A method for evaluating the operation status of water quality in the whole process based on key point analysis

Abstract

The present invention provides a method for evaluating the operation status of water quality in the whole process based on key point analysis, which relates to the technical field of operation status evaluation, including: collecting information on the points along the sewage treatment process, dividing the functional areas, and determining the anaerobic zone, the anoxic zone, and the aerobic zone; performing functional bacteria performance tests on multiple pollutants respectively, and establishing a water quality calibration data set according to the test results; obtaining the first pollutant type and the first pollutant concentration of the sewage treatment; performing full-process treatment prediction, and generating a treatment prediction residual concentration; performing deviation calculation according to a preset residual concentration threshold, and generating a treatment deviation coefficient, the treatment deviation coefficient having positive and negative directions; and adding the treatment deviation coefficient to the first state evaluation result for the first pollutant. The present invention solves the technical problem that the traditional sewage treatment evaluation method is usually based on single-point sampling and analysis, lacks a comprehensive evaluation of each key point in the entire treatment process, and leads to poor accuracy of the water quality evaluation results.

Description

Technical Field

[0001] The present invention relates to the technical field of operation status assessment, and in particular to a full-process water quality operation status assessment method based on key point analysis. Background Art

[0002] In traditional methods, the evaluation of sewage treatment effects is usually based on single-point sampling and analysis, or only focuses on the water quality of the inlet and outlet water. There is a lack of detailed evaluation of each key point in the treatment process, resulting in low accuracy of the overall water quality assessment. In addition, traditional methods have limited monitoring and optimization capabilities during the treatment process and are unable to adjust and optimize treatment parameters in real time, resulting in large fluctuations in treatment effects and difficulty in ensuring that the effluent water quality is stable and meets standards.

[0003] Comparative document (publication number CN114004552B): discloses a groundwater quality risk assessment method, the method includes generating a three-dimensional model according to the assessment area, dividing the three-dimensional model into regions, and obtaining sub-regions containing impact values; calculating the water quality risk value of each sampling point in the assessment area according to the impact value, and when the water quality risk value reaches a preset risk threshold, marking the corresponding sampling point, and generating a detection route according to the marked sampling point. This comparative document only performs water quality analysis on fixed sampling points, and lacks overall full-process analysis for flowing waters, resulting in low accuracy of water quality assessment. Summary of the invention

[0004] This application provides a full-process water quality operation status assessment method based on key point analysis, aiming to solve the technical problem that traditional sewage treatment assessment methods are usually based on single-point sampling and analysis, lack of comprehensive assessment of each key point in the entire treatment process, and lead to poor accuracy of water quality assessment results.

[0005] In view of the above problems, this application provides a full-process water quality operation status assessment method based on key point analysis.

[0006] The first aspect disclosed in the present application provides a method for evaluating the water quality operation status of the entire process based on key point analysis, the method comprising: collecting information on point layout along the sewage treatment process, dividing the functional areas according to the point layout information, and determining anaerobic zones, anoxic zones and aerobic zones, wherein each zone has an area identification; performing functional bacteria performance tests on the anaerobic zones, anoxic zones and aerobic zones for multiple pollutants, respectively, and establishing a water quality calibration data set according to the test results; obtaining a first pollutant type and a first pollutant concentration of a first pollutant in sewage treatment; based on the water quality calibration data set, in combination with the first pollutant type and the first pollutant concentration, performing a full-process treatment prediction on the first pollutant to generate a treatment prediction residual concentration, wherein the treatment prediction residual concentration is the residual concentration after the sewage treatment process; performing deviation calculation on the treatment prediction residual concentration according to a preset residual concentration threshold to generate a treatment deviation coefficient, wherein the treatment deviation coefficient has positive and negative directions; and adding the treatment deviation coefficient to the first state evaluation result of the sewage treatment process for the first pollutant.

[0007] The second aspect disclosed in the present application provides a full-process water quality operation status assessment system based on key point analysis, and the system is used for the above-mentioned full-process water quality operation status assessment method based on key point analysis, and the system includes: a functional area division module, the functional area division module is used to collect the along-process point information of the sewage treatment process, and divide the functional areas according to the along-process point information to determine the anaerobic zone, the anoxic zone and the aerobic zone, wherein each zone has a zone identification; a performance testing module, the performance testing module is used to perform functional bacteria performance tests of multiple pollutants on the anaerobic zone, the anoxic zone and the aerobic zone, respectively, and establish a water quality calibration data set according to the test results; a pollutant acquisition module, the pollutant acquisition module is used to obtain the first A first pollutant type and a first pollutant concentration of a pollutant; a treatment prediction module, the treatment prediction module is used to perform a full-process treatment prediction on the first pollutant based on the water quality calibration data set, in combination with the first pollutant type and the first pollutant concentration, to generate a treatment prediction residual concentration, wherein the treatment prediction residual concentration is the residual concentration after the sewage treatment process; a deviation calculation module, the deviation calculation module is used to perform a deviation calculation on the treatment prediction residual concentration according to a preset residual concentration threshold, and generate a treatment deviation coefficient, wherein the treatment deviation coefficient has a positive and negative direction; an evaluation result acquisition module, the evaluation result acquisition module is used to add the treatment deviation coefficient to the first state evaluation result of the sewage treatment process for the first pollutant.

[0008] According to a third aspect disclosed in the present application, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any step of the first aspect disclosed in the present application is implemented.

[0009] The fourth aspect disclosed in the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the first aspect disclosed in the present application.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] By collecting information about points along the way and dividing functional areas, water quality assessment can be performed based on the characteristics of different areas, thereby more accurately assessing the water quality status during sewage treatment; by establishing a water quality calibration data set and combining the type and concentration of the first pollutant, the first pollutant is predicted for the entire process of treatment, generating a predicted residual concentration for treatment, and improving the accuracy of the prediction of the treatment effect; by calculating the deviation of the predicted residual concentration for treatment according to a preset residual concentration threshold, and adding the treatment deviation coefficient to the evaluation result, an accurate analysis of the treatment process is achieved, thereby improving the accuracy of the operating status evaluation results. In summary, this method solves the technical problems of inaccurate water quality assessment and inaccurate prediction of treatment effects in traditional sewage treatment processes, and achieves targeted water quality assessment and accurate prediction of treatment effects, achieving the technical effect of improving the accuracy of the evaluation.

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic diagram of a flow chart of a method for evaluating the operation status of water quality in the entire process based on key point analysis provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the structure of a full-process water quality operation status assessment system based on key point analysis provided in an embodiment of the present application;

[0015] Figure 3 This is a diagram of the internal structure of a computer device provided in an embodiment of the present application.

[0016] Explanation of the reference numerals: functional area division module 10 , performance testing module 20 , pollutant acquisition module 30 , processing prediction module 40 , deviation calculation module 50 , evaluation result acquisition module 60 . DETAILED DESCRIPTION

[0017] The embodiment of the present application solves the technical problem that traditional sewage treatment assessment methods are usually based on single-point sampling and analysis, lack of comprehensive assessment of each key point in the entire treatment process, and lead to poor accuracy of water quality assessment results by providing a full-process water quality operation status assessment method based on key point analysis.

[0018] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0019] like Figure 1 As shown, the embodiment of the present application provides a method for evaluating the operation status of water quality in the whole process based on key point analysis, and the method includes:

[0020] Collecting the point distribution information along the sewage treatment process, dividing the functional areas according to the point distribution information along the process, and determining the anaerobic area, the anoxic area and the aerobic area, wherein each area has an area identification;

[0021] Key collection points are determined in the sewage treatment process. These points are arranged along different stages of the sewage treatment process. Corresponding collection equipment is installed at the selected collection points for real-time monitoring and collection of water quality parameters. Commonly used water quality parameters include oxygen demand, temperature, pH value, etc.

[0022] According to the water quality parameters at different collection points, the changing characteristics of sewage in the treatment process are analyzed. Through preliminary analysis of the data, the water quality characteristics at different locations in the sewage treatment process are analyzed to identify the different functional areas in the sewage treatment process. According to the analysis results, the sewage treatment process is divided into different functional areas such as anaerobic zone, anoxic zone and aerobic zone. Among them, the anaerobic zone is usually set in the primary stage of sewage treatment, characterized by extremely low or close to zero dissolved oxygen content, and organic matter is decomposed by anaerobic microorganisms; the dissolved oxygen content in the anoxic zone is low but not zero, and nitrate is reduced to nitrogen gas by denitrifying bacteria to achieve nitrogen removal; the dissolved oxygen content in the aerobic zone is high, and organic matter and ammonia nitrogen are further decomposed by aerobic microorganisms to achieve further purification of sewage. Each functional area divided is marked to ensure that each area can be clearly identified in the subsequent data analysis and processing process.

[0023] The functional bacteria performance tests of various pollutants are respectively performed on the anaerobic zone, the anoxic zone and the aerobic zone, and a water quality calibration data set is established according to the test results;

[0024] Determine the types of functional bacteria that play a major role in the anaerobic zone, anoxic zone and aerobic zone. The anaerobic zone includes anaerobic methanogens, anaerobic acidifying bacteria, etc.; the anoxic zone includes denitrifying bacteria, facultative aerobic bacteria, etc.; the aerobic zone includes nitrifying bacteria, aerobic heterotrophic bacteria, etc. Collect sewage samples from the anaerobic zone, anoxic zone and aerobic zone, and separate and culture the functional bacteria in each area under laboratory conditions to ensure the purity of the tested strains and determine the types of pollutants that need to be tested, including organic matter, nitrogen compounds, phosphorus compounds, etc.

[0025] Under laboratory conditions, the performance of functional bacteria is tested. For example, culture conditions suitable for the growth and metabolism of each functional bacteria are set, such as temperature, pH value, dissolved oxygen content, etc., and pollutants of known concentrations are added to the culture medium. The removal efficiency of the functional bacteria is evaluated by measuring the changes in pollutant concentrations at different time points, and the data such as pollutant concentration, bacterial count, and culture conditions at each time point are recorded.

[0026] The data obtained during the experiment were organized to establish a water quality calibration data set, which includes pollutant removal performance data of each functional bacteria in different areas, as well as pollutant removal efficiency and metabolite data, providing basic data support for subsequent sewage treatment process optimization and water quality assessment.

[0027] Obtaining a first pollutant type and a first pollutant concentration of a first pollutant in sewage treatment;

[0028] Determine the types of pollutants that need to be treated, such as organic matter, nitrogen compounds, phosphorus compounds, etc., to obtain the first pollutant type. Collect water samples and analyze them in the laboratory, including using potassium dichromate method (COD) and five-day culture method (BOD) to determine the organic content in the water; using spectrophotometry, ion chromatography, etc. to determine the concentration of ammonia nitrogen, nitrate nitrogen, and nitrite nitrogen, and determine the first pollutant concentration after analysis.

[0029] Based on the water quality calibration data set, combined with the first pollutant type and the first pollutant concentration, a full-process treatment prediction is performed on the first pollutant to generate a treatment prediction residual concentration, wherein the treatment prediction residual concentration is a residual concentration after the sewage treatment process;

[0030] From the established water quality calibration data set, data related to the first pollutant are extracted, including the removal performance data of the pollutant by each functional bacteria in different functional areas, namely, the anaerobic zone, the anoxic zone and the aerobic zone. According to the sewage treatment process, the removal of pollutants at different stages is determined, and combined with the removal efficiency in the calibration data set, a complete treatment flow chart is formed.

[0031] Illustratively, based on the removal efficiency of functional bacteria for pollutants, a pollutant removal model is established in different functional zones. Combined with the flow and transfer of sewage between treatment units, the change process of pollutant concentration is simulated. The initial concentration of the first pollutant is used as the input condition of the model, and the treatment prediction model is run to simulate the removal process of pollutants in the entire sewage treatment process. The model gradually calculates the removal effect of pollutants in anaerobic zones, anoxic zones and aerobic zones, and outputs the pollutant concentration in each stage. According to the output of the prediction model, the final residual concentration of the first pollutant after the sewage passes through the treatment process is calculated, which is the pollutant concentration value at the end of the treatment process.

[0032] Performing deviation calculation on the treatment predicted residual concentration according to a preset residual concentration threshold to generate a treatment deviation coefficient, wherein the treatment deviation coefficient has a positive and negative direction;

[0033] According to environmental standards, regulatory requirements or specific process requirements, the preset residual concentration threshold of the first pollutant is determined. This threshold represents the maximum allowable residual concentration. If this value is exceeded, the treatment effect is considered to not meet the requirements.

[0034] Compare the treatment predicted residual concentration with the preset residual concentration threshold, and calculate the deviation between them. The deviation can be calculated by a simple subtraction operation, that is, the treatment predicted residual concentration minus the preset residual concentration threshold. The treatment effect and the expected deviation are judged according to the positive or negative deviation. If the deviation is positive, it means that the treatment predicted residual concentration exceeds the preset threshold, and the treatment effect is poor; if the deviation is negative, it means that the treatment predicted residual concentration is lower than the preset threshold, and the treatment effect is good. Divide the deviation by the preset residual concentration threshold, and calculate the ratio of the deviation to the threshold. In this way, the deviation can be quantified as a relative value, which is convenient for comparison under different conditions.

[0035] The treatment deviation coefficient is added to the first state evaluation result of the sewage treatment process for the first pollutant.

[0036] The calculated processing deviation coefficient is integrated into the status assessment result. The direction of its influence on the assessment result is determined according to the positive or negative value of the processing deviation coefficient. When the processing deviation coefficient is positive, the assessment result decreases, and vice versa.

[0037] Furthermore, it also includes:

[0038] Continuously collecting environmental data at the deployment locations to establish an environmental data set, wherein the environmental data set has a mapping relationship with the anaerobic zone, the anoxic zone, and the aerobic zone, and the environmental data set at least includes temperature data, humidity data, oxygen concentration data, and pH data;

[0039] The water quality calibration data set is corrected based on the environmental data corresponding to the anaerobic zone, the anoxic zone and the aerobic zone.

[0040] Monitoring points are set up in the anaerobic, anoxic and aerobic areas to ensure comprehensive monitoring of the environmental conditions in each area, and suitable sensors are selected for continuous monitoring of environmental data, including temperature, humidity, oxygen concentration, pH and other parameters. Sensor equipment is installed at pre-determined monitoring points, and environmental data from each monitoring point is continuously collected. The obtained environmental data is mapped with the corresponding anaerobic, anoxic and aerobic areas to establish an environmental data set, and each functional area has corresponding environmental data records.

[0041] The collected environmental data are integrated with the water quality calibration data set. Each environmental data corresponds to the corresponding water quality calibration data. According to the characteristics of the environmental data and the changing rules of the water quality calibration data, the environmental factors that may affect the water quality calibration data are determined. For example, factors such as temperature, oxygen concentration, and pH value will affect the activity of microorganisms and the degradation efficiency of pollutants. Based on the determined correction factors, the water quality calibration data set is corrected. That is, according to the actual environmental data, the correction coefficient under each functional area is calculated and applied to the water quality calibration data. The corrected water quality data reflects the impact of environmental factors on the water quality calibration data and is closer to the actual treatment conditions.

[0042] Furthermore, the water quality calibration data set is corrected based on the environmental data corresponding to the anaerobic zone, the anoxic zone and the aerobic zone, including:

[0043] Acquire a sample environment information set according to the pollutant treatment data record of the first area, wherein the first area is any one of the anaerobic area, the anoxic area and the aerobic area;

[0044] Obtaining a set of sample environmental impact coefficients that affect the treatment effect of pollutant treatment under the sample environmental information set;

[0045] Using the sample environmental information set and the sample environmental impact coefficient set to construct an environmental impact analysis model;

[0046] Based on the environmental impact analysis model, the environmental data corresponding to the anaerobic zone, the anoxic zone and the aerobic zone are analyzed to generate a plurality of environmental impact coefficients;

[0047] The water quality calibration data set is corrected and calculated based on the multiple environmental impact coefficients.

[0048] A first zone is randomly selected from the anaerobic zone, the anoxic zone and the aerobic zone, and relevant data is extracted from the pollutant treatment data records of the selected first zone, including information such as pollutant removal rate and treatment effect. Environmental information related to the selected first zone is collected, including data such as temperature, humidity, oxygen concentration, pH, etc., to form a sample environmental information set.

[0049] Quantitative analysis is performed on each environmental factor to determine its degree of influence on the pollutant treatment effect, and it is expressed as an environmental impact coefficient, which can be obtained through statistical analysis or experimental testing. The quantified environmental impact coefficients are combined into a sample environmental impact coefficient set, and each coefficient corresponds to an environmental factor, reflecting the degree of influence of the factor on the pollutant treatment effect.

[0050] An environmental impact analysis model is established based on a neural network. The input variables of the model are the various environmental parameters in the sample environmental information set, and the output variables are the sample environmental impact coefficient set. The model parameters are estimated and fitted using sample data to determine the weight of the impact of various environmental parameters on the pollutant treatment effect. The established environmental impact analysis model is verified using part of the data set to evaluate the model's fitting degree and prediction accuracy. The model is adjusted and optimized based on the verification results to improve the model's prediction ability and applicability, and ultimately an environmental impact analysis model that meets the preset accuracy is obtained.

[0051] Prepare the environmental data corresponding to the anaerobic zone, anoxic zone and aerobic zone, including environmental parameters such as temperature, humidity, oxygen concentration, pH, etc. Use the constructed environmental impact analysis model to analyze the environmental data, obtain the influence of various environmental factors on the pollutant treatment effect, and generate multiple environmental impact coefficients.

[0052] The obtained environmental impact coefficient is applied to the water quality calibration data set to correct the impact of environmental factors on the water quality calibration data and obtain corrected water quality data, which can better reflect the impact of actual environmental conditions on water quality.

[0053] Furthermore, it also includes:

[0054] When the processing deviation coefficient is positive, a parameter optimization instruction is generated, and a first processing area is located, wherein the first processing area is a main processing area of ​​the first pollutant;

[0055] Based on the parameter optimization instruction, taking the preset residual concentration threshold as a target, optimizing the control parameters of the first processing area to generate optimized control parameters;

[0056] The first pollutant is optimized and controlled based on the optimization control parameters.

[0057] According to the positive or negative situation of the treatment deviation coefficient, a judgment is made. If the treatment deviation coefficient is positive, it means that the predicted residual concentration of the treatment is higher than the preset residual concentration threshold, indicating that the treatment effect is lower than expected and parameter optimization is required. In this case, a parameter optimization instruction is generated to guide the subsequent parameter adjustment and optimization process. Determine the main treatment area of ​​the first pollutant, that is, the first treatment area, which can be determined by analyzing the treatment effects of different areas in the water quality treatment process to ensure the targeted optimization control.

[0058] The preset residual concentration threshold is used as the optimization target of the control parameters, that is, the control parameters are adjusted so that the treatment effect reaches the expected value. Based on the generated parameter optimization instructions, the control parameters of the first treatment area are optimized to find the optimal control parameter combination. According to the parameter optimization results, the optimized control parameters are generated to achieve the target of the preset residual concentration threshold.

[0059] According to the generated optimized control parameters, the control parameters of the first processing area are adjusted to achieve the expected goals.

[0060] Furthermore, taking the preset residual concentration threshold as a target, optimizing the control parameters of the first processing area to generate optimized control parameters includes:

[0061] Introducing a parameter optimization function to analyze the predicted residual concentration and the preset residual concentration threshold, obtaining a feedback adjustment parameter, and outputting the feedback adjustment parameter as the optimization control parameter;

[0062] Wherein, the parameter optimization function is as follows:

[0063]

[0064] in, is the preset residual concentration threshold, C i To process the predicted residual concentration, ε is the variable adjustment coefficient, ε=ε1+ε2+ε3+ε4, ε1 is the temperature variable adjustment coefficient, ε2 is the humidity variable adjustment coefficient, ε3 is the oxygen concentration variable adjustment coefficient, ε4 is the pH variable adjustment coefficient, n is the number of initial pollutant concentrations, n>0.

[0065] Specifically, the parameter optimization function is as follows:

[0066]

[0067] This parameter optimization function is a mathematical model used to evaluate the difference between the treatment predicted residual concentration and the preset residual concentration threshold. The goal of this function is to minimize the loss function L by adjusting the influence coefficients of environmental factors, that is, adjusting ε1, ε2, ε3, and ε4. That is, to make the treatment predicted residual concentration as close as possible to the preset residual concentration threshold. The optimization goal is to make the actual residual concentration in the treatment process meet the preset requirements as much as possible, so as to achieve better treatment effects.

[0068] Collect data on environmental factors, including temperature, humidity, oxygen concentration, pH, etc., apply the collected data to the parameter optimization function, calculate the average value of the sum of squares of errors as the optimization target, analyze the changing trend of the optimization target, understand the difference between the predicted residual concentration and the preset residual concentration threshold, and the impact of environmental factors on this difference, use optimization algorithms such as gradient descent method and genetic algorithm to optimize the parameter optimization function, adjust the influence coefficients ε1, ε2, ε3, ε4 of environmental factors, find the feedback adjustment parameters that minimize the error, and use the obtained feedback adjustment parameters as the output of the optimization control parameters. These parameters are used to adjust the operating parameters in the sewage treatment process to achieve optimal control of the residual concentration.

[0069] Furthermore, it also includes:

[0070] When the treatment deviation coefficient is negative, collecting an operation data set and a treatment effect set, wherein the operation data set includes an environmental data set, an operation parameter set, and a microbial community set, and the treatment effect set includes a pollutant concentration set, and the operation data set and the treatment effect set have time marks;

[0071] According to the time mark, a time series graph is drawn for the environmental data set, the operating parameter set, the pollutant concentration set, the microbial community set, and the pollutant concentration set to generate a plurality of time series curves;

[0072] A correlation analysis between the operating parameters and the processing effects is performed based on the multiple time series curves, and a key operating parameter set is generated according to the correlation analysis result.

[0073] When the treatment deviation coefficient is negative, collect the operation data set, including the environmental data set, the operating parameter set and the microbial community set. The environmental data set includes data on environmental factors such as temperature, humidity, oxygen concentration, pH, etc. The operating parameter set includes the operating parameters of the treatment equipment, such as stirring rate, ventilation volume, dosage of dosing, etc. The microbial community set includes data on the type, quantity and activity of microorganisms. Collect treatment effect data including the pollutant concentration set. The pollutant concentration set includes the pollutant concentration data after treatment, which reflects the treatment effect. Both the operation data set and the treatment effect set have time tags for subsequent time series analysis and correspondence.

[0074] According to the time stamp, the collected environmental data, operating parameters, pollutant concentrations and microbial community data are sorted in chronological order. For each data set, a corresponding time series graph is drawn. The horizontal axis of each graph is time and the vertical axis is the corresponding data value.

[0075] Perform correlation analysis on each data set in the time series diagram to find the correlation between them. Use correlation analysis methods, such as Pearson correlation coefficient, Spearman correlation coefficient, etc., to calculate the correlation between different data sets. According to the results of correlation analysis, identify the key operating parameters that are closely related to the treatment effect. These parameters play an important role in the pollutant removal efficiency and microbial activity in the sewage treatment process.

[0076] Furthermore, a set of key operating parameters is generated based on the correlation analysis results, including:

[0077] Obtaining multiple correlation coefficients corresponding to multiple operating parameters according to the correlation analysis results;

[0078] A plurality of correlation coefficients satisfying preset correlation coefficients are obtained from the plurality of correlation coefficients, and a plurality of corresponding operating parameters are obtained as the key operating parameters, and the parameters are combined into the key operating parameter set.

[0079] Correlation analysis methods, such as the Pearson correlation coefficient and the Spearman correlation coefficient, are used to calculate the correlation coefficient between each operating parameter and the treatment effect, such as the pollutant concentration. These correlation coefficients characterize the degree of linear correlation between the two variables.

[0080] Determine the preset correlation coefficient threshold based on actual conditions and specific needs to screen out correlation coefficients that meet the requirements. Generally, the closer the correlation coefficient is to 1 or -1, the stronger the correlation between the two variables is. For the correlation coefficients that pass the screening, determine the corresponding operating parameters as key operating parameters. These parameters are closely related to the treatment effect and are of great significance to the control of the sewage treatment process. The screened key operating parameters are combined into a key operating parameter set.

[0081] In summary, the full-process water quality operation status assessment method based on key point analysis provided in the embodiment of the present application has the following technical effects:

[0082] 1. Through the collection of information on points along the way and the division of functional areas, water quality assessment can be carried out according to the characteristics of different areas, so as to more accurately assess the water quality status in the sewage treatment process;

[0083] 2. By establishing a water quality calibration data set and combining the type and concentration of the first pollutant, a full-process treatment prediction of the first pollutant is performed to generate a treatment prediction residual concentration, thereby improving the prediction accuracy of the treatment effect;

[0084] 3. By calculating the deviation of the treatment predicted residual concentration according to the preset residual concentration threshold and adding the treatment deviation coefficient to the evaluation result, an accurate analysis of the treatment process is achieved, thereby improving the accuracy of the operation status evaluation results.

[0085] In summary, this method solves technical problems such as inaccurate water quality assessment and inaccurate treatment effect prediction in traditional sewage treatment processes, realizes targeted water quality assessment and accurate treatment effect prediction, and achieves the technical effect of improving assessment accuracy.

[0086] Based on the same inventive concept as the full-process water quality operation status evaluation method based on key point analysis in the aforementioned embodiment, Figure 2 As shown, the present application provides a full-process water quality operation status assessment system based on key point analysis, the system comprising:

[0087] Functional area division module 10, the functional area division module 10 is used to collect the point distribution information along the sewage treatment process, divide the functional areas according to the point distribution information along the process, and determine the anaerobic area, the anoxic area and the aerobic area, wherein each area has an area identification;

[0088] A performance testing module 20, wherein the performance testing module 20 is used to perform functional bacteria performance tests on various pollutants in the anaerobic zone, the anoxic zone and the aerobic zone, respectively, and to establish a water quality calibration data set according to the test results;

[0089] A pollutant acquisition module 30, wherein the pollutant acquisition module 30 is used to obtain a first pollutant type and a first pollutant concentration of a first pollutant in sewage treatment;

[0090] A treatment prediction module 40, the treatment prediction module 40 is used to perform a full-process treatment prediction of the first pollutant based on the water quality calibration data set, combined with the first pollutant type and the first pollutant concentration, to generate a treatment prediction residual concentration, wherein the treatment prediction residual concentration is a residual concentration after the sewage treatment process;

[0091] A deviation calculation module 50, the deviation calculation module 50 is used to perform deviation calculation on the treatment predicted residual concentration according to a preset residual concentration threshold value to generate a treatment deviation coefficient, wherein the treatment deviation coefficient has a positive and negative direction;

[0092] The evaluation result acquisition module 60 is used to add the treatment deviation coefficient to the first state evaluation result of the sewage treatment process for the first pollutant.

[0093] Furthermore, the system further includes a calibration data set correction module to perform the following operation steps:

[0094] Continuously collecting environmental data at the deployment locations to establish an environmental data set, wherein the environmental data set has a mapping relationship with the anaerobic zone, the anoxic zone, and the aerobic zone, and the environmental data set at least includes temperature data, humidity data, oxygen concentration data, and pH data;

[0095] The water quality calibration data set is corrected based on the environmental data corresponding to the anaerobic zone, the anoxic zone and the aerobic zone.

[0096] Furthermore, the system further comprises a correction calculation module to perform the following operation steps:

[0097] Acquire a sample environment information set according to the pollutant treatment data record of the first area, wherein the first area is any one of the anaerobic area, the anoxic area and the aerobic area;

[0098] Obtaining a set of sample environmental impact coefficients that affect the treatment effect of pollutant treatment under the sample environmental information set;

[0099] Using the sample environmental information set and the sample environmental impact coefficient set to construct an environmental impact analysis model;

[0100] Based on the environmental impact analysis model, the environmental data corresponding to the anaerobic zone, the anoxic zone and the aerobic zone are analyzed to generate a plurality of environmental impact coefficients;

[0101] The water quality calibration data set is corrected and calculated based on the multiple environmental impact coefficients.

[0102] Furthermore, the system further includes an optimization control module to perform the following operation steps:

[0103] When the processing deviation coefficient is positive, a parameter optimization instruction is generated, and a first processing area is located, wherein the first processing area is a main processing area of ​​the first pollutant;

[0104] Based on the parameter optimization instruction, taking the preset residual concentration threshold as a target, optimizing the control parameters of the first processing area to generate optimized control parameters;

[0105] The first pollutant is optimized and controlled based on the optimization control parameters.

[0106] Furthermore, the system further includes an optimization control parameter acquisition module to perform the following operation steps:

[0107] Introducing a parameter optimization function to analyze the predicted residual concentration and the preset residual concentration threshold, obtaining a feedback adjustment parameter, and outputting the feedback adjustment parameter as the optimization control parameter;

[0108] Wherein, the parameter optimization function is as follows:

[0109]

[0110] in, is the preset residual concentration threshold, C i To process the predicted residual concentration, ε is the variable adjustment coefficient, ε=ε1+ε2+ε3+ε4, ε1 is the temperature variable adjustment coefficient, ε2 is the humidity variable adjustment coefficient, ε3 is the oxygen concentration variable adjustment coefficient, ε4 is the pH variable adjustment coefficient, n is the number of initial pollutant concentrations, n>0.

[0111] Furthermore, the system further includes a key operating parameter set generation module to perform the following operation steps:

[0112] When the treatment deviation coefficient is negative, collecting an operation data set and a treatment effect set, wherein the operation data set includes an environmental data set, an operation parameter set, and a microbial community set, and the treatment effect set includes a pollutant concentration set, and the operation data set and the treatment effect set have time marks;

[0113] According to the time mark, a time series graph is drawn for the environmental data set, the operating parameter set, the pollutant concentration set, the microbial community set, and the pollutant concentration set to generate a plurality of time series curves;

[0114] A correlation analysis between the operating parameters and the processing effects is performed based on the multiple time series curves, and a key operating parameter set is generated according to the correlation analysis result.

[0115] Furthermore, the system further includes a key operating parameter set acquisition module to perform the following operation steps:

[0116] Obtaining multiple correlation coefficients corresponding to multiple operating parameters according to the correlation analysis results;

[0117] A plurality of correlation coefficients satisfying preset correlation coefficients are obtained from the plurality of correlation coefficients, and a plurality of corresponding operating parameters are obtained as the key operating parameters, and the parameters are combined into the key operating parameter set.

[0118] Through the above-mentioned detailed description of the full-process water quality operation status assessment method based on key point analysis in this specification, those skilled in the art can clearly know the full-process water quality operation status assessment system based on key point analysis in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0119] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the processor of the computer device is used to provide computing and control capabilities; the memory of the computer device includes a non-volatile storage medium and an internal memory, wherein the non-volatile storage medium stores an operating system, a computer program and a database, and the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium; the network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a full-process water quality operation status assessment method based on key point analysis.

[0120] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0121] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A full-process water quality operation status assessment method based on key point analysis, characterized in that: The method comprises: Collecting the point distribution information along the sewage treatment process, dividing the functional areas according to the point distribution information along the process, and determining the anaerobic area, the anoxic area and the aerobic area, wherein each area has an area identification; The functional bacteria performance tests of various pollutants are respectively carried out on the anaerobic zone, the anoxic zone and the aerobic zone, and a water quality calibration data set is established according to the test results. The environmental data are continuously collected at the distribution point locations to establish an environmental data set, wherein the environmental data set has a mapping relationship with the anaerobic zone, the anoxic zone and the aerobic zone, and the environmental data set at least includes temperature data, humidity data, oxygen concentration data, and pH data; the water quality calibration data set is corrected based on the environmental data corresponding to the anaerobic zone, the anoxic zone and the aerobic zone; Obtaining a first pollutant type and a first pollutant concentration of a first pollutant in sewage treatment; Based on the water quality calibration data set, combined with the first pollutant type and the first pollutant concentration, a full-process treatment prediction is performed on the first pollutant to generate a treatment prediction residual concentration, wherein the treatment prediction residual concentration is a residual concentration after the sewage treatment process; Performing deviation calculation on the treatment predicted residual concentration according to a preset residual concentration threshold to generate a treatment deviation coefficient, wherein the treatment deviation coefficient has a positive and negative direction; The treatment deviation coefficient is added to the sewage treatment process for optimizing and controlling the first state evaluation result of the first pollutant.

2. The method according to claim 1, characterized in that The water quality calibration data set is corrected based on the environmental data corresponding to the anaerobic zone, the anoxic zone and the aerobic zone, including: Acquire a sample environment information set according to the pollutant treatment data record of the first area, wherein the first area is any one of the anaerobic area, the anoxic area and the aerobic area; Obtaining a set of sample environmental impact coefficients that affect the treatment effect of pollutant treatment under the sample environmental information set; Using the sample environmental information set and the sample environmental impact coefficient set to construct an environmental impact analysis model; Based on the environmental impact analysis model, the environmental data corresponding to the anaerobic zone, the anoxic zone and the aerobic zone are analyzed to generate a plurality of environmental impact coefficients; The water quality calibration data set is corrected and calculated based on the multiple environmental impact coefficients.

3. The method according to claim 1, characterized in that Also includes: When the processing deviation coefficient is positive, a parameter optimization instruction is generated, and a first processing area is located, wherein the first processing area is a main processing area of ​​the first pollutant; Based on the parameter optimization instruction, taking the preset residual concentration threshold as a target, optimizing the control parameters of the first processing area to generate optimized control parameters; The first pollutant is optimized and controlled based on the optimization control parameters.

4. The method according to claim 3, characterized in that Taking the preset residual concentration threshold as a target, optimizing the control parameters of the first processing area to generate optimized control parameters includes: Introducing a parameter optimization function to analyze the predicted residual concentration and the preset residual concentration threshold, obtaining a feedback adjustment parameter, and outputting the feedback adjustment parameter as the optimization control parameter; Wherein, the parameter optimization function is as follows: ； in, To preset the residual concentration threshold, To process the predicted residue concentration, is the variable adjustment coefficient, , is the temperature variable adjustment coefficient is the humidity variable adjustment coefficient, is the oxygen concentration variable adjustment coefficient, is the pH variable adjustment coefficient, n is the number of initial pollutant concentrations, n>

0.

5. The method according to claim 1, characterized in that Also includes: When the treatment deviation coefficient is negative, collecting an operation data set and a treatment effect set, wherein the operation data set includes an environmental data set, an operation parameter set, and a microbial community set, and the treatment effect set includes a pollutant concentration set, and the operation data set and the treatment effect set have time marks; According to the time mark, a time series graph is drawn for the environmental data set, the operating parameter set, the pollutant concentration set, the microbial community set, and the pollutant concentration set to generate a plurality of time series curves; A correlation analysis between the operating parameters and the processing effects is performed based on the multiple time series curves, and a key operating parameter set is generated according to the correlation analysis result.

6. The method according to claim 5, characterized in that Generate a set of key operating parameters based on the correlation analysis results, including: Obtaining multiple correlation coefficients corresponding to multiple operating parameters according to the correlation analysis results; A plurality of correlation coefficients satisfying preset correlation coefficients are obtained from the plurality of correlation coefficients, and a plurality of corresponding operating parameters are obtained as the key operating parameters, and the parameters are combined into the key operating parameter set.

7. The whole process water quality operation status evaluation system based on key point analysis is characterized by: A system for implementing the full-process water quality operation status assessment method based on key point analysis according to any one of claims 1 to 6, comprising: A functional area division module, which is used to collect the point distribution information along the sewage treatment process, divide the functional areas according to the point distribution information along the process, and determine the anaerobic area, the anoxic area and the aerobic area, wherein each area has an area identification; A performance testing module, wherein the performance testing module is used to perform functional bacteria performance tests of various pollutants on the anaerobic zone, the anoxic zone and the aerobic zone, respectively, establish a water quality calibration data set according to the test results, and continuously collect environmental data for the distribution point locations to establish an environmental data set, wherein the environmental data set has a mapping relationship with the anaerobic zone, the anoxic zone and the aerobic zone, and the environmental data set at least includes temperature data, humidity data, oxygen concentration data, and pH data; the water quality calibration data set is corrected based on the environmental data corresponding to the anaerobic zone, the anoxic zone and the aerobic zone; A pollutant acquisition module, the pollutant acquisition module is used to obtain a first pollutant type and a first pollutant concentration of a first pollutant for sewage treatment; A treatment prediction module, the treatment prediction module is used to perform a full-process treatment prediction of the first pollutant based on the water quality calibration data set, combined with the first pollutant type and the first pollutant concentration, to generate a treatment prediction residual concentration, wherein the treatment prediction residual concentration is a residual concentration after the sewage treatment process; A deviation calculation module, the deviation calculation module is used to perform deviation calculation on the treatment predicted residual concentration according to a preset residual concentration threshold value to generate a treatment deviation coefficient, wherein the treatment deviation coefficient has a positive or negative direction; An evaluation result acquisition module is used to add the treatment deviation coefficient to the sewage treatment process for optimizing the control of the first state evaluation result of the first pollutant.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the full-process water quality operation status assessment method based on key point analysis described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the full-process water quality operation status assessment method based on key point analysis described in any one of claims 1 to 6 are implemented.

Images