Industrial wastewater denitrification pollution treatment equipment operation state intelligent monitoring system

Abstract

The application provides an industrial wastewater denitrification pollution treatment equipment operation state intelligent monitoring system, and belongs to the technical field of wastewater denitrification pollution treatment. The industrial wastewater denitrification pollution treatment equipment operation state intelligent monitoring system is characterized in that it comprises a dissolved oxygen collection module, which collects dissolved oxygen concentration data of each spatial sampling point in real time; an oxygen spectrum generation and identification module, which is used for mapping the dissolved oxygen concentration data to a three-dimensional spatial coordinate system corresponding to a target nitrification tank, and constructing an oxygen spatial distribution spectrum of the target nitrification tank; an analysis and calculation module, which constructs an oxygen demand load factor; and a comprehensive analysis module, which is used for correlating the oxygen consumption factor and the oxygen demand load factor, constructing an oxygen transfer efficiency factor, and performing evaluation and optimization. Through layering of the water body and adjustment of the Roots blower air supply, precise oxygen supply is realized, and energy waste and denitrification efficiency reduction caused by excess or insufficient oxygen are avoided.

Description

Technical Field

[0001] This invention relates to the field of wastewater denitrification and pollution control technology, and more specifically, to an intelligent monitoring system for the operating status of denitrification and pollution control equipment in industrial wastewater. Background Technology

[0002] Large-scale printing and dyeing enterprises will discharge industrial wastewater, which contains a large amount of heavy metals, nitrogenous substances, etc. If this industrial wastewater is discharged into rivers without treatment, it will cause river pollution and ecological pollution.

[0003] Currently, dyeing and printing enterprises treat industrial wastewater before discharging it. The treatment process includes aerobic nitrification, which is a crucial step and one of the key links in achieving nitrogen conversion and final removal. Nitrification refers to the process by which nitrifying bacteria (mainly ammonia-oxidizing bacteria and nitrite-oxidizing bacteria) gradually convert ammonia nitrogen in wastewater into nitrate nitrogen under aerobic conditions.

[0004] Current treatment systems collect data from the bottom of the pool and supply oxygen there. This leads to excessive oxygen levels in the upper layers of the water, resulting in over-aeration. Over-aeration increases the operating power of the blowers, increasing their energy consumption and reducing system efficiency. Furthermore, existing systems do not adequately collect and consider key factors such as the concentration of microorganisms and heavy metals in industrial wastewater, neglecting their impact on the oxygen consumption rate of the water. This makes it difficult to accurately control the oxygen supply, hindering the optimization and energy-saving effects of wastewater denitrification processes.

[0005] Therefore, an intelligent monitoring system for the operation status of denitrification pollution treatment equipment in industrial wastewater is proposed to solve this problem. Summary of the Invention

[0006] To overcome the above deficiencies, the present invention provides an intelligent monitoring system for the operating status of denitrification pollution treatment equipment in industrial wastewater that overcomes or at least partially solves the above technical problems.

[0007] This invention is implemented as follows:

[0008] This invention provides an intelligent monitoring system for the operating status of denitrification pollution treatment equipment in industrial wastewater, comprising:

[0009] The dissolved oxygen acquisition module is used to deploy multiple oxygen delivery channels in the target nitrification tank. Each oxygen delivery channel is equipped with a nano-aeration head at the end and multiple spatial sampling points are set inside. A dissolved oxygen sensor is deployed at each spatial sampling point to collect dissolved oxygen concentration data at each spatial sampling point in real time and map the dissolved oxygen concentration data to the three-dimensional spatial coordinate system corresponding to the target nitrification tank.

[0010] The oxygen map generation and recognition module is used to map dissolved oxygen concentration data to the three-dimensional spatial coordinate system corresponding to the target nitrification tank, construct the oxygen spatial distribution map of the target nitrification tank, including the upper oxygen map, the middle oxygen map, and the bottom oxygen map. Based on the oxygen spatial distribution map of the target nitrification tank, and combined with the dissolved oxygen concentration data of each spatial sampling point, the oxygen spatial distribution map of the target nitrification tank is labeled as first-level label, second-level label, and third-level label, respectively.

[0011] The analysis and calculation module is used to mark the spatial distribution map of oxygen in the target nitrification tank, and then to further analyze the microbial concentration C in the i-th layer region of the target nitrification tank. m，i Heavy metal concentration C h，i and water viscosity μ i Data was collected and an internal factor dataset was constructed. An oxygen consumption factor (OCF) was then built from this dataset. The target nitrification tank depth (H) and influent disturbance intensity (A) were then considered. in and the flow velocity V of the i-th layer of liquid i Collect and construct an external factor dataset, and construct the oxygen demand load factor (ODLF) based on the external factor dataset;

[0012] The comprehensive analysis module is used to correlate the oxygen consumption factor (OCF) and the oxygen demand load factor (ODLF), construct the oxygen delivery efficiency factor (OEF), and evaluate and optimize it.

[0013] In a preferred embodiment, the dissolved oxygen acquisition module includes a spatial deployment unit, an acquisition unit, and a three-dimensional spatial coordinate construction unit;

[0014] The spatial layout unit is used to vertically set up multiple detection areas inside the target nitrification tank, and to set up a dissolved oxygen sensor at the spatial sampling point of each detection area.

[0015] The acquisition unit is used to collect wastewater dissolved oxygen concentration data in real time based on dissolved oxygen sensors located at spatial sampling points in each layer of the detection area.

[0016] The three-dimensional spatial coordinate construction unit is used to construct a three-dimensional spatial coordinate system inside the target nitrification tank, map the spatial sampling points of each layer of detection area to the three-dimensional spatial coordinate system, and obtain the three-dimensional coordinates x, y, z of the spatial sampling points of the i-th layer of detection area.

[0017] In a preferred embodiment, the oxygen spectrum generation and recognition module includes a spectrum segmentation unit and a labeling unit;

[0018] The map segmentation unit is used to divide the target nitrification tank into upper, middle and lower layers based on the z-axis direction in the three-dimensional spatial coordinate system of the target nitrification tank, and to divide the internal space of the target nitrification tank into several horizontal slice regions along the z-axis direction according to a set fixed interval, wherein the upper, middle and lower layers each correspond to one horizontal slice region, and construct the upper oxygen map, the middle oxygen map and the lower oxygen map;

[0019] The labeling unit is used to collect the dissolved oxygen content of each layer based on the upper layer oxygen spectrum, the middle layer oxygen spectrum and the bottom layer oxygen spectrum and in combination with the dissolved oxygen sensor, and to label each layer with a first-level label, a second-level label and a third-level label according to the dissolved oxygen content of each layer.

[0020] In a preferred embodiment, the analysis and calculation module includes an internal factor dataset construction unit, an internal factor data extraction unit, an external factor dataset construction unit, and an external factor data extraction unit.

[0021] The internal factor dataset construction unit is used to set up several sampling pipes vertically inside the target nitrification tank. Based on the upper, middle and bottom layers, one sampling pipe is installed respectively. Each sampling pipe in each layer is independently connected to a solenoid valve and a micro diaphragm pump. The outlet of the micro diaphragm pump is connected to a water quality microbial detector. Wastewater from the upper, middle and bottom layers is extracted within a fixed time. The concentration of microorganisms in the i-th layer is analyzed by the water quality microbial detector.

[0022] Sampling probes were installed at the upper, middle and bottom layers of the target nitrification tank. The sampling probes used a fixed sampling frequency to extract 100 ml of wastewater and input it into a heavy metal analyzer for detection, so as to obtain the heavy metal concentration of the i-th layer.

[0023] Vibration sensors were installed at the top, middle, and bottom layers of the target nitrification tank to detect the water viscosity of the i-th layer in real time.

[0024] An internal factor dataset is constructed based on the concentration of microorganisms, heavy metals, and water viscosity in the i-th layer.

[0025] In a preferred embodiment, the internal factor data extraction unit is used to extract microbial concentration, heavy metal content, and water viscosity from the internal factor dataset, and obtain the oxygen consumption factor (OCF) in the following manner;

[0026] The specific method for obtaining the oxygen consumption factor (OCF) is as follows:

[0027] Microbial concentration C m The relationship between the first oxygen-consuming sub-item OCF1;

[0028] The oxygen in the target nitrification tank is mainly used by microorganisms to decompose organic matter. Its oxygen consumption, first sub-item OCF1, is related to the concentration of microorganisms in the i-th layer, C. m，i The oxygen consumption rate OCF1 is obtained by calculation based on the direct proportionality of the oxygen consumption rate OCF1, where OCF1 = α × C. m，i In the formula, α is the proportionality coefficient, and the first oxygen consumption term OCF1 is the oxygen consumption rate.

[0029] The concentration of heavy metals C in the i-th layer of the target nitrification tank h，i For the concentration of microorganisms C in the i-th layer m，i It has an inhibitory effect, and this inhibitory effect varies with the concentration C of the heavy metal in the i-th layer. h，i The toxicity effect increases with increasing concentration, exhibiting a certain non-linear relationship. Empirically, an exponential decay factor is widely used to represent the toxicity effect. The toxicity effect refers to the increase in the concentration of heavy metal C at the i-th layer. h，i For the concentration of microorganisms C in the i-th layer m，i There is a phenomenon of inhibition, which leads to a decrease in oxygen consumption. Based on this, the second sub-item of oxygen consumption, OCF2, is calculated.

[0030] In the formula, β is the heavy metal inhibition coefficient, and e is the base of the natural logarithm, which takes a value of 2.718. It is an exponential decay factor;

[0031] Finally, the viscosity μ of the i-th layer of water in the target nitrification tank i It will reduce the diffusion rate of dissolved oxygen in water;

[0032] Oxygen is transported in wastewater through diffusion. The higher the viscosity, the slower the diffusion and the lower the oxygen consumption efficiency. Based on this, the third sub-item of oxygen consumption, OCF3, is calculated.

[0033] In the formula, γ is the coefficient of influence of water viscosity;

[0034] Based on the calculated oxygen consumption sub-item OCF1, oxygen consumption sub-item OCF2, and oxygen consumption sub-item OCF3, the oxygen consumption factor OCF is obtained through the following calculation formula;

[0035] OCF = OCF1 × OCF2 × OCF3.

[0036] In a preferred embodiment, the analysis and calculation module further includes an oxygen consumption assessment unit, which is used to preset an oxygen consumption threshold Q and compare the oxygen consumption threshold Q with the oxygen consumption factor OCF to generate an oxygen consumption assessment instruction, including:

[0037] When OCF > Q, it indicates that the oxygen consumption rate per unit time or unit volume in the current water body is abnormal, and an oxygen supplementation strategy is generated. This includes reducing the sludge concentration inside the target nitrification tank by 10%-17% through intermittent sludge discharge, thereby reducing the concentration of the i-th layer of microorganisms in the wastewater inside the target nitrification tank by 2%-9%, increasing the amount of precipitant added by 21%-27% (the precipitant includes lime and iron salts to remove heavy metal ions such as copper ions), and increasing the amount of clean water added to the target nitrification tank by 5%-13% to dilute the viscosity of the i-th layer of water.

[0038] When OCF≤Q, it indicates that the oxygen consumption rate per unit time or unit volume in the current water body is normal. The current aeration scheme inside the target nitrification tank should be maintained and continuous monitoring should be carried out.

[0039] In a preferred embodiment, the external factor dataset construction unit is used to directly obtain the internal depth of the target nitrification tank by consulting the construction drawings of the target nitrification tank, to collect the instantaneous influent flow velocity data in real time by installing an electromagnetic flow meter at the inlet point of the target nitrification tank, and to evaluate the influent disturbance intensity based on the fluctuation amplitude of the instantaneous flow velocity over time. Ultrasonic Doppler flow meters are deployed at heights of 1 / 4H, 1 / 2H, and 3 / 4H inside the target nitrification tank to coordinately measure the instantaneous flow velocity at the upper, middle, and bottom layers, construct the vertical liquid flow velocity distribution data of the i-th layer, and establish the external factor dataset.

[0040] In a preferred embodiment, the external factor data extraction unit is used to extract the target nitrification tank internal depth, influent disturbance intensity, and i-th layer liquid flow velocity from the external factor dataset, and after dimensionless processing, obtain the oxygen demand load factor ODLF in the following manner;

[0041] The specific method for obtaining the oxygen demand load factor ODLF is as follows:

[0042] First, based on the target nitrification tank internal depth H and the influent disturbance intensity A. in It is an important factor affecting oxygen supply efficiency. The deeper the water inside the target nitrification tank, the longer the oxygen transport path, and the greater the difficulty in supplying oxygen. The influent disturbance intensity A... in Changes in these factors can also affect the oxygen distribution and mixing effect within the water body, depending on the target nitrification tank depth H and the influent disturbance intensity A. in The influent disturbance intensity factor ODLF was obtained through a calculation formula. A ;

[0043] Secondly, based on the flow velocity V of the i-th layer of liquid i Its high flow rate increases oxygen mixing and transport, but it also carries away oxygen, according to the flow rate V of the i-th layer liquid. i The average fluid velocity factor ODLF is obtained through a calculation formula.v ;

[0044] Based on the inflow disturbance intensity factor ODLF A and average fluid velocity factor ODLF v Correspondingly, after dimensionless processing, the oxygen demand load factor (ODLF) is obtained through a calculation formula.

[0045] In a preferred embodiment, the analysis and calculation module further includes an oxygen demand assessment unit, which is used to preset an oxygen demand load threshold E and compare the oxygen demand load threshold E with the oxygen demand load factor ODLF to generate an oxygen demand load strategy.

[0046] When ODLF > E, it indicates that the oxygen demand in the current target nitrification tank is abnormal, and an oxygen supply strategy is generated, including reducing the blower air volume by 8%-9% and reducing the flow rate of the i-th layer by 3%-7%.

[0047] When ODLF≤E, it indicates that the oxygen demand in the target nitrification tank is normal. Continue to maintain the current wastewater denitrification treatment plan and continue monitoring.

[0048] In a preferred embodiment, the integrated analysis module includes an oxygen delivery tube distribution unit, an association unit, and an oxygen delivery efficiency evaluation unit;

[0049] The oxygen supply pipe distribution unit is used to install a Roots blower outside the target nitrification tank and install a main channel at the outlet end of the Roots blower. Several branch pipes are set on the main channel, wherein the p-th branch pipe is set in the upper layer, the c-th branch pipe is set in the middle layer, and the v-th branch pipe is set in the bottom layer.

[0050] The associated unit is used to associate the oxygen consumption factor OCF and the oxygen demand load factor ODLF, and after dimensionless processing, the oxygen delivery efficiency factor OEF is obtained by calculation.

[0051] The oxygen delivery efficiency assessment unit is used to preset the oxygen delivery efficiency threshold R, compare the oxygen delivery efficiency threshold R with the oxygen delivery efficiency factor OEF, and generate an oxygen delivery efficiency assessment instruction, including:

[0052] When OEF > R, it indicates that there is an anomaly in the amount of oxygen supplied to the target nitrification tank. An oxygen supply strategy is generated, including reducing the power of the Roots blower by 13%-19% and reducing the opening of the upper air valve of the p-th branch pipe by 30%-40% to reduce the oxygen enrichment phenomenon in the upper layer, reducing the opening of the upper air valve of the c-th branch pipe by 20%-34%, and increasing the opening of the upper air valve of the v-th branch pipe by 15%-19% to increase the oxygen supply and infiltration effect at the bottom of the target nitrification tank.

[0053] When OEF≤R, it indicates that the current oxygen supply to the target nitrification tank is normal. Continue the current oxygen supply plan and continue monitoring.

[0054] The intelligent monitoring system for the operation status of denitrification pollution treatment equipment in industrial wastewater provided by this invention has the following beneficial effects:

[0055] 1. By deploying multiple spatial sampling points and installing dissolved oxygen sensors inside the target nitrification tank, real-time acquisition and three-dimensional spatial mapping of dissolved oxygen concentration can be achieved, constructing a refined oxygen spatial distribution map. This provides an intuitive basis for subsequent oxygen supply regulation, breaking through the limitations of traditional two-dimensional or single-point monitoring. Based on the data from each spatial sampling point, the oxygen map is intelligently labeled and divided into three levels: Level 1 (normal), Level 2 (low), and Level 3 (abnormal). This effectively enables rapid identification of areas with uneven oxygen distribution and abnormal oxygen supply, allowing for precise oxygen supplementation to different layers of the water body.

[0056] 2. By combining internal factors such as microbial concentration, heavy metal concentration, and water viscosity, as well as external factors such as nitrification tank depth, influent disturbance intensity, and liquid flow velocity, the impact on oxygen transport and dissipation is analyzed. The oxygen consumption factor OCF and the oxygen demand load factor ODLF are correlated to form the oxygen transport efficiency factor OEF. This index can be used to dynamically assess the oxygen supply efficiency, identify oxygen transport bottlenecks, and optimize the operation of blower equipment, thereby avoiding energy waste and denitrification efficiency decline caused by oxygen excess or deficiency. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0058] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Example 1, referring to Figure 1This invention provides a technical solution: an intelligent monitoring system for the operating status of denitrification pollution treatment equipment in industrial wastewater, comprising;

[0061] The dissolved oxygen acquisition module is used to deploy multiple oxygen delivery channels in the target nitrification tank. Each oxygen delivery channel is equipped with a nano-aeration head at the end and multiple spatial sampling points are set inside. A dissolved oxygen sensor is deployed at each spatial sampling point to collect dissolved oxygen concentration data at each spatial sampling point in real time and map the dissolved oxygen concentration data to the three-dimensional spatial coordinate system corresponding to the target nitrification tank.

[0062] The oxygen map generation and recognition module is used to map dissolved oxygen concentration data to the three-dimensional spatial coordinate system corresponding to the target nitrification tank, construct the oxygen spatial distribution map of the target nitrification tank, including the upper oxygen map, the middle oxygen map, and the bottom oxygen map. Based on the oxygen spatial distribution map of the target nitrification tank, and combined with the dissolved oxygen concentration data of each spatial sampling point, the oxygen spatial distribution map of the target nitrification tank is labeled as first-level label, second-level label, and third-level label, respectively.

[0063] The analysis and calculation module is used to mark the spatial distribution map of oxygen in the target nitrification tank, and then to further analyze the microbial concentration C in the i-th layer region of the target nitrification tank. m，i Heavy metal concentration C h，i and water viscosity μ i Data was collected and an internal factor dataset was constructed. An oxygen consumption factor (OCF) was then built from this dataset. The target nitrification tank depth (H) and influent disturbance intensity (A) were then considered. in and the flow velocity V of the i-th layer of liquid i Collect and construct an external factor dataset, and construct the oxygen demand load factor (ODLF) based on the external factor dataset;

[0064] The comprehensive analysis module is used to correlate the oxygen consumption factor (OCF) and the oxygen demand load factor (ODLF), construct the oxygen delivery efficiency factor (OEF), and evaluate and optimize it.

[0065] In this embodiment, by deploying multiple spatial sampling points and installing dissolved oxygen sensors inside the target nitrification tank, real-time acquisition and three-dimensional spatial mapping of dissolved oxygen concentration can be achieved, constructing a refined oxygen spatial distribution map. This provides an intuitive basis for subsequent oxygen supply regulation, breaking through the limitations of traditional two-dimensional or single-point monitoring. Based on the data from each spatial sampling point, the oxygen map is intelligently labeled and divided into first-level (normal), second-level (low), and third-level (abnormal) label areas, which can effectively achieve rapid identification of areas with uneven oxygen distribution and abnormal oxygen supply, providing a basis for subsequent precise adjustment.

[0066] By combining internal factors such as microbial concentration, heavy metal concentration, and water viscosity, as well as external factors such as nitrification tank depth, influent disturbance intensity, and liquid flow velocity, an oxygen consumption factor (OCF) and an oxygen demand load factor (ODLF) can be constructed. Their impact on oxygen transport and dissipation can then be analyzed. The OCF and ODLF can be correlated to form an oxygen delivery efficiency factor (OEF). This index can be used to dynamically assess oxygen supply efficiency, identify oxygen delivery bottlenecks, and optimize air volume distribution strategies, thereby avoiding energy waste and decreased denitrification efficiency caused by oxygen excess or deficiency.

[0067] Example 2 is an explanation of Example 1; please refer to it. Figure 1 Specifically, the dissolved oxygen acquisition module includes a spatial layout unit, an acquisition unit, and a three-dimensional spatial coordinate construction unit;

[0068] The spatial layout unit is used to vertically set up multiple detection areas inside the target nitrification tank, and to set up a dissolved oxygen sensor at the spatial sampling point of each detection area.

[0069] The acquisition unit is used to collect wastewater dissolved oxygen concentration data in real time based on dissolved oxygen sensors located at spatial sampling points in each layer of the detection area.

[0070] The three-dimensional spatial coordinate construction unit is used to construct a three-dimensional spatial coordinate system inside the target nitrification tank, map the spatial sampling points of each layer of detection area to the three-dimensional spatial coordinate system, and obtain the three-dimensional coordinates x, y, z of the spatial sampling points of the i-th layer of detection area.

[0071] In this embodiment, the target nitrification tank is vertically layered using spatial deployment units, and dissolved oxygen sensors are installed at sampling points in each layer. This forms a multi-dimensional sensing and monitoring network that runs vertically and covers horizontally, significantly improving the completeness and representativeness of dissolved oxygen monitoring. The dissolved oxygen acquisition module can acquire dissolved oxygen concentration data at different spatial locations in each layer in real time, effectively revealing the differences in oxygen distribution at different depths and locations, providing a precise basis for subsequent oxygen supply regulation and anomaly detection. The three-dimensional spatial coordinate construction unit can map the physical location of the sampling points to a three-dimensional coordinate system, generating a data model with precise spatial positioning, thereby supporting the generation of three-dimensional visualization maps and regional labeling, improving the system's intuitive expression and analytical accuracy of dissolved oxygen distribution.

[0072] Based on real monitoring data and a three-dimensional spatial model for each layer, the oxygen supply status of each area can be dynamically analyzed, enabling refined airflow adjustment, stratified aeration control, and optimized allocation of oxygen resources. This significantly improves the oxygen supply efficiency and denitrification stability of the nitrification tank. The combination of three-dimensional coordinates and dissolved oxygen concentration data can accurately locate local abnormal areas such as insufficient oxygen supply, uneven mixing, or blockage, and provide support for issuing targeted early warning commands to the system, thereby enhancing the safety and intelligence level of system operation.

[0073] Example 3 is an explanation of Example 1; please refer to the provided text. Figure 1 Specifically, the oxygen spectrum generation and recognition module includes a spectrum division unit and a labeling unit;

[0074] The map segmentation unit is used to divide the target nitrification tank into upper, middle and lower layers based on the z-axis direction in the three-dimensional spatial coordinate system of the target nitrification tank, and to divide the internal space of the target nitrification tank into several horizontal slice regions along the z-axis direction according to a set fixed interval, wherein the upper, middle and lower layers each correspond to one horizontal slice region, and construct the upper oxygen map, the middle oxygen map and the lower oxygen map;

[0075] The labeling unit is used to collect the dissolved oxygen content of each layer based on the upper layer oxygen spectrum, the middle layer oxygen spectrum and the bottom layer oxygen spectrum and in combination with the dissolved oxygen sensor, and to label each layer with a first-level label, a second-level label and a third-level label according to the dissolved oxygen content of each layer.

[0076] In this embodiment, the map segmentation unit divides the upper, middle, and lower layers based on the z-axis direction (i.e., the depth direction) in the three-dimensional spatial coordinate system and constructs corresponding oxygen maps. This can intuitively and systematically reflect the oxygen distribution in different water layers, facilitating hierarchical management and control. By dividing the target nitrification tank into multiple horizontally sliced ​​areas with fixed intervals, the analytical accuracy of oxygen spatial distribution can be effectively enhanced, providing basic data support for subsequent precise aeration adjustment and local fault identification. The labeling unit combines the oxygen maps of different levels with the measured data from the dissolved oxygen sensor to evaluate the oxygen content of each layer and assign first-level, second-level, and third-level labels, enabling rapid classification and hierarchical management of the oxygen supply status in the nitrification tank.

[0077] The combination of hierarchical mapping and tagging mechanisms enables the system to quickly identify problem areas such as insufficient oxygen supply, local hypoxia, or uneven aeration, significantly improving the system's response speed and identification accuracy to abnormal states. Different levels of tags correspond to different dissolved oxygen states, which can provide a basis for subsequent oxygen adjustment strategies, enabling precise adjustment of fan output or nano-aeration intensity by region, improving oxygen supply efficiency, and reducing energy consumption.

[0078] Example 4 is an explanation of Example 1; please refer to the provided text. Figure 1 Specifically, the analysis and calculation module includes an internal factor dataset construction unit, an internal factor data extraction unit, an external factor dataset construction unit, and an external factor data extraction unit.

[0079] The internal factor dataset construction unit is used to set up several sampling pipes vertically inside the target nitrification tank. Based on the upper, middle and bottom layers, one sampling pipe is installed respectively. Each sampling pipe in each layer is independently connected to a solenoid valve and a micro diaphragm pump. The outlet of the micro diaphragm pump is connected to a water quality microbial detector. Wastewater from the upper, middle and bottom layers is extracted within a fixed time. The concentration of microorganisms in the i-th layer is analyzed by the water quality microbial detector.

[0080] Sampling probes were installed at the upper, middle and bottom layers of the target nitrification tank. The sampling probes used a fixed sampling frequency to extract 100 ml of wastewater and input it into a heavy metal analyzer for detection, so as to obtain the heavy metal concentration of the i-th layer.

[0081] Vibration sensors were installed at the top, middle, and bottom layers of the target nitrification tank to detect the water viscosity of the i-th layer in real time.

[0082] An internal factor dataset is constructed based on the concentration of microorganisms, heavy metals, and water viscosity in the i-th layer.

[0083] In this embodiment, by deploying independent sampling pipes, heavy metal analysis probes, and water viscosity sensors in the upper, middle, and lower layers, core parameters reflecting the system's operating status can be comprehensively acquired, enabling three-dimensional information capture of the operating environment of each water layer. A solenoid valve + micro diaphragm pump structure is used to quantitatively sample wastewater from different layers, and a professional water quality microbial detector is connected to detect microbial concentration. This provides highly sensitive, quantitatively accurate, and spatially stratified microbial concentration information. Using fixed-frequency sampling and connecting to a heavy metal analyzer effectively reflects the accumulation trend of heavy metal concentration in different water layers, providing real-time dynamic data support for assessing toxicity risks and analyzing inhibitory effects.

[0084] Vibration sensors have strong real-time viscosity sensing capabilities and can continuously output viscosity change trends. This helps determine the impact of changes in water rheological properties on oxygen diffusion efficiency, microbial activity, and other factors, and constructs a more complete internal factor data view. By building a structured dataset with parameters such as microbial concentration, heavy metal concentration, and water viscosity as the basic unit of the i-th layer, the system's ability to perceive and respond to the operational status of each layer is improved, which is helpful for the subsequent realization of regional and hierarchical precise operation control strategies.

[0085] Example 5 is an explanation of Example 1; please refer to it. Figure 1 Specifically, the internal factor data extraction unit is used to extract microbial concentration, heavy metal content and water viscosity from the internal factor dataset, and obtains the oxygen consumption factor (OCF) in the following ways;

[0086] The specific method for obtaining the oxygen consumption factor (OCF) is as follows:

[0087] Microbial concentration C m The relationship between the first oxygen-consuming sub-item OCF1;

[0088] The oxygen in the target nitrification tank is mainly used by microorganisms to decompose organic matter. Its oxygen consumption, first sub-item OCF1, is related to the concentration of microorganisms in the i-th layer, C. m，i Proportional to each other, the first oxygen consumption term OCF1 is obtained by calculation, OCF1 = α × C m，i In the formula, α is the proportionality coefficient, and the first sub-term of oxygen consumption, OCF1, is actually the oxygen consumption rate.

[0089] The following is a sample table of data collection for the first sub-item of oxygen consumption, OCF1, as shown in Table 1 below;

[0090] The data from this experiment were collected under outdoor temperatures of 25℃-28℃ and water pH of approximately 6-7.5.

[0091] Number of sample layers……1……2……3……1……2……3;

[0092] The i-th layer of micro

[0093] biological concentration

[0094] (mg / L)…10…15…8…12…20…11.2;

[0095] The proportionality coefficient is 0.15.

[0096] The first sub-item of oxygen consumption is: 1.50, 2.25, 1.20, 1.80, 3.00, and 1.68.

[0097] The proportionality coefficient α in Table 1 was obtained by referring to the empirical coefficients of microbial oxygen consumption rate and microbial concentration under similar conditions in existing relevant research literature or industry standards, and the proportionality coefficient α value was 0.15.

[0098] The concentration of heavy metals C in the i-th layer of the target nitrification tank h，i For the concentration of microorganisms C in the i-th layer m，i It has an inhibitory effect, and this inhibitory effect varies with the concentration C of the heavy metal in the i-th layer. h，iThe toxicity effect increases with increasing concentration, exhibiting a certain non-linear relationship. Empirically, an exponential decay factor is widely used to represent the toxicity effect. The toxicity effect refers to the increase in the concentration of heavy metal C at the i-th layer. h，i For the concentration of microorganisms C in the i-th layer m，i There is a phenomenon of inhibition, which leads to a decrease in oxygen consumption. Based on this, the second sub-item of oxygen consumption, OCF2, is calculated.

[0099] In the formula, β is the heavy metal inhibition coefficient, and e is the base of the natural logarithm, which takes a value of 2.718. is the exponential decay factor; e is the base of the natural logarithm. Empirically, many toxicity inhibition effects and biological processes use exponential functions with the natural base to express the intensity of the effect. Based on historical data, the base is derived to be 2.718.

[0100] For example, assuming β is 0.1, C h，i If the concentration is 5 (mg / L), then OCF2 = e -0.1×5 =e -0.5 ≈0.6065;

[0101] This indicates that at the current heavy metal concentration, the oxygen consumption capacity of microorganisms has been suppressed to about 60% of its original level.

[0102] Based on the experimental environment and water quality conditions in Table 1, the concentration C of heavy metals in the i-th layer of wastewater in the target nitrification tank was further collected. h，i As shown in Table 2 below;

[0103] Number of sample layers……1……2……3……1……2……3;

[0104] The i-th layer

[0105] Metal concentration

[0106] (mg / L)…1.0…2.5…4.0…5.5…6.2…7.0;

[0107] Heavy metal inhibition

[0108] Control coefficient……0.12……0.12……0.12……0.12……0.12……0.12;

[0109] Second oxygen demand

[0110] Sub-items……0.87……0.74……0.62……0.51……0.47……0.44;

[0111] The heavy metal inhibition coefficient β in Table 2 is the optimal parameter obtained by fitting the actual situation of the decrease in microbial activity under different concentrations in the study. It can better reflect the inhibitory effect of heavy metals on microbial oxygen consumption. It is an empirical value obtained through a large number of experiments and field monitoring, specifically taken as 0.12.

[0112] Finally, the viscosity μ of the i-th layer of water in the target nitrification tank i It will reduce the diffusion rate of dissolved oxygen in water;

[0113] Oxygen is transported in wastewater through diffusion. The higher the viscosity, the slower the diffusion and the lower the oxygen consumption efficiency. Based on this, the third sub-item of oxygen consumption, OCF3, is calculated.

[0114] In the formula, is the coefficient of influence of water viscosity;

[0115] For example, suppose the viscosity of the i-th water layer is μ. i Given that the Pa is 1 mPa and γ is 0.5, then;

[0116] This indicates that because the wastewater has a certain viscosity, the oxygen transport efficiency decreases to 66.7%.

[0117] Based on the experimental environment and water quality conditions in Table 1, the viscosity μ of the i-th water layer was collected. i A sample data example table is shown in Table 3 below;

[0118] Number of sample layers……1……2……3……1……2……3;

[0119] i layer of water

[0120] Volume viscosity

[0121] (mPa·s)…0.8…1.0…1.2…1.5…1.8…2.0;

[0122] Water viscosity

[0123] Influence coefficient……0.6……0.6……0.6……0.6……0.6……0.6;

[0124] Third oxygen consumption

[0125] Sub-items……0.68……0.63……0.58……0.53……0.48……0.45;

[0126] The water viscosity influence coefficient γ in Table 3 is based on regression analysis of viscosity and oxygen consumption rate in different water bodies (such as urban sewage, papermaking wastewater, and chemical wastewater) in actual engineering projects. The commonly recommended value range is usually between 0.4 and 0.8. Taking 0.6 is an intermediate stable value, which can more accurately reflect the influence of water viscosity on oxygen diffusion efficiency in general industrial wastewater treatment scenarios.

[0127] Based on the calculated oxygen consumption sub-item OCF1, oxygen consumption sub-item OCF2, and oxygen consumption sub-item OCF3, the oxygen consumption factor OCF is obtained through the following calculation formula;

[0128] OCF = OCF1 × OCF2 × OCF3.

[0129] The following is a sample table of OCF (Oxygen Depletion Factor) samples collected;

[0130] Based on the experimental environment and water quality conditions in Table 1, an example table of Oxygen Depletion Factor (OCF) sample data was constructed, as shown in Table 4 below.

[0131] Number of sample layers……1……2……3……1……2……3;

[0132] The first sub-item of oxygen consumption is: 1.50, 2.25, 1.20, 1.80, 3.00, and 1.68.

[0133] The second sub-item of oxygen consumption is: ... 0.87 ... 0.47 ... 0.62 ... 0.51 ... 0.47 ... 0.44;

[0134] The third sub-item of oxygen consumption is: ... 0.68 ... 0.63 ... 0.58 ... 0.53 ... 0.48 ... 0.45;

[0135] Oxygen consumption factor (OCF)……0.89……1.05……0.43……0.49……0.68……0.33;

[0136] In this embodiment, by integrating three key internal factors—microbial concentration, heavy metal toxicity inhibition, and water viscosity limitation effect—into the model, the constructed oxygen consumption factor (OCF) can accurately reflect the actual oxygen consumption capacity of the i-th layer region of the target nitrification tank, exhibiting stronger adaptability and accuracy. Based on the positive linear correlation between microbial concentration and the first sub-item of oxygen consumption, the contribution of microbial concentration to the oxygen consumption rate is clarified, providing a quantifiable basis for monitoring microbial activity and evaluating the biochemical performance of the system.

[0137] The Oxygen Consumption Factor (OCF), composed of multiple sub-items, comprehensively considers biological factors (microorganisms), chemical factors (heavy metals), and physical factors (water viscosity), providing a scientific basis and algorithmic support for the system to carry out intelligent monitoring, early warning of operational status, and optimized control.

[0138] Example 6 is an explanation of Example 1; please refer to it. Figure 1 Specifically, the analysis and calculation module further includes an oxygen consumption assessment unit, which is used to preset an oxygen consumption threshold Q and compare the oxygen consumption threshold Q with the oxygen consumption factor OCF to generate an oxygen consumption assessment instruction, including:

[0139] When OCF > Q, it indicates that the oxygen consumption rate per unit time or unit volume in the current water body is abnormal, and an oxygen supplementation strategy is generated. This includes reducing the sludge concentration inside the target nitrification tank by 10%-17% through intermittent sludge discharge, thereby reducing the concentration of the i-th layer of microorganisms in the wastewater inside the target nitrification tank by 2%-9%, increasing the amount of precipitant added by 21%-27% (the precipitant includes lime and iron salts to remove heavy metal ions such as copper ions), and increasing the amount of clean water added to the target nitrification tank by 5%-13% to dilute the viscosity of the i-th layer of water.

[0140] When OCF≤Q, it indicates that the oxygen consumption rate per unit time or unit volume in the current water body is normal. The current aeration scheme inside the target nitrification tank should be maintained and continuous monitoring should be carried out.

[0141] The following table shows an example comparison of data from the Oxygen Consumption Factor (OCF) sample data with the oxygen consumption threshold (Q).

[0142] Number of sample layers……1……2……3……1……2……3;

[0143] Oxygen consumption

[0144] Factor OCF……0.89……1.05……0.43……0.49……0.68……0.33;

[0145] Oxygen consumption

[0146] Threshold Q……0.50……0.50……0.50……0.50……0.50……0.50;

[0147] Assessment results... Abnormal, oxygen supplementation strategy implemented... Abnormal, oxygen supplementation strategy implemented... Normal... Normal... Abnormal, oxygen supplementation strategy implemented... Normal;

[0148] The oxygen consumption threshold Q in Table 5 and the oxygen consumption factor OCF are comprehensive indicators constructed based on multiple internal factors affecting oxygen consumption, such as microbial concentration, heavy metal concentration, and water viscosity. They are dimensionless processed by normalization to stabilize their value in the [0,1] range. To facilitate the system's automatic identification of abnormal oxygen consumption, the oxygen consumption threshold Q = 0.5 is set. This value is selected based on the statistical results of historical operating data and represents the dividing point between normal and excessive oxygen consumption in the system.

[0149] In this embodiment, by setting a preset oxygen consumption threshold Q and comparing it with the real-time calculated oxygen consumption factor OCF, it is possible to determine in real time whether the oxygen consumption rate inside the current target nitrification tank is abnormal, thereby achieving efficient and intelligent oxygen consumption status identification and effectively supporting system status regulation. The oxygen supplementation strategy is not a single means, but rather a comprehensive improvement of microbial load, heavy metal toxicity, and water physical properties through the synergistic effect of multiple control methods, forming a closed-loop mechanism of oxygen consumption status—factor identification—multi-source regulation—system recovery.

[0150] Example 7 is an explanation of Example 1; please refer to it. Figure 1 Specifically, the external factor dataset construction unit is used to directly obtain the internal depth of the target nitrification tank by consulting the construction drawings of the target nitrification tank, to collect the instantaneous influent flow velocity data in real time by installing an electromagnetic flow meter at the inlet point of the target nitrification tank, and to evaluate the influent disturbance intensity based on the fluctuation amplitude of the instantaneous flow velocity over time. Ultrasonic Doppler flow meters are deployed at heights of 1 / 4H, 1 / 2H, and 3 / 4H inside the target nitrification tank to coordinate the measurement of the instantaneous flow velocity at the upper, middle, and bottom layers, to construct the vertical liquid flow velocity distribution data of the i-th layer, and to establish the external factor dataset.

[0151] In this embodiment, by combining the structural information of the target nitrification tank with real-time flow velocity data acquisition, the system can accurately and comprehensively construct a dataset of external factors, including water depth, influent disturbance intensity, and multi-layer liquid flow velocity, thereby providing basic support for oxygen supply strategy optimization. By directly reading the nitrification tank construction drawings, depth parameters can be quickly obtained, avoiding redundant measurements or repeated modeling, significantly reducing deployment costs and on-site operation difficulties, and improving system implementation efficiency.

[0152] By using an electromagnetic flowmeter to collect the instantaneous flow velocity fluctuation amplitude at the inlet, the intensity of inlet disturbance can be quantified, enabling real-time capture and interference identification of hydraulic disturbance trends. This provides a basis for subsequent oxygen supply load response. The establishment of an external factor dataset provides a structural, dynamic, and quantifiable foundation for understanding the external environment of the intelligent monitoring system, which is helpful for further developing data-driven intelligent optimization control strategies.

[0153] Example 8 is an explanation of Example 1; please refer to it. Figure 1 Specifically, the external factor data extraction unit is used to extract the internal depth of the target nitrification tank, the influent disturbance intensity, and the liquid flow velocity of the i-th layer from the external factor dataset, and after dimensionless processing, obtain the oxygen demand load factor ODLF in the following manner;

[0154] The specific method for obtaining the oxygen demand load factor ODLF is as follows:

[0155] First, based on the target nitrification tank internal depth H and the influent disturbance intensity A. in It is an important factor affecting oxygen supply efficiency. The deeper the water inside the target nitrification tank, the longer the oxygen transport path, and the greater the difficulty in supplying oxygen. The influent disturbance intensity A... in Changes in these factors can also affect the oxygen distribution and mixing effect within the water body, depending on the target nitrification tank depth H and the influent disturbance intensity A. in The influent disturbance intensity factor ODLF was obtained through a calculation formula. A Its expression is In the formula, ρ1 is an empirical coefficient that reflects the degree of influence of water depth on sample demand. In this invention, the empirical coefficient ρ1 is taken as 0.85, which is selected by combining the historical operation data of the target nitrification tank and typical oxygen supply modeling literature to fully reflect the degree of influence of water depth on oxygen supply mixing efficiency.

[0156] Based on the experimental environment in Table 1, we continued to collect data on the internal depth H of the target nitrification tank and the influent disturbance intensity A. in The sample data is shown in Table 6 below;

[0157] Sample number……1……2……3……4……5……6;

[0158] Water inflow disturbance

[0159] Strength A in (m / s)…0.511…0.886…0.732…0.661…0.389…0.560;

[0160] Target nitration

[0161] The pool is deep

[0162] Degree H (m)……4.0……4.0……4.0……4.0……4.0……4.0……4.0;

[0163] Water inflow disturbance

[0164] Intensity factor……0.409……0.709……0.586……0.529……0.311……0.448;

[0165] Secondly, based on the flow velocity V of the i-th layer of liquid i Its high flow rate increases oxygen mixing and transport, but it also carries away oxygen, according to the flow rate V of the i-th layer liquid. i The average fluid velocity factor ODLF is obtained through a calculation formula. v Its expression is In the formula, n is the number of layers, and ρ2 is an empirical coefficient;

[0166] Based on the experimental environment in Table 1, the flow velocity V of the i-th layer of liquid will continue to be collected. j A sample data example table is shown in Table 7;

[0167] Number of sample layers……1……2……3-1……2……3-1……2……3;

[0168] i-th layer of liquid

[0169] Volume flow velocity V i (m / s)……0.12……0.15……0.18

[0170] -0.20……0.22……0.24-

[0171] 0.10……0.08……0.12;

[0172] ...0.15-0.22-0.10;

[0173] Empirical coefficient... 1.5-1.5-1.5;

[0174] Average liquid

[0175] Flow rate factor ODLF v 0.225-0.330-0.150;

[0176] The empirical coefficient ρ2 in Table 7 shows that, based on extensive research into wastewater treatment processes, the liquid flow velocity in the range of 0.1–0.4 m / s has a significant impact on oxygen distribution. The appropriate empirical coefficient is generally selected between 1.2 and 1.6. In industrial applications, engineers have found through operational parameter calibration that using ρ2 = 1.5 can better match the actual blower oxygen supply curve, DO distribution, and aeration uniformity, and therefore it is used as a recommended initial value in many design projects.

[0177] Based on the inflow disturbance intensity factor ODLF A and average fluid velocity factor ODLF v Relatedly, after dimensionless processing, the oxygen demand load factor ODLF is obtained through the following calculation formula;

[0178] ODLF=ω1×ODLF A +ω2×ODLFv ;

[0179] In the formula, ω1 and ω2 are weighting coefficients, satisfying ω1+ω2=1.

[0180] Based on the experimental environment in Table 1, we will continue to construct a sample data instance table of oxygen demand load factor ODLF, as shown in Table 8 below.

[0181] Sample number……1……2……3;

[0182] Water inflow disturbance

[0183] Intensity factor ODLF A ……0.409……0.709……0.586;

[0184] Average liquid

[0185] Flow rate factor ODLF v ……0.225……0.330……0.150;

[0186] Weighting coefficients ω1……0.6……0.6……0.6;

[0187] Weighting coefficients ω2……0.4……0.4……0.4;

[0188] Oxygen demand

[0189] Loading factor……0.283……0.402……0.218;

[0190] The weighting coefficients ω1 and ω2 in Table 8 are derived from a large amount of historical data, based on the weights of the influent disturbance intensity factor and the average liquid velocity factor in the system. Combined with historical data, the influence of the influent disturbance intensity factor on the oxygen demand load factor ODLF is greater than that of the average liquid velocity factor. Based on the weight settings in the historical data, the values ​​of ω1 and ω2 are derived to be 0.6 and 0.4.

[0191] In this embodiment, key parameters such as water depth, influent disturbance intensity, and liquid flow velocity of the target nitrification tank are extracted, and a mathematical model is constructed to calculate the oxygen demand load factor (ODLF). This enables dynamic and accurate assessment of oxygen supply demand, improves the system's responsiveness to operational fluctuations, and uses dimensionless disturbance intensity and flow velocity factors as the basis for calculation. This gives the model good engineering adaptability and cross-system comparison capability, making it easy to reuse and promote in different types or scales of nitrification tanks.

[0192] Example 9, this example is an explanation of Example 1, please refer to it. Figure 1Specifically, the analysis and calculation module also includes an oxygen demand assessment unit, which is used to preset an oxygen demand load threshold E and compare the oxygen demand load threshold E with the oxygen demand load factor ODLF to generate an oxygen demand load strategy, including:

[0193] When ODLF > E, it indicates that the oxygen demand in the current target nitrification tank is abnormal, and an oxygen supply strategy is generated, including reducing the blower air volume by 8%-9% and reducing the flow velocity of the i-th layer by 3%-7% to reduce the intensity of disturbance to the water body.

[0194] When ODLF≤E, it indicates that the oxygen demand in the target nitrification tank is normal. Continue to maintain the current wastewater denitrification treatment plan and continue monitoring.

[0195] Based on the experimental environment in Table 1, we will continue to construct a sample data example table for comparing the oxygen demand load threshold E and the oxygen demand load factor ODLF, as shown in Table 9 below.

[0196] Sample number……1……2……3

[0197] Oxygen demand

[0198] Loading factor ODLF……0.283……0.402……0.218

[0199] Oxygen demand

[0200] Load threshold E……0.30……0.30……0.30

[0201] Assessment results: ...normal...abnormal; oxygen supply strategy implemented: ...normal.

[0202] The oxygen demand load threshold E in Table 9 is derived as 0.30 by averaging the oxygen demand load threshold E set based on the oxygen load characteristics of industrial wastewater treatment of similar scale and historical data of multiple projects.

[0203] In this embodiment, by precisely controlling the two factors of air volume and flow rate, the coupled control between oxygen supply efficiency and water disturbance is achieved. Under the premise of ensuring the denitrification effect, energy consumption and system load are effectively reduced. The proposed strategy parameters are clear (such as the adjustment percentage range), which is easy to deploy directly in the control system, reduces the ambiguity of manual intervention, and enhances the consistency and practicality of strategy execution.

[0204] Example 10: This example is an explanation of Example 1. Please refer to the provided text. Figure 1 Specifically, the comprehensive analysis module includes an oxygen delivery tube distribution unit, an association unit, and an oxygen delivery efficiency evaluation unit;

[0205] The oxygen supply pipe distribution unit is used to install a Roots blower outside the target nitrification tank and install a main channel at the outlet end of the Roots blower. Several branch pipes are set on the main channel, wherein the p-th branch pipe is set in the upper layer, the c-th branch pipe is set in the middle layer, and the v-th branch pipe is set in the bottom layer.

[0206] The associated unit is used to associate the oxygen consumption factor OCF and the oxygen demand load factor ODLF. After dimensionless processing, the oxygen delivery efficiency factor OEF is calculated by the following formula.

[0207]

[0208] In the formula, ε is a very small positive number to prevent division by zero;

[0209] The oxygen consumption factor (OCF) was normalized using a formula to obtain the OCF. norm As shown below;

[0210]

[0211] The oxygen demand load factor (ODLF) was normalized using a formula to obtain ODLF. norm As shown below;

[0212]

[0213] Based on the experimental environment in Table 1, we will continue to construct an example table of sample data for the oxygen transport efficiency factor (OEF), as shown in Table 10 below.

[0214] Sample number……1……2;

[0215] Number of floors……1……2……3-1……2……3;

[0216] Oxygen consumption

[0217] Factor…0.89…1.05…0.43-0.49…0.68…0.33;

[0218] After normalization

[0219] The oxygen consumption factor (OCF) is 0.775, 1.00, 0.114-0.156, 0.455, and 0.01.

[0220] oxygen demand negative

[0221] Charge factor……0.283……0.283……0.283-0.402……0.402……0.402;

[0222] Normalized

[0223] Oxygen demand loading factor ODLF……0.01……0.01……0.01-1.00……1.00……1.00;

[0224] To prevent division by zero, the smallest positive number ε is defined as follows: ε = 0.01 - 0.01.

[0225] Oxygen transport efficiency factor (OEF)……0.473……0.604……0.071-0.494……0.673……0.406;

[0226] The values ​​of ε in Table 10 are the minimum normal ranges commonly used in wastewater treatment automatic control systems, which are 0.001 to 0.05. Setting ε to 0.01 is the most common compromise and can meet the accuracy requirements of most actual data.

[0227] The oxygen delivery efficiency assessment unit is used to preset the oxygen delivery efficiency threshold R, compare the oxygen delivery efficiency threshold R with the oxygen delivery efficiency factor OEF, and generate an oxygen delivery efficiency assessment instruction, including:

[0228] When OEF > R, it indicates that there is an anomaly in the amount of oxygen supplied to the target nitrification tank. An oxygen supply strategy is generated, including reducing the power of the Roots blower by 13%-19% and reducing the opening of the upper air valve of the p-th branch pipe by 30%-40% to reduce the oxygen enrichment phenomenon in the upper layer, reducing the opening of the upper air valve of the c-th branch pipe by 20%-34%, and increasing the opening of the upper air valve of the v-th branch pipe by 15%-19% to increase the oxygen supply and infiltration effect at the bottom of the target nitrification tank.

[0229] When OEF≤R, it indicates that the current oxygen supply to the target nitrification tank is normal. Continue the current oxygen supply plan and continue monitoring.

[0230] Based on the experimental environment in Table 1, we will continue to construct a sample data example table for comparing the oxygen delivery efficiency threshold R and the oxygen delivery efficiency factor OEF, as shown in Table 11 below.

[0231] Number of floors……1……2……3……1……2……3;

[0232] Oxygen transport efficiency factor (OEF)……0.473……0.604……0.071……0.494……0.673……0.406;

[0233] Oxygen delivery efficiency threshold R……0.45……0.45……0.45……0.45……0.45……0.45;

[0234] Assessment results...abnormal, oxygen administration strategy executed...normal...abnormal, oxygen administration strategy executed...abnormal, oxygen administration strategy executed...abnormal, oxygen administration strategy executed...normal;

[0235] The oxygen delivery efficiency threshold R in Table 11 was calculated based on the actual values ​​of the oxygen delivery efficiency factor (OEF) collected from multiple operating cycles. Samples were selected from cycles with good operating conditions, and statistical methods were used for analysis. The range of the oxygen delivery efficiency threshold R was also statistically analyzed. Based on historical experience, the oxygen delivery efficiency threshold R was set to 0.45.

[0236] The comprehensive analysis module mathematically correlates the oxygen consumption factor (OCF) with the oxygen demand load factor (ODLF) and calculates the oxygen delivery efficiency factor (OEF) to accurately assess the supply and demand balance, providing a quantitative basis for system operation decisions. This enables dynamic matching between oxygen supply behavior and biochemical oxygen demand. By setting up oxygen delivery branch pipelines (p-th, c-th, and v-th) at different water layers and forming an adjustable oxygen delivery network with the Roots blower main pipeline, independent airflow control is achieved for the upper, middle, and lower layers. This avoids oxygen enrichment or hypoxia caused by uneven oxygen supply and improves oxygen utilization efficiency.

[0237] The entire oxygen supply control logic is based on a closed-loop mechanism of "consumption-demand-transportation", which enables the system to have dynamic monitoring, autonomous judgment and real-time adjustment capabilities. It can effectively cope with influent fluctuations, changes in operating conditions and water quality complexity, improve the system's robustness and intelligence level, and effectively support the synergistic process of nitrification and denitrification by optimizing oxygen supply efficiency and controlling the spatial distribution and concentration gradient of oxygen in the nitrification tank. This promotes the efficient removal of pollutants such as ammonia nitrogen and total nitrogen, and improves the overall denitrification performance.

[0238] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0239] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. An intelligent monitoring system for the operation state of a device for treating nitrogen pollution in industrial wastewater, characterized in that, The method comprises the steps of: The dissolved oxygen collection module is used for arranging a plurality of oxygen supply channels in the target nitrification tank, installing a nano aeration head at the end of each oxygen supply channel, and arranging a plurality of spatial sampling points in the interior, and arranging a dissolved oxygen sensor at each spatial sampling point, collecting the dissolved oxygen concentration data of each spatial sampling point in real time, and mapping the dissolved oxygen concentration data to the corresponding three-dimensional spatial coordinate system of the target nitrification tank; The oxygen map generation and identification module is used for mapping the dissolved oxygen concentration data to the corresponding three-dimensional spatial coordinate system of the target nitrification tank, constructing the oxygen spatial distribution map of the target nitrification tank, including the upper oxygen map, the middle oxygen map and the bottom oxygen map, marking the oxygen spatial distribution map of the target nitrification tank based on the oxygen spatial distribution map of the target nitrification tank and the dissolved oxygen concentration data of each spatial sampling point, and marking as a first grade label, a second grade label and a third grade label respectively. An analysis calculation module is configured to, after marking the oxygen spatial distribution map of the target nitrification tank, continue to calculate the microbial concentration C m，i , heavy metal concentration C h，i , and water viscosity μ i of the i-th layer region in the target nitrification tank m，i , heavy metal content C h，i , and water viscosity μ i in the internal factor data set, respectively calculate the oxygen consumption first subterm OCF1, the oxygen consumption second subterm OCF2, and the oxygen consumption third subterm OCF3, multiply the three terms, construct the oxygen consumption factor OCF, and collect the depth H of the target nitrification tank, the influent disturbance intensity A in , and the i-th layer liquid flow rate V i to construct an external factor data set, and calculate the influent disturbance intensity factor ODLF A and the average liquid flow rate factor ODLF v according to the external factor data set, and then combine the weight coefficients ω1 and ω2 to construct the oxygen demand load factor ODLF; The comprehensive analysis module is configured to correlate the oxygen consumption factor OCF and the oxygen demand load factor ODLF, construct an oxygen efficiency factor OEF, and adopt a formula The oxygen efficiency factor OEF is calculated, evaluated and optimized, where ε is a minimum positive number to prevent division by zero.

2. The intelligent monitoring system for the operation state of the industrial wastewater denitrification pollution treatment equipment according to claim 1, characterized in that, The dissolved oxygen collection module comprises a spatial arrangement unit, a collection unit and a three-dimensional spatial coordinate construction unit. The spatial arrangement unit is used for vertically arranging a plurality of detection areas in the interior of the target nitrification tank, and arranging a dissolved oxygen sensor at each spatial sampling point of each detection area; The collection unit is used for collecting the wastewater dissolved oxygen concentration data of each layer based on the dissolved oxygen sensor at the spatial sampling point position of each detection area; The three-dimensional spatial coordinate construction unit is used for constructing a three-dimensional spatial coordinate system in the interior of the target nitrification tank, mapping each detection area spatial sampling point to the three-dimensional spatial coordinate system, and obtaining the three-dimensional coordinates x, y and z of the i-th detection area spatial sampling point.

3. The intelligent monitoring system for the operation state of the industrial wastewater denitrification pollution treatment equipment according to claim 2, characterized in that, The oxygen map generation and identification module comprises a map division unit and a marking unit. The map division unit is used for dividing the upper layer, the middle layer and the bottom layer based on the z-axis direction of the three-dimensional spatial coordinate system of the target nitrification tank, and dividing the interior space of the target nitrification tank into a plurality of horizontal slice regions along the z-axis direction according to a set fixed interval, wherein the upper layer, the middle layer and the bottom layer correspond to one horizontal slice region respectively, and the upper oxygen map, the middle oxygen map and the bottom oxygen map are constructed; The marking unit is used for marking the dissolved oxygen content of each layer based on the upper oxygen map, the middle oxygen map and the bottom oxygen map and the dissolved oxygen sensor, and marking as a first grade label, a second grade label and a third grade label respectively according to the dissolved oxygen content of each layer.

4. The intelligent monitoring system for the operation state of the industrial wastewater denitrification pollution treatment equipment according to claim 3, characterized in that, The analysis calculation module comprises an internal factor data set construction unit, an internal factor data extraction unit, an external factor data set construction unit and an external factor data extraction unit; The internal factor data set construction unit is used for arranging a plurality of sampling pipelines in the interior of the target nitrification tank vertically, arranging one sampling pipeline corresponding to the upper layer, the middle layer and the bottom layer respectively, and independently connecting an electromagnetic valve and a micro diaphragm pump to the sampling pipeline in each layer, connecting the water outlet end of the micro diaphragm pump with a water quality microorganism detector, and extracting wastewater at the positions of the upper layer, the middle layer and the bottom layer in a fixed time, and analyzing the i-th microbial concentration by the water quality microorganism detector; By setting sampling probes at upper, middle and bottom positions in the target nitrification tank, the sampling probes are used to extract 100 ml wastewater at a fixed sampling frequency and input into a heavy metal analyzer for detection to obtain the heavy metal concentration of the ith layer; By installing vibration sensors at the upper, middle and bottom positions of the target nitrification tank, the water viscosity of the ith layer is detected in real time; The internal factor dataset is constructed based on the microbial concentration, heavy metal concentration and water viscosity of the ith layer.

5. The intelligent monitoring system for the operation state of the industrial wastewater denitrification pollution treatment equipment according to claim 4, characterized in that, The internal factor data extraction unit is configured to extract the microbial concentration, heavy metal content and water viscosity from the internal factor dataset, and obtain an oxygen consumption factor OCF by the following method: The specific method for obtaining the oxygen consumption factor OCF is as follows: Microorganism concentration C m The relationship between the oxygen consumption first sub-item OCF1; The oxygen in the target nitrification tank is mainly used by microorganisms to decompose organic matters, and the oxygen consumption first sub-item OCF1 and the microorganism concentration Ci in the i-th layer are proportional to each other, so that the oxygen consumption rate OCF1 is obtained by calculation, OCF1 = α × C m，i ; in the formula, α is a proportional coefficient, and the oxygen consumption first sub-item OCF1 is an oxygen consumption rate. m，i ; in the formula, α is a proportional coefficient, and the oxygen consumption first sub-item OCF1 is an oxygen consumption rate. The concentration of the heavy metal in the i th layer in the target nitrification tank C h，i The concentration of the microorganism in the i th layer C m，i Has an inhibitory effect, and the inhibitory effect increases with the concentration of the heavy metal in the i th layer C h，i Has a certain nonlinear relationship, and the toxicity effect is empirically expressed by an exponential decay factor, which means that the toxicity effect is proportional to the concentration of the heavy metal in the i th layer C h，i The concentration of the microorganism in the i th layer C m，i Has an inhibitory effect, which causes the phenomenon of oxygen consumption to decrease, and the second subterm OCF2 of oxygen consumption is calculated. where β is the heavy metal inhibition coefficient, e is the base of the natural logarithm, and has a value of 2.718, is the exponential decay factor; Finally, the viscosity μ of the i-th layer of water in the target nitrification basin i will reduce the rate of diffusion of dissolved oxygen in the water; Oxygen diffuses and transfers in wastewater by diffusion, and the higher the viscosity, the slower the diffusion and the lower the oxygen consumption efficiency. The oxygen consumption third sub-item OCF3 is calculated in this way; where γ is a water body viscosity influence coefficient; The oxygen consumption factor OCF is obtained by combining the calculated oxygen consumption first sub-item OCF1, oxygen consumption second sub-item OCF2 and oxygen consumption third sub-item OCF3 by the following calculation formula: OCF = OCF1 × OCF2 × OCF3.

6. The intelligent monitoring system for the operation state of the industrial wastewater denitrification pollution treatment equipment according to claim 5, characterized in that, The analysis and calculation module further includes an oxygen consumption evaluation unit configured to preset an oxygen consumption threshold Q and compare the oxygen consumption threshold Q with the oxygen consumption factor OCF to generate an oxygen consumption evaluation instruction, including: When OCF > Q, it indicates that the oxygen consumption rate per unit time or unit volume in the current water body is abnormal, and a supplemental oxygen strategy is generated, including reducing the internal sludge concentration of the target nitrification tank by 10%-17% through intermittent deslagging, thereby reducing the ith layer microbial concentration in the wastewater in the target nitrification tank by 2%-9%, increasing the sedimentation agent dosage by 21%-27%, and adding 5%-13% of clean water to the target nitrification tank; When OCF ≤ Q, it indicates that the oxygen consumption rate per unit time or unit volume in the current water body is normal, and the current aeration scheme for the target nitrification tank is maintained for continuous monitoring.

7. The intelligent monitoring system for the operation state of the industrial wastewater denitrification pollution treatment equipment according to claim 6, characterized in that, The internal factor dataset construction unit is configured to directly obtain the internal depth of the target nitrification tank by consulting the construction drawings of the target nitrification tank, and to real-time collect the instantaneous flow rate data of the influent by installing an electromagnetic flowmeter at the influent point of the target nitrification tank, and to evaluate the influent disturbance intensity based on the fluctuation amplitude of the instantaneous flow rate over time, and to arrange ultrasonic Doppler flowmeters at 1 / 4H, 1 / 2H and 3 / 4H height positions inside the target nitrification tank for cooperative measurement of the instantaneous flow rates at the upper, middle and bottom positions to construct the ith layer liquid flow rate distribution data in the vertical direction, and to establish the external factor dataset.

8. The intelligent monitoring system for the operation state of the industrial wastewater denitrification pollution treatment equipment according to claim 7, characterized in that, The external factor data extraction unit is configured to extract the internal depth of the target nitrification tank, the influent disturbance intensity and the ith layer liquid flow rate from the external factor dataset, and to obtain an oxygen demand load factor ODLF by the following method: The specific method for obtaining the oxygen demand load factor ODLF is as follows: Firstly, based on the internal depth H of the target nitrification tank and the influent disturbance intensity A in , which are important factors affecting the oxygen supply efficiency, because the deeper the water body inside the target nitrification tank, the longer the oxygen transfer path, the more difficult the oxygen supply, and the change of the influent disturbance intensity A in will also affect the oxygen distribution and mixing effect inside the water body, according to the internal depth H of the target nitrification tank and the influent disturbance intensity A in , the influent disturbance intensity factor ODLF A is obtained through the calculation formula. Second, based on the liquid flow rate V i , its high flow rate will increase oxygen mixing transmission, but also take away oxygen, according to the liquid flow rate V i , the average liquid flow rate factor ODLF v is obtained by calculation formula Based on the intensity factor of the water inflow disturbance ODLF A and the average liquid flow rate factor ODLF v associated, after being dimensionless, the oxygen demand load factor ODLF is obtained by calculation.

9. The intelligent monitoring system for the operation state of a device for treating nitrogen pollution in industrial wastewater according to claim 8, characterized in that, The analysis calculation module further comprises an oxygen demand evaluation unit, the oxygen demand evaluation unit is used for presetting an oxygen demand load threshold E, comparing the oxygen demand load threshold E with an oxygen demand load factor ODLF, and generating an oxygen demand load strategy, comprising: When ODLF>E, it indicates that the oxygen demand state in the current target nitrification tank is abnormal, an oxygen supply strategy is generated, comprising reducing the fan air volume by 8%-9% and reducing the flow rate of the i-th layer by 3%-7%; When ODLF≤E, it indicates that the oxygen demand state in the current target nitrification tank is normal, the current wastewater denitrification treatment scheme is continued, and monitoring is continued.

10. The intelligent monitoring system for the operation state of the industrial wastewater denitrification pollution treatment equipment according to claim 9, characterized in that, The comprehensive analysis module comprises an oxygen supply pipe distribution unit, an association unit and an oxygen supply efficiency evaluation unit; The oxygen supply pipe distribution unit is used for setting a Roots blower outside the target nitrification tank, and installing a main channel at the air outlet end of the Roots blower, and a plurality of branch pipes are arranged on the main channel, wherein the p-th branch pipe is arranged at an upper layer position, the c-th branch pipe is arranged at a middle layer position, and the v-th branch pipe is arranged at a bottom layer position; The association unit is used for associating an oxygen consumption factor OCF and an oxygen demand load factor ODLF, and obtaining an oxygen supply efficiency factor OEF through calculation after dimensionless processing; The oxygen supply efficiency evaluation unit is used for presetting an oxygen supply efficiency threshold R, comparing the oxygen supply efficiency threshold R with the oxygen supply efficiency factor OEF, and generating an oxygen supply efficiency evaluation instruction, comprising: When OEF>R, it indicates that the amount of oxygen delivered to the target nitrification tank is abnormal, an oxygen supply strategy is generated, comprising reducing the power of the Roots blower by 13%-19%, reducing the opening degree of the air valve on the p-th branch pipe by 30%-40% to reduce the upper layer oxygen enrichment phenomenon, reducing the opening degree of the air valve on the c-th branch pipe by 20%-34%, and increasing the opening degree of the air valve on the v-th branch pipe by 15%-19%; When OEF≤R, it indicates that the amount of oxygen delivered to the target nitrification tank is normal, the current oxygen supply scheme is maintained, and monitoring is continued.

Images