A control system and method based on a DO model

Abstract

The present application relates to the technical field of environmental engineering, and more particularly to a control system and method based on a DO model. The method comprises the following steps: collecting data through an online data acquisition instrument during the water inlet process to obtain sewage basic data, wherein the sewage basic data includes organic matter data and water inlet basic data; constructing a DO model according to the sewage basic data to obtain the DO model; processing system running state data according to the DO model; and judging biological phase balance according to the system running state data to obtain biological phase balance judgment data for biochemical system process operation auxiliary work. The present application can realize real-time monitoring of the sewage treatment process, precise model construction, system running state optimization and real-time adjustment of process operation, thereby improving the operation efficiency and treatment level of the sewage treatment system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental engineering, and in particular to a control system and method based on a DO model. BACKGROUND

[0002] Traditional wastewater treatment systems usually rely on experience and manual operation, which has problems such as low operation efficiency, high energy consumption, and unstable treatment effect. In order to solve these problems, in recent years, the field of environmental engineering technology has begun to use advanced automatic control methods, among which the control method based on the DO (dissolved oxygen) model has become an important technical means. DO is one of the important indicators in the wastewater treatment process, which can reflect the oxygen content in the wastewater, so as to evaluate the biological activity and biochemical reaction. However, the traditional DO model-based control method has some limitations, such as relying only on a simple oxygen dissolution model or lacking accurate judgment of the biological phase balance state. SUMMARY

[0003] The present application provides a control system and method based on a DO model to solve at least one of the above technical problems.

[0004] The present application provides a control method based on a DO model, which comprises:

[0005] S1, data acquisition is performed by an online data acquisition instrument during the influent process to obtain wastewater basic data, wherein the wastewater basic data includes organic matter data and influent basic data;

[0006] S2, a DO model is constructed according to the wastewater basic data to obtain the DO model;

[0007] S3, system operation state processing is performed according to the DO model to obtain system operation state data;

[0008] S4, biological phase balance judgment is performed according to the system operation state data to obtain biological phase balance judgment data, so as to perform biochemical system process operation auxiliary work.

[0009] In the present application, the organic matter data and other basic data in the influent are obtained in real time by online data acquisition instruments, so that the system can timely understand the characteristics and load conditions of the sewage, and realize real-time monitoring and analysis of the sewage treatment process. Based on the obtained sewage basic data, a DO model is constructed, which can more accurately describe the variation law of dissolved oxygen in the system, thereby improving the accuracy and reliability of the model. Through system operation state processing of the DO model, system operation state data is obtained, which can monitor and evaluate the operation state of the sewage treatment system in real time, help to timely discover abnormal conditions in the system, guide the operation personnel to adjust and optimize, and improve the stability and processing efficiency of the system. According to the biological phase balance judgment data obtained by judging the biological phase balance according to the system operation state data, the activity of biological reaction and the growth state of microorganisms in the system can be evaluated, and the biochemical system process operation can be guided to optimize the sewage treatment process. Through the biological phase balance judgment data, the process operation of the biochemical system can be adjusted in time, including but not limited to adjusting the aeration amount, sludge reflux ratio and other parameters, so as to maintain the stable operation and treatment effect of the system.

[0010] Optionally, the data acquisition by the online data acquisition instrument during the influent process obtains sewage basic data, including:

[0011] S11, data acquisition by online DO.ORP instrument, online temperature sensing device, influent quantity detection device and influent PH value monitoring device during the influent process obtains dissolved oxygen concentration data, oxidation-reduction state data, influent temperature data, influent quantity data and influent PH value data;

[0012] S11, organic matter species data acquisition by a chemical reaction device during the influent process obtains organic matter species data;

[0013] S12, organic matter concentration data acquisition by an optical sensor during the influent process obtains organic matter concentration data;

[0014] S13, influent basic data generation of the dissolved oxygen concentration data, the oxidation-reduction state data, the influent temperature data, the influent quantity data and the influent PH value data obtains influent basic data;

[0015] S14, organic matter data generation of the organic matter species data and the organic matter concentration data obtains organic matter data;

[0016] S15, data integration of the influent basic data and the organic matter data obtains sewage basic data.

[0017] The online data acquisition instrument in the application can monitor and record various parameter data in the water inlet process in real time, can provide accurate and timely data to reflect the actual situation of water inlet, avoids the errors and delays existing in manual sampling, and thus improves the accuracy and reliability of data. Using the online data acquisition instrument can realize automatic acquisition and processing of data without manual intervention and operation, saves human resources and time cost, and improves the efficiency of data acquisition and processing. Integrating data collected by different instruments can form basic sewage data, which provides basic data support for subsequent DO model construction and system operation state processing. At the same time, this also provides a reliable data basis for comprehensive analysis and evaluation of the operation state of the sewage treatment system.

[0018] Optionally, the organic matter concentration data obtained by the optical sensor in the water inlet process includes:

[0019] The optical sensor is controlled to measure light emission and acceptance in the water inlet process to obtain water inlet optical property data;

[0020] Organic matter concentration calculation is performed according to the water inlet optical property data and the organic matter type data to obtain preliminary organic matter concentration data;

[0021] Data correction is performed on the preliminary organic matter concentration data according to the water inlet temperature data, the water inlet quantity data and the water inlet PH value data to obtain organic matter concentration data.

[0022] The optical sensor in the application can monitor the light emission and acceptance in the water inlet process in real time, obtain the optical property data of the water inlet, and provide accurate water inlet property information, which is helpful for real-time understanding of the pollution of the water inlet. According to the optical property data of the water inlet and the known organic matter type data, a correlation model between the organic matter concentration and the optical property can be established. Through this model, the preliminary organic matter concentration data can be accurately calculated, which provides a basis for subsequent data correction. After obtaining the preliminary organic matter concentration data, the data is corrected in combination with the water inlet temperature, the water inlet quantity and the water inlet PH value and the like, which can consider the influence of the water inlet conditions, and improves the accuracy and reliability of the organic matter concentration data. The optical sensor can realize real-time monitoring and data acquisition without manual intervention, realizes the automation and real-time of data acquisition, and improves the real-time and reliability of data. Through the acquisition and analysis of the optical property data and the organic matter concentration data of the water inlet, the pollution and characteristics of the sewage can be comprehensively understood, which provides an important basis for subsequent sewage treatment process operation and optimization.

[0023] Optionally, the organic matter concentration calculation according to the water inlet optical property data and the organic matter type data to obtain preliminary organic matter concentration data includes:

[0024] According to the organic matter species data, an organic matter concentration optical characteristic mapping is performed through a preset organic matter concentration optical characteristic correlation model, to obtain organic matter concentration optical characteristic mapping data;

[0025] According to the organic matter concentration optical characteristic mapping data and the inlet water optical characteristic data, an organic matter concentration calculation is performed, to obtain preliminary organic matter concentration data;

[0026] The construction step of the organic matter concentration optical characteristic correlation model is specifically as follows:

[0027] Obtain organic matter concentration test data, organic matter species test data and light characteristic test data under different conditions;

[0028] According to the light characteristic test data, feature extraction is performed, to obtain absorbance feature data and transmittance feature data;

[0029] According to the organic matter concentration test data, the organic matter species test data, the absorbance feature data and the transmittance feature data, correlation analysis is performed, to obtain organic matter species concentration optical characteristic correlation data;

[0030] According to the organic matter species concentration optical characteristic correlation data, a fitting model derivation process is performed, to obtain a first organic matter concentration optical characteristic correlation model;

[0031] According to the organic matter concentration test data, the organic matter species test data and the light characteristic test data, a multiple regression mapping relationship construction is performed, to obtain a second organic matter concentration optical characteristic correlation model.

[0032] The organic matter concentration optical property correlation model can quantitatively describe the relationship between the optical property data and the organic matter concentration, considers the correlation between different organic matter types and different optical properties, and can improve the accuracy and reliability of the organic matter concentration calculation. Using an optical sensor for data acquisition and calculation can realize real-time monitoring and calculation of the organic matter concentration, saving time and labor cost, and improving the efficiency and real-time performance of data processing. Through feature extraction and correlation analysis, combined with absorbance characteristics, transmittance characteristics and organic matter concentration test data, the influence of various factors on the optical properties and organic matter concentration can be comprehensively considered, so that the organic matter concentration optical property correlation model can be more accurately constructed. Based on the multivariate regression mapping relationship, the organic matter concentration optical property correlation model can more finely fit the relationship between the organic matter concentration and the optical properties, improve the applicability and prediction ability of the model. The preliminary organic matter concentration data obtained by this method is more accurate, which is helpful for subsequent data correction and system operation state analysis, so as to provide more accurate data support for biochemical system process operation, and improve the performance and efficiency of the sewage treatment system.

[0033] Optionally, the DO model construction according to the sewage basic data comprises:

[0034] S21, constructing an oxygen mass transfer model according to the sewage basic data to obtain oxygen mass transfer model data; wherein the oxygen mass transfer model is constructed by an oxygen transfer equation, and the oxygen transfer equation is specifically:

[0035]

[0036] c t is oxygen concentration change rate data, D is oxygen diffusion coefficient data, is dissolved oxygen diffusion degree data, k v is dissolved oxygen transfer rate data, c is dissolved oxygen concentration data, c eq is dissolved oxygen equilibrium concentration data.

[0037] S22, constructing an oxygen reaction kinetics model according to the sewage basic data to obtain oxygen reaction kinetics model data; wherein the oxygen reaction kinetics model is constructed by an oxygen reaction kinetics equation, and the oxygen reaction kinetics equation is specifically:

[0038]

[0039] R is biological respiration rate data, k r is biological respiration rate constant data, c is dissolved oxygen concentration data, K DO is Michaelis-Menten constant data.

[0040] S23, constructing an oxygen dissolution kinetics model according to the oxygen mass transfer model data and the oxygen reaction kinetics model data, to obtain oxygen dissolution kinetics model data;

[0041] S24, constructing a DO model according to the sewage basic data and the oxygen dissolution kinetics model data.

[0042] In the present application, by establishing the oxygen mass transfer model and the oxygen reaction kinetics model, the change of dissolved oxygen concentration in sewage with time and space can be simulated. The mass transfer model considers the diffusion process of oxygen in water, and the reaction kinetics model considers the influence of biological respiration rate on dissolved oxygen concentration, so that the change rule of dissolved oxygen can be more accurately described. By introducing the oxygen reaction kinetics model, the influence of biological respiration rate on dissolved oxygen concentration can be considered, instead of simply considering the transport process of oxygen. Through the construction of the oxygen mass transfer model and the oxygen reaction kinetics model, the parameters in the model can be determined, such as oxygen diffusion coefficient, biological respiration rate constant and Michelson-Mentone constant. The oxygen mass transfer model and the oxygen reaction kinetics model are integrated to construct a complete oxygen dissolution kinetics model. Through this model, the influence of oxygen transport process and biological respiration process on dissolved oxygen concentration is considered, so that the change rule of DO can be more accurately described. The constructed DO model can predict the running state of the system according to the sewage basic data, to provide auxiliary operation for biochemical system process operation. The accurate DO model helps to optimize the process operation, improve the treatment efficiency and water quality stability.

[0043] Optionally, the constructing an oxygen dissolution kinetics model according to the oxygen mass transfer model data and the oxygen reaction kinetics model data, to obtain oxygen dissolution kinetics model data, comprises:

[0044] S231, performing oxygen transport simulation on the oxygen mass transfer model data according to the sewage basic data, to obtain oxygen transport simulation data;

[0045] S232, performing reaction kinetics simulation according to the oxygen reaction kinetics model data and the oxygen transport simulation data, to obtain reaction kinetics simulation data;

[0046] S233, performing oxygen transport simulation on the oxygen mass transfer model data according to the reaction kinetics simulation data, to obtain new oxygen transport simulation data, and performing change rate calculation according to the new oxygen transport simulation data and the oxygen transport simulation data, to obtain dissolved oxygen concentration transport change rate data;

[0047] S234, repeating S231 to S233 until the dissolved oxygen concentration transmission change rate data is determined to be less than or equal to the preset change rate threshold data, to generate oxygen dissolution kinetics model data.

[0048] In the present application, through the steps of S231 and S232, oxygen transmission simulation and reaction kinetics simulation are performed respectively, considering the transmission of oxygen and the biological reaction process. This comprehensive simulation can more accurately describe the transmission of oxygen in water and the biological oxidation process. In the step of S233, the oxygen transmission model data is simulated again according to the reaction kinetics simulation data, to obtain new oxygen transmission simulation data. By repeating this process until the dissolved oxygen concentration transmission change rate data is less than or equal to the preset change rate threshold data, the model can be dynamically adjusted to be closer to the actual situation. Through continuous iteration simulation and adjustment, the dissolved oxygen concentration transmission change rate of the model gradually converges to the preset change rate threshold. The obtained oxygen dissolution kinetics model data has higher accuracy and stability, and can better reflect the dynamic changes of the actual system. This method can help to determine appropriate model parameters and initial conditions, thereby optimizing and improving the oxygen dissolution kinetics model. By continuously adjusting the model to be closer to the actual situation, the reliability and applicability of the model are improved.

[0049] Optionally, the DO model is constructed according to the sewage basic data and the oxygen dissolution kinetics model data, to obtain the DO model, including:

[0050] According to the sewage basic data and the oxygen dissolution kinetics model data, the influent organic matter load is processed to obtain sewage dissolved oxygen load data;

[0051] According to the sewage basic data and the sewage dissolved oxygen load data, oxygen transmission processing is performed to obtain sewage dissolved oxygen transmission data;

[0052] According to the sewage basic data and the sewage dissolved oxygen transmission data, biological oxidation simulation is performed to obtain sewage dissolved oxygen biological oxidation data;

[0053] Obtain aeration data, and construct a DO model according to the aeration data and the sewage dissolved oxygen biological oxidation data, to obtain the DO model.

[0054] The present application considers the influence of organic matter on dissolved oxygen by processing the organic matter load in the influent, further perfecting the construction of the DO model. At the same time, combined with oxygen transfer processing, the transmission process of oxygen in the water body is considered, making the model more close to the actual situation. Through biological oxidation simulation, the consumption of dissolved oxygen by the activity of organisms in the sewage is considered. Such consideration makes the model more detailed and can more accurately reflect the influence of biological reaction in the sewage treatment system. Aeration data is obtained, and combined with biological oxidation data, the DO model is constructed. The aeration data considers the oxygen supply of gas to the water body, and the biological oxidation data considers the consumption of dissolved oxygen by biological activity. The influence of the two aspects is comprehensively considered to construct a complete DO model.

[0055] Optionally, the system operation state processing according to the DO model obtains system operation state data, including:

[0056] According to the DO model, the DO concentration change trend processing obtains DO concentration change trend data;

[0057] According to the DO model, the DO concentration spatial distribution processing obtains DO concentration spatial distribution data;

[0058] Obtain sludge concentration data, and according to the DO model and the sludge concentration data, the biological reaction activity processing obtains biological reaction activity data;

[0059] According to the DO concentration change trend data, the DO concentration spatial distribution data and the biological reaction activity data, the data integration obtains system operation state data.

[0060] In the present application, by processing the DO model, data about the dissolved oxygen concentration change trend, spatial distribution and biological reaction activity in the system can be obtained, which can deeply reflect the operation state of the sewage treatment system and help users better understand the working condition of the system. Processing the DO concentration change trend can realize real-time monitoring of the dissolved oxygen concentration and timely discover the change trend of the DO concentration, providing early warning information for the operator and helping them take corresponding control measures to avoid problems. By processing the DO concentration spatial distribution data, the DO concentration distribution in different regions of the sewage treatment system can be understood, which helps to find local abnormalities or unevenness and guide users to take targeted measures for adjustment and optimization. According to the biological reaction activity data, the activity level of biological reaction in the system can be evaluated to provide a reference basis for further adjustment and optimization. Integrating and analyzing the DO concentration change trend data, the DO concentration spatial distribution data and the biological reaction activity data can comprehensively evaluate the operation state of the system, which helps to find potential problems in the system and provides guiding opinions and suggestions to improve the operation efficiency and stability of the system.

[0061] Optionally, the biophase balance judgment according to the system running state data comprises:

[0062] microbial growth condition evaluation according to the system running state data, to obtain microbial growth condition evaluation data;

[0063] biological reaction stability evaluation according to the sewage basic data and the system running state data, to obtain biological reaction stability evaluation;

[0064] biophase balance judgment according to the microbial growth condition evaluation data and the biological reaction stability evaluation, to obtain biophase balance judgment data.

[0065] In the present application, the microbial growth condition evaluation according to the system running state data can understand the growth of microorganisms in the sewage treatment system, which is helpful to evaluate the activity and proliferation of microorganisms in the system and provide data support for further biophase balance judgment. The biological reaction stability evaluation according to the sewage basic data and the system running state data can evaluate the stability and health condition of the biological reaction in the system, including understanding the information of microbial population structure, metabolic activity and biodiversity of biological community in the biological reaction process, so as to judge the stability and adaptability of the biological reaction. Combined with the microbial growth condition evaluation data and the biological reaction stability evaluation data, the biophase balance judgment can comprehensively consider the microbial ecological environment and the running state of the biological reaction in the system, which is helpful to determine whether there is microbial growth imbalance or biological reaction instability in the system, and to take corresponding adjustment measures to maintain the stable operation of the system. Through the biophase balance judgment data, the changes of the microbial ecological environment and the health condition of the biological reaction in the system can be understood in time, so as to adjust and optimize the operation strategy in time and ensure the efficient and stable operation of the system.

[0066] Optionally, the present application also provides a DO model-based control system for executing the DO model-based control method as described above, the DO model-based control system comprising:

[0067] a sewage basic data acquisition module for acquiring data through online data acquisition instruments during the water inlet process to obtain sewage basic data, wherein the sewage basic data comprises organic matter data and water inlet basic data;

[0068] a DO model construction module for constructing a DO model according to the sewage basic data to obtain the DO model;

[0069] The system operation state processing module is configured to process system operation state data according to the DO model.

[0070] The biological phase balance judgment module is configured to judge biological phase balance according to the system operation state data to obtain biological phase balance judgment data for assisting biochemical system process operation.

[0071] The present application aims to obtain sewage basic data by using online data acquisition instruments during water inlet process, and construct a DO model according to these data, thereby realizing comprehensive monitoring and analysis of the sewage treatment system. Based on the constructed DO model, the operation state of the sewage treatment system can be monitored and analyzed in real time by processing system operation state data, including evaluation of dissolved oxygen concentration changes, biological reaction activity, organic matter content, etc., which helps to find abnormal conditions and potential problems in the system and take timely measures for adjustment and optimization. Combined with system operation state data, especially biological phase balance judgment data, the microbial ecological environment and biological reaction process in the sewage treatment system can be accurately evaluated, which helps to judge the stability and health status of the biological phase and timely find and solve problems affecting the system. Based on the biological phase balance judgment data, the biochemical system process operation can be assisted by the operator. The present application can improve the operator's grasp and control ability of the system operation state, thereby realizing efficient and stable operation of the sewage treatment system. By continuously monitoring and analyzing system operation state data, real-time adjustment and optimization of biochemical system operation can be realized. This timely feedback and adjustment can ensure the stability and efficiency of the system, while reducing operating costs and energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0072] Other features, objects, and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the drawings:

[0073] Fig. 1 A step flowchart of a control method based on a DO model of an embodiment is shown;

[0074] Fig. 2 A step flowchart of a sewage basic data acquisition method of an embodiment is shown;

[0075] Fig. 3 A step flowchart of a DO model construction method of an embodiment is shown;

[0076] Fig. 4 A step flowchart of an oxygen dissolution kinetics model construction method of an embodiment is shown;

[0077] The implementation of the present application, functional features and advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0078] The technical method of the patent of the application will be described clearly and completely below in combination with the drawings. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0079] In addition, the drawings are only schematic illustrations of the application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated description thereof will be omitted. Some block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0080] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be called a second element, and similarly a second element can be called a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0081] Referring to Figs. 1 to 4 The application provides a control method based on a DO model, which comprises the following steps:

[0082] S1, collecting data by an online data collection instrument during the water inlet process to obtain sewage basic data, wherein the sewage basic data comprises organic matter data and water inlet basic data;

[0083] Specifically, online chemical sensors, biological sensors or optical sensors and other equipment are installed to monitor the concentration of organic matter and other water quality parameters in the water inlet. At the same time, a flow meter and other equipment are used to measure the flow of the water inlet. These devices can be connected to the data collection system through various interfaces to collect data in real time.

[0084] S2, constructing a DO model according to the sewage basic data to obtain the DO model;

[0085] Specifically, based on the collected influent basic data, a DO model is established. Mathematical modeling methods can be used to establish models that describe the changes of dissolved oxygen concentration over time and space, such as chemical kinetics equations, mass transfer equations, etc., by combining parameters such as influent organic matter data and influent quantity data. A dissolved oxygen mass transfer model is constructed according to the oxygen transfer equation. This model describes the law of change of dissolved oxygen concentration over time and space, taking into account the diffusion, transport and reaction of dissolved oxygen, etc. An oxygen reaction kinetics model is constructed according to the oxygen reaction kinetics equation. This model describes the relationship between biological respiration rate and dissolved oxygen concentration, taking into account the influence of biological activity on dissolved oxygen. The dissolved oxygen mass transfer model and the oxygen reaction kinetics model are combined to establish a complete DO model. Numerical simulation methods are used to combine the measured data to fit and verify the model, ensuring the accuracy and reliability of the model.

[0086] S3, processing the system running state according to the DO model to obtain system running state data;

[0087] Specifically, the established DO model is used to process the running state of the system. According to the real-time collected data, input into the model for calculation, to obtain the predicted value of the dissolved oxygen concentration in the system, such as model parameter updating, state estimation, etc. Calculation process, and verification and adjustment of the model output results.

[0088] For dynamic systems, model parameters change with time and operating conditions. Therefore, first, the parameters of the DO model need to be updated according to the real-time collected data, such as the dissolved oxygen concentration change rate constant, the dissolved oxygen equilibrium concentration, etc. The real-time collected influent basic data is input into the DO model, and the current state of the dissolved oxygen concentration in the system is estimated according to the calculation formula and parameters of the model, which is realized by numerical methods such as numerical solution of differential equations or numerical simulation. The dissolved oxygen concentration calculated by the model is compared with the actual measured value to verify the output results. If there is a difference between the model predicted value and the actual measured value, the model needs to be adjusted and optimized to improve the accuracy and reliability of the model, such as using parameter identification, fitting optimization, etc. to adjust the parameters of the model, so that the model better fits the actual situation. The dissolved oxygen concentration data obtained after model processing and adjustment are combined with other system running state data to generate system running state data, including the change trend of dissolved oxygen concentration, spatial distribution, etc.

[0089] S4, judging the biological phase balance according to the system running state data to obtain biological phase balance judgment data for biochemical system process operation auxiliary work.

[0090] Specifically, based on the simulated system operation state data, the biological phase balance is judged. For example, the relationship between dissolved oxygen concentration and sludge concentration, biological reaction rate, etc. is analyzed to evaluate the activity and growth state of microorganisms in the system. According to the evaluation results, the biochemical system process operation is guided, and parameters such as aeration quantity and sludge return ratio are adjusted to maintain the biological phase balance and stable operation of the system.

[0091] Specifically, the dissolved oxygen concentration data and sludge concentration data obtained by simulation are statistically analyzed to understand their change trend and correlation. The influence of dissolved oxygen concentration on microbial growth and activity is explored, such as the relationship between dissolved oxygen concentration and biological reaction rate, and a model is established to describe the quantitative relationship between the two. Based on the simulation data, the growth rate, metabolic activity, etc. of microorganisms in the system, as well as the structure and stability of the microbial community are evaluated. Abnormal conditions in the microbial community, such as eutrophication, odor, etc. are analyzed to evaluate the health status of microorganisms and the ecological balance. According to the evaluation results, the corresponding biochemical system operation strategy is formulated, and key parameters such as aeration quantity and sludge return ratio are adjusted to maintain the balance of the microbial community and the stable operation of the system. For example, if the evaluation result shows that the microbial activity is insufficient, the aeration quantity can be increased to increase the dissolved oxygen concentration; if the sludge concentration is too high, the sludge return ratio can be adjusted to reduce excessive sedimentation. According to the simulation data analysis, it is found that the dissolved oxygen concentration and sludge concentration are negatively correlated, while the biological reaction rate is positively correlated. The evaluation result shows that the activity of microorganisms in the system is low due to insufficient dissolved oxygen concentration. In view of this, it is decided to increase the aeration quantity, increase the dissolved oxygen concentration, and appropriately adjust the sludge return ratio to promote the growth and metabolic activity of microorganisms, maintain the biological phase balance of the system and stable operation.

[0092] In the present application, the organic matter data and other basic data in the influent are obtained in real time by online data acquisition instruments, so that the system can timely understand the characteristics and load of the sewage, and realize real-time monitoring and analysis of the sewage treatment process. Based on the obtained sewage basic data, the DO model is constructed, which can more accurately describe the variation law of dissolved oxygen in the system, thereby improving the accuracy and reliability of the model. Through system operation state processing of the DO model, system operation state data is obtained, which can monitor and evaluate the operation state of the sewage treatment system in real time, which helps to timely discover abnormal conditions in the system, guide the operation personnel to adjust and optimize, and improve the stability and processing efficiency of the system. According to the system operation state data, the biological phase balance judgment data is obtained, which helps to evaluate the activity of biological reaction and the growth state of microorganisms in the system, guide the biochemical system process operation, and optimize the sewage treatment process. Through the biological phase balance judgment data, the process operation of the biochemical system can be adjusted in time, including but not limited to adjusting parameters such as aeration quantity and sludge return ratio, to maintain the stable operation and treatment effect of the system.

[0093] Optionally, the data acquisition during the water inflow process is performed by an online data acquisition instrument to obtain sewage basic data, including:

[0094] S11, data acquisition during the water inflow process is performed by an online DO / ORP instrument, an online temperature sensing device, an inflow volume detection device, and an inflow pH value monitoring device to obtain dissolved oxygen concentration data, oxidation-reduction state data, inflow temperature data, inflow volume data, and inflow pH value data.

[0095] Specifically, data acquisition during the water inflow process is performed by an online DO / ORP instrument, an online temperature sensing device, an inflow volume detection device, and an inflow pH value monitoring device to obtain dissolved oxygen concentration data, oxidation-reduction state data, inflow temperature data, inflow volume data, and inflow pH value data. These data reflect the physical and chemical properties of the inflow water, including dissolved oxygen levels, water temperature, pH value, etc.

[0096] S11, data acquisition of organic species during the water inflow process is performed by a chemical reaction device to obtain organic species data.

[0097] Specifically, a liquid chromatography-mass spectrometry (LC-MS) instrument is used for organic species data acquisition. LC-MS is a high-efficiency analytical instrument that can accurately qualitatively and quantitatively analyze organic matter in the inflow water, thereby obtaining organic species data. For example, the collected data includes the types and content information of organic matter such as phenol, phenethyl alcohol, and benzyl alcohol.

[0098] S12, organic matter concentration data is collected during the water inflow process by an optical sensor to obtain organic matter concentration data.

[0099] Specifically, an ultraviolet-visible spectrophotometer (UV-Vis Spectrophotometer) is used for organic matter concentration data acquisition. UV-Vis spectrophotometer is a commonly used optical sensor that can measure the absorbance of organic matter in water samples, and then calculate the concentration of organic matter. For example, by analyzing the absorption spectrum of the water sample, the concentration data of organic matter such as phenol and phenethyl alcohol can be obtained.

[0100] S13, the dissolved oxygen concentration data, the oxidation-reduction state data, the inflow temperature data, the inflow volume data, and the inflow pH value data are used to generate inflow basic data to obtain inflow basic data.

[0101] Specifically, the dissolved oxygen concentration, oxidation-reduction state, influent temperature, influent quantity and influent pH value and other data are collected using a multi-parameter water quality monitor. The multi-parameter water quality monitor usually includes a dissolved oxygen sensor, a temperature sensor, a pH sensor and the like, can monitor multiple water quality parameters of the influent in real time, and transmit the data to a data processing system.

[0102] S14, organic matter data is generated by combining the organic matter species data and the organic matter concentration data, and the organic matter data is obtained.

[0103] Specifically, the collected organic matter species data and organic matter concentration data are integrated into the same data table or database to ensure that the corresponding relationship of the data is accurate. The organic matter species data is coded or labeled. Each organic matter is assigned a unique identifier or code. The format of the organic matter species data and the organic matter concentration data is ensured to be consistent and associated.

[0104] S15, the influent basic data and the organic matter data are integrated to obtain sewage basic data.

[0105] Specifically, the data collected from different sensors or instruments is integrated into a unified data processing platform, such as using a data acquisition system or monitoring software. After data integration, the relationship between different parameters can be analyzed to provide basic data support for subsequent sewage treatment processes.

[0106] The online data acquisition instrument in the application can monitor and record various parameter data in the influent process in real time, can provide accurate and timely data to reflect the actual situation of the influent, avoids the errors and delays of manual sampling, and thus improves the accuracy and reliability of the data. Using the online data acquisition instrument can realize automatic data acquisition and processing without manual intervention and operation, saves human resources and time cost, and improves the efficiency of data acquisition and processing. Integrating the data collected by different instruments can form sewage basic data, which provides basic data support for subsequent DO model construction and system operation state processing. At the same time, this also provides a reliable data basis for comprehensive analysis and evaluation of the operation state of the sewage treatment system.

[0107] Optionally, the organic matter concentration data is obtained by controlling the optical sensor to measure light emission and acceptance during the influent process, and the organic matter concentration data is obtained.

[0108] The optical sensor is controlled to measure light emission and acceptance during the influent process to obtain influent optical property data.

[0109] Specifically, the optical sensor is controlled to measure and receive light emission during the water inlet process to obtain water inlet optical characteristic data. The optical sensor can be used to detect specific optical characteristics in the water inlet, such as absorbance, transmittance, etc., which can reflect information such as the concentration of organic matter in the water.

[0110] Organic matter concentration calculation is performed according to the water inlet optical characteristic data and the organic matter species data to obtain preliminary organic matter concentration data;

[0111] Specifically, organic matter concentration calculation is performed according to the water inlet optical characteristic data and the organic matter species data to obtain preliminary organic matter concentration data. Through the optical characteristic data and the known organic matter species information, a correlation model between the organic matter concentration and the optical characteristic can be established to calculate the preliminary organic matter concentration data.

[0112] Y = β0+ β1× X + ∈;

[0113] Y is the preliminary organic matter concentration data, β0is the preset initial organic matter concentration data, β1is the water inlet optical characteristic weighting data, X is the water inlet optical characteristic data, and ∈ is the error adjustment term; wherein β0and β1are generated by training historical data, and the steps of training generation are as follows:

[0114]

[0115]

[0116] i is the historical data sequence item, n is the historical quantity data, X i is the i-th historical water inlet optical characteristic data corresponding to the organic matter species data, is the average value data of the historical water inlet optical characteristic data, Y i is the i-th historical preliminary organic matter concentration data corresponding to the organic matter species data, is the average value data of the historical preliminary organic matter concentration data.

[0117] The preliminary organic matter concentration data is corrected according to the water inlet temperature data, the water inlet quantity data and the water inlet PH value data to obtain organic matter concentration data.

[0118] Specifically, the preliminary organic matter concentration data is corrected according to the water inlet temperature data, the water inlet quantity data and the water inlet PH value data to obtain organic matter concentration data. The factors such as water inlet temperature, water inlet quantity and water inlet PH value all have an impact on the measurement of organic matter concentration, so correction is needed to improve the accuracy and reliability of the data.

[0119] The concentration of organic matter is usually affected by temperature, and temperature correction is performed using the known relationship between temperature and organic matter concentration. A correction model is established through the existing temperature-concentration relationship of the data, and the measured organic matter concentration data is corrected according to the influent temperature. For example, if a set of organic matter concentration data at different temperatures has been obtained, a linear or nonlinear regression model (such as using linear regression, polynomial regression, exponential function fitting, etc. to establish the model) can be established to predict the organic matter concentration at different temperatures. Then, according to the relationship between the actual influent temperature and the model-predicted organic matter concentration, the measured organic matter concentration is corrected.

[0120] Changes in the amount of influent will cause changes in the concentration of organic matter, so flow correction is needed. The relationship between the amount of influent and the concentration of organic matter can be established, for example, a correction model can be established using the existing data of the amount of influent-concentration relationship (such as using linear regression, polynomial regression, exponential function fitting, etc. to establish the model). Then, according to the relationship between the actual influent amount and the model-predicted organic matter concentration, the measured organic matter concentration is corrected.

[0121] pH has a great influence on the solubility and ionization degree of organic matter, so pH correction is also needed. A correction model can be established using the known relationship between pH and organic matter concentration (such as using linear regression, polynomial regression, exponential function fitting, etc. to establish the model). Then, according to the relationship between the actual pH of the influent and the model-predicted organic matter concentration, the measured organic matter concentration is corrected.

[0122] In the present application, the optical sensor can monitor the light emission and reception in the influent in real time, obtain the optical property data of the influent, and provide accurate information about the characteristics of the influent, which helps to understand the pollution situation of the influent in real time. According to the optical property data of the influent and the known data of the organic matter species, a correlation model between the concentration of organic matter and the optical property can be established. Through this model, the preliminary organic matter concentration data can be accurately calculated, providing a basis for subsequent data correction. After obtaining the preliminary organic matter concentration data, combined with the parameters such as influent temperature, influent amount and influent pH, the data is corrected, which can take into account the influence of the influent conditions, and improve the accuracy and reliability of the organic matter concentration data. The optical sensor can realize real-time monitoring and data acquisition without manual intervention, realizing the automation and real-time of data acquisition, and improving the real-time and reliability of the data. Through the acquisition and analysis of the optical property data and the organic matter concentration data of the influent, the pollution situation and characteristics of the sewage can be fully understood, which provides an important basis for the subsequent operation and optimization of the sewage treatment process.

[0123] Optionally, the organic matter concentration calculation according to the influent optical property data and the organic matter species data obtains preliminary organic matter concentration data, including:

[0124] The organic matter concentration optical property mapping is performed according to the organic matter species data through a preset organic matter concentration optical property correlation model to obtain organic matter concentration optical property mapping data.

[0125] Specifically, the preset organic matter concentration optical property correlation model is established, which is based on known experimental data or theoretical assumptions. For example, a linear regression, a polynomial regression, a support vector machine, or other machine learning algorithms can be used to establish the model. For each organic matter species, according to the preset correlation model, it is mapped to the corresponding optical property to obtain the data as the organic matter concentration optical property mapping data.

[0126] The organic matter concentration calculation is performed according to the organic matter concentration optical property mapping data and the influent optical property data to obtain preliminary organic matter concentration data.

[0127] Specifically, the influent optical property data is input into the established mapping model, and the preliminary organic matter concentration data is calculated through the model.C org = A x O im + θ, C org is the preliminary organic matter concentration data, A is the weighted term data in the organic matter concentration optical property mapping data corresponding to the influent optical property data, O im is the influent optical property data, and θ is the initial term data in the organic matter concentration optical property mapping data, wherein A and θ can be solved from the following multiple linear regression analysis.

[0128] The construction steps of the organic matter concentration optical property correlation model are specifically as follows:

[0129] Obtain organic matter concentration test data, organic matter species test data, and light property test data under different conditions;

[0130] Specifically, an optical sensor is used to monitor and record the optical property data such as absorbance and transmittance of the influent in real time during the influent treatment process. At the same time, the species data and concentration data of the organic matter are collected.

[0131] According to the light property test data, feature extraction is performed to obtain absorbance feature data and transmittance feature data.

[0132] Specifically, feature extraction is performed on the light property data. The main features include absorbance and transmittance. Absorbance is a measure of the degree of light absorption in a substance. Absorbance feature data can be obtained by calculating the absorption rate or absorbance at different wavelengths, reflecting the absorption characteristics of the substance at different wavelengths. Transmittance is a measure of the degree of light remaining in its original direction after passing through a substance. Transmittance feature data can be obtained by calculating the transmittance at different wavelengths, reflecting the degree of light transmission of the substance at different wavelengths.

[0133] Correlation analysis is performed on the organic matter concentration test data, the organic matter species test data, the absorbance feature data, and the transmittance feature data to obtain organic matter species concentration optical feature correlation data.

[0134] Specifically, correlation analysis is performed on the data, and statistical methods such as Pearson correlation coefficient and Spearman correlation coefficient can be used to measure the linear correlation or nonlinear correlation between two variables. On the basis of correlation analysis, absorbance features and transmittance features with high correlation with organic matter concentration are selected. Generally, features with an absolute correlation coefficient greater than a certain threshold (such as 0.5) can be considered to have strong correlation.

[0135] Pearson correlation coefficient is used to measure the linear correlation between two continuous variables. Its value ranges from -1 to 1, where: when the correlation coefficient is close to 1, it indicates that there is a strong positive correlation between the two variables, i.e. when one variable increases, the other variable also increases. When the correlation coefficient is close to -1, it indicates that there is a strong negative correlation between the two variables, i.e. when one variable increases, the other variable decreases. When the correlation coefficient is close to 0, it indicates that there is no linear correlation between the two variables.

[0136]

[0137] r is the test correlation coefficient data, j is the test data sequence item, m is the test quantity data, a j is the jth organic matter concentration test data, is the average value data of the organic matter concentration test data, b j is the jth absorbance feature data or the jth transmittance feature data, is the average value data of the absorbance feature data or the average value data of the transmittance feature data.

[0138] The Spearman correlation coefficient measures a monotonic relationship between two variables, meaning that an increase in one variable results in an increase or decrease in the other. It does not require a linear relationship, making it more suitable for non-linear relationships. The Spearman correlation coefficient ranges from -1 to 1, and its calculation involves sorting the original data and then calculating the Pearson correlation coefficient between the sorted data.

[0139] Based on the correlation data of optical characteristics of organic species concentration, a fitting model is derived to obtain the first correlation model of optical characteristics of organic species concentration.

[0140] Specifically, using existing data and techniques such as least squares, the optimal linear model is fitted. The goal of least squares is to minimize the sum of squared errors between the actual observed values ​​and the model's predicted values. To evaluate the goodness of fit of the model, indices such as goodness of fit can be used to evaluate the model's fit. The closer the goodness of fit is to 1, the better the model fits. from sklearn.linear_model import LinearRegression; import numpy as np # Assuming existing organic matter concentration data X = np.array([[1],[2],[3],[4],[5]]) # Assuming existing optical property data y = np.array([2,3.5,4.2,5.1,6]) # Create a linear regression model object model = LinearRegression() # Fit the model using organic matter concentration data model.fit(X,y) # Output the slope and intercept of the model print("slope:",model.coef_[0]); print("intercept:",model.intercept_).

[0141] Based on the organic matter concentration test data, the organic matter type test data, and the light characteristic test data, a multivariate regression mapping relationship is constructed to obtain a second organic matter concentration optical characteristic correlation model.

[0142] Specifically, multiple regression analysis is used to establish a relationship model between multiple independent variables and one dependent variable. In this case, organic matter concentration, organic matter type, and optical properties are independent variables, while organic matter concentration can be the dependent variable. Multiple linear regression analysis is used to establish a relationship model between organic matter concentration, organic matter type, and optical properties. The general form of the multiple linear regression model is y = α0 + α1x1 + α2x2... + α n x n +θ, where y is the dependent variable (optical properties), x1, x2, ..., x nare independent variables (organic matter concentration, organic matter species, etc.), a0, a1, a2, …, a n are regression coefficients, and θ is an error term.

[0143] In the present application, by establishing an organic matter concentration optical property correlation model, the relationship between optical property data and organic matter concentration can be quantitatively described, considering the correlation between different organic matter species and different optical properties, which can improve the accuracy and reliability of organic matter concentration calculation. Using optical sensors for data acquisition and calculation, real-time monitoring and calculation of organic matter concentration can be realized, saving time and labor cost, improving the efficiency and real-time performance of data processing. Through feature extraction and correlation analysis, combined with absorbance characteristics, transmittance characteristics and organic matter concentration test data, the influence of various factors on the relationship between optical properties and organic matter concentration can be comprehensively considered, so that the organic matter concentration optical property correlation model can be more accurately constructed. Based on the multiple regression mapping relationship, the organic matter concentration optical property correlation model can be more accurately fitted, improving the applicability and prediction ability of the model. The preliminary organic matter concentration data obtained by this method is more accurate, which is helpful for subsequent data correction and system operation state analysis, so as to provide more accurate data support for biochemical system process operation, and improve the performance and efficiency of the wastewater treatment system.

[0144] Optionally, the DO model construction according to the sewage basic data comprises:

[0145] S21, constructing an oxygen mass transfer model according to the sewage basic data to obtain oxygen mass transfer model data; wherein the oxygen mass transfer model is constructed by an oxygen transfer equation, and the oxygen transfer equation is specifically:

[0146]

[0147] c t is the oxygen concentration change rate data, D is the oxygen diffusion coefficient data, is the dissolved oxygen diffusion degree data, k v is the dissolved oxygen transfer rate data, c is the dissolved oxygen concentration data, c eq is the dissolved oxygen equilibrium concentration data.

[0148] Specifically, the oxygen mass transfer model is constructed according to the sewage basic data to obtain oxygen mass transfer model data. In this step, the model is constructed by using the oxygen transfer equation, which describes the change of dissolved oxygen concentration with time and space.

[0149] S22, constructing an oxygen reaction kinetics model according to the sewage basic data to obtain oxygen reaction kinetics model data; wherein the oxygen reaction kinetics model is constructed by an oxygen reaction kinetics equation, and the oxygen reaction kinetics equation is specifically:

[0150]

[0151] R is biological respiration rate data, k r is biological respiration rate constant data, c is dissolved oxygen concentration data, and K DO is the Michaelis-Menten constant data;

[0152] Specifically, the oxygen reaction kinetics model data is obtained by constructing an oxygen reaction kinetics model according to the sewage basic data. In this step, the model is constructed by using the oxygen reaction kinetics equation, which describes the relationship between the biological respiration rate and the dissolved oxygen concentration.

[0153] S23, constructing an oxygen dissolution kinetics model according to the oxygen mass transfer model data and the oxygen reaction kinetics model data to obtain oxygen dissolution kinetics model data;

[0154] Specifically, the oxygen dissolution kinetics model is obtained by integrating the mass transfer model and the reaction kinetics model. The mass transfer and reaction processes are integrated together to directly obtain the overall equation describing the oxygen dissolution kinetics. Generally, this equation will consider the coupling effect between mass transfer and reaction to more comprehensively describe the dynamic change of oxygen in water. For example, the influence of biological oxidation reaction on the oxygen transfer rate can be considered, or the reaction rate can be directly embedded into the mass transfer equation.

[0155] Specifically, the oxygen dissolution kinetics model data is obtained by constructing an oxygen dissolution kinetics model using a coupling iteration method, which simulates the mass transfer and reaction processes as a whole, but considers the mutual influence between mass transfer and reaction in the solving process through iteration. First, the distribution of dissolved oxygen is solved according to the mass transfer model, then the biological reaction rate is calculated according to the reaction kinetics model, and finally the dissolved oxygen concentration is updated and the process is repeated until convergence.

[0156] S24, constructing a DO model according to the sewage basic data and the oxygen dissolution kinetics model data to obtain the DO model.

[0157] Specifically, the oxygen mass transfer model is used to predict the transport process of dissolved oxygen in wastewater based on parameters such as influent temperature, influent flow rate, etc. During the transport process, the diffusion of oxygen in water and other factors affecting oxygen transport are considered. The oxygen reaction kinetics model is used to predict the consumption rate of dissolved oxygen during biological respiration based on parameters such as influent temperature, influent flow rate, influent pH value, etc. The utilization and consumption of oxygen by organisms are considered. The oxygen mass transfer model and the oxygen reaction kinetics model are integrated to obtain a complete DO model. This model describes the influence of the transport of dissolved oxygen in water and the biological respiration process on the concentration of dissolved oxygen.

[0158] Specifically, there is another case, which is described in detail in the rest of the invention.

[0159] In the present invention, by establishing the oxygen mass transfer model and the oxygen reaction kinetics model, the change of dissolved oxygen concentration in wastewater with time and space can be simulated. The mass transfer model considers the diffusion process of oxygen in water, and the reaction kinetics model considers the influence of biological respiration rate on dissolved oxygen concentration, so that the variation law of dissolved oxygen can be described more accurately. By introducing the oxygen reaction kinetics model, the influence of biological respiration rate on dissolved oxygen concentration can be considered, not just the simple consideration of oxygen transport process. By constructing the oxygen mass transfer model and the oxygen reaction kinetics model, the parameters in the model can be determined, such as oxygen diffusion coefficient, biological respiration rate constant and Michaelis-Menten constant, etc. The oxygen mass transfer model and the oxygen reaction kinetics model are integrated to construct a complete oxygen dissolution kinetics model. Through this model, the influence of oxygen transport process and biological respiration process on dissolved oxygen concentration is considered, so that the variation law of DO can be described more accurately. The constructed DO model can predict the running state of the system according to the basic data of wastewater, and provide auxiliary operation for the operation of biochemical system process. Accurate DO model helps to optimize process operation, improve treatment efficiency and water quality stability.

[0160] Optionally, the oxygen dissolution kinetics model construction according to the oxygen mass transfer model data and the oxygen reaction kinetics model data obtains oxygen dissolution kinetics model data, including:

[0161] S231, oxygen transport simulation is performed on the oxygen mass transfer model data according to the basic data of wastewater to obtain oxygen transport simulation data;

[0162] Specifically, oxygen transport simulation is performed on the oxygen mass transfer model data according to the basic data of wastewater to obtain oxygen transport simulation data. By simulating the transport process of oxygen in wastewater, the transport of dissolved oxygen in water is obtained, including the concentration distribution of oxygen at different times and spatial positions.

[0163] S232, performing reaction kinetics simulation according to the oxygen reaction kinetics model data and the oxygen transfer simulation data to obtain reaction kinetics simulation data;

[0164] Specifically, reaction kinetics simulation is performed according to the oxygen reaction kinetics model data and the oxygen transfer simulation data to obtain reaction kinetics simulation data. By simulating the reaction process of oxygen and water organisms, the change of reaction rate with time and space, and the generation of reaction products are obtained.

[0165] S233, performing oxygen transfer simulation on the oxygen mass transfer model data according to the reaction kinetics simulation data to obtain new oxygen transfer simulation data, and performing change rate calculation according to the new oxygen transfer simulation data and the oxygen transfer simulation data to obtain dissolved oxygen concentration transfer change rate data;

[0166] Specifically, oxygen transfer simulation is performed on the oxygen mass transfer model data according to the reaction kinetics simulation data to obtain new oxygen transfer simulation data, and calculation of dissolved oxygen concentration transfer change rate is performed according to the new oxygen transfer simulation data and the previous transfer simulation data. The simulation process is iterated continuously until the dissolved oxygen concentration transfer change rate reaches the preset change rate threshold value to ensure the accuracy and stability of the simulation results.

[0167] S234, repeating S231 to S233 until the dissolved oxygen concentration transfer change rate data is determined to be less than or equal to the preset change rate threshold value data to generate oxygen dissolution kinetics model data.

[0168] Specifically, steps S231 to S233 are repeatedly executed until the dissolved oxygen concentration transfer change rate data is determined to be less than or equal to the preset change rate threshold value data. By iteratively simulating the process, the model is gradually optimized until the preset accuracy and stability requirements are met to generate the final oxygen dissolution kinetics model data.

[0169] In the present application, oxygen transfer simulation and reaction kinetics simulation are performed in steps S231 and S232, respectively, considering oxygen transfer and biological reaction processes. This comprehensive simulation can more accurately describe the oxygen transfer and biological oxidation processes in water. In step S233, the oxygen transfer model data is simulated again based on the reaction kinetics simulation data to obtain new oxygen transfer simulation data. By repeating this process until the dissolved oxygen concentration transfer change rate data is less than or equal to the preset change rate threshold data, the model can be dynamically adjusted to be closer to the actual situation. Through continuous iteration and adjustment, the dissolved oxygen concentration transfer change rate of the model gradually converges to the preset change rate threshold. The obtained oxygen dissolution kinetics model data has higher accuracy and stability, and can better reflect the dynamic changes of the actual system. This method can help determine appropriate model parameters and initial conditions, thereby optimizing and improving the oxygen dissolution kinetics model. By continuously adjusting the model to make it closer to the actual situation, the reliability and applicability of the model are improved.

[0170] Optionally, the DO model is constructed according to the sewage basic data and the oxygen dissolution kinetics model data, including:

[0171] According to the sewage basic data and the oxygen dissolution kinetics model data, the influent organic matter load is processed to obtain sewage dissolved oxygen load data.

[0172] Specifically, the influent organic matter load is processed according to the sewage basic data and the oxygen dissolution kinetics model data to obtain sewage dissolved oxygen load data. According to the organic matter load in the sewage and the oxygen dissolution kinetics model data, the dissolved oxygen load in the sewage is calculated to understand the content and distribution of dissolved oxygen in the sewage.

[0173] According to the sewage basic data and the sewage dissolved oxygen load data, the oxygen transfer is processed to obtain sewage dissolved oxygen transfer data.

[0174] Specifically, the oxygen transfer is processed according to the sewage basic data and the sewage dissolved oxygen load data to obtain sewage dissolved oxygen transfer data. The transfer process of dissolved oxygen in sewage is simulated, including the diffusion, transfer and distribution of oxygen, and the interaction with other components in the sewage. The calculated dissolved oxygen concentration is multiplied by the influent flow to obtain the dissolved oxygen load in the sewage per unit time, which represents the amount of dissolved oxygen contained in a unit volume of sewage in a given time. The calculated dissolved oxygen load is combined with the influent flow, temperature, organic matter concentration and other information to understand the content and distribution of dissolved oxygen in the sewage. For example, the dissolved oxygen load in different time periods or different parts can be analyzed to reveal the spatiotemporal variation of dissolved oxygen.

[0175] performing biological oxidation simulation according to the sewage basic data and the sewage dissolved oxygen transmission data to obtain sewage dissolved oxygen biological oxidation data;

[0176] Specifically, biological oxidation simulation is performed according to the sewage basic data and the sewage dissolved oxygen transmission data to obtain sewage dissolved oxygen biological oxidation data. Through simulating the utilization and consumption process of dissolved oxygen in sewage by organisms, the absorption and release of dissolved oxygen by organisms under different conditions are understood. According to the set simulation conditions and the established biological reaction kinetics model (obtaining biological population data in the sewage tank, such as collecting data by accessing a preset database, constructing a biological reaction kinetics model according to the obtained biological population data, and obtaining the biological reaction kinetics model), simulation calculation is performed to simulate the absorption and release of dissolved oxygen by organisms under different conditions. The model can be solved by numerical calculation methods (such as numerical integration, iterative solution, etc.) to obtain the change of dissolved oxygen concentration in the biological reaction process. By analyzing the simulation results, the absorption and release of dissolved oxygen by organisms under different conditions are understood. The change curve of dissolved oxygen concentration with time can be observed, and the influence of different environmental factors on biological reaction and the utilization efficiency of dissolved oxygen by organisms are analyzed.

[0177] obtaining aeration data, and performing DO model construction according to the aeration data and the sewage dissolved oxygen biological oxidation data to obtain a DO model.

[0178] Specifically, aeration data is obtained, and DO model construction is performed according to the aeration data and the sewage dissolved oxygen biological oxidation data to obtain a DO model. According to the influence of aeration operation on dissolved oxygen and the simulation results of the biological oxidation process in sewage, a dynamic DO model is constructed for predicting and controlling the dissolved oxygen content in the sewage treatment system to realize stable operation and efficient treatment of the system. Mathematical modeling methods are used, such as establishing a dynamic mass balance model based on the mass conservation principle and reaction kinetics theory, considering the influence of aeration process, biological degradation process and other factors to obtain the DO model. Mass conservation principle equation: is the dissolved oxygen concentration rate data, I is the influent flux data, E is the effluent flux data, G is the dissolved oxygen generation rate data, and C is the dissolved oxygen consumption rate data.

[0179] In the present application, the influence of organic matter on dissolved oxygen is considered by treating the organic matter load in the influent, further improving the construction of the DO model. At the same time, combined with the oxygen transfer treatment, the transmission process of oxygen in the water body is considered, making the model more close to the actual situation. Through biological oxidation simulation, the consumption of dissolved oxygen by the activity of organisms in the sewage is considered. Such consideration makes the model more detailed and can more accurately reflect the influence of biological reaction in the sewage treatment system. Obtain aeration data and combine biological oxidation data to construct a DO model. The aeration data considers the oxygen supply of gas to the water body, while the biological oxidation data considers the consumption of dissolved oxygen by biological activity. The influence of the two aspects is comprehensively considered to construct a complete DO model.

[0180] Optionally, the system operation state processing according to the DO model obtains system operation state data, including:

[0181] According to the DO model, the DO concentration change trend processing obtains DO concentration change trend data;

[0182] Specifically, the DO concentration change trend processing according to the DO model obtains DO concentration change trend data. By analyzing the change trend of DO concentration with time, the change of dissolved oxygen content in the system is understood, including periodic change, trend change, etc., to evaluate the stability and operation state of the system.

[0183] According to the DO model, the DO concentration spatial distribution processing obtains DO concentration spatial distribution data;

[0184] Specifically, the DO concentration spatial distribution processing according to the DO model obtains DO concentration spatial distribution data. By analyzing the DO concentration distribution at different positions, the spatial distribution characteristics of dissolved oxygen in the system are understood, including concentration gradient, heterogeneous distribution, etc., to evaluate the aeration and mixing of the system. By comparing the dissolved oxygen concentrations at different positions, the concentration gradient in the system is analyzed. The concentration gradient can reflect the transmission and mixing of dissolved oxygen in the system, which helps to evaluate the aeration and mixing effect of the system, for example, calculating the concentration difference or slope between different positions to evaluate the concentration gradient. Heterogeneity analysis is performed on the spatial distribution of dissolved oxygen concentration to understand the non-uniformity and local difference in the system. Statistical methods such as variance analysis, Kriging interpolation, etc. can be used to evaluate the spatial heterogeneity of dissolved oxygen concentration and generate the corresponding spatial heterogeneity map. According to the concentration gradient and heterogeneity analysis results, the aeration and mixing of the system are evaluated. By analyzing the spatial distribution characteristics of dissolved oxygen, it can be judged whether there is a problem of insufficient aeration or uneven mixing in the system, and corresponding measures are taken for improvement and optimization.

[0185] obtaining sludge concentration data and performing biological reaction activity processing according to the DO model and the sludge concentration data to obtain biological reaction activity data;

[0186] Specifically, sludge concentration data is obtained (through experimental sampling or automated sampling by terminal equipment, uploaded to a cloud server), and biological reaction activity processing is performed according to the DO model and the sludge concentration data to obtain biological reaction activity data. By analyzing the relationship between biological activity in sludge and dissolved oxygen concentration, the activity level of organisms under different conditions is understood to evaluate the efficiency and stability of biological reactions. For example, by combining sludge concentration data with an established DO model, the relationship between biological activity in sludge and dissolved oxygen concentration is analyzed, and statistical analysis methods, correlation analysis or regression analysis are used to determine the activity level of biological reactions.

[0187] Feature extraction is performed on the data, such as extracting the average concentration, maximum concentration, and change rate of sludge from the sludge concentration data, and extracting the average value and dynamic change trend of dissolved oxygen concentration from the DO model data. Statistical analysis methods such as Pearson correlation coefficient analysis or Spearman correlation coefficient analysis are used to evaluate the linear or nonlinear relationship between sludge concentration and dissolved oxygen concentration. According to the correlation analysis results, the degree of association between the two is determined. When there is a significant association between sludge concentration and dissolved oxygen concentration, a mathematical model is established using regression analysis to further explore the functional relationship between them. According to the regression analysis results, the impact of sludge concentration on dissolved oxygen concentration is evaluated, and the activity level of biological reactions is inferred.

[0188] Data integration is performed according to the DO concentration change trend data, the DO concentration spatial distribution data, and the biological reaction activity data to obtain system operation state data.

[0189] Specifically, data integration is performed according to the DO concentration change trend data, the DO concentration spatial distribution data, and the biological reaction activity data to obtain system operation state data. The various data obtained from the previous analysis are integrated and comprehensively analyzed to obtain the overall operation state of the system, including the dynamic change of dissolved oxygen and the level of biological activity, which provides a reference for subsequent system regulation and optimization.

[0190] In the present application, by processing the DO model, data about the variation trend of dissolved oxygen concentration, spatial distribution, and biological reaction activity in the system can be obtained, which can deeply reflect the operation state of the wastewater treatment system and help users better understand the working condition of the system. Processing the variation trend of DO concentration can realize real-time monitoring of the dissolved oxygen concentration and timely find the variation trend of DO concentration, providing early warning information for the operators and helping them take appropriate control measures to avoid problems. By processing the spatial distribution data of DO concentration, the DO concentration distribution in different regions of the wastewater treatment system can be understood, which helps to find local abnormalities or unevenness and guide users to take targeted measures for adjustment and optimization. According to the biological reaction activity data, the activity level of biological reaction in the system can be evaluated, which provides a reference basis for further adjustment and optimization. Integrating and analyzing the DO concentration variation trend data, DO concentration spatial distribution data, and biological reaction activity data can comprehensively evaluate the operation state of the system, which helps to find potential problems in the system and provides guiding opinions and suggestions to improve the operation efficiency and stability of the system.

[0191] Optionally, the biological phase balance judgment according to the system operation state data includes:

[0192] According to the system operation state data, the microbial growth condition is evaluated to obtain microbial growth condition evaluation data.

[0193] Specifically, the microbial growth condition is evaluated according to the system operation state data to obtain microbial growth condition evaluation data. By analyzing the growth of microorganisms in the system, including the number of microorganisms, species structure, growth rate, and other indicators, the health condition and activity level of microbial growth are evaluated. For example, sludge microbial data is obtained, and the microbial growth condition is evaluated according to the microbial characteristic data in the sludge microbial data and the system operation state data to obtain microbial growth condition evaluation data; the growth condition of microorganisms is evaluated according to the microbial characteristic data in the sludge microbial data and the system operation state data, and the growth state of microorganisms is evaluated by analyzing the abundance and activity of microbial species, combined with the dissolved oxygen concentration, temperature, and other factors in the system operation state data. The growth state of microorganisms can also be calculated by regression according to the dissolved oxygen concentration data and the anaerobic degree data in the microbial characteristic data.

[0194] According to the wastewater basic data and the system operation state data, the biological reaction stability is evaluated to obtain biological reaction stability evaluation;

[0195] Specifically, the biological reaction stability evaluation is performed based on the sewage basic data and the system operation state data, and the biological reaction stability evaluation data is obtained. By analyzing the stability and reliability of the biological reaction in the system, including the stability of the biological reaction process, the anti-interference ability, etc., the overall operation state of the biological reaction system is evaluated. Based on the system operation state data and the biological reaction process analysis results, the stability indexes of the biological reaction system are calculated. These indexes include the DO concentration change rate, the sludge activity change rate, the biological degradation efficiency, etc., which are used to quantitatively evaluate the stability of the biological reaction system.

[0196] By using the regression analysis method, the dissolved oxygen concentration data and the anaerobic degree data in the microbial characteristic data are taken as independent variables, and the microbial growth condition evaluation data is taken as dependent variable, and a regression model is established. Through regression calculation, the influence degree of dissolved oxygen concentration and microbial characteristics on microbial growth condition can be quantified.

[0197] According to the microbial growth condition evaluation data and the biological reaction stability evaluation, the biological phase balance judgment is performed, and the biological phase balance judgment data is obtained.

[0198] Specifically, the biological phase balance judgment is performed according to the microbial growth condition evaluation data and the biological reaction stability evaluation, and the biological phase balance judgment data is obtained. The evaluation results of the above two aspects are comprehensively analyzed to judge the balance degree and stability of the biological phase in the system, including whether the structure and function of the biological community are good, whether there is an imbalance phenomenon, etc.

[0199] Microbial growth condition evaluation data analysis: Analyze the data of microbial species abundance and activity to evaluate the structure and activity level of the microbial community. Check whether there is a situation that some microbial species are too much or too little, and whether there are signs of low microbial activity. According to the microbial growth condition evaluation data, the health status of the microbial community in the system is evaluated. Biological reaction stability evaluation data analysis: Analyze the biological reaction stability evaluation data, including the stability of the biological reaction process, the anti-interference ability, etc. Check whether the biological reaction system is stable under the influence of external environment, and whether there are abnormal fluctuations or unstable phenomena. Comprehensive analysis and judgment: The microbial growth condition evaluation data and the biological reaction stability evaluation data are comprehensively analyzed. It is judged whether the structure and function of the microbial community are good, and whether the biological reaction process is stable. It is noted that whether there is an imbalance phenomenon of the microbial community, such as over-proliferation or reduction of some microorganisms, leading to abnormal system function.

[0200] In the present application, by evaluating the microbial growth condition based on the system operation state data, the growth of microorganisms in the wastewater treatment system can be understood, which helps to evaluate the activity and proliferation of microorganisms in the system and provides data support for further biological phase balance judgment. According to the wastewater basic data and the system operation state data, the biological reaction stability is evaluated, which can evaluate the stability and health condition of the biological reaction in the system, including understanding the information of microbial population structure, metabolic activity and biodiversity of biological community in the biological reaction process, so as to judge the stability and adaptability of the biological reaction. Combined with the microbial growth condition evaluation data and the biological reaction stability evaluation data, the biological phase balance judgment is carried out, which can comprehensively consider the microbial ecological environment and the operation state of the biological reaction in the system, which helps to determine whether there is microbial growth imbalance or biological reaction instability in the system, and take corresponding adjustment measures to maintain the stable operation of the system. Through the biological phase balance judgment data, the change of the microbial ecological environment and the health condition of the biological reaction in the system can be understood in time, so as to adjust and optimize the operation strategy in time, and ensure the efficient and stable operation of the system.

[0201] Optionally, the present application also provides a DO model-based control system for executing the DO model-based control method as described above, the DO model-based control system comprising:

[0202] a wastewater basic data acquisition module for acquiring data by online data acquisition instrument during the water inlet process to obtain wastewater basic data, wherein the wastewater basic data comprises organic matter data and water inlet basic data;

[0203] a DO model construction module for constructing a DO model according to the wastewater basic data;

[0204] a system operation state processing module for processing system operation state according to the DO model to obtain system operation state data;

[0205] a biological phase balance judgment module for judging biological phase balance according to the system operation state data to obtain biological phase balance judgment data for biochemical system process operation auxiliary work.

[0206] The purpose of the present application is to realize comprehensive monitoring and analysis of the wastewater treatment system by obtaining basic wastewater data using online data acquisition instruments during the water inlet process and constructing a DO model based on these data. Based on the constructed DO model, the operation status data of the system can be processed to monitor and analyze the operation status of the wastewater treatment system in real time, including evaluation of the change of dissolved oxygen concentration, biological reaction activity, organic matter content, etc., which helps to find abnormal conditions and potential problems in the system and take timely measures for adjustment and optimization. Combined with the system operation status data, especially the biological phase balance judgment data, the microbial ecological environment and biological reaction process in the wastewater treatment system can be accurately evaluated, which helps to judge the stability and health status of the biological phase and find and solve problems affecting the system in a timely manner. Based on the biological phase balance judgment data, the operation personnel can be assisted in the biochemical system process operation. The present application can improve the grasp and control ability of the operation personnel on the system operation status, thereby realizing efficient and stable operation of the wastewater treatment system. Through continuous monitoring and analysis of the system operation status data, real-time adjustment and optimization of the biochemical system operation can be realized. This timely feedback and adjustment can ensure the stability and efficiency of the system, while reducing the operation cost and energy consumption.

[0207] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application being defined by the attached application file and not by the above description, therefore all variations falling within the meaning and scope of the equivalent requirements of the application file are intended to be included within the present application.

[0208] The above description is only a specific implementation of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control method based on a DO model, characterized by, The method comprises: S1, collecting data by an online data collection instrument during the water inflow process to obtain sewage basic data, wherein the sewage basic data comprises organic matter data and water inflow basic data; S2, constructing a DO model according to the sewage basic data to obtain the DO model; S3, processing system operation state according to the DO model to obtain system operation state data; S4, judging the biological phase balance according to the system operation state data to obtain biological phase balance judgment data for biochemical system process operation auxiliary work; Wherein, the method of collecting data by an online data collection instrument during the water inflow process to obtain sewage basic data comprises: S11, collecting data by an online DO.ORP instrument, online temperature sensing equipment, water inflow quantity detection equipment and water inflow PH value monitoring equipment during the water inflow process to obtain dissolved oxygen concentration data, oxidation-reduction state data, water inflow temperature data, water inflow quantity data and water inflow PH value data; S11, collecting organic matter type data by a chemical reaction device during the water inflow process to obtain organic matter type data; S12, collecting organic matter concentration data by an optical sensor during the water inflow process to obtain organic matter concentration data; S13, generating water inflow basic data by the dissolved oxygen concentration data, the oxidation-reduction state data, the water inflow temperature data, the water inflow quantity data and the water inflow PH value data to obtain water inflow basic data; S14, generating organic matter data by the organic matter type data and the organic matter concentration data to obtain organic matter data; S15, integrating the water inflow basic data and the organic matter data to obtain sewage basic data; Wherein, the method of collecting organic matter concentration data by an optical sensor during the water inflow process to obtain organic matter concentration data comprises: Controlling the optical sensor to measure light emission and acceptance during the water inflow process to obtain water inflow optical property data; Calculating organic matter concentration according to the water inflow optical property data and the organic matter type data to obtain preliminary organic matter concentration data; Data correcting the preliminary organic matter concentration data according to the water inflow temperature data, the water inflow quantity data and the water inflow PH value data to obtain organic matter concentration data; The method of constructing a DO model according to the sewage basic data to obtain the DO model comprises: S21, constructing an oxygen mass transfer model according to the sewage basic data to obtain oxygen mass transfer model data; wherein the oxygen mass transfer model is constructed by an oxygen transfer equation, and the oxygen transfer equation is specifically: ; is oxygen concentration rate of change data, is oxygen diffusion coefficient data, is dissolved oxygen diffusion extent data, is dissolved oxygen transport rate data, is dissolved oxygen concentration data, is dissolved oxygen equilibrium concentration data; S22, constructing an oxygen reaction kinetics model according to the sewage basic data to obtain oxygen reaction kinetics model data; wherein the oxygen reaction kinetics model is constructed by an oxygen reaction kinetics equation, and the oxygen reaction kinetics equation is specifically: ; biological respiration rate data, biological respiration rate constant data, dissolved oxygen concentration data, mitchell-menton constant data; S23, constructing an oxygen dissolution kinetics model according to the oxygen mass transfer model data and the oxygen reaction kinetics model data to obtain oxygen dissolution kinetics model data; S24, constructing a DO model according to the sewage basic data and the oxygen dissolution kinetics model data, to obtain the DO model.

2. The method of claim 1, wherein, The organic matter concentration calculation according to the water inlet optical property data and the organic matter species data obtains preliminary organic matter concentration data, which includes: According to the organic matter species data, an organic matter concentration optical property mapping is performed through a preset organic matter concentration optical property correlation model to obtain organic matter concentration optical property mapping data; According to the organic matter concentration optical property mapping data and the water inlet optical property data, an organic matter concentration calculation is performed to obtain preliminary organic matter concentration data; The construction steps of the organic matter concentration optical property correlation model are specifically: Obtain organic matter concentration test data, organic matter species test data, and light property test data under different conditions; According to the light property test data, feature extraction is performed to obtain absorbance feature data and transmittance feature data; According to the organic matter concentration test data, the organic matter species test data, the absorbance feature data, and the transmittance feature data, correlation analysis is performed to obtain organic matter species concentration optical feature correlation data; According to the organic matter species concentration optical feature correlation data, a fitting model derivation process is performed to obtain a first organic matter concentration optical property correlation model; According to the organic matter concentration test data, the organic matter species test data, and the light property test data, a multivariate regression mapping relationship is constructed to obtain a second organic matter concentration optical property correlation model.

3. The method of claim 1, wherein, The oxygen dissolution kinetics model construction according to the oxygen mass transfer model data and the oxygen reaction kinetics model data obtains oxygen dissolution kinetics model data, which includes: S231, oxygen transfer simulation is performed on the oxygen mass transfer model data according to the sewage basic data to obtain oxygen transfer simulation data; S232, reaction kinetics simulation is performed according to the oxygen reaction kinetics model data and the oxygen transfer simulation data to obtain reaction kinetics simulation data; S233, oxygen transfer simulation is performed on the oxygen mass transfer model data according to the reaction kinetics simulation data to obtain new oxygen transfer simulation data, and a change rate calculation is performed according to the new oxygen transfer simulation data and the oxygen transfer simulation data to obtain dissolved oxygen concentration transfer change rate data; S234, repeat S231 to S233 until the dissolved oxygen concentration transfer change rate data is less than or equal to a preset change rate threshold data to generate oxygen dissolution kinetics model data.

4. The method of claim 1, wherein, The DO model construction according to the sewage basic data and the oxygen dissolution kinetics model data obtains a DO model, which includes: According to the sewage basic data and the oxygen dissolution kinetics model data, an inlet organic matter load processing is performed to obtain sewage dissolved oxygen load data; According to the sewage basic data and the sewage dissolved oxygen load data, an oxygen transfer processing is performed to obtain sewage dissolved oxygen transfer data; biological oxidation simulation is performed according to the sewage basic data and the sewage dissolved oxygen transmission data, to obtain sewage dissolved oxygen biological oxidation data; aeration data is acquired, and a DO model is constructed according to the aeration data and the sewage dissolved oxygen biological oxidation data, to obtain the DO model.

5. The method of claim 1, wherein, The system running state processing according to the DO model includes: DO concentration change trend processing according to the DO model is performed, to obtain DO concentration change trend data; DO concentration spatial distribution processing according to the DO model is performed, to obtain DO concentration spatial distribution data; sludge concentration data is acquired, and biological reaction activity processing is performed according to the DO model and the sludge concentration data, to obtain biological reaction activity data; data integration is performed according to the DO concentration change trend data, the DO concentration spatial distribution data, and the biological reaction activity data, to obtain system running state data.

6. The method of claim 1, wherein, The biological phase balance judgment according to the system running state data includes: microbial growth condition evaluation is performed according to the system running state data, to obtain microbial growth condition evaluation data; biological reaction stability evaluation is performed according to the sewage basic data and the system running state data, to obtain biological reaction stability evaluation data; biological phase balance judgment is performed according to the microbial growth condition evaluation data and the biological reaction stability evaluation, to obtain biological phase balance judgment data.

7. A DO model based control system, characterized by The DO model-based control system for performing the DO model-based control method according to claim 1 includes: a sewage basic data acquisition module, configured to acquire data by an online data acquisition instrument during an influent process, to obtain sewage basic data, wherein the sewage basic data includes organic matter data and influent basic data; a DO model construction module, configured to construct a DO model according to the sewage basic data, to obtain the DO model; a system running state processing module, configured to perform system running state processing according to the DO model, to obtain system running state data; a biological phase balance judgment module, configured to perform biological phase balance judgment according to the system running state data, to obtain biological phase balance judgment data, to perform biochemical system process operation auxiliary work.

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