A dynamic monitoring and early warning system for wastewater treatment

Abstract

This invention discloses a dynamic monitoring and early warning system for wastewater treatment, relating to the field of wastewater treatment technology. It addresses the high cost of dynamic monitoring and early warning systems for wastewater treatment in existing technologies. The invention calculates pollution assessment coefficients for each monitoring area based on wastewater index data; obtains the collection frequency for each monitoring area based on the pollution assessment coefficients and environmental data; inputs the wastewater inflow from the equalization tank into a wastewater inflow prediction model to obtain a predicted wastewater inflow; controls the operation of the booster pump based on the predicted inflow and water level information; and regulates the wastewater treatment facilities based on the early warning level. Furthermore, this invention obtains the maximum derivative value in the pollution degree fluctuation derivative function and marks it as a water quality stability characteristic value. This ensures that the calculated water quality stability characteristic value accurately reflects the water quality fluctuations in each monitoring area, thus reducing the cost of dynamic monitoring and early warning for wastewater treatment.

Description

Technical Field

[0001] This invention belongs to the field of wastewater treatment technology, specifically a dynamic monitoring and early warning system for wastewater treatment. Background Technology

[0002] With the acceleration of urbanization, urban sewage treatment has become an increasingly prominent issue. Especially in the context of smart city construction, how to treat sewage efficiently and intelligently has become an important topic. Traditional sewage treatment methods mostly rely on physical, chemical and biological treatment technologies. Although these methods are efficient in treating large-scale sewage, they usually lack sufficient flexibility and real-time response capabilities, making it difficult to effectively treat specific pollutants. In recent years, with the introduction of Internet of Things (IoT) technology, by monitoring sewage quality in real time and remotely controlling treatment facilities, IoT technology can improve the level of intelligence in sewage treatment.

[0003] Existing technologies achieve dynamic monitoring and early warning of wastewater treatment by continuously collecting water quality data from wastewater treatment ponds and monitoring changes in water quality. However, in actual wastewater treatment processes, the degree of pollution and the influent volume of wastewater fluctuate significantly, making it difficult for existing technologies to collect stable wastewater indicator data. This results in low accuracy for dynamic monitoring and early warning of wastewater treatment. Furthermore, most existing technologies use a fixed sampling frequency to obtain wastewater water quality data. However, if the sampling frequency is not reduced when the wastewater water quality is relatively stable, the cost of dynamic monitoring and early warning of wastewater treatment will increase.

[0004] This invention proposes a dynamic monitoring and early warning system for wastewater treatment to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes a dynamic monitoring and early warning system for wastewater treatment, which addresses the issue that in actual wastewater treatment processes, the pollution level and influent volume of wastewater fluctuate significantly, making it difficult for existing technologies to collect stable wastewater indicator data, thus resulting in low accuracy in dynamic monitoring and early warning of wastewater treatment. Furthermore, most existing technologies use a fixed sampling frequency to obtain wastewater quality data, but when the wastewater quality is relatively stable, not reducing the sampling frequency will lead to increased costs for dynamic monitoring and early warning of wastewater treatment.

[0006] To achieve the above objectives, a first aspect of the present invention provides a dynamic monitoring and early warning system for wastewater treatment, comprising: a data processing module, and a data acquisition module and a monitoring and early warning module connected thereto;

[0007] The data acquisition module is used to divide the equalization tank into several monitoring areas; collect wastewater index data and environmental data of each monitoring area in real time; and monitor the wastewater inflow and water level information of the equalization tank in real time. Among them, the wastewater index data includes nitrogen content, phosphorus content, pH and dissolved oxygen concentration; and the environmental data includes temperature and wind speed.

[0008] The data processing module is used to: calculate the pollution assessment coefficient for each monitoring area based on wastewater index data; obtain the collection frequency for each monitoring area based on the pollution assessment coefficient and environmental data; and...

[0009] The wastewater inflow rate of the equalization tank is input into the wastewater inflow rate prediction model to obtain the predicted wastewater inflow rate; the operating status of the booster pump is controlled based on the predicted inflow rate and water level information; the operating status includes on and off, and the wastewater inflow rate prediction model is obtained through artificial intelligence model training;

[0010] The monitoring and early warning module is used to determine the early warning level based on the pollution assessment coefficient; and to regulate the sewage treatment facilities based on the early warning level; wherein the early warning levels include Level 1, Level 2 and Level 3.

[0011] Preferably, dividing the regulating pool into several monitoring areas includes:

[0012] The equal area of ​​the regulating pond is divided into several monitoring areas, and the monitoring areas are marked as i; where i = 1, 2, ..., n, and n is the total number of monitoring areas.

[0013] Preferably, the calculation of the pollution assessment coefficient for each monitoring area based on wastewater index data includes:

[0014] A1: Extract wastewater indicator data;

[0015] A2: The pollution assessment coefficient WXi of monitoring area i is calculated using the formula WXi=a×DHi+b×LHi+c×|PHi-7|+d×YHi; where DHi is the nitrogen content, LHi is the phosphorus content, PHI is the pH, YHi is the dissolved oxygen concentration, and a, b, c, and d are all proportionality coefficients greater than 0.

[0016] Preferably, the step of obtaining the collection frequency for each monitoring area based on pollution assessment coefficients and environmental data includes:

[0017] B1: Extract the pollution assessment coefficient WXi and environmental data for each monitoring area;

[0018] B2: The water quality stability status of each monitoring area is obtained based on the pollution assessment coefficient; whereby the water quality stability status includes high stability and low stability.

[0019] B3: The sampling frequency CPi for monitoring areas with low water quality stability is calculated using the formula CPi = (e × WXi + f × WD) / (h × FS + g); where WD is temperature, FS is wind speed, e, f, and h are all proportionality coefficients greater than 0, and g is a non-zero constant. <g<0.1;

[0020] B4: For monitoring areas with high water quality stability, the sampling frequency is once a day.

[0021] It should be noted that the collection frequency refers to the number of times the data acquisition module collects water quality data of the wastewater in the monitoring area each day. When the calculated collection frequency CPi is a decimal, it is rounded up.

[0022] This invention obtains the water quality stability status of the monitoring area through a pollution assessment coefficient and adopts a corresponding sampling frequency according to the different water quality stability statuses of the monitoring area. For monitoring areas with low water quality stability, the corresponding sampling frequency is calculated by formula based on the pollution assessment coefficient and environmental data. This allows for adaptive adjustment of the sampling frequency according to changes in pollution levels and the environment, which is beneficial to improving the accuracy of dynamic monitoring and early warning of wastewater treatment. For monitoring areas with high water quality stability, the sampling frequency is set to once a day, which can save energy and consumables of the detection devices required during the pollution index data collection process, and help reduce the cost of dynamic monitoring and early warning of wastewater treatment.

[0023] Preferably, the step of obtaining the water quality stability status of each monitoring area based on the pollution assessment coefficient includes:

[0024] C1: Generates a pollution level fluctuation function based on pollution assessment coefficients;

[0025] C2: Differentiate the pollution level fluctuation function to obtain the pollution level fluctuation derivative function;

[0026] C3: Obtain the maximum derivative value in the pollution degree fluctuation derivative function and mark the maximum derivative value as the water quality stability characteristic value;

[0027] C4: Determine whether the water quality stability characteristic value is greater than the preset fluctuation threshold; if yes, mark the water quality stability of the corresponding monitoring area as low stability; if no, mark the water quality stability of the corresponding monitoring area as high stability.

[0028] This invention generates a pollution degree fluctuation function based on a pollution assessment coefficient, and then differentiates the pollution degree fluctuation function to obtain a pollution degree fluctuation derivative function. The maximum derivative value of this derivative function is obtained and marked as a water quality stability characteristic value. This ensures that the calculated water quality stability characteristic value accurately reflects the water quality fluctuation in each monitoring area, facilitating the setting of corresponding sampling frequencies based on the water quality stability of the monitoring area. This improves the accuracy of dynamic monitoring and early warning of wastewater treatment and reduces the cost of such monitoring and early warning.

[0029] Preferably, the step of generating the pollution level fluctuation function based on the pollution assessment coefficient includes:

[0030] Pollution assessment coefficients for each equalization pool in several consecutive periods are extracted; pollution degree fluctuation functions for each equalization pool are plotted with time as the independent variable and pollution assessment coefficients as the dependent variable.

[0031] Preferably, the wastewater inflow prediction model is obtained through training an artificial intelligence model, including:

[0032] The wastewater inflow data for several consecutive periods are extracted and integrated into several sets of training and testing data. The training data is used to train the artificial intelligence model. The testing data is used to test the trained artificial intelligence model, and the artificial intelligence model is adjusted according to the testing results. Finally, a wastewater inflow prediction model is obtained, with the input being the wastewater inflow data for the most recent several consecutive periods and the output being the wastewater inflow data for the predicted period. The artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0033] This invention extracts pollution assessment coefficients to train an artificial intelligence model. After training, a wastewater inflow prediction model is obtained. By inputting the wastewater inflow of several consecutive cycles into the wastewater inflow prediction model, the wastewater inflow of the prediction cycle is obtained. This model can predict the water level changes in the regulating tank, which facilitates timely adjustment of the operating status of the booster pump. This keeps the workload of the wastewater treatment equipment relatively stable, thereby improving the efficiency of dynamic monitoring and early warning of wastewater treatment.

[0034] Preferably, the control of the booster pump's operating status based on predicted inflow and water level information includes:

[0035] D1: Extract predicted inflow and water level information;

[0036] D2: Obtain the predicted water level based on the predicted inflow and water level information;

[0037] D3: Determine whether the predicted water level is greater than the preset maximum water level; if yes, control the operation of the booster pump to be on; otherwise, jump to D4.

[0038] D4: Determine whether the predicted water level is lower than the preset minimum water level; if yes, control the operation of the booster pump to be off; otherwise, jump to D1.

[0039] Preferably, obtaining the predicted water level based on the predicted inflow and water level information includes:

[0040] Extract the predicted inflow and water level information; calculate the predicted water level YSW using the formula YSW=α×SW+β×YJS+γ; where SW is the water level information, YJS is the predicted inflow, α is the influence coefficient of the water level information, β is the influence coefficient of the predicted inflow, and γ is a constant, and α, β, and γ are all greater than 0.

[0041] Preferably, determining the early warning level based on the pollution assessment coefficient includes:

[0042] E1: Extract the pollution assessment coefficient WXi;

[0043] E2: Set the pollution range threshold [WY1, WY2], and WY1 <WY2;

[0044] E3: Determine whether the pollution assessment coefficient WXi is within the pollution range threshold [WY1, WY2]; if yes, mark the warning level as Level II warning; otherwise, proceed to E4.

[0045] E4: Determine whether the pollution assessment coefficient WXi is greater than WY2; if yes, mark the warning level as Level 1 warning; if no, mark the warning level as Level 3 warning.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] 1. This invention obtains the water quality stability status of the monitoring area through a pollution assessment coefficient, and adopts a corresponding sampling frequency according to the different water quality stability statuses of the monitoring area. For monitoring areas with low water quality stability, the corresponding sampling frequency is calculated by formula based on the pollution assessment coefficient and environmental data. This allows for adaptive adjustment of the sampling frequency according to changes in pollution levels and the environment, which is beneficial to improving the accuracy of dynamic monitoring and early warning of wastewater treatment. For monitoring areas with high water quality stability, the sampling frequency is set to once a day, which can save energy and consumables of the detection devices required during the pollution index data collection process, and help reduce the cost of dynamic monitoring and early warning of wastewater treatment.

[0048] 2. This invention generates a pollution degree fluctuation function based on a pollution assessment coefficient, and then differentiates the pollution degree fluctuation function to obtain a pollution degree fluctuation derivative function. The maximum derivative value of the pollution degree fluctuation derivative function is obtained and marked as a water quality stability characteristic value. This ensures that the calculated water quality stability characteristic value accurately reflects the water quality fluctuation in each monitoring area, facilitating the setting of corresponding sampling frequencies based on the water quality stability of the monitoring area. This improves the accuracy of dynamic monitoring and early warning of wastewater treatment and reduces the cost of such monitoring and early warning.

[0049] 3. This invention extracts pollution assessment coefficients to train an artificial intelligence model. After training, a wastewater inflow prediction model is obtained. By inputting the wastewater inflow of several consecutive cycles into the wastewater inflow prediction model, the wastewater inflow of the prediction cycle is obtained. This model can predict the water level changes in the regulating tank, which facilitates timely adjustment of the operating status of the booster pump. This keeps the workload of the wastewater treatment equipment relatively stable, thereby improving the efficiency of dynamic monitoring and early warning of wastewater treatment. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a schematic diagram of the dynamic monitoring and early warning system for wastewater treatment in this invention.

[0052] Figure 2 This is an overall flowchart of the dynamic monitoring and early warning system for wastewater treatment in this invention;

[0053] Figure 3 This is a flowchart for obtaining the water quality stability status of each monitoring area in this invention. Detailed Implementation

[0054] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see Figures 1-3The first aspect of the present invention provides a dynamic monitoring and early warning system for wastewater treatment, including: a data processing module, and a data acquisition module and a monitoring and early warning module connected thereto;

[0056] Data acquisition module: used to divide the equalization tank into several monitoring areas; to collect wastewater index data and environmental data of each monitoring area in real time; to monitor the wastewater inflow and water level information of the equalization tank in real time; among which, wastewater index data includes nitrogen content, phosphorus content, pH and dissolved oxygen concentration; environmental data includes temperature and wind speed;

[0057] Data processing module: used to calculate the pollution assessment coefficient for each monitoring area based on wastewater index data; to obtain the data collection frequency for each monitoring area based on the pollution assessment coefficient and environmental data; and,

[0058] The wastewater inflow rate of the equalization tank is input into the wastewater inflow rate prediction model to obtain the predicted wastewater inflow rate; the operating status of the booster pump is controlled based on the predicted inflow rate and water level information; the operating status includes on and off, and the wastewater inflow rate prediction model is obtained through artificial intelligence model training;

[0059] Monitoring and early warning module: used to determine the early warning level based on the pollution assessment coefficient; and to regulate the wastewater treatment facilities based on the early warning level; the early warning levels include Level 1, Level 2 and Level 3.

[0060] In this embodiment, the regulating pool is divided into several monitoring areas, including:

[0061] The equal area of ​​the regulating pond is divided into several monitoring areas, and the monitoring areas are marked as i; where i = 1, 2, ..., n, and n is the total number of monitoring areas.

[0062] For example, the equal area of ​​the regulating pond is divided into four monitoring areas, namely monitoring area 1, monitoring area 2, monitoring area 3 and monitoring area 4.

[0063] In this embodiment, the pollution assessment coefficient for each monitoring area is calculated based on wastewater index data, including:

[0064] A1: Extract wastewater indicator data;

[0065] A2: The pollution assessment coefficient WXi of monitoring area i is calculated using the formula WXi=a×DHi+b×LHi+c×|PHi-7|+d×YHi; where DHi is the nitrogen content, LHi is the phosphorus content, PHI is the pH, YHi is the dissolved oxygen concentration, and a, b, c, and d are all proportionality coefficients greater than 0.

[0066] For example, a = 1, b = 1.8, c = 20, d = 15; nitrogen content DH2 = 30 mg / L, phosphorus content LH2 = 12 mg / L, pH2 = 6.4, dissolved oxygen concentration YH2 = 2.8 mg / L; the pollution assessment coefficient WX2 = 105.6 for monitoring area 2 is calculated using the formula.

[0067] In this embodiment, the collection frequency for each monitoring area is obtained based on the pollution assessment coefficient and environmental data, including:

[0068] B1: Extract the pollution assessment coefficient WXi and environmental data for each monitoring area;

[0069] B2: The water quality stability status of each monitoring area is obtained based on the pollution assessment coefficient; whereby the water quality stability status includes high stability and low stability.

[0070] B3: The sampling frequency CPi for monitoring areas with low water quality stability is calculated using the formula CPi = (e × WXi + f × WD) / (h × FS + g); where WD is temperature, FS is wind speed, e, f, and h are all proportionality coefficients greater than 0, and g is a non-zero constant. <g<0.1;

[0071] B4: For monitoring areas with high water quality stability, the sampling frequency is once a day.

[0072] It should be noted that the collection frequency refers to the number of times the data acquisition module collects water quality data of the wastewater in the monitoring area each day. When the calculated collection frequency CPi is a decimal, it is rounded up.

[0073] For example, setting e=1, f=2, h=5, g=0.01, temperature WD=27℃, wind speed FS=3.5m / s, the water quality stability of monitoring area 1 is high, the water quality stability of monitoring area 2 is low, and the pollution assessment coefficient corresponding to monitoring area 2 is WX2=105.6; the sampling frequency CP2≈9.11 is calculated by formula, and rounded up to obtain sampling frequency CP2=10; since the water quality stability of monitoring area 1 is high, the sampling frequency of monitoring area 1 is set to once a day.

[0074] This invention obtains the water quality stability status of the monitoring area through a pollution assessment coefficient and adopts a corresponding sampling frequency according to the different water quality stability statuses of the monitoring area. For monitoring areas with low water quality stability, the corresponding sampling frequency is calculated by formula based on the pollution assessment coefficient and environmental data. This allows for adaptive adjustment of the sampling frequency according to changes in pollution levels and the environment, which is beneficial to improving the accuracy of dynamic monitoring and early warning of wastewater treatment. For monitoring areas with high water quality stability, the sampling frequency is set to once a day, which can save energy and consumables of the detection devices required during the pollution index data collection process, and help reduce the cost of dynamic monitoring and early warning of wastewater treatment.

[0075] In this embodiment, the water quality stability status of each monitoring area is obtained based on the pollution assessment coefficient, including:

[0076] C1: Generates a pollution level fluctuation function based on pollution assessment coefficients;

[0077] C2: Differentiate the pollution level fluctuation function to obtain the pollution level fluctuation derivative function;

[0078] C3: Obtain the maximum derivative value in the pollution degree fluctuation derivative function and mark the maximum derivative value as the water quality stability characteristic value;

[0079] C4: Determine whether the water quality stability characteristic value is greater than the preset fluctuation threshold; if yes, mark the water quality stability of the corresponding monitoring area as low stability; if no, mark the water quality stability of the corresponding monitoring area as high stability.

[0080] For example, the maximum derivative value in the pollution degree fluctuation derivative function corresponding to monitoring area 1 is set to 0.3, and the maximum derivative value is marked as the water quality stability characteristic value; the fluctuation threshold is set to 0.5. Since the water quality stability characteristic value is less than the preset fluctuation threshold, the water quality stability state of monitoring area 1 is marked as high stability.

[0081] This invention generates a pollution degree fluctuation function based on a pollution assessment coefficient, and then differentiates the pollution degree fluctuation function to obtain a pollution degree fluctuation derivative function. The maximum derivative value of this derivative function is obtained and marked as a water quality stability characteristic value. This ensures that the calculated water quality stability characteristic value accurately reflects the water quality fluctuation in each monitoring area, facilitating the setting of corresponding sampling frequencies based on the water quality stability of the monitoring area. This improves the accuracy of dynamic monitoring and early warning of wastewater treatment and reduces the cost of such monitoring and early warning.

[0082] In this embodiment, a pollution level fluctuation function is generated based on the pollution assessment coefficient, including:

[0083] Pollution assessment coefficients for each equalization pool in several consecutive periods are extracted; pollution degree fluctuation functions for each equalization pool are plotted with time as the independent variable and pollution assessment coefficients as the dependent variable.

[0084] In this embodiment, the wastewater inflow prediction model is obtained through training an artificial intelligence model, including:

[0085] The wastewater inflow volume of several consecutive periods is extracted and integrated into several sets of raw data. 80% of the raw data is used as training data and 20% as test data. The training data is used to train the artificial intelligence model. The test data is used to test the trained artificial intelligence model, and the artificial intelligence model is adjusted according to the test results. Finally, a wastewater inflow prediction model is obtained, which takes the wastewater inflow volume of the most recent several consecutive periods as input and outputs the wastewater inflow volume of the predicted period. The artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0086] This invention extracts pollution assessment coefficients to train an artificial intelligence model. After training, a wastewater inflow prediction model is obtained. By inputting the wastewater inflow of several consecutive cycles into the wastewater inflow prediction model, the wastewater inflow of the prediction cycle is obtained. This model can predict the water level changes in the regulating tank, which facilitates timely adjustment of the operating status of the booster pump. This keeps the workload of the wastewater treatment equipment relatively stable, thereby improving the efficiency of dynamic monitoring and early warning of wastewater treatment.

[0087] In this embodiment, controlling the operating status of the booster pump based on predicted inflow and water level information includes:

[0088] D1: Extract predicted inflow and water level information;

[0089] D2: Obtain the predicted water level based on the predicted inflow and water level information;

[0090] D3: Determine whether the predicted water level is greater than the preset maximum water level; if yes, control the operation of the booster pump to be on, and the booster pump will pump the sewage in the regulating tank to the settling tank; if no, jump to D4.

[0091] D4: Determine whether the predicted water level is lower than the preset minimum water level; if yes, control the operation of the booster pump to be off; otherwise, jump to D1.

[0092] For example, the predicted water level YSW is set to 3.6m, the preset maximum water level is 3.2m, and the preset minimum water level is 2m. Since the predicted water level is greater than the preset maximum water level, the operation status of the lift pump is controlled to be turned on, and the lift pump pumps the sewage in the equalization tank to the sedimentation tank.

[0093] This invention obtains the predicted water level by predicting the influent flow and the current water level information of the equalization tank, and determines whether the predicted water level is between the preset minimum and maximum water levels. Based on the determination result, the operating status of the booster pump is controlled so that the water level of the equalization tank is stabilized within the preset range. This reduces the impact of water level fluctuations in the equalization tank on the collection of pollution index data and helps to improve the accuracy of dynamic monitoring and early warning of sewage treatment.

[0094] In this embodiment, obtaining the predicted water level based on the predicted inflow and water level information includes:

[0095] Extract the predicted inflow and water level information; calculate the predicted water level YSW using the formula YSW=α×SW+β×YJS+γ; where SW is the water level information, YJS is the predicted inflow, α is the influence coefficient of the water level information, β is the influence coefficient of the predicted inflow, and γ is a constant, and α, β, and γ are all greater than 0.

[0096] For example, α = 1 and β = 0.05 / m are set. 2 γ = 0.2m; water level information SW = 2.4m, predicted inflow YJS = 20m³ 3 The predicted water level YSW is calculated to be 3.6m using the formula.

[0097] In this embodiment, the early warning level is determined based on the pollution assessment coefficient, including:

[0098] E1: Extract the pollution assessment coefficient WXi;

[0099] E2: Set the pollution range threshold [WY1, WY2], and WY1 <WY2;

[0100] E3: Determine whether the pollution assessment coefficient WXi is within the pollution range threshold [WY1, WY2]; if yes, mark the warning level as Level II warning; otherwise, proceed to E4.

[0101] E4: Determine whether the pollution assessment coefficient WXi is greater than WY2; if yes, mark the warning level as Level 1 warning; if no, mark the warning level as Level 3 warning.

[0102] For example, the pollution assessment coefficient WX1 is set to 113.6, and the pollution range threshold [WY1,WY2] is set to [100,120]. Since the pollution assessment coefficient WX1 is located at the pollution range threshold [WY1,WY2], the warning level is marked as Level II warning.

[0103] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0104] Working principle of the invention:

[0105] This invention divides the equalization tank into several monitoring zones; collects wastewater index data and environmental data for each monitoring zone in real time; monitors the wastewater inflow and water level information of the equalization tank in real time; calculates the pollution assessment coefficient for each monitoring zone based on the wastewater index data; obtains the collection frequency for each monitoring zone based on the pollution assessment coefficient and environmental data; inputs the wastewater inflow of the equalization tank into a wastewater inflow prediction model to obtain the predicted wastewater inflow; controls the operation status of the booster pump based on the predicted inflow and water level information; determines the early warning level based on the pollution assessment coefficient; and regulates the wastewater treatment facilities based on the early warning level.

[0106] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A dynamic monitoring and early warning system for wastewater treatment, comprising: A data processing module, and a data acquisition module and a monitoring and early warning module connected thereto; characterized in that, The data acquisition module is used to divide the equalization tank into several monitoring areas; collect wastewater index data and environmental data of each monitoring area in real time; and monitor the wastewater inflow and water level information of the equalization tank in real time. Among them, the wastewater index data includes nitrogen content, phosphorus content, pH and dissolved oxygen concentration; and the environmental data includes temperature and wind speed. The data processing module is used to: calculate the pollution assessment coefficient for each monitoring area based on wastewater index data; obtain the collection frequency for each monitoring area based on the pollution assessment coefficient and environmental data; and... The wastewater inflow rate of the equalization tank is input into the wastewater inflow rate prediction model to obtain the predicted wastewater inflow rate; the operating status of the booster pump is controlled based on the predicted inflow rate and water level information; the operating status includes on and off, and the wastewater inflow rate prediction model is obtained through artificial intelligence model training; The monitoring and early warning module is used to determine the early warning level based on the pollution assessment coefficient; and to regulate the wastewater treatment facilities based on the early warning level; wherein the early warning levels include Level 1, Level 2, and Level 3. The division of the equalization pool into several monitoring areas includes: The equal area of ​​the equalization pool is divided into several monitoring areas, and the monitoring areas are marked as i; where i = 1, 2, ..., n, and n is the total number of monitoring areas; The calculation of pollution assessment coefficients for each monitoring area based on wastewater index data includes: A1: Extract wastewater indicator data; A2: The pollution assessment coefficient WXi for monitoring area i is calculated using the formula WXi=a×DHi+b×LHi+c×|PHi-7|+d×YHi; where DHi is the nitrogen content, LHi is the phosphorus content, PHI is the pH, and YHi is the dissolved oxygen concentration. The units for nitrogen content, phosphorus content, and dissolved oxygen concentration are all mg / L; a, b, c, and d are all proportionality coefficients greater than 0, and a=1, b=1.8, c=20, and d=15 are set. The acquisition of the collection frequency for each monitoring area based on pollution assessment coefficients and environmental data includes: B1: Extract the pollution assessment coefficient WXi and environmental data for each monitoring area; B2: The water quality stability status of each monitoring area is obtained based on the pollution assessment coefficient; whereby the water quality stability status includes high stability and low stability. B3: The sampling frequency CPi for monitoring areas with low water quality stability is calculated using the formula CPi=(e×WXi+f×WD) / (h×FS+g); where WD is the temperature in °C; FS is the wind speed in m / s; e, f, and h are all proportionality coefficients greater than 0, and g is a non-zero constant, with e=1, f=2, h=5, and g=0.

01. B4: Set the sampling frequency for monitoring areas with high water quality stability to once a day; The process of obtaining the water quality stability status of each monitoring area based on the pollution assessment coefficient includes: C1: Generates a pollution level fluctuation function based on pollution assessment coefficients; C2: Differentiate the pollution level fluctuation function to obtain the pollution level fluctuation derivative function; C3: Obtain the maximum derivative value in the pollution degree fluctuation derivative function and mark the maximum derivative value as the water quality stability characteristic value; C4: Determine whether the water quality stability characteristic value is greater than the preset fluctuation threshold; if yes, mark the water quality stability of the corresponding monitoring area as low stability; if no, mark the water quality stability of the corresponding monitoring area as high stability.

2. The dynamic monitoring and early warning system for wastewater treatment according to claim 1, characterized in that, The pollution level fluctuation function generated based on the pollution assessment coefficient includes: Pollution assessment coefficients for each equalization pool in several consecutive periods are extracted; pollution degree fluctuation functions for each equalization pool are plotted with time as the independent variable and pollution assessment coefficients as the dependent variable.

3. The dynamic monitoring and early warning system for wastewater treatment according to claim 1, characterized in that, The wastewater inflow prediction model is obtained through training an artificial intelligence model, including: The wastewater inflow data for several consecutive periods are extracted and integrated into several sets of training and testing data. The training data is used to train the artificial intelligence model. The testing data is used to test the trained artificial intelligence model, and the artificial intelligence model is adjusted according to the testing results. Finally, a wastewater inflow prediction model is obtained, with the input being the wastewater inflow data for the most recent several consecutive periods and the output being the wastewater inflow data for the predicted period. The artificial intelligence model includes a BP neural network model or an RBF neural network model.

4. The dynamic monitoring and early warning system for wastewater treatment according to claim 1, characterized in that, The method of controlling the operation of the booster pump based on predicted inflow and water level information includes: D1: Extract predicted inflow and water level information; D2: Obtain the predicted water level based on the predicted inflow and water level information; D3: Determine whether the predicted water level is greater than the preset maximum water level; if yes, control the operation of the booster pump to be on; otherwise, jump to D4. D4: Determine whether the predicted water level is lower than the preset minimum water level; if yes, control the operation of the booster pump to be off; otherwise, jump to D1.

5. A dynamic monitoring and early warning system for wastewater treatment according to claim 4, characterized in that, The process of obtaining the predicted water level based on predicted inflow and water level information includes: Extract the predicted inflow and water level information; calculate the predicted water level YSW using the formula YSW=α×SW+β×YJS+γ; where SW is the water level information in meters (m); and YJS is the predicted inflow in meters (m). 3 α is the influence coefficient of water level information, β is the influence coefficient of predicted inflow, and γ is a constant. All three α, β, and γ are greater than 0. Therefore, α = 1 and β = 0.05 / m. 2 γ=0.2m.

6. The dynamic monitoring and early warning system for wastewater treatment according to claim 1, characterized in that, The determination of the early warning level based on the pollution assessment coefficient includes: E1: Extract pollution assessment coefficient; E2: Set the pollution range threshold [WY1, WY2], and WY1 <WY2; E3: Determine whether the pollution assessment coefficient WXi is within the pollution range threshold [WY1, WY2]; if yes, mark the warning level as Level II warning; otherwise, proceed to E4. E4: Determine whether the pollution assessment coefficient is greater than WY2; if yes, mark the warning level as Level 1; if no, mark the warning level as Level 3.

Images