponding to the previous time after the correction and adjustment is completed, and input the historical influent water data corresponding to the effluent monitoring data at the current time and the process control scheme corresponding to the current time of the sewage treatment system into the updated sewage treatment mechanism model corresponding to the previous time as input data, and output corresponding effluent water quality re-simulation data.

[0065] The iterative loop module is configured to determine whether a simulation error between the simulated effluent water quality data and the monitored effluent water quality data is less than a preset simulation error. If yes, the corresponding sewage treatment mechanism model is a feasible model.

[0066] If no, the next model parameter with the highest adjustment sensitivity is selected, and the correction and adjustment module is repeatedly executed until the simulated effluent water quality data and the monitored effluent water quality data are within the preset simulation error. At this time, the corresponding sewage treatment mechanism model is determined to be a feasible model.

[0067] The model updating module is configured to update the sewage treatment mechanism model at the previous moment according to the corrected and adjusted model parameters when the corresponding sewage treatment mechanism model is determined to be a feasible model.

[0068] The server further comprises:

[0069] The aerobic tank control module is configured to monitor the DO value of the aerobic tank in the sewage treatment system when controlling each control device in the sewage treatment system, and determine whether the corresponding DO value meets a preset DO value. If no, the oxygen demand parameter in each intermediate control parameter in the process control scheme calculated by the control module is corrected based on a preset oxygen demand correction model. In this embodiment, the oxygen demand correction model is also constructed by using the existing random forest algorithm. According to the corresponding DO value, the required air quantity is calculated, which is converted into the final working frequency of the fan device. After executing the control parameter, it is determined whether the control parameter executed according to the data feedback of the DO electrode of the aerobic tank needs to be further adjusted.

[0070] The embodiment also discloses a sewage treatment control method based on a mechanism model, which uses the sewage treatment control system based on a mechanism model.

[0071] The above is only an embodiment of the present application, and the common knowledge of the specific structure and characteristics in the scheme is not described too much. The ordinary skilled person in the art knows all the ordinary technical knowledge in the field of the application before the application date or the priority date, can know all the prior art in the field, and has the ability to apply conventional experimental means before that date. The ordinary skilled person in the art can improve and implement the present scheme based on the disclosure given in the present application, and some typical known structures or known methods should not be an obstacle for the ordinary skilled person in the art to implement the present application. It should be noted that, for those skilled in the art, without departing from the structure of the present application, a number of modifications and improvements can be made, which should be regarded as the protection scope of the present application. The scope of protection of the present application should be subject to the contents of its claims, and the specific embodiments in the description can be used to explain the contents of the claims.

Claims

1. A wastewater treatment control system based on a mechanistic model, characterized by: The service end comprises: The service end comprises: The real-time water inlet monitoring module is configured to monitor real-time water inlet data of the sewage treatment system to form real-time water inlet data. The abnormality judgment module is configured to determine whether the real-time water inlet data at the current time is abnormal based on the real-time water inlet data. The comparison and selection cycle module is configured to simulate sewage treatment of the sewage treatment system based on the sewage treatment mechanism model at the previous time according to the real-time water inlet data at the current time and the process control scheme corresponding to the current time of the sewage treatment system. The control module is configured to calculate control parameters of control equipment corresponding to each intermediate control parameter in the process control scheme based on each intermediate control parameter in the process control scheme corresponding to the current time when the process control scheme corresponding to the current time of the sewage treatment system is determined to be a feasible scheme. The effluent monitoring module is configured to monitor real-time effluent quality monitoring data of the sewage treatment system. The effluent judgment module is configured to determine whether the effluent quality monitoring data meets a preset water quality value based on the real-time effluent quality monitoring data. The effluent judgment module comprises: The first retrieval module is configured to retrieve historical effluent quality monitoring data corresponding to the previous time and the time before the previous time and historical water inlet data corresponding to the current effluent quality monitoring data and the historical effluent quality monitoring data from a historical database after the current effluent quality monitoring data at the current time is obtained. An error calculation module is configured to calculate a prediction error value of the corresponding sewage treatment mechanism model at the current time according to the effluent quality monitoring data at the current time, the historical effluent quality monitoring data corresponding to the previous time and the time before the previous time, and the effluent quality simulation data corresponding to each time, and determine whether the corresponding prediction error value is greater than a preset error threshold. If yes, it is determined that the sewage treatment mechanism model at the previous time needs to be adjusted; otherwise, it does not need to be adjusted. The prediction error value calculation formula is: In the formula, is a prediction error value, is the current time of the effluent water quality monitoring data, is the current time of the effluent water quality simulation data, , , is a corresponding dynamic weight value, the greater the corresponding effluent water quality error at the current time, the greater the corresponding weight value. An ordering module is configured to, when the determination result is that the sewage treatment mechanism model at the previous time needs to be adjusted, identify the adjustment sensitivity of each model parameter in the model parameter group corresponding to the sewage treatment mechanism model at the previous time, and sort the adjustment sensitivity in descending order to form a sensitivity ordering table. A selection module is configured to select the model parameter with the highest adjustment sensitivity according to the sensitivity ordering table. A correction and adjustment module is configured to, after selecting the corresponding intermediate control parameter, correct and adjust the model parameter based on a preset adjustment amount. After correction, the sewage treatment mechanism model corresponding to the previous time is updated, and the historical influent data corresponding to the effluent monitoring data at the current time and the process control scheme corresponding to the current time of the sewage treatment system are input into the updated sewage treatment mechanism model corresponding to the previous time as input data, and the corresponding effluent quality re-simulation data is output. An iteration cycle module is configured to determine whether the simulation error between the effluent quality re-simulation data and the effluent quality monitoring data is less than a preset simulation error. If yes, the corresponding sewage treatment mechanism model is a feasible model. If not, the model parameter with the highest adjustment sensitivity is selected, and the correction and adjustment module is repeatedly executed until the effluent quality re-simulation data and the effluent quality monitoring data are within the preset simulation error. At this time, it is determined that the corresponding sewage treatment mechanism model is a feasible model. A model updating module is configured to, when the determination result is that the corresponding sewage treatment mechanism model is a feasible model, update the sewage treatment mechanism model at the previous time according to the corrected and adjusted model parameters.

2. The mechanism model based sewage treatment control system according to claim 1, characterized in that: The anomaly determination module is further configured to, when the determination result is yes, correct the real-time influent data monitored at the current time, determine that the corrected real-time influent data is accurate data, and retrieve the intermediate control parameter of the sewage treatment system at the previous time as the process control scheme corresponding to the current time of the sewage treatment system. The server further comprises a data cleaning module configured to, when it is determined that the real-time influent data monitored at the current time is accurate data, clean the monitored real-time influent data.

3. The mechanism model based sewage treatment control system according to claim 2, characterized in that: The server further comprises: An aerobic tank control module is configured to, when controlling each control device in the sewage treatment system, monitor the DO value of the aerobic tank in the sewage treatment system, and determine whether the corresponding DO value meets a preset DO value. If not, the oxygen demand parameter in each intermediate control parameter in the process control scheme calculated by the control module is corrected based on a preset oxygen demand correction model.

4. The mechanism model based sewage treatment control system according to claim 3, characterized in that: The intermediate control parameters include air quantity parameters, DO values, oxygen demand parameters, internal reflux ratio parameters, external reflux ratio parameters, sludge discharge quantity parameters, and dosing quantity parameters corresponding to the sewage treatment system.

5. A method for control of sewage treatment based on a mechanistic model, characterized by: A mechanism model based sewage treatment control system according to any one of claims 1 to 4.

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