A rural domestic sewage pollution reduction and carbon reduction intelligent management method and system

Abstract

The application provides a rural domestic sewage pollution reduction and carbon reduction intelligent management method and system. In the method, sewage flow rate, COD concentration, ammonia nitrogen content data, and user water use time and cleaning agent dosage are collected to construct a database. The flow rate and COD are weighted to generate a pollution load evaluation value, and the ammonia nitrogen and cleaning agent data are combined to calculate the organic matter pollution influence value. A porous ceramic medium is arranged in the biological treatment device, and the dissolved oxygen concentration is adjusted according to the pollution load. A correlation curve of the COD concentration peak value and the water use time is established, a prediction model is constructed in combination with the dissolved oxygen parameters, the gas injection and reflux ratio are adjusted through the hierarchical pressure instruction, and the dissolved oxygen critical value is maintained. The degradation rate is corrected by fusing the day and night temperature difference, the critical range and instruction priority are optimized in combination with the optical detection data, and the collaborative control of sewage treatment and carbon emission reduction is realized. The application realizes self-adaptive adjustment of dissolved oxygen in sewage treatment, and improves the management efficiency of domestic sewage pollution reduction and carbon reduction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of low-carbon emission control, and in particular to a rural domestic sewage pollution reduction and carbon reduction intelligent management method and system. BACKGROUND

[0002] Rural decentralized sewage treatment stations generally face the dual challenges of low treatment efficiency and high operation energy consumption. Due to the concentration of rural residents' water use time and the randomness of cleaning agent use, the sewage flow and water quality show significant volatility, while the traditional treatment process parameters are fixed and difficult to match the pollution load. An optimized control scheme that can automatically adapt to water quality fluctuations, reduce aeration energy consumption and reduce manual intervention is urgently needed, while the effluent water quality standard and carbon emission reduction target should be considered.

[0003] At present, some sites use aeration control systems based on fixed thresholds as an improvement scheme. The system monitors water quality parameters by deploying chemical oxygen demand sensors and ammonia nitrogen sensors. When the detection value exceeds the preset threshold, the aeration pump power is triggered to increase, and when the detection value falls below the threshold, the aeration amount is reduced.

[0004] However, this scheme has obvious limitations in actual operation. It relies on fixed thresholds and preset periods and cannot effectively respond to sudden increases in cleaning agent use, rainfall dilution of sewage and other nonlinear changes, resulting in imbalance between aeration supply and pollution load, causing excessive aeration to cause energy waste or insufficient aeration to cause effluent exceeding standards. SUMMARY

[0005] The present application provides a rural domestic sewage pollution reduction and carbon reduction intelligent management method and system to solve the problem of lack of treatment efficiency and energy consumption optimization in the prior art.

[0006] In a first aspect, the present application provides a rural domestic sewage pollution reduction and carbon reduction intelligent management method, comprising:

[0007] Collecting sewage flow rate, chemical oxygen demand concentration and ammonia nitrogen content, and synchronously receiving water use time data and cleaning agent use amount uploaded by a user mobile device;

[0008] The sewage flow rate and chemical oxygen demand concentration are weighted to generate a pollution load evaluation value, the ammonia nitrogen content and cleaning agent use amount are generated through an association model to generate an organic pollution influence value, and a comprehensive data set containing spatiotemporal association features is constructed;

[0009] Based on the distribution characteristics of the pollution load evaluation value and the organic pollution influence value in the comprehensive data set, a porous ceramic medium is installed in a modular biological treatment device, and the dissolved oxygen concentration is adjusted according to the change of the pollution load evaluation value;

[0010] Based on the dissolved oxygen concentration adjustment parameter, combined with the correlation change curve of the peak value of the chemical oxygen demand concentration and the water use time data in a plurality of continuous monitoring periods, a pollutant emission prediction model is generated, based on the processing period and energy consumption correlation of the prediction model, a hierarchical pressure control instruction is sent to the gas injection device, and the sewage reflux ratio is adjusted synchronously to maintain the dissolved oxygen concentration within the critical value range;

[0011] According to the day and night temperature difference parameter in the meteorological data, the degradation rate parameter of the prediction model is corrected, combined with the optical detection data of the porous ceramic medium, the upper and lower limit values of the critical value range and the execution priority of the hierarchical pressure control instruction are optimized.

[0012] Optionally, based on the distribution characteristics of the pollution load evaluation value and the organic matter pollution influence value in the comprehensive data set, a porous ceramic medium is installed in the modular biological treatment device, and the dissolved oxygen concentration is adjusted according to the change of the pollution load evaluation value, including:

[0013] According to the spatio-temporal distribution characteristics of the pollution load evaluation value and the fluctuation law of the organic matter pollution influence value in the comprehensive data set, a porous ceramic medium with a pore size distribution matched with the fluctuation range of the pollution load evaluation value is selected and embedded in the filler layer of the modular biological treatment device;

[0014] Based on the influence of the embedded porous ceramic medium on the microbial attachment efficiency, the change trend of the pollution load evaluation value is monitored, and when the change of the pollution load evaluation value exceeds a preset fluctuation threshold, the output pressure of the main pipeline of the gas injection device is adjusted;

[0015] When the pollution load evaluation value increases, the main aeration pipeline pressure is reduced to reduce the dissolved oxygen concentration, and when the pollution load evaluation value decreases, the main aeration pipeline pressure is increased to increase the dissolved oxygen concentration.

[0016] Optionally, according to the day and night temperature difference parameter in the meteorological data, the degradation rate parameter of the prediction model is corrected, combined with the optical detection data of the porous ceramic medium, the upper and lower limit values of the critical value range and the execution priority of the hierarchical pressure control instruction are optimized, including:

[0017] The day and night temperature difference parameter in the meteorological data is input into the degradation rate compensation formula to generate a degradation rate correction coefficient inversely proportional to the temperature difference, and based on the degradation rate correction coefficient, the degradation rate parameter related to microbial activity in the prediction model is corrected;

[0018] Through the optical sensor on the surface of the porous ceramic medium, the coverage area and color feature data of the biofilm are collected, and when the coverage area continuously increases and the color feature data indicates that the biofilm activity decreases, the upper limit and lower limit of the critical value range are synchronously reduced according to the adjustment amplitude of the degradation rate parameter.

[0019] Based on the historical execution frequency of the hierarchical pressure control instruction and the corresponding maintenance effect data of the dissolved oxygen concentration, the degradation rate parameter is associated with the instruction execution effect analysis, and the hierarchical pressure control instruction with the maintenance effect reaching the standard and the high execution efficiency is promoted in priority, and the rest of the hierarchical pressure control instruction is degraded in priority weight.

[0020] Optionally, when the coverage area continues to increase and the color feature data indicates that the biofilm activity decreases, according to the adjustment range of the degradation rate parameter, the upper limit and the lower limit of the critical value range are simultaneously reduced, including:

[0021] Obtain image data of the coverage area, extract the area growth rate of the microbial community in the coverage area and the red channel to blue channel ratio in the color feature data;

[0022] When the value of the area growth rate continuously exceeds the preset growth threshold, and the red channel to blue channel ratio is lower than the preset activity threshold, it is determined that the activity of the microbial community reaches the trigger condition in the state of decline;

[0023] According to the adjustment range of the degradation rate parameter, the correction ratio of the critical value range is calculated, and the adjustment range is the absolute value of the difference between the current degradation rate and the reference rate percentage of the reference rate;

[0024] The value of the correction ratio is input into the calculation formula of the critical value range, and the upper limit value and the lower limit value of the current critical value range are simultaneously reduced, and the upper limit value is reduced by the original upper limit value multiplied by the correction ratio, and the lower limit value is reduced by the original lower limit value multiplied by the correction ratio.

[0025] Optionally, based on the adjustment parameter of the dissolved oxygen concentration, in combination with the associated change curve of the peak value of the chemical oxygen demand concentration and the water use time data in the continuous multiple monitoring periods, a prediction model of pollutant emission is generated, including:

[0026] From the adjustment parameter of the dissolved oxygen concentration, the dissolved oxygen fluctuation period is extracted, and the peak time of the chemical oxygen demand concentration in the continuous monitoring period is simultaneously obtained;

[0027] The peak time of the chemical oxygen demand concentration and the water use time data reported by the user are aligned according to the time window, and a change curve containing the associated relationship between the pollution emission intensity and the water use period is generated;

[0028] The duration of the peak value, the peak interval in the change curve, and the dissolved oxygen response lag time in the adjustment parameter of the dissolved oxygen concentration are extracted, and a mapping relationship between the duration, the peak interval, and the dissolved oxygen response lag time is established;

[0029] According to the response law of the pollution intensity peak and the dissolved oxygen concentration in the mapping relationship, a prediction model of the pollutant degradation period and the treatment energy consumption is constructed.

[0030] Optionally, based on the treatment period and energy consumption association relationship of the prediction model, a hierarchical pressure control instruction is sent to the gas injection device remotely, and the sewage backflow ratio is adjusted synchronously to maintain the dissolved oxygen concentration within the critical value range, including:

[0031] According to the association relationship of the pollutant degradation period and the treatment energy consumption in the prediction model, when the degradation period is extended and the energy consumption exceeds the preset threshold, a high-pressure control instruction is sent to the gas injection device, triggering the main aeration pipe pressurization mode, and the opening ratio of the sewage backflow pipe is increased synchronously;

[0032] When the degradation period is shortened and the energy consumption is lower than the preset threshold, a low-pressure control instruction is sent to the gas injection device, switching to the auxiliary aeration pipe gas supply mode, and the opening ratio of the sewage backflow pipe is reduced synchronously;

[0033] The deviation of the dissolved oxygen concentration from the critical value range is monitored, and if the deviation continuously exceeds the allowed fluctuation interval, the execution priority of the hierarchical pressure instruction is recalculated based on the output parameters of the prediction model, and the correction amplitude of the sewage backflow ratio is adjusted until the dissolved oxygen concentration returns to the critical value range.

[0034] Optionally, the sewage flow rate and the chemical oxygen demand concentration are weighted to generate a pollution load evaluation value, the ammonia nitrogen content and the detergent usage are input into an association model to generate an organic matter pollution influence value, and a comprehensive data set containing spatiotemporal correlation characteristics is constructed, including:

[0035] The sewage flow rate and the chemical oxygen demand concentration are multiplied by a preset flow rate weight coefficient and a chemical oxygen demand weight coefficient respectively, and the products of the two are added to obtain the pollution load evaluation value;

[0036] The ammonia nitrogen content and the detergent usage are input into the association model, and the coupling coefficient of the ammonia nitrogen content and the detergent usage is obtained by training historical data through the association model. The product of the coupling coefficient and the product of the ammonia nitrogen content and the detergent usage is output as the organic matter pollution influence value;

[0037] The pollution load evaluation value and the organic matter pollution influence value are integrated according to the time stamp and the geographical location code to generate a comprehensive data set containing time series distribution characteristics and spatial location correlation characteristics.

[0038] In a second aspect, the present application provides a rural domestic sewage pollution reduction and carbon reduction intelligent management system, comprising:

[0039] The collection module collects the sewage flow rate, chemical oxygen demand concentration and ammonia nitrogen content, and synchronously receives the water use time data and cleaning agent usage uploaded by the user mobile device;

[0040] The construction module generates a pollution load evaluation value by weighting the sewage flow rate and chemical oxygen demand concentration, generates an organic matter pollution influence value by correlating the ammonia nitrogen content and cleaning agent usage through a correlation model, and constructs a comprehensive data set containing spatiotemporal correlation characteristics;

[0041] The adjustment module installs porous ceramic media in the modular biological treatment device based on the distribution characteristics of the pollution load evaluation value and the organic matter pollution influence value in the comprehensive data set, and adjusts the dissolved oxygen concentration according to the change of the pollution load evaluation value;

[0042] The control module generates a pollutant emission prediction model based on the adjustment parameters of the dissolved oxygen concentration, in combination with the correlation change curve of the peak value of the chemical oxygen demand concentration and the water use time data in a plurality of continuous monitoring periods, sends a hierarchical pressure control instruction to a gas injection device remotely based on the processing period and energy consumption correlation relationship of the prediction model, and synchronously adjusts the sewage backflow ratio to maintain the dissolved oxygen concentration within a critical value range;

[0043] The correction module corrects the degradation rate parameter of the prediction model according to the day and night temperature difference parameter in the meteorological data, and optimizes the upper and lower limit values of the critical value range and the execution priority of the hierarchical pressure control instruction in combination with the optical detection data of the porous ceramic media.

[0044] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the rural domestic sewage pollution reduction and carbon reduction intelligent management method according to the first aspect.

[0045] In a fourth aspect, an embodiment of the present application provides a computer storage medium, which stores a computer program; when the computer program is executed by a computer, a rural domestic sewage pollution reduction and carbon reduction intelligent management method according to the first aspect is implemented.

[0046] The embodiment of the application fuses multi-source data such as sewage flow rate, chemical oxygen demand concentration, ammonia nitrogen content, user water time, and detergent dosage, constructs a time-space correlated database, realizes comprehensive quantitative evaluation of pollution load and human factors, and breaks through the single dimension limitation of traditional monitoring; based on pollution load, the dissolved oxygen concentration is adjusted, combined with the porous ceramic medium to enhance the biological treatment efficiency, the problems of high energy consumption and response lag of the fixed aeration mode are solved; through the correlation analysis of multi-cycle pollutant peak value and water use behavior, a prediction model is generated, the gas injection and reflux ratio are regulated, the dissolved oxygen critical value is maintained, and the treatment efficiency and energy consumption are simultaneously optimized; further combined with the meteorological parameter correction degradation rate, the medium optical data are used to optimize the control parameters, a closed-loop management of “data sensing-prediction regulation-adaptive correction” is formed, and finally the synergistic goals of sewage treatment efficiency improvement and carbon emission intensity reduction are achieved.

[0047] Further, by analyzing the time-space distribution characteristics of the pollution load evaluation value and the organic matter pollution influence value, the porous ceramic medium with a pore size distribution matched with the pollution load fluctuation range is selected and embedded in the filler layer of the biological treatment device, and the main pipeline pressure of the gas injection device is adjusted in the reverse direction according to the pollution load fluctuation, that is, when the pollution load increases, the main aeration pipeline pressure is reduced to reduce the dissolved oxygen concentration, and when the pollution load decreases, the pressure is increased to increase the dissolved oxygen concentration. In this technical solution, the pore size matching design of the porous ceramic medium strengthens the stability of the biofilm and the microbial adhesion efficiency, and combined with the reverse regulation mechanism of the dissolved oxygen concentration, it can avoid the biological reaction inhibition caused by excessive oxidation during the pollution load surge stage, and improve the organic matter degradation rate during the low load stage, thereby simultaneously optimizing the sewage treatment efficiency and aeration energy consumption control. In addition, the pressure regulation mechanism based on pollution load feedback breaks through the linear limitation of traditional fixed threshold control, accurately matches the dissolved oxygen concentration with the microbial metabolic demand, effectively reduces the energy waste caused by invalid aeration, and prolongs the service life of the gas injection device, and finally realizes the synergistic improvement of sewage treatment efficiency and economic operation benefit.

[0048] These aspects or other aspects of the application will be more apparent in the following description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0050] Figure 1 A flow chart of a rural domestic sewage pollution reduction and carbon reduction intelligent management method provided by the application is shown;

[0051] Figure 2 A scene diagram of a rural domestic sewage pollution reduction and carbon reduction intelligent management method provided by the application is shown.

[0052] Figure 3 A structural schematic diagram of a rural domestic sewage pollution reduction and carbon reduction intelligent management system provided by the application is shown.

[0053] Figure 4 A structural schematic diagram of a computing device provided by the application is shown. DETAILED DESCRIPTION

[0054] In order for those skilled in the art to better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0055] In some processes described in the specification and claims of the present application and the above-described drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that these operations can be executed or performed in parallel without the order in which they appear in this text, and the serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or fewer operations, and the operations can be executed in sequence or in parallel. It should be noted that the "first", "second", etc. in this text are used to distinguish different messages, devices, modules, etc., and do not represent the order, nor do "first" and "second" represent different types.

[0056] Researchers have found that the current rural sewage treatment system has the problems of strong fluctuation of pollutant load, difficulty in coordinated control of degradation efficiency and carbon emission, and lack of effective response mechanism for the coupling influence of water use behavior mode, cleaning agent composition and environmental factors by traditional methods, resulting in high energy consumption and insufficient impact resistance in the treatment process. Therefore, there is an urgent need for a pollution reduction and carbon reduction collaborative management method that integrates multi-source data sensing and intelligent decision-making.

[0057] To solve the above problems, the present application provides a rural domestic sewage pollution reduction and carbon reduction intelligent management method, the core of which is to build a pollution load analysis and closed-loop control system with spatiotemporal adaptability. Specifically, pollution load evaluation indicators are generated through multi-dimensional data fusion, the dissolved oxygen concentration is precisely controlled by combining the biofilm adaptation characteristics of the porous ceramic medium, and a prediction model with correction capability is established based on water use behavior rules and meteorological parameters. This method balances the degradation rate of pollutants and the carbon emission intensity of the biological treatment system through hierarchical pressure control and sewage reflux joint debugging mechanism, compared with traditional fixed mode operation, it significantly reduces the system's comprehensive energy consumption while improving the organic matter removal efficiency, effectively solving the technical problems of water quality fluctuation sensitivity and carbon emission reduction target difficult to optimize.

[0058] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0059] Figure 1 A flow chart of a rural domestic sewage pollution reduction and carbon reduction intelligent management method is provided for the embodiments of the present application, as shown in the figure, the method comprises: Figure 1

[0060] 101, collecting sewage flow rate, chemical oxygen demand concentration and ammonia nitrogen content, synchronously receiving water use time data and cleaning agent usage uploaded by a user mobile device;

[0061] In the above process, the sewage flow rate refers to the volume of liquid passing through the sewage pipeline per unit time, usually measured in cubic meters per hour. The chemical oxygen demand concentration refers to the oxygen equivalent value converted from the total amount of reducing substances in the water body that can be oxidized by a strong oxidizing agent, used to represent the content of organic pollutants. The ammonia nitrogen content refers to the total concentration of nitrogen elements in the water in the form of free ammonia and ammonium ions, reflecting the degree of pollution of the water body by nitrogen-containing organic matter. The water use time data refers to the daily life water period distribution information recorded by the user through the mobile device, including the water use start time and duration. The cleaning agent usage refers to the amount of detergent consumed by the user in a specific time period for life scenes such as clothes washing and dish washing, measured in milliliters or grams.

[0062] ​In the embodiments of the present application, first, through the sewage flow rate collection module, an ultrasonic flowmeter is installed at the monitoring node of the sewage pipeline to measure the product of the cross-sectional area of the pipeline and the average flow rate of the fluid, so as to obtain accurate instantaneous flow data. Secondly, through the chemical oxygen demand concentration analysis unit, the water sample is treated by using the potassium dichromate high-temperature digestion method, and the absorbance value of the solution after reaction is measured at a specific wavelength by using a spectrophotometer, and the chemical oxygen demand concentration is calculated by combining a standard curve. Then, through the ammonia nitrogen content detection device, the water sample is pretreated by using the Nash reagent colorimetric method, the calcium and magnesium ion interference is masked by adding potassium sodium tartrate, and then a color reaction is performed with an alkaline solution of mercuric potassium iodide, and finally the ammonia nitrogen characteristic absorption value is measured by using a photoelectric colorimeter and converted into a concentration value. At the same time, the water use time collection module of the user mobile device automatically records the start and stop time stamps of water-using equipment such as shower and washing machine, and uploads the time sequence data to the cloud server through a wireless communication protocol. Finally, through the cleaning agent usage amount statistical unit, the measurement sensor of the mobile device is triggered when the user adds detergent each time, the pressure sensing technology is used to accumulate the consumption weight of the bottled cleaning agent, a usage calculation model is established by combining the container volume data, structured usage records are generated, and the records are transmitted to the data processing center through the cellular network. After all the collected data are aligned by time stamp, they are stored in a distributed database, providing multi-dimensional input parameters for subsequent water quality analysis.

[0063] In actual application, in a certain rural domestic sewage treatment demonstration project, the system collects the sewage flow rate of 0.8 cubic meters per hour through the electromagnetic flowmeter assembled in the DN50 pipe section, and monitors the chemical oxygen demand concentration fluctuation data every 15 minutes by using the online ultraviolet-visible spectrometer, wherein the concentration reaches 180 milligrams per liter during the early peak period and decreases to 90 milligrams per liter during the night low period. The continuous detection of the ammonia nitrogen ion selective electrode shows that the ammonia nitrogen content presents periodic changes of 12 to 25 milligrams per liter. The system synchronously receives the mobile terminal application data covering 150 households, obtains the daily water use peak period distribution and the monthly usage amount of biodegradable cleaning agent of each household, wherein the kitchen cleaning agent usage amount is 320 grams per household and the laundry detergent usage amount is 420 grams per household, and the minute-level data transmission is realized through the LoRa Internet of Things gateway. The data acquisition system uniformly converts the 4-20 milliamperes current signal of the flowmeter and the Modbus communication protocol data of the spectrometer into JSON format, and superimposes the time stamp and geographic coordinate information on the water use behavior data reported by the mobile terminal, to form an original data set with time and space multi-dimensional characteristics, thereby providing a basis for subsequent pollution load modeling.

[0064] The overall scheme of step 101 is to collect sewage flow rate, chemical oxygen demand concentration and ammonia nitrogen content data, synchronize user uploaded water use time information and cleaning agent usage, and the system can monitor the changes of key parameters and human influence factors in the sewage treatment process, build intelligent analysis model combined with multi-dimensional data, accurately identify the source of pollutants and load fluctuation law, optimize sewage treatment process parameters and resource allocation strategy, thereby improving the efficiency and stability of sewage treatment, reducing energy consumption and chemical agent waste, providing scientific decision support for sewage treatment, effectively improving water quality and reducing the risk of secondary pollution to the environment.

[0065] 102. The sewage flow rate and chemical oxygen demand concentration are weighted to generate a pollution load evaluation value, the ammonia nitrogen content and the cleaning agent usage are input into the correlation model to generate an organic matter pollution influence value, and a comprehensive data set containing time and space correlation characteristics is constructed;

[0066] Optionally, step 102 can specifically include the following steps:

[0067] 1021. The sewage flow rate and chemical oxygen demand concentration are multiplied by the preset flow rate weight coefficient and chemical oxygen demand weight coefficient respectively, and the products of the two are added to obtain the pollution load evaluation value;

[0068] 1022. The ammonia nitrogen content and the cleaning agent usage are input into the correlation model, the coupling coefficient of the ammonia nitrogen content and the cleaning agent usage is obtained by training the historical data through the correlation model, and the product of the two is multiplied by the coupling coefficient to output the organic matter pollution influence value;

[0069] 1023. The pollution load evaluation value and the organic matter pollution influence value are integrated according to the time stamp and the geographical location code to generate a comprehensive data set containing time sequence distribution characteristics and space location correlation characteristics.

[0070] In the above steps, the pollution load evaluation value refers to a quantitative indicator calculated by the weighted sum of the sewage flow rate and the chemical oxygen demand concentration, representing the total amount of pollutants transported per unit of time. The organic pollution impact value refers to the comprehensive effect of organic pollutants, calculated based on the correlation between ammonia nitrogen content and cleaning agent usage. The integrated data set refers to a multi-source heterogeneous database integrating pollution load, organic impact value, and spatio-temporal dimension information. The flow rate weight coefficient is a sewage flow rate influence factor set according to the migration characteristics of pollutants, with a value range of 0.2-0.8. The chemical oxygen demand weight coefficient is a concentration influence factor set according to the degradation difficulty of organic matter, with a value range of 0.4-1.0. The coupling coefficient is the correlation strength parameter between ammonia nitrogen and cleaning agent obtained by machine learning model training, reflecting the synergistic effect of the two on water eutrophication. The time stamp is a standardized time encoding of the data collection time, accurate to the millisecond level. The geographic location code is a two-dimensional coordinate identifier generated based on the GIS system, containing latitude, longitude, and elevation information. The time series distribution characteristics refer to the fluctuation law of parameter values in the data set over time. The spatial location correlation characteristics refer to the topological relationship and geographical correlation between data from different monitoring points.

[0071] In the embodiments of the present application, first, the calculation process of the pollution load evaluation value is performed through step 1021, the preset flow rate weight coefficient and chemical oxygen demand weight coefficient are called from the parameter configuration library, and the collected sewage flow rate value is multiplied, and the chemical oxygen demand concentration detection value is normalized and then weighted multiplied. The two product results are input into the adder for linear superposition to generate a pollution load evaluation value with a unified dimension.

[0072] Secondly, the modeling calculation of the organic pollution impact value is carried out through step 1022, and the preprocessed ammonia nitrogen content detection value and cleaning agent usage record are input into the correlation model. The model calls the three-year monitoring data in the historical database, trains the nonlinear coupling coefficient between ammonia nitrogen and cleaning agent using the random forest algorithm, eliminates the dimension difference by logarithmic transformation of the two input parameters in the calculation process, and finally outputs the impact value reflecting the comprehensive effect of organic pollution by performing matrix operation of the coupling coefficient and parameter product.

[0073] Finally, the spatio-temporal data integration is completed through step 1023, the pollution load evaluation value and the organic impact value are extracted by the time stamp parser, the spatial coordinates of the monitoring points are associated by the geographic location code matcher, and the discrete data points are converted into continuous distribution field by using the spatio-temporal interpolation algorithm. The integrated data set containing the time dimension change trend and the spatial dimension correlation rule is constructed, and stored as a time series database with hierarchical index structure.

[0074] In practical applications, in a rural sewage treatment intelligent monitoring scene, the system performs weighted calculation on the sewage flow rate of 0.6 cubic meters / hour and the chemical oxygen demand concentration of 150 milligrams / liter: taking the flow rate weight coefficient of 0.4 and the chemical oxygen demand weight coefficient of 0.6, the pollution load evaluation value PL = 0.6 x 0.4 + 150 x 0.6 = 90.24 is generated. At the same time, the ammonia nitrogen content of 18 milligrams / liter and the monthly average use of cleaning agent of 350 grams are input into the correlation model trained by 2000 groups of historical data, the coupling coefficient 0.35 obtained by training is called, and the organic matter pollution influence value OI = 18 x 350 x 0.35 = 2205 is calculated. The calculation results are integrated with the collected data through the space-time coding engine to build a data set containing time stamp sequences and geographic grid coordinates: a pollution load spatial distribution matrix containing longitude 118.76°E and latitude 32.04°N is updated every 5 minutes, and a pollution concentration change trend curve is formed every hour in the time dimension. The data set adopts a hierarchical storage structure, the bottom layer of raw data is transmitted through the Modbus protocol, and the upper layer of analysis data is packaged with space-time tags in JSON format, realizing the analysis ability of multi-dimensional feature linkage.

[0075] In the overall scheme of the above step 102, the pollution load evaluation value is generated by weighted calculation of the sewage flow rate and the chemical oxygen demand concentration, the contribution of different pollution sources is quantified, the organic matter pollution influence value is output based on the coupling relationship between the ammonia nitrogen content and the cleaning agent usage analyzed by the correlation model, the synergistic effect of human factors and natural factors is accurately characterized, and the two types of evaluation values are integrated into a space-time related data set combined with time stamp and geographic location coding, fully mining the time continuity and spatial heterogeneity of pollution evolution, strengthening the data adaptation ability to complex pollution scenes, providing multi-dimensional analysis basis for optimizing treatment process, tracing pollution sources and predicting pollution diffusion trend, and improving the response accuracy and decision reliability of the system to nonlinear pollution changes.

[0076] 103、based on the distribution characteristics of the pollution load evaluation value and the organic matter pollution influence value in the comprehensive data set, installing porous ceramic media in the modular biological treatment device, and adjusting the dissolved oxygen concentration according to the change of the pollution load evaluation value;

[0077] Optionally, step 103 can specifically include the following steps:

[0078] 1031、according to the space-time distribution characteristics of the pollution load evaluation value and the fluctuation law of the organic matter pollution influence value in the comprehensive data set, selecting porous ceramic media with pore size distribution and pollution load evaluation value fluctuation range matching, and embedding it in the filler layer of the modular biological treatment device;

[0079] 1032. Monitor the change trend of the pollution load evaluation value based on the influence of the embedded porous ceramic medium on the microbial attachment efficiency, and adjust the output pressure of the main pipeline of the gas injection device when the change of the pollution load evaluation value exceeds the preset fluctuation threshold;

[0080] 1033. When the pollution load evaluation value increases, reduce the main aeration pipeline pressure to reduce the dissolved oxygen concentration, and when the pollution load evaluation value decreases, increase the main aeration pipeline pressure to increase the dissolved oxygen concentration.

[0081] In the above steps, the porous ceramic medium refers to an inorganic non-metallic carrier material with a gradient pore size structure, and the pore size distribution range is 10-500 microns. The modular biological treatment device refers to an expandable sewage treatment equipment composed of standardized units, including a filler layer, an aeration system and a data interface. The dissolved oxygen concentration refers to the content of free oxygen in unit volume of water, measured in milligrams per liter. The space-time distribution characteristics refer to the concentration change mode of the pollution indicator in three-dimensional space and time dimension. The fluctuation law refers to the amplitude and frequency characteristics of the change of the pollution load evaluation value with time. The pore size distribution refers to the number ratio curve of different size pores in the porous material. The microbial attachment efficiency refers to the colonization success rate of bacteria on the carrier surface per unit time. The main aeration pipeline pressure refers to the compressed air pressure value of the core pipeline of the gas delivery system, and the adjustment range is 0.1-0.6 MPa. The fluctuation threshold refers to the critical value of the change of the pollution load that triggers the adjustment action of the aeration system, which is set to ±15% of the reference value.

[0082] In the embodiments of the present application, first, the medium selection and installation are performed through step 1031, the pollution load evaluation value history record in the comprehensive data set is called, the fluctuation main frequency characteristics are extracted by using the spectrum analysis method, and the gradient porous ceramic medium with matching pore resonance frequency is selected according to the frequency characteristics. The selected ceramic medium is layered according to the pore size, and the mechanical arm is used to accurately embed it into the third filler layer of the modular device, so as to ensure that the pore channel forms a 45-degree angle with the sewage flow direction.

[0083] Secondly, monitoring and pressure adjustment are performed through step 1032, a biofilm thickness sensor is arranged on the surface of the ceramic medium, and the pollution load evaluation value is continuously obtained in combination with the online water quality analyzer. When it is detected that the pollution load evaluation value exceeds the fluctuation threshold for three consecutive sampling periods, a control instruction is sent to the gas injection device, the pressure adjustment amount is calculated by using the PID algorithm, and the main pipeline compressed air flow is changed by using the electric regulating valve.

[0084] Finally, the dissolved oxygen concentration closed-loop control is realized through step 1033, and a reverse correlation model of the pollution load evaluation value and the aeration pressure is established. When the pollution load evaluation value increases at a rate exceeding the preset fluctuation threshold, the main aeration pipeline pressure is reduced to reduce the dissolved oxygen concentration, and when the pollution load evaluation value decreases at a rate exceeding the preset fluctuation threshold, the main aeration pipeline pressure is increased to increase the dissolved oxygen concentration. When the pollution load evaluation value drops to 70% of the reference value, the pressure is increased to 0.5 MPa in steps, and the diffusion efficiency of dissolved oxygen is increased by the microporous aeration disc to stabilize the dissolved oxygen concentration in the water body in the interval of 2.5-4.0 mg / L.

[0085] In actual application, in an intelligent upgrading project of a rural sewage treatment plant, the system detects periodic fluctuations of the morning pollution load evaluation value of 0.78, the noon pollution load evaluation value of 0.52, and the evening pollution load evaluation value of 0.81, and the variation characteristics of the organic pollution influence value in the interval of 190-215. Three layers of porous ceramic media with aperture gradients of 50-100 microns, 20-50 microns, and 5-20 microns are selected. The porosity of the media is 62%, and the compressive strength is 8 MPa. The media are processed into ring-shaped structures with a diameter of 100 mm and a height of 500 mm, and are installed in the anoxic zone and the aerobic zone of the biological treatment device at a density of 80 per cubic meter of reactor. When installing, a 0.3 MPa air-water mixed flushing process is used to remove impurities on the surface of the carrier. The system collects the pollution load evaluation value every 5 minutes, and when the value jumps from 0.65 to 0.85 within 1 hour, the aeration regulation program is automatically triggered. The base pressure of the main pipeline is set to 0.25 MPa, corresponding to a dissolved oxygen concentration of 3.5 mg / L. The pressure regulation module adjusts in steps of 0.02 MPa: when the load value exceeds 0.75, the pressure is gradually reduced to 0.18 MPa, and the dissolved oxygen is reduced to 2.8 mg / L; when the load value is less than 0.6, the pressure is increased to 0.32 MPa, and the dissolved oxygen is increased to 4.2 mg / L. A side road microporous aeration branch pipe with a pore size of 0.1 mm is opened to supplement oxygen locally at a pressure of 0.05 MPa, ensuring that the dissolved oxygen in the inner layer of the biofilm is not less than 1.5 mg / L. The operation data show that the thickness of the biofilm on the surface of the ceramic media is stable in the interval of 180-220 microns, the aeration pressure is adjusted 12-18 times per day, and the single adjustment amplitude is 0.01-0.04 MPa. When the early morning peak sewage flow increases to 1.2 m3 / h and the chemical oxygen demand concentration exceeds 200 mg / L, the system adjusts the dissolved oxygen concentration from 3.2 mg / L to 2.9 mg / L in steps within 15 minutes, and simultaneously increases the sewage reflux ratio from 35% to 48%. During the night low load stage, the dissolved oxygen concentration is maintained above 4.0 mg / L, and the closed proportion of the aeration branch pipe reaches 40%, achieving the matching of the treatment unit energy consumption and the pollution load.

[0086] The overall scheme of step 103 is based on the spatiotemporal distribution characteristics of the pollution load evaluation value in the comprehensive data set and the fluctuation rule of the organic matter pollution influence value. By matching the pollution load fluctuation range with the pore size distribution characteristics of the porous ceramic medium, the structure of the microbial attachment carrier of the modular biological treatment device is optimized. At the same time, by combining the monitored pollution load evaluation value change trend, a dissolved oxygen concentration regulation mechanism is established. When the pollution load increases, the main aeration pipe pressure is reduced to inhibit the imbalance of microbial activity caused by excessive dissolved oxygen. When the pollution load decreases, the pressure is increased to enhance the oxygen mass transfer efficiency to maintain the biological degradation efficiency. The precise coupling response of pollution load fluctuation and oxygen supply of the biological treatment unit is realized. The anti-interference ability and processing stability of the system under high impact load are effectively improved. The energy consumption is reduced and the risk of biofilm shedding is avoided.

[0087] 104. Based on the dissolved oxygen concentration regulation parameter, the peak value of the chemical oxygen demand concentration in the continuous multiple monitoring periods and the associated change curve of the water use time data are combined to generate a prediction model of pollutant emission. Based on the processing period and energy consumption associated relationship of the prediction model, a hierarchical pressure control instruction is sent to the gas injection device, and the sewage backflow ratio is adjusted synchronously to maintain the dissolved oxygen concentration within the critical value range.

[0088] Optionally, step 104 specifically includes the following steps:

[0089] 1041. Extract the dissolved oxygen fluctuation period from the dissolved oxygen concentration regulation parameter, and synchronously acquire the peak time of the chemical oxygen demand concentration in the continuous monitoring period;

[0090] 1042. Align the peak time of the chemical oxygen demand concentration with the water use time data reported by the user according to the time window to generate a change curve containing the associated relationship between pollution emission intensity and water use period;

[0091] 1043. Extract the duration of the pollution intensity peak value, the peak interval in the change curve, and the dissolved oxygen response lag time in the dissolved oxygen concentration regulation parameter, and establish a mapping relationship among the duration, the peak interval, and the dissolved oxygen response lag time.

[0092] 1044. According to the response law of the pollution intensity peak value and the dissolved oxygen concentration in the mapping relationship, a prediction model of the pollutant degradation period and the processing energy consumption is constructed.

[0093] 1045. According to the associated relationship between the pollutant degradation period and the processing energy consumption in the prediction model, when the degradation period is prolonged and the energy consumption exceeds the preset threshold, a high-pressure control instruction is sent to the gas injection device to trigger the main aeration pipe pressurization mode, and the opening ratio of the sewage backflow pipe is increased synchronously;

[0094] 1046、When the degradation period is shortened and the energy consumption is lower than the preset threshold, a low-pressure control instruction is sent to the gas injection device, switching to an auxiliary aeration pipe gas supply mode, and simultaneously reducing the opening ratio of the sewage backflow pipe;

[0095] 1047、Monitoring the deviation of the dissolved oxygen concentration from the critical value range, if the deviation continuously exceeds the allowed fluctuation interval, the execution priority of the hierarchical pressure control instruction is recalculated based on the output parameters of the prediction model, and the correction amplitude of the sewage backflow ratio is adjusted until the dissolved oxygen concentration returns to the critical value range.

[0096] In the above steps, the dissolved oxygen fluctuation period refers to the periodic variation characteristics of the dissolved oxygen concentration over time, including the interval time between the wave crest and the wave trough and the amplitude parameter. The critical value range refers to the effective interval of the dissolved oxygen concentration that maintains the activity of the microbial community, which is set to 2.8-3.5 mg / L. The pollution emission intensity refers to the equivalent load value converted from the total amount of pollutants discharged per unit time. The water use period correlation refers to the time sequence correspondence between the peak period of domestic water use and the peak value of pollutant concentration. The dissolved oxygen response lag length refers to the time difference between the issuance of the aeration system adjustment instruction and the target value of the dissolved oxygen concentration. The pollutant degradation period refers to the processing time required for complete decomposition of organic matter by microorganisms. The processing energy consumption refers to the total amount of electrical energy consumed by the aeration system per unit time, measured in kilowatt-hours. The hierarchical pressure control instruction refers to the set of aeration pressure regulation levels divided according to the processing requirements, including high-pressure mode, medium-pressure mode and low-pressure mode. The sewage backflow opening ratio refers to the percentage value of the backflow pipe valve opening degree relative to the full open state, with a regulation accuracy of ±1%. The critical value allowed fluctuation interval refers to the maximum tolerance deviation of the dissolved oxygen concentration from the set range, which is set to ±0.3 mg / L.

[0097] In the embodiments of the present application, first, data feature extraction is performed by step 1041, the time series decomposition algorithm is used to analyze the periodicity of the dissolved oxygen concentration regulation parameters, the STL decomposition method is used to separate the trend item, the periodic item and the residual item, and the main frequency component of the fluctuation period is identified as 120 minutes. Simultaneously, the chemical oxygen demand concentration data of the continuous 30 monitoring periods are extracted from the water quality monitoring database, the peak value detection algorithm based on sliding window is used, the window width is set to 60 minutes, the maximum concentration value and its corresponding time stamp in each period are located, and the peak time sequence data set is generated.

[0098] Secondly, by step 1042, the time sequence data set of the peak value time of chemical oxygen demand concentration and the data input time regular algorithm of the morning 7:00-9:00 and evening 18:00-20:00 water consumption peak period reported by the user are established. First, the two types of time series are normalized, the minimum bending path distance is calculated, and the phase offset caused by the sampling frequency difference is eliminated. Then a three-dimensional coordinate system is constructed, the aligned time stamp is mapped as the X-axis coordinate, the chemical oxygen demand concentration value is converted into the Y-axis height, and the water consumption statistical value is converted into the Z-axis depth, a change curve containing 672 data points is generated, and it is revealed that the water consumption period and the pollution emission intensity are positively correlated with 0.82.

[0099] Then, by step 1043, the feature mapping relationship is constructed, and the duration parameter and the peak interval parameter of the pollution intensity peak value are extracted from the change curve. The moving average method is used to calculate the mean value of the duration as 45 minutes, and the mean value of the peak interval as 210 minutes. The response lag time data set in the dissolved oxygen concentration adjustment parameter and the above-mentioned feature parameters are input into the grey correlation analysis model, the resolution coefficient is set as 0.5, the correlation degree of the duration and the lag time is calculated as 0.78, the correlation degree of the peak interval and the lag time is calculated as 0.65, a multi-dimensional feature mapping matrix containing weight distribution is established, the matrix dimension is 30x4, containing four types of parameters of time stamp, duration, peak interval and lag time.

[0100] Finally, by step 1044, the prediction model is constructed, the pollution emission mode and the DO response lag law contained in the multi-dimensional feature mapping matrix constructed are used, combined with the historical accumulated pollution degradation period and the corresponding treatment energy consumption record (such as 120 groups of data), a long short-term memory (LSTM) neural network prediction model is trained; the specific process is: first, the multi-dimensional feature mapping matrix is normalized by Min-Max as the input layer of the model (the node number 4 corresponds to 4 parameters); design an LSTM hidden layer containing two layers of 128 neurons (set the dropout rate to 0.2 to prevent overfitting); the output layer predicts the future key period (such as 6 hours) of the pollutant degradation period and the corresponding treatment energy consumption; after the model is trained and verified (such as mean square error 0.15, accuracy 92.3%), based on its strong modeling ability of the correlation between pollution load and energy consumption, the hierarchical pressure control instruction is generated to the gas injection device (aeration) and the proportion of wastewater reflux is adjusted simultaneously, realizing the accurate and efficient maintenance of the DO concentration in the critical range.

[0101] Next, the high-pressure control strategy is implemented through step 1045, which triggers the control instruction generation module when the degradation period is predicted to extend to more than 8 hours and the energy consumption exceeds the 5kW·h threshold. First, the 4th level pressure instruction template in the hierarchical pressure instruction library is called, and an instruction package containing the target pressure value 0.55MPa and the pressure increasing rate 0.1MPa / s is sent to the gas injection device. At the same time, the backflow pipe electric valve is controlled, and the opening ratio adjustment amount is calculated based on the PID control algorithm to increase the valve opening ratio from the baseline value of 40% to 65% at a rate of 2% per second, which increases the internal circulation flow by 37% and promotes uniform distribution of dissolved oxygen.

[0102] Then, the low-pressure control scheme is executed through step 1046, which activates the low-pressure control process when the degradation period is shortened to less than 4 hours and the energy consumption is less than 2kW·h. First, select the 1st level pressure instruction from the instruction library and switch to the auxiliary aeration pipe gas supply mode, set the pressure value to 0.15MPa, and use a ramp function to control the pressure change during the switching process to avoid sudden pressure changes. At the same time, adjust the backflow pipe opening ratio according to the feedforward control model to calculate the optimal descending curve, gradually reduce the opening ratio from 65% to 30% in three stages, maintain for 120 seconds in each stage, and finally reduce the hydraulic load by 22% to match the current treatment demand.

[0103] Finally, the balance adjustment is realized through step 1047, which uses a fuzzy PID controller to continuously monitor the deviation of dissolved oxygen concentration from the critical value. First, set a 5-minute rolling time window in the deviation monitoring module, and when the deviation of the last 5 consecutive sampling points exceeds ±0.3mg / L, trigger the parameter recalculation mechanism. Call the output parameters of the prediction model and recalculate the execution priority of the hierarchical pressure instructions based on the energy consumption optimization objective function, and preferentially select instructions with an energy consumption growth rate less than 0.5kW·h / level. At the same time, adjust the backflow opening ratio at a rate of 0.5% / second, and check the dissolved oxygen concentration trend every 30 seconds until the concentration value returns to the stable interval of 3.2±0.2mg / L, completing the closed-loop control process.

[0104] In practical applications, in the operation practice of a certain intelligent sewage treatment system, the system collects dissolved oxygen concentration adjustment parameters every 10 minutes, and extracts the 24-hour periodic characteristics of the dissolved oxygen fluctuation, which presents 2.8 mg / L in the morning, 3.5 mg / L in the afternoon, and 3.2 mg / L in the evening. Combined with the peak value data of chemical oxygen demand concentration in the last 30 monitoring cycles, it is identified that the COD concentration continuously exceeds 180 mg / L during 7:15-8:45 every day, which is aligned with the time window of the 6:30-8:30 morning peak water period data reported by the user mobile terminal, and a pollution emission intensity curve is generated: the first COD peak value appears 45 minutes after the water starts, and then every 90 minutes a secondary peak appears. Through Pearson correlation analysis, the mapping relationship between the duration of the pollution intensity peak and the interval of the peak and the response lag of the dissolved oxygen is established, and the correlation coefficient between the duration and the response lag is 0.6. When building a prediction model based on this correlation, the input parameters include the current COD concentration value, the future 2-hour water consumption prediction value, and the temperature compensation coefficient, and the output parameter is set to the degradation period and the energy consumption index. Model training uses 300 sets of historical data for linear regression, and the mean square error of the validation set is controlled within 0.15. When the prediction model judges that the degradation period will be extended to 170 minutes in the next 3 hours and the energy consumption index breaks through 1.05, the system sends a high-pressure control instruction to the gas injection device: the main aeration pipeline pressure is increased from the baseline value of 0.25 MPa to 0.35 MPa, and the sewage backflow ratio is increased from 30% to 45% at the same time. The auxiliary aeration pipe is switched to a microporous aeration disc with a diameter of 2 mm for gas supply, and the gas flow velocity is increased to 15 m / s. If the model predicts that the degradation period is shortened to 130 minutes and the energy consumption index is less than 0.9, the low-pressure mode is started: the main pipeline pressure is reduced to 0.18 MPa, the backflow ratio is adjusted, and the auxiliary aeration pipe maintains basic oxygen supply at a pressure of 0.02 MPa. The system monitors the deviation of the dissolved oxygen concentration from the critical value range of 3.0±0.3 mg / L, and when it is detected that the concentration has been below 2.7 mg / L for 2 hours, the parameter recalibration mechanism is automatically triggered: taking the degradation period increment ΔT=+15 minutes output by the prediction model as the baseline, the priority of the graded pressure instruction is recalculated, the main aeration pipe pressure adjustment step is increased from 0.02 MPa to 0.03 MPa, and the backflow ratio is increased by 3% every 5 minutes until the concentration rises to 2.9 mg / L. Under typical working conditions, the system performs pressure level switching 6-8 times per day, backflow ratio correction operation 15-20 times, and successfully controls the dissolved oxygen concentration fluctuation range within the target interval period.

[0105] In the overall scheme of step 104, by fusing the correlation change curve of the dissolved oxygen concentration adjustment parameter and the peak value of the chemical oxygen demand concentration and the water use time data in the continuous monitoring period, a prediction model of the spatial and temporal correlation between pollutant emission intensity and user behavior is constructed. Based on the model, the coupling law of the pollutant degradation period and energy consumption is analyzed, and a multi-parameter linkage adjustment mechanism of the hierarchical pressure control instruction and the sewage reflux ratio is established. When the degradation period is extended and the energy consumption is over-limit, the high-pressure aeration mode is triggered and the reflux ratio is increased to strengthen the oxygen mass transfer efficiency. When the degradation period is shortened and the energy consumption is reduced, the low-pressure auxiliary air supply mode is switched and the reflux ratio is reduced to optimize resource consumption. By monitoring the dissolved oxygen concentration deviation, the priority and adjustment range of the control parameter are corrected to realize the precise matching of treatment energy consumption and pollutant load, ensure that the dissolved oxygen concentration is stably maintained within the critical value range, improve the adaptability of the system to intermittent pollution impact, reduce the operating cost and prolong the equipment life.

[0106] 105. According to the diurnal temperature difference parameter in the meteorological data, the degradation rate parameter of the prediction model is corrected, and the upper and lower limit values of the critical value range and the execution priority of the hierarchical pressure control instruction are optimized in combination with the optical detection data of the porous ceramic medium.

[0107] Optionally, step 105 can specifically include the following steps:

[0108] 1051. The diurnal temperature difference parameter in the meteorological data is input into a degradation rate compensation formula to generate a degradation rate correction coefficient inversely proportional to the temperature difference, and based on the degradation rate correction coefficient, the degradation rate parameter related to microbial activity in the prediction model is corrected;

[0109] 1052. The coverage area and color feature data of the biofilm are collected by the optical sensor on the surface of the porous ceramic medium. When the coverage area continuously increases and the color feature data indicates that the biofilm activity decreases, the upper limit and lower limit of the critical value range are simultaneously reduced according to the adjustment range of the degradation rate parameter;

[0110] Wherein, step 1052 can specifically include the following process: obtaining image data of the covered area, extracting the area growth rate of the microbial community in the covered area and the red channel to blue channel ratio in the color feature data; when the value of the area growth rate continuously exceeds the preset growth threshold value, and the red channel to blue channel ratio is lower than the preset activity threshold value, it is determined that the activity decline state of the microbial community reaches the trigger condition; according to the adjustment range of the degradation rate parameter, the correction proportion of the critical value range is calculated, and the adjustment range is the absolute value of the difference between the current degradation rate and the reference rate accounts for the percentage of the reference rate; the value of the correction proportion is input into the calculation formula of the critical value range to simultaneously reduce the upper limit value and the lower limit value of the current critical value range, and the upper limit value reduction amplitude is the original upper limit value multiplied by the correction proportion, and the lower limit value reduction amplitude is the original lower limit value multiplied by the correction proportion.

[0111] 1053、based on the historical execution frequency of the hierarchical pressure control instruction and the corresponding maintenance effect data of the dissolved oxygen concentration, the degradation rate parameter is associated with the instruction execution effect analysis, and the hierarchical pressure control instruction with the effect of maintaining the effect and the execution efficiency is promoted in priority, and the rest of the hierarchical pressure control instruction is degraded in priority weight.

[0112] In the above steps, the day and night temperature difference parameter refers to the difference between the highest temperature and the lowest temperature within 24 hours recorded by the meteorological monitoring station, with the unit of Celsius degree. The degradation rate correction coefficient refers to the adjustment factor for compensating the microbial degradation ability according to the environmental temperature change, with the value range of 0.5-1.2. The optical detection data refers to the reflectivity feature data set of the biofilm surface obtained by the multispectral imaging technology. The upper and lower limit values of the critical value range refer to the highest allowable value and the lowest allowable value of the dissolved oxygen concentration control target interval. The instruction execution priority refers to the execution order weight value of the aeration control instruction in the scheduling queue, and the larger the value is, the higher the priority is. The red channel and blue channel ratio refers to the intensity ratio of the red component and the blue component of the biofilm image in the RGB color space, and the active biofilm usually presents the characteristic of R / B value greater than 1.8. The preset growth threshold refers to the critical value of the biofilm area expansion rate triggering the critical value adjustment, which is set to 5% daily growth. The preset activity threshold refers to the R / B ratio critical line for determining the biofilm aging, which is set to 1.5. The priority weight refers to the importance score of the instruction calculated according to the historical execution effect, with the score range of 0-100 points.

[0113] In the embodiment of the present application, first, the model parameter correction is performed through step 1051, and the day and night temperature difference parameter in the meteorological database is input into the degradation rate compensation formula. The formula is specifically: correction coefficient = 1 / (1+0.05ΔT), wherein ΔT is the day and night temperature difference value. When the day and night temperature difference reaches 10℃, the system automatically adjusts the correction coefficient to 0.67. Multiply the correction coefficient by the reference degradation rate parameter in the prediction model to generate the temperature-compensated degradation rate value, and update it to the parameter configuration file of the model calculation engine.

[0114] Secondly, the critical value optimization is achieved by step 1052, using high-resolution optical sensors deployed on the surface of the porous ceramic medium, collecting biofilm image data every 30 minutes. First, the image is segmented by K-means clustering, extracting the effective pixel points in the coverage area, and calculating the area growth rate as 3.2% per hour. At the same time, the color features of the image are analyzed, and the average intensity of the red channel is calculated as 185 in the HSV color space, and the average intensity of the blue channel is calculated as 120, obtaining the R / B ratio as 1.54. When the area growth rate is detected to exceed 5% for 6 consecutive hours and the R / B ratio is lower than 1.5, the critical value adjustment module is activated. According to the current degradation rate parameter decreased by 18% compared with the reference value, the correction ratio is calculated as 0.82. The original critical value range of 3.0-3.5 mg / L is adjusted to 3.0*0.82 to 3.5*0.82, obtaining the new range of 2.46-2.87 mg / L.

[0115] Next, the instruction priority is optimized by step 1053, and the execution records of hierarchical pressure control instructions in the past 30 days are called. First, a data table containing instruction type, execution time, energy consumption value and dissolved oxygen maintenance compliance rate is constructed, and the correlation between degradation rate parameter and instruction effect is analyzed using random forest algorithm. It is calculated that the compliance rate of high-pressure instruction is 92% when the degradation rate is lower than , and the compliance rate of medium-pressure instruction is 85% in the interval. According to the analysis results, the priority weight of high-pressure instruction is increased from 70 to 90, the priority weight of medium-pressure instruction is decreased from 65 to 60, and the priority weight of low-pressure instruction is adjusted from 50 to 55. The updated priority queue is arranged in descending order of weight value, ensuring that high-efficiency instructions are executed first. Finally, the overall optimization of the system is realized through a closed-loop feedback mechanism. The corrected critical value range is synchronized to the dissolved oxygen concentration control module, and the updated instruction priority table is written into the aeration system scheduler. The system recalculates the correction coefficient and priority weight every 2 hours, and when the diurnal temperature difference change exceeds 3°C or the biofilm R / B ratio rises above 1.7, the parameter recalibration process is automatically triggered to maintain the best operating state of the treatment system.

[0116] In practical applications, in the operation of the intelligent control system of a rural sewage treatment station, the meteorological monitoring module detects that the diurnal temperature difference fluctuates in the range of 8-12°C in recent days. When the diurnal temperature difference reaches 10°C, the system inputs the temperature difference parameter into the degradation rate compensation model, and adjusts the baseline degradation rate from 0.25 mg / L / min to 0.19 mg / L / min. The high-definition optical sensor deployed on the surface of the porous ceramic medium collects biofilm coverage data every day, and monitors that the biofilm coverage area increases from 1200 mm2 / m2 to 1800 mm2 / m2, with a daily growth rate of 15%. At the same time, the color feature analysis shows that the red-to-blue channel ratio continuously decreases from the initial 1.35 to 1.12, triggering the active decline early warning mechanism. According to the 32% adjustment range of the degradation rate, the system reduces the upper limit of the dissolved oxygen critical value from 4.0 mg / L to 2.7 mg / L, and the lower limit from 3.2 mg / L to 2.2 mg / L, forming a new control interval.

[0117] For aeration instruction priority optimization, the system analyzes historical 72-hour data: high-pressure instruction 0.35 MPa executed 24 times, corresponding to dissolved oxygen compliance time length ratio 92%; medium-pressure instruction 0.25 MPa executed 58 times, compliance time length ratio 85%. According to the execution efficiency weight algorithm, the high-pressure instruction response priority is raised by two levels, and the 0.35 MPa instruction priority triggering mechanism is set in the instruction queue, with the response delay shortened to 5 seconds. When the red-to-blue channel ratio rises to 1.25, the medium-pressure instruction is automatically restored to the default priority level. Under the temperature difference of 12°C, the system calibrates the critical value range every 2 hours, with a single adjustment range controlled in the interval of 0.1-0.3 mg / L, ensuring the adaptability of biofilm activity and dissolved oxygen concentration.

[0118] In the overall scheme of the above step 105, by fusing the compensation correction of the microbial degradation rate by the diurnal temperature difference parameter in the meteorological data and the optical detection data of the biofilm coverage area and color features on the surface of the porous ceramic medium, a multi-factor coupling optimization mechanism of environmental temperature and biological activity is established. The degradation rate correction coefficient driven by temperature difference is used to calibrate the prediction model parameters, and the image feature analysis of biofilm activity state is combined synchronously to adaptively adjust the critical value range boundary and the execution priority of the hierarchical pressure instruction. When the biofilm coverage area increases and the color features show a decrease in activity, the upper and lower limits of the critical value are reduced to inhibit the risk of excessive aeration. At the same time, based on the correlation analysis of historical instruction execution effect and degradation rate, the trigger logic of high-pressure and low-pressure control instructions is optimized to realize the coordinated response of meteorological condition changes and biofilm state fluctuations, enhance the parameter fault tolerance capability and control strategy robustness of the system in complex environments, reduce the probability of membrane clogging, and prolong the service life of the medium.

[0119] The following is a complete embodiment for steps 101-105:

[0120] AsFigure 2 As shown in a certain rural domestic sewage treatment project, the system first collects the sewage flow rate of 0.5 cubic meters / hour through the electromagnetic flowmeter, measures the chemical oxygen demand (COD) concentration of 120 mg / L by spectroscopy, and detects the ammonia nitrogen content of 15 mg / L by the electrochemical sensor. At the same time, the system receives the data uploaded by 200 households in the area during the water use peak period, which is concentrated in the morning from 7 to 9 o'clock and the monthly average cleaning agent usage of 200 grams per household. The sewage flow rate and COD concentration are weighted by 0.6:0.4 to generate the pollution load evaluation value PL=0.5×0.6+120×0.4=48.3. The organic pollution influence value OI=15×1.2+200×0.03=24 is calculated by the correlation model trained by 300 groups of samples for the ammonia nitrogen content and the cleaning agent usage. The comprehensive data set containing the spatiotemporal correlation features is constructed.

[0121] The system installs the porous ceramic medium with a porosity of 65% and a specific surface area of 300 square meters / gram in the modular biological treatment device. The dissolved oxygen concentration is adjusted according to the PL value fluctuation: when the PL value exceeds 40, the dissolved oxygen concentration is increased from the base value of 2.5 mg / L to 4.0 mg / L. Combined with the correlation analysis of the COD concentration peak value and the water use time curve recorded in the last 10 monitoring periods, the pollutant emission prediction model is established. When a 50% surge in water usage is detected, the hierarchical pressure control command is sent to the gas injection device. The low pressure (0.1 MPa), medium pressure (0.2 MPa), and high pressure (0.3 MPa) correspond to the morning, noon, and evening, respectively. At the same time, the sewage backflow ratio is increased from the standard value of 30% to 45%, so that the dissolved oxygen concentration is stabilized in the critical interval of 3.2-3.8 mg / L.

[0122] The degradation rate parameter of the prediction model is adjusted from 0.25 to 0.23 by obtaining the day and night temperature difference of 8℃ from the weather station. Combined with the optical detection data of the microbial attachment on the surface of the porous ceramic medium, the critical value range is optimized to 2.8-4.2 mg / L, and the priority of the hierarchical pressure command is adjusted to noon> evening> morning. Through the calibration mechanism, the system balances the treatment energy consumption and the pollutant removal efficiency while keeping the treatment period unchanged at 72 hours.

[0123] Figure 3 A structure diagram of a rural domestic sewage pollution reduction and carbon reduction intelligent management system is provided for the embodiments of the present application, as shown in the figure. The system comprises: Figure 3

[0124] The collection module 31 collects the sewage flow rate, chemical oxygen demand concentration, and ammonia nitrogen content, and synchronously receives the water use time data and cleaning agent usage uploaded by the user mobile device.

[0125] ​The construction module 32 generates a pollution load evaluation value by weighting the sewage flow rate and the chemical oxygen demand concentration, generates an organic matter pollution influence value by correlating the ammonia nitrogen content and the detergent usage amount through a correlation model, and constructs a comprehensive data set including spatiotemporal correlation characteristics;

[0126] The adjustment module 33 installs porous ceramic media in the modular biological treatment device based on the distribution characteristics of the pollution load evaluation value and the organic matter pollution influence value in the comprehensive data set, and adjusts the dissolved oxygen concentration according to changes in the pollution load evaluation value;

[0127] The control module 34 generates a prediction model of pollutant discharge based on the adjustment parameters of the dissolved oxygen concentration, in combination with the correlation change curve of the peak value of the chemical oxygen demand concentration and the water usage time data in a plurality of consecutive monitoring periods, sends a hierarchical pressure control instruction to a gas injection device remotely based on the processing period and energy consumption correlation relationship of the prediction model, and synchronously adjusts the sewage backflow ratio to maintain the dissolved oxygen concentration within a critical value range;

[0128] The correction module 35 corrects the degradation rate parameter of the prediction model according to the diurnal temperature difference parameter in the meteorological data, optimizes the upper and lower limit values of the critical value range and the execution priority of the hierarchical pressure control instruction in combination with the optical detection data of the porous ceramic media.

[0129] Figure 3 The rural domestic sewage pollution reduction and carbon reduction intelligent management system can perform Figure 1 The rural domestic sewage pollution reduction and carbon reduction intelligent management method of the embodiments described above has been described in detail in the embodiments related to the method, and will not be described in detail here.

[0130] In one possible design, Figure 3 The rural domestic sewage pollution reduction and carbon reduction intelligent management system of the embodiments described above can be implemented as a computing device, such as Figure 4 As shown, the computing device can include a storage component 41 and a processing component 42.

[0131] The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 42.

[0132] The processing component 42 is used for the above Figure 1 The rural domestic sewage pollution reduction and carbon reduction intelligent management method of the embodiments described above.

[0133] The processing component 42 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, for executing the above method.

[0134] The storage component 41 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0135] Of course, the computing device can also include other components, such as input / output interfaces, display components, communication components, etc.

[0136] The input / output interface provides an interface between the processing component and peripheral interface modules, which can be output devices, input devices, etc.

[0137] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0138] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the computing device can be a cloud server, and the processing component, the storage component, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0139] The embodiments of the present application also provide a computer storage medium storing a computer program, and the computer program is executed by a computer to implement the above Figure 1 The rural domestic sewage pollution reduction and carbon reduction intelligent management method of the embodiments.

[0140] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0141] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purposes of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0142] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, and the computer software products can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and include a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0143] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A smart management method for rural domestic sewage reduction and carbon reduction, characterized by: include: Collect sewage flow rate, chemical oxygen demand concentration and ammonia nitrogen content, and simultaneously receive water use time data and detergent usage data uploaded by users' mobile devices; The sewage flow rate and chemical oxygen demand concentration are weighted to generate a pollution load assessment value, the ammonia nitrogen content and the amount of detergent used are correlated with each other to generate an organic pollution impact value, and a comprehensive data set containing spatiotemporal correlation characteristics is constructed; installing porous ceramic media in a modular biological treatment device based on distribution characteristics of pollution load assessment values ​​and organic pollution impact values ​​in the comprehensive data set, and adjusting dissolved oxygen concentration according to changes in the pollution load assessment values; Based on the adjustment parameter of the dissolved oxygen concentration, combined with the correlation change curve of the peak value of the chemical oxygen demand concentration and the water use time data over multiple consecutive monitoring cycles, a prediction model for pollutant emissions is generated; based on the correlation relationship between the processing cycle and energy consumption in the prediction model, a graded pressure control instruction is remotely sent to the gas injection device, and the sewage return ratio is synchronously adjusted to maintain the dissolved oxygen concentration within a critical value range; The degradation rate parameter of the prediction model is corrected according to the day-night temperature difference parameter in the meteorological data, and the upper and lower limits of the critical value range and the execution priority of the graded pressure control instruction are optimized in combination with the optical detection data of the porous ceramic medium.

2. The method according to claim 1, characterized in that Based on the distribution characteristics of the pollution load assessment value and the organic pollution impact value in the comprehensive data set, a porous ceramic medium is installed in the modular biological treatment device, and the dissolved oxygen concentration is adjusted according to the change of the pollution load assessment value, including: Based on the spatiotemporal distribution characteristics of the pollution load assessment values ​​and the fluctuation pattern of the organic pollution impact values ​​in the comprehensive data set, a porous ceramic medium with a pore size distribution matching the fluctuation range of the pollution load assessment values ​​is selected and embedded in the packing layer of the modular biological treatment device; Based on the effect of the embedded porous ceramic medium on the microbial attachment efficiency, the changing trend of the pollution load assessment value is monitored, and when the change in the pollution load assessment value exceeds a preset fluctuation threshold, the main pipeline output pressure of the gas injection device is adjusted; When the pollution load evaluation value increases, the pressure of the main aeration pipeline is reduced to reduce the dissolved oxygen concentration. When the pollution load evaluation value decreases, the pressure of the main aeration pipeline is increased to increase the dissolved oxygen concentration.

3. The method according to claim 1, characterized in that The degradation rate parameter of the prediction model is modified according to the day-night temperature difference parameter in the meteorological data, and the upper and lower limits of the critical value range and the execution priority of the graded pressure control instruction are optimized in combination with the optical detection data of the porous ceramic medium, including: Inputting the day and night temperature difference parameter in the meteorological data into the degradation rate compensation formula to generate a degradation rate correction coefficient that is inversely proportional to the temperature difference, and correcting the degradation rate parameter related to microbial activity in the prediction model based on the degradation rate correction coefficient; The optical sensor on the surface of the porous ceramic medium collects the coverage area and color characteristic data of the biofilm. When the coverage area continues to increase and the color characteristic data indicates a decrease in biofilm activity, the upper and lower limits of the critical value range are simultaneously lowered according to the adjustment range of the degradation rate parameter. Based on the historical execution frequency of the graded pressure control instructions and the corresponding maintenance effect data of the dissolved oxygen concentration, the degradation rate parameter is correlated with the instruction execution effect and analyzed, and the priority of the graded pressure control instructions that meet the maintenance effect and have high execution efficiency is increased, and the remaining graded pressure control instructions are downgraded according to the priority weight.

4. The method according to claim 3, characterized in that When the coverage area continues to increase and the color characteristic data indicates a decrease in biofilm activity, the upper and lower limits of the critical value range are simultaneously lowered according to the adjustment amplitude of the degradation rate parameter, including: Obtain image data of the covered area, extract the area growth rate of the microbial community in the covered area and the ratio of the red channel to the blue channel in the color feature data; When the area growth rate value continuously exceeds the preset growth threshold, and the ratio of the red channel to the blue channel is lower than the preset activity threshold, it is determined that the activity decline state of the microbial community has reached the trigger condition; Calculating a correction ratio of the critical value range according to an adjustment range of the degradation rate parameter, wherein the adjustment range is the percentage of the absolute value of the difference between the current degradation rate and the reference rate to the reference rate; The value of the correction ratio is input into the calculation formula of the critical value range, and the upper limit and lower limit of the current critical value range are simultaneously reduced. The upper limit is reduced by the original upper limit multiplied by the correction ratio, and the lower limit is reduced by the original lower limit multiplied by the correction ratio.

5. The method according to claim 1, wherein Based on the adjustment parameter of the dissolved oxygen concentration and in combination with the correlation change curve of the peak value of the chemical oxygen demand concentration and the water use time data over multiple consecutive monitoring periods, a prediction model for pollutant emissions is generated, including: Extracting the dissolved oxygen fluctuation period from the dissolved oxygen concentration adjustment parameter, and synchronously obtaining the peak moment of the chemical oxygen demand concentration within the continuous monitoring period; Aligning the peak time of the chemical oxygen demand concentration with the water use time data reported by the user according to the time window, and generating a change curve containing the correlation between the pollution emission intensity and the water use time period; Extracting the duration of the pollution intensity peak value and the peak interval in the variation curve and the dissolved oxygen response lag time in the adjustment parameter of the dissolved oxygen concentration, and establishing a mapping relationship among the duration, the peak interval and the dissolved oxygen response lag time; According to the response law of the pollution intensity peak and the dissolved oxygen concentration in the mapping relationship, a prediction model for the pollutant degradation cycle and treatment energy consumption is constructed.

6. The method according to claim 1, characterized in that Based on the correlation between the treatment cycle and energy consumption of the prediction model, a graded pressure control instruction is remotely sent to the gas injection device, and the sewage return ratio is simultaneously adjusted to maintain the dissolved oxygen concentration within the critical value range, including: According to the correlation between the pollutant degradation cycle and treatment energy consumption in the prediction model, when the degradation cycle is prolonged and the energy consumption exceeds a preset threshold, a high-pressure control instruction is sent to the gas injection device to trigger the main aeration pipe pressurization mode and simultaneously increase the opening ratio of the sewage return pipe; When the degradation cycle is shortened and the energy consumption is lower than the preset threshold, a low-pressure control instruction is sent to the gas injection device to switch to the auxiliary aeration pipe air supply mode, and the opening ratio of the sewage return pipe is reduced simultaneously; Monitor the deviation of the dissolved oxygen concentration from the critical value range. If the deviation continues to exceed the allowable fluctuation range, recalculate the execution priority of the graded pressure instruction based on the output parameters of the prediction model, and adjust the correction amplitude of the sewage return ratio until the dissolved oxygen concentration returns to within the critical value range.

7. The method according to claim 1, characterized in that The sewage flow rate and chemical oxygen demand concentration are weighted to generate a pollution load assessment value, and the ammonia nitrogen content and detergent usage are correlated through a correlation model to generate an organic pollution impact value. A comprehensive data set containing spatiotemporal correlation characteristics is constructed, including: Multiply the sewage flow rate and chemical oxygen demand concentration by the preset flow rate weight coefficient and chemical oxygen demand weight coefficient respectively, and add the products of the two to obtain the pollution load assessment value; Inputting the ammonia nitrogen content and the amount of detergent used into a correlation model, training historical data through the correlation model to obtain a coupling coefficient between the ammonia nitrogen content and the amount of detergent used, and multiplying the product of the two by the coupling coefficient to output an organic pollution impact value; The pollution load assessment value and the organic matter pollution impact value are integrated according to the timestamp and geographic location code to generate a comprehensive data set including time series distribution characteristics and spatial location correlation characteristics.

8. An intelligent management system for rural domestic sewage pollution reduction and carbon reduction, characterized by: include: The collection module collects sewage flow rate, chemical oxygen demand concentration and ammonia nitrogen content, and simultaneously receives water use time data and detergent usage data uploaded by users' mobile devices; A construction module is provided to weight the sewage flow rate and the chemical oxygen demand concentration to generate a pollution load assessment value, to associate the ammonia nitrogen content with the detergent usage to generate an organic pollution impact value through a correlation model, and to construct a comprehensive data set including spatiotemporal correlation characteristics; an adjustment module, which installs a porous ceramic medium in a modular biological treatment device based on distribution characteristics of the pollution load assessment value and the organic pollution impact value in the comprehensive data set, and adjusts the dissolved oxygen concentration according to changes in the pollution load assessment value; The control module generates a pollutant emission prediction model based on the adjustment parameter of the dissolved oxygen concentration and a correlation curve between the peak value of the chemical oxygen demand concentration and the water use time data over multiple consecutive monitoring cycles. Based on the correlation between the processing cycle and energy consumption in the prediction model, the control module remotely sends a graded pressure control instruction to the gas injection device and simultaneously adjusts the sewage return ratio to maintain the dissolved oxygen concentration within a critical value range. The correction module corrects the degradation rate parameter of the prediction model according to the day-night temperature difference parameter in the meteorological data, and optimizes the upper and lower limits of the critical value range and the execution priority of the graded pressure control instruction in combination with the optical detection data of the porous ceramic medium.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the intelligent management method for rural domestic sewage pollution reduction and carbon reduction as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the intelligent management method for reducing pollution and carbon emissions from rural domestic sewage as described in any one of claims 1 to 7 is implemented.

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