AOA intelligent nitrogen system operation monitoring method and device

Abstract

The present invention discloses an operation monitoring method and device of an AOA intelligent nitrogen system, which relates to the field of water treatment technology, including: obtaining an inlet water portrait; obtaining an AOA intelligent nitrogen operation mode; in a full-range nitrification mode, obtaining a COD predicted concentration and a first NH3-N predicted concentration, and performing an oxygen demand prediction to obtain a first theoretical oxygen demand; in a short-range nitrification mode, obtaining a second NH3-N predicted concentration and a DO predicted concentration, and performing an oxygen demand prediction to obtain a second theoretical oxygen demand; based on the configuration of an aeration regulating valve opening calibration table, obtaining an aeration regulating valve desired opening; in a full-range nitrification mode, monitoring the COD predicted concentration, the first NH3-N predicted concentration, and the aeration regulating valve desired opening through a sensor array, and monitoring the second NH3-N predicted concentration, the DO predicted concentration, and the aeration regulating valve desired opening in a short-range nitrification mode. The present invention solves the technical problem of untimely monitoring in the prior art when performing nitrogen removal, and achieves the technical effect of accurate monitoring and abnormal early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of water treatment, and in particular to an operation monitoring method and device of an AOA intelligent nitrogen system. Background Art

[0002] With the continuous development of sewage treatment technology, nitrogen removal is an important part of water treatment, and its stability and efficiency are crucial to improving water quality. Traditional nitrogen removal systems mostly use full nitrification mode or short-range nitrification mode to adjust the nitrogen removal process. However, these traditional systems generally have problems such as low monitoring accuracy and delayed real-time feedback. Summary of the invention

[0003] The present application provides an operation monitoring method and device of the AOA intelligent nitrogen system, which is used to solve the technical problem of the prior art that monitoring is not timely during nitrogen removal.

[0004] In view of the above problems, the present application provides an operation monitoring method and device of the AOA intelligent nitrogen system.

[0005] In a first aspect of the present application, an operation monitoring method of an AOA intelligent nitrogen system is provided, the method comprising:

[0006] Please obtain an influent portrait, wherein the influent portrait includes a substance detection type label list and a substance detection concentration label list; obtain the AOA intelligent nitrogen operation mode, wherein the AOA intelligent nitrogen operation mode includes a full-range nitrification mode and a short-range nitrification mode; when it belongs to the full-range nitrification mode, analyze the COD concentration and NH3-N concentration at the outlet of the aerobic pool according to the substance detection type label list, the substance detection concentration label list and the preset influent flow rate to obtain the COD predicted concentration and the first NH3-N predicted concentration; predict the oxygen demand according to the COD predicted concentration and the first NH3-N predicted concentration to obtain the first theoretical oxygen demand; when it belongs to the short-range nitrification mode, analyze the COD concentration and NH3-N concentration at the outlet of the aerobic pool according to the substance detection type label list, the substance detection concentration label list and the preset influent flow rate to obtain the COD predicted concentration and the first NH3-N predicted concentration; predict the oxygen demand according to the COD predicted concentration and the first NH3-N predicted concentration to obtain the first theoretical oxygen demand; The NH3-N concentration and DO concentration at the outlet of the aerobic tank are analyzed based on the label list and the preset inlet flow rate to obtain the second NH3-N predicted concentration and DO predicted concentration; the oxygen demand is predicted based on the second NH3-N predicted concentration and the DO predicted concentration to obtain the second theoretical oxygen demand; according to the first theoretical oxygen demand or the second theoretical oxygen demand, based on the configuration of the aeration regulating valve opening calibration table, the expected opening of the aeration regulating valve is obtained; in the full-range nitrification mode, the COD predicted concentration, the first NH3-N predicted concentration and the expected opening of the aeration regulating valve are monitored by the sensor array, and in the short-range nitrification mode, the second NH3-N predicted concentration, the DO predicted concentration and the expected opening of the aeration regulating valve are monitored.

[0007] The second aspect of the present application provides an operation monitoring device of the AOA intelligent nitrogen system, the device comprising:

[0008] an influent portrait acquisition module, wherein the influent portrait acquisition module acquires an influent portrait, wherein the influent portrait includes a substance detection type label list and a substance detection concentration label list; an operation mode acquisition module, wherein the operation mode acquisition module acquires an AOA intelligent nitrogen operation mode, wherein the AOA intelligent nitrogen operation mode includes a full-range nitrification mode and a short-range nitrification mode; a first concentration analysis module, wherein the first concentration analysis module is used to analyze the COD concentration and NH3-N concentration at the outlet of the aerobic tank according to the substance detection type label list, the substance detection concentration label list and the preset influent flow rate when it belongs to the full-range nitrification mode, and obtain a COD predicted concentration and a first NH3-N predicted concentration; a first oxygen demand prediction module, wherein the first oxygen demand prediction module performs oxygen demand prediction according to the COD predicted concentration and the first NH3-N predicted concentration to obtain a first theoretical oxygen demand; a second concentration analysis module, wherein the second concentration analysis module is used to analyze the COD concentration and the NH3-N concentration at the outlet of the aerobic tank according to the substance detection type label list, the substance detection concentration label list and the preset influent flow rate when it belongs to the short-range nitrification mode The quality inspection type label list, the substance detection concentration label list and the preset water inlet flow rate are used to analyze the NH3-N concentration and DO concentration at the outlet of the aerobic tank to obtain the second NH3-N predicted concentration and DO predicted concentration; the second oxygen demand prediction module, the second oxygen demand prediction module predicts the oxygen demand according to the second NH3-N predicted concentration and the DO predicted concentration to obtain the second theoretical oxygen demand; the aeration regulating valve expected opening acquisition module, the aeration regulating valve expected opening acquisition module obtains the aeration regulating valve expected opening according to the first theoretical oxygen demand or the second theoretical oxygen demand based on the aeration regulating valve opening calibration table configuration; the monitoring module, the monitoring module is used to monitor the COD predicted concentration, the first NH3-N predicted concentration and the aeration regulating valve expected opening through the sensor array when it belongs to the full nitrification mode, and monitor the second NH3-N predicted concentration and DO predicted concentration and the aeration regulating valve expected opening when it belongs to the short-range nitrification mode.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The present application obtains an influent portrait, wherein the influent portrait includes a substance detection type label list and a substance detection concentration label list; obtains an AOA smart nitrogen operation mode, wherein the AOA smart nitrogen operation mode includes a full-range nitrification mode and a short-range nitrification mode; when it belongs to the full-range nitrification mode, the COD concentration and NH3-N concentration at the outlet of the aerobic pool are analyzed according to the substance detection type label list, the substance detection concentration label list and the preset influent flow rate to obtain a COD predicted concentration and a first NH3-N predicted concentration; oxygen demand is predicted according to the COD predicted concentration and the first NH3-N predicted concentration to obtain a first theoretical oxygen demand; when it belongs to the short-range nitrification mode, the COD concentration and NH3-N concentration at the outlet of the aerobic pool are analyzed according to the substance detection type label list, the substance detection concentration label list and the preset influent flow rate to obtain a COD predicted concentration and a first NH3-N predicted concentration; oxygen demand is predicted according to the COD predicted concentration and the first NH3-N predicted concentration to obtain a first theoretical oxygen demand; The NH3-N concentration and DO concentration at the outlet of the aerobic tank are analyzed based on the degree label list and the preset inlet flow rate to obtain the second NH3-N predicted concentration and DO predicted concentration; the oxygen demand is predicted according to the second NH3-N predicted concentration and the DO predicted concentration to obtain the second theoretical oxygen demand; according to the first theoretical oxygen demand or the second theoretical oxygen demand, based on the configuration of the aeration regulating valve opening calibration table, the expected opening of the aeration regulating valve is obtained; when it belongs to the full nitrification mode, the COD predicted concentration, the first NH3-N predicted concentration and the expected opening of the aeration regulating valve are monitored by the sensor array, and when it belongs to the short-range nitrification mode, the second NH3-N predicted concentration, the DO predicted concentration and the expected opening of the aeration regulating valve are monitored. The present invention solves the technical problem of untimely monitoring during nitrogen removal in the prior art, by acquiring an influent portrait, analyzing the type and concentration of substance detection, and predicting relevant concentrations according to a full-range nitrification mode or a short-range nitrification mode, and combining an aeration regulating valve opening calibration table, the desired aeration regulating valve opening is obtained, and key parameters are monitored in real time through a sensor array, thereby achieving the technical effects of precise monitoring and abnormal warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic diagram of the operation monitoring method of the AOA intelligent nitrogen system provided in an embodiment of the present application;

[0013] Figure 2 This is a schematic diagram of the structure of the operation monitoring device of the AOA intelligent nitrogen system provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: water inlet portrait acquisition module 11 , operation mode acquisition module 12 , first concentration analysis module 13 , first oxygen demand prediction module 14 , second concentration analysis module 15 , second oxygen demand prediction module 16 , aeration regulating valve desired opening acquisition module 17 , monitoring module 18 . DETAILED DESCRIPTION

[0015] The present application provides an operation monitoring method and device for the AOA intelligent nitrogen system, aiming to solve the technical problem of untimely monitoring in the prior art during nitrogen removal. By obtaining an influent portrait, analyzing the type and concentration of substance detection, and predicting relevant concentrations according to the full-range nitrification mode or the short-range nitrification mode, the desired aeration regulating valve opening is obtained in combination with an aeration regulating valve opening calibration table. The key parameters are monitored in real time through a sensor array, thereby achieving the technical effects of precise monitoring and abnormal warning.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides an operation monitoring method of the AOA intelligent nitrogen system, the method comprising:

[0019] Step S100: Obtaining an influent portrait, wherein the influent portrait includes a substance detection type label list and a substance detection concentration label list.

[0020] In an embodiment of the present application, first, various physical and chemical parameters of the incoming water are collected in real time through monitoring equipment such as sensors, analytical instruments, etc., including the types and concentrations of substances in the water. These data include the detection results of substances such as organic matter, nitrides, and phosphides. In order to process these data in a systematic way, they are converted into substance detection type labels and substance detection concentration labels. In this process, the substance detection type label records all the types of substances in the water; and the substance detection concentration label records the concentration of each substance. For example, the COD concentration indicates the degree of pollution of organic matter in the water, and the NH3-N concentration indicates the nitrogen content in the water.

[0021] Next, the water inflow data is classified and labeled through real-time monitoring and analysis. For example, water quality testing instruments obtain the concentration of substances such as COD and NH3-N through chemical analysis or sensors, and pair this information with the substance type to form a complete substance detection type label list and substance detection concentration label list. The substance detection concentration label list is a concentration value list that corresponds one-to-one with the substance detection type label list, recording the concentration of each substance in the water.

[0022] Finally, the substance detection type label list and the substance detection concentration label list are integrated to obtain the influent profile.

[0023] Step S200: obtaining an AOA intelligent nitrogen operation mode, wherein the AOA intelligent nitrogen operation mode includes a full-range nitrification mode and a short-range nitrification mode.

[0024] In the embodiment of the present application, the AOA smart nitrogen operation mode includes a full-range nitrification mode and a short-range nitrification mode. The AOA smart nitrogen operation mode is obtained by interacting with the AOA smart nitrogen system.

[0025] Step S300: When it belongs to the full nitrification mode, the COD concentration and NH3-N concentration at the outlet of the aerobic tank are analyzed according to the substance detection type label list, the substance detection concentration label list and the preset water inlet flow rate to obtain the COD predicted concentration and the first NH3-N predicted concentration.

[0026] In an embodiment of the present application, in the full nitrification mode, the COD concentration and NH3-N concentration at the outlet of the aerobic pool are analyzed based on the substance detection type label list, the substance detection concentration label list and the preset water inlet flow rate. The specific process includes first obtaining the full nitrification additive type list and the full nitrification additive addition amount list, and then constructing the first, second, and N-level sampling constraints, and collecting full nitrification samples in turn. Samples at each level include records such as substance detection type, concentration, and water inlet flow rate. By aggregating the sample data at these different levels, the COD predicted concentration and the first NH3-N predicted concentration are finally generated.

[0027] Further, in the method provided in the embodiment of the application, when it belongs to the full nitrification mode, the COD concentration and NH3-N concentration at the outlet of the aerobic pool are analyzed according to the substance detection type label list, the substance detection concentration label list and the preset inlet flow rate to obtain the COD predicted concentration and the first NH3-N predicted concentration, and also includes:

[0028] Obtain a list of full-process nitrification additive types and a list of full-process nitrification additive addition amounts; construct a first-level sampling constraint based on the list of full-process nitrification additive types, the list of full-process nitrification additive addition amounts, the list of substance detection type labels, the list of substance detection concentration labels, and the preset water inlet flow rate, collect full-process nitrification samples, and obtain a first-level full-process nitrification sample, wherein the first-level full-process nitrification sample has a first-level sample substance detection type label record list, a first-level sample substance detection concentration label record list, a first-level sample water inlet preset record flow rate, a first-level sample COD record concentration, and a first-level sample NH3-N record concentration; construct a second-level sampling constraint based on the first-level sample substance detection type label record list, the first-level sample substance detection concentration label record list, the first-level sample water inlet preset record flow rate, the list of full-process nitrification additive types, and the list of full-process nitrification additive addition amounts, collect full-process nitrification samples, and obtain a second-level full-process nitrification sample, wherein the second-level full-process nitrification sample has a second-level sample substance detection type label record list, a second-level sample substance detection concentration label record list, a second-level sample record list, secondary sample water inlet preset record flow, secondary sample COD record concentration and secondary sample NH3-N record concentration; until N-1 level sampling constraints are constructed according to the N-1 level sample substance detection type label record list, the N-1 level sample substance detection concentration label record list, the N-1 level sample water inlet preset record flow, the full nitrification additive type list, and the full nitrification additive addition amount list, and full nitrification sample collection is carried out to obtain N-level full nitrification samples, wherein the N-level full nitrification samples have N-level sample substance detection type label record list, N-level sample substance detection concentration label record list, N-level sample water inlet preset record flow, N-level sample COD record concentration and N-level sample NH3-N record concentration; the first-level sample COD record concentration and the first-level sample NH3-N record concentration, the second-level sample COD record concentration and the second-level sample NH3-N record concentration, until the N-level sample COD record concentration and the N-level sample NH3-N record concentration are aggregated to generate the COD predicted concentration and the first NH3-N predicted concentration.

[0029] In the embodiment of the present application, firstly, a list of types of full-process nitrification additives and a list of addition amounts of full-process nitrification additives are obtained from a preset database. These lists include various additives used in the full-process nitrification process and their corresponding addition amounts.

[0030] Then, based on the list of types of nitrification additives, the list of amounts of nitrification additives added, the list of substance detection type labels, the list of substance detection concentration labels, and the preset inlet flow rate, the first-level sampling constraints are constructed. These constraints include type constraints and quantitative constraints, which are designed to ensure that the collected sample data meets the predetermined standards and requirements; the quantitative constraints are verified by calculating the difference between the concentration data of the sample and the reference data, and if they meet the quantitative threshold, they are considered to meet the requirements.

[0031] After constructing the primary sampling constraints, the full-process nitrification samples are collected according to these constraints to obtain the primary full-process nitrification samples. The primary samples include the substance detection type label record, substance detection concentration label record, inlet preset flow rate, COD concentration and NH3-N concentration and other data.

[0032] Then, based on the data of the primary samples, the list of types of nitrification additives and the list of added amounts, the secondary sampling constraints are constructed. Similar to the primary sampling constraints, the secondary sampling constraints also include type constraints and quantitative constraints, aiming to ensure that the secondary sample collection meets the corresponding standards.

[0033] According to the secondary sampling constraints, samples are collected to obtain the secondary full-process nitrification samples, which also include material detection type label records, material detection concentration label records, inlet preset flow rate, COD concentration and NH3-N concentration data. Similarly, the process of constructing the third, fourth to N-level sampling constraints is the same as that of the first and second level samples, except that the sampling constraints of each level are adjusted and optimized according to the sample data of the previous level to ensure the accuracy and validity of the sample data.

[0034] After collecting the N-level full-process nitrification samples, data aggregation processing is performed. For the COD concentration and NH3-N concentration data in the N-level samples, a central trend analysis is first performed to obtain the COD predicted concentration and NH3-N predicted concentration of the N-level samples. Then, the predicted concentration of the N-level samples and the data of the N-1-level samples are subjected to another central trend analysis until the COD predicted concentration and NH3-N predicted concentration of the first-level samples are finally output. Finally, the COD predicted concentration and NH3-N predicted concentration of the first-level samples are set as the COD predicted concentration and the first NH3-N predicted concentration.

[0035] Further, in the method provided by the embodiment of the application, a first-level sampling constraint condition is constructed based on the full-process nitrification additive type list, the full-process nitrification additive addition amount list, the substance detection type label list, the substance detection concentration label list and the water inlet preset flow rate, and the full-process nitrification sample is collected to obtain a first-level full-process nitrification sample, wherein the first-level full-process nitrification sample has a first-level sample substance detection type label record list, a first-level sample substance detection concentration label record list, a first-level sample water inlet preset record flow rate, a first-level sample COD record concentration and a first-level sample NH3-N record concentration, and also includes:

[0036] Type constraints are constructed based on the full-process nitrification additive type list and the substance detection type label list; quantitative constraints are constructed based on the full-process nitrification additive addition amount list, the substance detection concentration label list and the preset water inlet flow rate; based on the type constraints and the quantitative constraints, the first-level full-process nitrification sample is collected with COD concentration and NH3-N concentration as retrieval targets, wherein the first-level full-process nitrification sample has the first-level sample substance detection type label record list, the first-level sample substance detection concentration label record list, the first-level sample water inlet preset record flow rate, the first-level sample COD record concentration and the first-level sample NH3-N record concentration.

[0037] Based on the list of total nitrification additive dosage, the list of substance detection concentration labels and the preset inlet flow rate, the quantitative constraint conditions are constructed. First, the quantitative standards of these three factors are considered. By combining these standards, a quantitative constraint function is formed to measure whether the sample meets the preset quantitative requirements. When the sample parameter value meets the quantitative condition, it is considered to meet the quantitative constraint condition. Through this process, a quantitative constraint condition based on factors such as additive dosage, concentration and flow rate is finally obtained.

[0038] Next, based on the constructed type constraints and quantitative constraints, COD concentration and NH3-N concentration were selected as retrieval targets to collect first-level full-process nitrification samples. By applying these two constraints, samples that meet the requirements are screened out from historical data, and a list of first-level sample substance detection type label records, a list of first-level sample substance detection concentration label records, a first-level sample water inlet preset flow record, a first-level sample COD concentration record, and a first-level sample NH3-N concentration record are obtained. Through this sampling process, a first-level full-process nitrification sample is obtained.

[0039] Furthermore, in the method provided in the embodiment of the application, the quantitative constraint condition is constructed according to the total nitrification additive addition amount list, the substance detection concentration label list and the preset water inlet flow rate, and further includes:

[0040] Construct a quantization constraint function:

[0041]

[0042] in, Characterize the quantization constraint parameters, represents the weight of the jth list, The quantitative value of the i-th attribute of the sample representing the j-th list, The reference ith attribute quantized value of the jth list, Characterize the sample quantization parameters, Characterizes a reference quantization parameter; when the quantization constraint parameter is less than or equal to a quantization constraint parameter threshold, it is deemed that the quantization constraint condition is met; when the quantization constraint parameter is greater than the quantization constraint parameter threshold, it is deemed that the quantization constraint condition is not met.

[0043] In the embodiment of the present application, a quantization constraint function is constructed, and the quantization constraint function is as follows ;in, Characterize the quantization constraint parameters, represents the weight of the jth list, which is pre-set by technical experts, The quantitative value of the i-th attribute of the sample representing the j-th list, The reference ith attribute quantized value of the jth list, Characterize the sample quantization parameters, Characterizes the reference quantization parameter.

[0044] When the calculated quantization constraint parameter is less than or equal to the preset quantization constraint parameter threshold, the quantization constraint condition is deemed to be satisfied; when the quantization constraint parameter is greater than the preset quantization constraint parameter threshold, the quantization constraint condition is deemed not to be satisfied. The quantization constraint parameter threshold is pre-set by technical experts based on historical experience.

[0045] Further, in the method provided in the embodiment of the application, the primary sample COD recorded concentration and the primary sample NH3-N recorded concentration, the secondary sample COD recorded concentration and the secondary sample NH3-N recorded concentration, until the N-level sample COD recorded concentration and the N-level sample NH3-N recorded concentration are aggregated to generate the COD predicted concentration and the first NH3-N predicted concentration, and further includes:

[0046] Perform central tendency analysis on the N-level sample COD recorded concentration and the N-level sample NH3-N recorded concentration, respectively, to obtain the N-level COD predicted concentration and the N-level NH3-N predicted concentration; perform central tendency analysis on the N-level COD predicted concentration and the N-1-level sample COD recorded concentration, the N-level NH3-N predicted concentration and the N-1-level sample NH3-N recorded concentration, respectively, to obtain the N-1-level COD predicted concentration and the N-1-level NH3-N predicted concentration; perform the analysis in a loop until the first-level COD predicted concentration and the first-level NH3-N predicted concentration are output, and are set as the COD predicted concentration and the first NH3-N predicted concentration.

[0047] In the embodiment of the present application, the COD concentration and NH3-N concentration in the N-level sample are first subjected to a central tendency analysis. For the COD and NH3-N concentration data, the arithmetic mean method is used to calculate the central tendency. Specifically, the COD concentration and NH3-N concentration of the N-level sample are calculated as their means, respectively. These two means are the N-level COD predicted concentration and the N-level NH3-N predicted concentration, which represent the average levels of COD and NH3-N concentrations in the N-level sample.

[0048] Next, the central tendency analysis was performed on the predicted COD concentration of level N and the recorded COD concentration of level N-1 samples. To this end, the arithmetic mean method was used to calculate the mean of the predicted concentration of level N and the concentration of level N-1 samples to obtain the predicted COD concentration of level N-1. Similarly, the predicted NH3-N concentration of level N and the recorded NH3-N concentration of level N-1 were averaged to obtain the predicted NH3-N concentration of level N-1.

[0049] As this process is carried out step by step, the arithmetic mean method is continued to be used to perform a central trend analysis on the N-1 level COD predicted concentration and the N-2 level sample COD recorded concentration to obtain the N-2 level COD predicted concentration; at the same time, the N-1 level NH3-N predicted concentration and the N-2 level sample NH3-N recorded concentration are processed in the same way to obtain the N-2 level NH3-N predicted concentration. This step is the same as the previous processing, that is, the data is averaged, so as to gradually move the prediction results forward.

[0050] Continue the analysis cycle until the first-level sample is reached. Perform arithmetic averaging on the first-level sample COD record concentration and the first-level sample NH3-N record concentration, and finally obtain the COD predicted concentration and the first NH3-N predicted concentration.

[0051] Step S400: predicting oxygen demand according to the predicted COD concentration and the first predicted NH3-N concentration to obtain a first theoretical oxygen demand.

[0052] In an embodiment of the present application, first, the relevant COD concentration record data set, NH3-N concentration data set and oxygen demand monitoring data set are collected with the full nitrification additive type list and addition amount list as constraints. Then, using the mean square loss error (MSE) as the loss function, with the oxygen demand monitoring data set as supervision, and the COD concentration data set and NH3-N concentration data set as input, the first oxygen demand prediction channel is trained. By screening data with loss values ​​greater than or equal to the threshold, a first-level loss training data set is formed, and the second oxygen demand prediction channel is trained based on this data set, and so on, until the data volume threshold is met. Finally, the outputs of all training channels are fully connected by the mean to obtain the final oxygen demand prediction model. Finally, the model is used to process the COD predicted concentration and the first NH3-N predicted concentration to obtain the first theoretical oxygen demand.

[0053] Furthermore, in the method provided in the embodiment of the application, the oxygen demand is predicted according to the predicted COD concentration and the first predicted NH3-N concentration to obtain a first theoretical oxygen demand, and further includes:

[0054] A list of types of nitrification additives and a list of addition amounts of nitrification additives are obtained; with the list of types of nitrification additives and the list of addition amounts of nitrification additives as constraints, a COD concentration record data set, an NH3-N concentration data set and an oxygen demand monitoring data set are collected; based on the mean square loss error, with the oxygen demand monitoring data set as supervision, and with the COD concentration record data set and the NH3-N concentration data set as input, a first oxygen demand prediction channel is trained; a first-level loss training data set whose loss value in the first oxygen demand prediction channel is greater than or equal to a loss threshold is obtained; when the first-level loss training data set is greater than or equal to a data volume threshold, a second oxygen demand prediction channel is trained based on the first-level loss training data set; until the Y-level loss training data set is less than the data volume threshold, the first oxygen demand prediction channel, the second oxygen demand prediction channel, and the Y-th oxygen demand prediction channel are mean fully connected to obtain an oxygen demand prediction model; the COD prediction concentration and the first NH3-N prediction concentration are processed according to the oxygen demand prediction model to obtain the first theoretical oxygen demand.

[0055] In the embodiment of the present application, firstly, a list of types of nitrification additives and a list of addition amounts of nitrification additives are obtained, and the list of types of nitrification additives and the list of addition amounts of nitrification additives are used as constraints to collect COD concentration record data sets, NH3-N concentration data sets, and oxygen demand monitoring data sets. Specifically, the list of types of nitrification additives and the list of addition amounts of nitrification additives are used as constraints to collect relevant data from the historical monitoring database in the water treatment process to obtain COD concentration record data sets, NH3-N concentration data sets, and oxygen demand monitoring data sets.

[0056] After data collection is completed, a deep neural network is selected as the basis for model training. In the early stage of model training, the oxygen demand prediction model is trained based on the mean square loss error as the loss function. During the training process, the oxygen demand monitoring data set is used as the supervision data, and the COD concentration and NH3-N concentration data sets are used as input data to enter the first oxygen demand prediction channel. The model is optimized by calculating the error between the actual oxygen demand and the predicted oxygen demand. Specifically, the mean square loss error formula is used to measure the difference between the predicted value and the actual value. The smaller the MSE value, the higher the accuracy of the model prediction.

[0057] During the training process, the loss value is evaluated. When the loss value in the first oxygen demand prediction channel is greater than or equal to the set loss threshold, the training data with loss values ​​greater than or equal to the set loss threshold are screened out to form a first-level loss training data set. The loss threshold is pre-set by technical experts.

[0058] Then, the first-level loss training data set is trained again to form the second oxygen demand prediction channel. This process will continue to cycle until the loss value is lower than the threshold and meets the data volume requirement. Each training will use a portion of the data to optimize the prediction channel so that the model can better adapt to different water quality and treatment conditions. Until the Yth training, the loss data set is less than the data volume threshold, and the training is stopped.

[0059] Then, the first, second, and up to the Yth oxygen demand prediction channels are processed with mean full connection, and the output results of multiple prediction channels are averaged to obtain a more stable and accurate oxygen demand prediction model.

[0060] Finally, according to the established oxygen demand prediction model, the COD predicted concentration and the first NH3-N predicted concentration data are input into the oxygen demand prediction model for processing to obtain the first theoretical oxygen demand required in the water treatment process.

[0061] Step S500: When it belongs to the short-range nitrification mode, the NH3-N concentration and DO concentration at the outlet of the aerobic tank are analyzed according to the substance detection type label list, the substance detection concentration label list and the preset water inlet flow rate to obtain the second NH3-N predicted concentration and DO predicted concentration.

[0062] In the embodiment of the present application, when it belongs to the short-range nitrification mode, the process is similar to the full-range nitrification mode. First, a list of short-range nitrification additive types and a list of short-range nitrification additive addition amounts are obtained from a preset database. These lists include various additives used in the short-range nitrification process and their corresponding addition amounts.

[0063] Then, based on the short-range nitrification additive type list, short-range nitrification additive addition amount list, substance detection type label list, substance detection concentration label list and water inlet preset flow rate, the first-level sampling constraint conditions are constructed. These constraints include type constraints and quantitative constraints, which are designed to ensure that the collected sample data meets the predetermined standards and requirements. The quantitative constraints are verified by the difference between the sample concentration data and the reference data.

[0064] After constructing the primary sampling constraints, short-range nitrification samples are collected according to these constraints to obtain primary short-range nitrification samples. The primary samples include substance detection type label records, substance detection concentration label records, inlet preset flow rate, COD concentration, NH3-N concentration and other data.

[0065] Next, based on the data of the first-level samples, the list of short-range nitrification additive types and the list of addition amounts, the second-level sampling constraints are constructed, and sample collection is performed to obtain the second-level short-range nitrification samples, including the corresponding substance detection type label records, substance detection concentration label records, inlet preset flow, COD concentration and NH3-N concentration data. This process will continue until the N-level short-range nitrification samples are collected.

[0066] After the N-level samples are collected, data aggregation processing is performed, and the central trend analysis is performed on the NH3-N concentration and DO concentration in the N-level samples to obtain the second NH3-N predicted concentration and DO predicted concentration.

[0067] Step S600: predicting oxygen demand according to the second predicted NH3-N concentration and the predicted DO concentration to obtain a second theoretical oxygen demand.

[0068] In an embodiment of the present application, the process of obtaining the second theoretical oxygen demand is similar to that of obtaining the first theoretical oxygen demand. Specifically, first, a list of short-range nitrification additive types and a list of addition amounts are obtained from a preset database as constraints. Then, based on these constraints, relevant data sets are collected, including NH3-N concentration data sets, DO concentration data sets, and oxygen demand monitoring data sets. Then, a deep neural network model is selected, and the oxygen demand monitoring data is used as supervision, and the predicted NH3-N concentration and DO concentration are used as input data to start training the oxygen demand prediction model. The model is optimized by calculating the error between the actual oxygen demand and the predicted oxygen demand.

[0069] During the training process, the loss value is evaluated. When the loss value is greater than or equal to the preset threshold, the data with larger loss is screened out for secondary training until the loss value meets the preset requirements. Then, the output results of multiple prediction channels are fully connected by the mean to obtain the final oxygen demand prediction model for the short-range nitrification mode.

[0070] Finally, the second NH3-N predicted concentration and DO predicted concentration are input into the oxygen demand prediction model for the short-range nitrification mode for prediction to obtain the second theoretical oxygen demand.

[0071] Step S700: obtaining a desired opening of an aeration regulating valve according to the first theoretical oxygen demand or the second theoretical oxygen demand and based on a configuration of an aeration regulating valve opening calibration table.

[0072] In the embodiment of the present application, the aeration regulating valve opening calibration table is a preset reference table, which lists the relationship between different oxygen demands and corresponding aeration regulating valve openings. According to the first theoretical oxygen demand or the second theoretical oxygen demand obtained, the corresponding valve opening value in the aeration regulating valve opening calibration table is searched, and through this process, the desired opening of the aeration regulating valve is obtained.

[0073] Step S800: When it belongs to the full-range nitrification mode, the COD predicted concentration, the first NH3-N predicted concentration and the desired opening of the aeration regulating valve are monitored through the sensor array; when it belongs to the short-range nitrification mode, the second NH3-N predicted concentration, the DO predicted concentration and the desired opening of the aeration regulating valve are monitored.

[0074] In the embodiment of the present application, when it belongs to the full nitrification mode, the actual values ​​of COD concentration, NH3-N concentration and aeration regulating valve opening are obtained through the sensor array. These real-time monitored data are compared with the COD predicted concentration, the first NH3-N predicted concentration and the expected opening of the aeration regulating valve. If the difference between the actual monitored value and the predicted value exceeds the preset tolerance range, the alarm mechanism is triggered to prompt the operator that there is a deviation or abnormality.

[0075] In the short-range nitrification mode, the sensor array monitors the actual values ​​of NH3-N concentration, DO concentration and aeration control valve opening in real time. These real-time data are compared with the second NH3-N predicted concentration, DO predicted concentration and the expected opening of the aeration control valve. If the difference between the actual value and the predicted value exceeds the set tolerance range, an alarm is also triggered to remind the operator to adjust or troubleshoot equipment problems.

[0076] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0077] The present application obtains an influent portrait, wherein the influent portrait includes a substance detection type label list and a substance detection concentration label list; obtains an AOA smart nitrogen operation mode, wherein the AOA smart nitrogen operation mode includes a full-range nitrification mode and a short-range nitrification mode; when it belongs to the full-range nitrification mode, the COD concentration and NH3-N concentration at the outlet of the aerobic pool are analyzed according to the substance detection type label list, the substance detection concentration label list and the preset influent flow rate to obtain a COD predicted concentration and a first NH3-N predicted concentration; oxygen demand is predicted according to the COD predicted concentration and the first NH3-N predicted concentration to obtain a first theoretical oxygen demand; when it belongs to the short-range nitrification mode, the COD concentration and NH3-N concentration at the outlet of the aerobic pool are analyzed according to the substance detection type label list, the substance detection concentration label list and the preset influent flow rate to obtain a COD predicted concentration and a first NH3-N predicted concentration; oxygen demand is predicted according to the COD predicted concentration and the first NH3-N predicted concentration to obtain a first theoretical oxygen demand; The NH3-N concentration and DO concentration at the outlet of the aerobic tank are analyzed based on the degree label list and the preset inlet flow rate to obtain the second NH3-N predicted concentration and DO predicted concentration; the oxygen demand is predicted according to the second NH3-N predicted concentration and the DO predicted concentration to obtain the second theoretical oxygen demand; according to the first theoretical oxygen demand or the second theoretical oxygen demand, based on the configuration of the aeration regulating valve opening calibration table, the expected opening of the aeration regulating valve is obtained; when it belongs to the full nitrification mode, the COD predicted concentration, the first NH3-N predicted concentration and the expected opening of the aeration regulating valve are monitored by the sensor array, and when it belongs to the short-range nitrification mode, the second NH3-N predicted concentration, the DO predicted concentration and the expected opening of the aeration regulating valve are monitored. The present invention solves the technical problem of untimely monitoring during nitrogen removal in the prior art, by acquiring an influent portrait, analyzing the type and concentration of substance detection, and predicting relevant concentrations according to a full-range nitrification mode or a short-range nitrification mode, and combining an aeration regulating valve opening calibration table, the desired aeration regulating valve opening is obtained, and key parameters are monitored in real time through a sensor array, thereby achieving the technical effects of precise monitoring and abnormal warning.

[0078] Embodiment 2 is based on the same inventive concept as the operation monitoring method of the AOA intelligent nitrogen system in the above embodiment. Figure 2 As shown, the present application provides an operation monitoring device for the AOA intelligent nitrogen system, and the device and method embodiments in the embodiments of the present application are based on the same inventive concept. The device includes:

[0079] an influent portrait acquisition module 11, wherein the influent portrait acquisition module 11 acquires an influent portrait, wherein the influent portrait includes a substance detection type label list and a substance detection concentration label list; an operation mode acquisition module 12, wherein the operation mode acquisition module 12 acquires an AOA intelligent nitrogen operation mode, wherein the AOA intelligent nitrogen operation mode includes a full-range nitrification mode and a short-range nitrification mode; a first concentration analysis module 13, wherein the first concentration analysis module 13 is used to analyze the COD concentration and NH3-N concentration at the outlet of the aerobic tank according to the substance detection type label list, the substance detection concentration label list and the preset influent flow rate when it belongs to the full-range nitrification mode, and obtain a COD predicted concentration and a first NH3-N predicted concentration; a first oxygen demand prediction module 14, wherein the first oxygen demand prediction module 14 performs oxygen demand prediction according to the COD predicted concentration and the first NH3-N predicted concentration to obtain a first theoretical oxygen demand; a second concentration analysis module 15, wherein the second concentration analysis module 15 is used to analyze the COD concentration and the NH3-N concentration at the outlet of the aerobic tank according to the substance detection type label list, the substance detection concentration label list and the preset influent flow rate when it belongs to the full-range nitrification mode, and obtain a COD predicted concentration and a first NH3-N predicted concentration. According to the substance detection type label list, the substance detection concentration label list and the preset water inlet flow rate, the NH3-N concentration and DO concentration at the aerobic tank outlet are analyzed to obtain the second NH3-N predicted concentration and DO predicted concentration; the second oxygen demand prediction module 16, the second oxygen demand prediction module 16 predicts the oxygen demand according to the second NH3-N predicted concentration and the DO predicted concentration to obtain the second theoretical oxygen demand; the aeration regulating valve expected opening acquisition module 17, the aeration regulating valve expected opening acquisition module 17 obtains the aeration regulating valve expected opening according to the first theoretical oxygen demand or the second theoretical oxygen demand and based on the aeration regulating valve opening calibration table configuration; the monitoring module 18, the monitoring module 18 is used to monitor the COD predicted concentration, the first NH3-N predicted concentration and the aeration regulating valve expected opening through the sensor array when it belongs to the full nitrification mode, and monitor the second NH3-N predicted concentration, DO predicted concentration and the aeration regulating valve expected opening when it belongs to the short-range nitrification mode.

[0080] Furthermore, the device is also used to achieve the following functions:

[0081] Obtain a list of full-process nitrification additive types and a list of full-process nitrification additive addition amounts; construct a first-level sampling constraint based on the list of full-process nitrification additive types, the list of full-process nitrification additive addition amounts, the list of substance detection type labels, the list of substance detection concentration labels, and the preset water inlet flow rate, collect full-process nitrification samples, and obtain a first-level full-process nitrification sample, wherein the first-level full-process nitrification sample has a first-level sample substance detection type label record list, a first-level sample substance detection concentration label record list, a first-level sample water inlet preset record flow rate, a first-level sample COD record concentration, and a first-level sample NH3-N record concentration; construct a second-level sampling constraint based on the first-level sample substance detection type label record list, the first-level sample substance detection concentration label record list, the first-level sample water inlet preset record flow rate, the list of full-process nitrification additive types, and the list of full-process nitrification additive addition amounts, collect full-process nitrification samples, and obtain a second-level full-process nitrification sample, wherein the second-level full-process nitrification sample has a second-level sample substance detection type label record list, a second-level sample substance detection concentration label record list, a second-level sample record list, secondary sample water inlet preset record flow, secondary sample COD record concentration and secondary sample NH3-N record concentration; until N-1 level sampling constraints are constructed according to the N-1 level sample substance detection type label record list, the N-1 level sample substance detection concentration label record list, the N-1 level sample water inlet preset record flow, the full nitrification additive type list, and the full nitrification additive addition amount list, and full nitrification sample collection is carried out to obtain N-level full nitrification samples, wherein the N-level full nitrification samples have N-level sample substance detection type label record list, N-level sample substance detection concentration label record list, N-level sample water inlet preset record flow, N-level sample COD record concentration and N-level sample NH3-N record concentration; the first-level sample COD record concentration and the first-level sample NH3-N record concentration, the second-level sample COD record concentration and the second-level sample NH3-N record concentration, until the N-level sample COD record concentration and the N-level sample NH3-N record concentration are aggregated to generate the COD predicted concentration and the first NH3-N predicted concentration.

[0082] Furthermore, the device is also used to achieve the following functions:

[0083] Type constraints are constructed based on the full-process nitrification additive type list and the substance detection type label list; quantitative constraints are constructed based on the full-process nitrification additive addition amount list, the substance detection concentration label list and the preset water inlet flow rate; based on the type constraints and the quantitative constraints, the first-level full-process nitrification sample is collected with COD concentration and NH3-N concentration as retrieval targets, wherein the first-level full-process nitrification sample has the first-level sample substance detection type label record list, the first-level sample substance detection concentration label record list, the first-level sample water inlet preset record flow rate, the first-level sample COD record concentration and the first-level sample NH3-N record concentration.

[0084] Furthermore, the device is also used to achieve the following functions:

[0085] Construct a quantization constraint function:

[0086]

[0087] in, Characterize the quantization constraint parameters, represents the weight of the jth list, The quantitative value of the i-th attribute of the sample representing the j-th list, The reference ith attribute quantized value of the jth list, Characterize the sample quantization parameters, Characterizes a reference quantization parameter; when the quantization constraint parameter is less than or equal to a quantization constraint parameter threshold, it is deemed that the quantization constraint condition is met; when the quantization constraint parameter is greater than the quantization constraint parameter threshold, it is deemed that the quantization constraint condition is not met.

[0088] Furthermore, the device is also used to achieve the following functions:

[0089] Perform central tendency analysis on the N-level sample COD recorded concentration and the N-level sample NH3-N recorded concentration, respectively, to obtain the N-level COD predicted concentration and the N-level NH3-N predicted concentration; perform central tendency analysis on the N-level COD predicted concentration and the N-1-level sample COD recorded concentration, the N-level NH3-N predicted concentration and the N-1-level sample NH3-N recorded concentration, respectively, to obtain the N-1-level COD predicted concentration and the N-1-level NH3-N predicted concentration; perform the analysis in a loop until the first-level COD predicted concentration and the first-level NH3-N predicted concentration are output, and are set as the COD predicted concentration and the first NH3-N predicted concentration.

[0090] Furthermore, the device is also used to achieve the following functions:

[0091] A list of types of nitrification additives and a list of addition amounts of nitrification additives are obtained; with the list of types of nitrification additives and the list of addition amounts of nitrification additives as constraints, a COD concentration record data set, an NH3-N concentration data set and an oxygen demand monitoring data set are collected; based on the mean square loss error, with the oxygen demand monitoring data set as supervision, and with the COD concentration record data set and the NH3-N concentration data set as input, a first oxygen demand prediction channel is trained; a first-level loss training data set whose loss value in the first oxygen demand prediction channel is greater than or equal to a loss threshold is obtained; when the first-level loss training data set is greater than or equal to a data volume threshold, a second oxygen demand prediction channel is trained based on the first-level loss training data set; until the Y-level loss training data set is less than the data volume threshold, the first oxygen demand prediction channel, the second oxygen demand prediction channel, and the Y-th oxygen demand prediction channel are mean fully connected to obtain an oxygen demand prediction model; the COD prediction concentration and the first NH3-N prediction concentration are processed according to the oxygen demand prediction model to obtain the first theoretical oxygen demand.

[0092] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0093] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0094] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. The operation monitoring method of the AOA intelligent nitrogen system is characterized by: include: Obtaining an influent water profile, wherein the influent water profile includes a substance detection type label list and a substance detection concentration label list; Obtaining an AOA intelligent nitrogen operation mode, wherein the AOA intelligent nitrogen operation mode includes a full-range nitrification mode and a short-range nitrification mode; When it belongs to the full nitrification mode, the COD concentration and NH3-N concentration at the outlet of the aerobic tank are analyzed according to the substance detection type label list, the substance detection concentration label list and the preset water inlet flow rate to obtain the COD predicted concentration and the first NH3-N predicted concentration; Predicting oxygen demand based on the predicted COD concentration and the first predicted NH3-N concentration to obtain a first theoretical oxygen demand; When it belongs to the short-range nitrification mode, the NH3-N concentration and DO concentration at the outlet of the aerobic tank are analyzed according to the substance detection type label list, the substance detection concentration label list and the preset water inlet flow rate to obtain the second NH3-N predicted concentration and DO predicted concentration; Perform oxygen demand prediction according to the second NH3-N predicted concentration and the DO predicted concentration to obtain a second theoretical oxygen demand; According to the first theoretical oxygen demand or the second theoretical oxygen demand, based on the configuration of the aeration regulating valve opening calibration table, obtaining the desired opening of the aeration regulating valve; When it belongs to the full nitrification mode, the COD predicted concentration, the first NH3-N predicted concentration and the desired opening of the aeration regulating valve are monitored by the sensor array; when it belongs to the short-range nitrification mode, the second NH3-N predicted concentration and the DO predicted concentration and the desired opening of the aeration regulating valve are monitored; Among them, when it belongs to the full nitrification mode, the COD concentration and NH3-N concentration at the outlet of the aerobic pool are analyzed according to the substance detection type label list, the substance detection concentration label list and the preset water inlet flow rate to obtain the COD predicted concentration and the first NH3-N predicted concentration, including: Obtaining a list of types of full-course nitrification additives and a list of addition amounts of full-course nitrification additives from a preset database; Based on the full-process nitrification additive type list, the full-process nitrification additive addition amount list, the substance detection type label list, the substance detection concentration label list and the water inlet preset flow rate, a first-level sampling constraint condition is constructed to collect a full-process nitrification sample to obtain a first-level full-process nitrification sample, wherein the first-level full-process nitrification sample has a first-level sample substance detection type label record list, a first-level sample substance detection concentration label record list, a first-level sample water inlet preset record flow rate, a first-level sample COD record concentration and a first-level sample NH3-N record concentration; According to the first-level sample substance detection type label record list, the first-level sample substance detection concentration label record list, the first-level sample water inlet preset record flow rate, the full-process nitrification additive type list, and the full-process nitrification additive addition amount list, a second-level sampling constraint condition is constructed to collect the full-process nitrification sample to obtain a second-level full-process nitrification sample, wherein the second-level full-process nitrification sample has a second-level sample substance detection type label record list, a second-level sample substance detection concentration label record list, a second-level sample water inlet preset record flow rate, a second-level sample COD record concentration, and a second-level sample NH3-N record concentration; Until N-level sampling constraints are constructed according to the N-1-level sample substance detection type label record list, the N-1-level sample substance detection concentration label record list, the N-1-level sample water inlet preset record flow rate, the full-process nitrification additive type list, and the full-process nitrification additive addition amount list, the full-process nitrification sample collection is carried out to obtain the N-level full-process nitrification sample, wherein the N-level full-process nitrification sample has the N-level sample substance detection type label record list, the N-level sample substance detection concentration label record list, the N-level sample water inlet preset record flow rate, the N-level sample COD record concentration and the N-level sample NH3-N record concentration; Aggregate the primary sample COD recorded concentration and the primary sample NH3-N recorded concentration, the secondary sample COD recorded concentration and the secondary sample NH3-N recorded concentration, until the N-level sample COD recorded concentration and the N-level sample NH3-N recorded concentration, to generate the COD predicted concentration and the first NH3-N predicted concentration; Wherein, based on the full-process nitrification additive type list, the full-process nitrification additive addition amount list, the substance detection type label list, the substance detection concentration label list and the water inlet preset flow rate, a first-level sampling constraint condition is constructed to collect the full-process nitrification sample and obtain the first-level full-process nitrification sample, wherein the first-level full-process nitrification sample has a first-level sample substance detection type label record list, a first-level sample substance detection concentration label record list, a first-level sample water inlet preset record flow rate, a first-level sample COD record concentration and a first-level sample NH3-N record concentration, including: Constructing type constraints according to the full-process nitration additive type list and the substance detection type label list; Constructing a quantitative constraint condition according to the total nitrification additive addition amount list, the substance detection concentration label list and the water inlet preset flow rate; According to the type constraint and the quantitative constraint, taking COD concentration and NH3-N concentration as the search targets, the first-level full-process nitrification sample is collected, wherein the first-level full-process nitrification sample has the first-level sample substance detection type label record list, the first-level sample substance detection concentration label record list, the first-level sample water inlet preset record flow, the first-level sample COD record concentration and the first-level sample NH3-N record concentration; Among them, when it belongs to the short-range nitrification mode, the NH3-N concentration and DO concentration at the outlet of the aerobic tank are analyzed according to the substance detection type label list, the substance detection concentration label list and the preset water inlet flow rate to obtain the second NH3-N predicted concentration and DO predicted concentration, including: After replacing the full-process nitrification additive type list with the short-process nitrification additive type list, replacing the full-process nitrification additive addition amount list with the short-process nitrification additive addition amount list, and replacing the COD concentration of the retrieval target attribute with the DO concentration, the same process as the full-process nitrification concentration analysis is executed to realize the prediction of the second NH3-N predicted concentration and the DO predicted concentration.

2. The method according to claim 1, characterized in that The quantitative constraint conditions are constructed according to the total nitrification additive addition amount list, the substance detection concentration label list and the water inlet preset flow rate, including: Construct a quantization constraint function: ； in, Characterize the quantization constraint parameters, represents the weight of the jth list, The quantitative value of the i-th attribute of the sample representing the j-th list, The quantized value of the reference i-th attribute representing the j-th list, Characterize the sample quantization parameters, Characterizing reference quantitative parameters; When the quantization constraint parameter is less than or equal to the quantization constraint parameter threshold, it is deemed that the quantization constraint condition is met; When the quantization constraint parameter is greater than the quantization constraint parameter threshold, it is regarded that the quantization constraint condition is not satisfied.

3. The method according to claim 1, characterized in that Aggregating the primary sample COD recorded concentration and the primary sample NH3-N recorded concentration, the secondary sample COD recorded concentration and the secondary sample NH3-N recorded concentration, until the N-level sample COD recorded concentration and the N-level sample NH3-N recorded concentration, to generate the COD predicted concentration and the first NH3-N predicted concentration, including: Performing central tendency analysis on the recorded COD concentration of the N-level samples and the recorded NH3-N concentration of the N-level samples, respectively, to obtain the predicted COD concentration of the N-level and the predicted NH3-N concentration of the N-level; Performing central tendency analysis on the N-level COD predicted concentration and the N-1-level sample COD recorded concentration, the N-level NH3-N predicted concentration and the N-1-level sample NH3-N recorded concentration, respectively, to obtain the N-1-level COD predicted concentration and the N-1-level NH3-N predicted concentration; The analysis is repeated until the first-level COD predicted concentration and the first-level NH3-N predicted concentration are output, which are set as the COD predicted concentration and the first NH3-N predicted concentration.

4. The method according to claim 1, characterized in that Predicting oxygen demand according to the predicted COD concentration and the first predicted NH3-N concentration to obtain a first theoretical oxygen demand includes: Obtain a list of types of nitrification additives and a list of amounts of nitrification additives added throughout the process; With the full-process nitrification additive type list and the full-process nitrification additive addition amount list as constraints, COD concentration record data set, NH3-N concentration data set and oxygen demand monitoring data set are collected; Based on the mean square loss error, the oxygen demand monitoring data set is used as supervision, and the COD concentration record data set and the NH3-N concentration data set are used as input to train a first oxygen demand prediction channel; Obtaining a first-level loss training data set in which the loss value in the first oxygen demand prediction channel is greater than or equal to the loss threshold; When the primary loss training data set is greater than or equal to a data volume threshold, training a second oxygen demand prediction channel based on the primary loss training data set; When the Y-level loss training data set is less than the data volume threshold, the first oxygen demand prediction channel, the second oxygen demand prediction channel, and the Y-th oxygen demand prediction channel are fully connected by means to obtain an oxygen demand prediction model; The COD predicted concentration and the first NH3-N predicted concentration are processed according to the oxygen demand prediction model to obtain the first theoretical oxygen demand.

5. The operation monitoring device of AOA intelligent nitrogen system is characterized by: The device is used to implement the operation monitoring method of the AOA intelligent nitrogen system according to any one of claims 1 to 4, comprising: An inlet water portrait acquisition module, wherein the inlet water portrait acquisition module obtains an inlet water portrait, wherein the inlet water portrait includes a substance detection type label list and a substance detection concentration label list; An operation mode acquisition module, wherein the operation mode acquisition module obtains the AOA intelligent nitrogen operation mode, wherein the AOA intelligent nitrogen operation mode includes a full-range nitrification mode and a short-range nitrification mode; A first concentration analysis module, which is used to analyze the COD concentration and NH3-N concentration at the outlet of the aerobic tank according to the substance detection type label list, the substance detection concentration label list and the preset water inlet flow rate when belonging to the full nitrification mode, to obtain the COD predicted concentration and the first NH3-N predicted concentration; A first oxygen demand prediction module, wherein the first oxygen demand prediction module performs oxygen demand prediction according to the COD predicted concentration and the first NH3-N predicted concentration to obtain a first theoretical oxygen demand; A second concentration analysis module, which is used to analyze the NH3-N concentration and DO concentration at the outlet of the aerobic tank according to the substance detection type label list, the substance detection concentration label list and the preset water inlet flow rate when belonging to the short-range nitrification mode, and obtain a second NH3-N predicted concentration and DO predicted concentration; A second oxygen demand prediction module, wherein the second oxygen demand prediction module performs oxygen demand prediction according to the second NH3-N predicted concentration and the DO predicted concentration to obtain a second theoretical oxygen demand; an aeration regulating valve desired opening acquisition module, wherein the aeration regulating valve desired opening acquisition module obtains the desired opening of the aeration regulating valve according to the first theoretical oxygen demand or the second theoretical oxygen demand and based on the configuration of the aeration regulating valve opening calibration table; A monitoring module, wherein the monitoring module is used to monitor the COD predicted concentration, the first NH3-N predicted concentration and the desired opening of the aeration regulating valve through a sensor array when the full-range nitrification mode is in progress, and to monitor the second NH3-N predicted concentration, the DO predicted concentration and the desired opening of the aeration regulating valve when the short-range nitrification mode is in progress.

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