A disinfection monitoring system based on centralized water supply

Abstract

The present invention discloses a disinfection monitoring system based on centralized water supply, which relates to the technical field of drinking water safety assurance. The system includes water quality monitoring equipment, a data acquisition and storage unit, an adaptive sampling unit, a water quality data analysis unit, a monitoring report generation and management unit, and an early warning information feedback unit, wherein the water quality monitoring equipment is used to measure the real-time monitoring data of the collected sample water, and the water quality monitoring equipment is deployed in the centralized water supply system; the adaptive sampling unit is used to adjust the sampling method of the sample water using an adaptive sampling algorithm based on the changes in the real-time monitoring data; and perform sampling operations based on the adjusted sampling method; the early warning information feedback unit is used to send the abnormal water quality early warning information of the centralized water supply source to the management end in real time when the monitoring report indicates that the disinfectant concentration is higher than the preset concentration threshold. The present application realizes adaptive monitoring and prediction of water quality.

Description

Technical Field

[0001] The present application relates to the technical field of drinking water safety assurance, and in particular to a disinfection monitoring system based on centralized water supply. Background Art

[0002] Pathogens in drinking water are one of the major threats to public health. Disinfection is a key step in killing pathogens in water, usually using disinfectants such as chlorine. However, disinfectant residues can be harmful to human health.

[0003] Therefore, it is necessary to monitor the concentration of disinfectants in water. At present, commonly used monitoring methods include high performance liquid chromatography (HPLC) and electrochemical methods, but these methods have some problems, such as complex operation, long time consumption, high cost, etc.

[0004] In view of this, this patent proposes a disinfection monitoring system based on centralized water supply. Summary of the invention

[0005] In view of the shortcomings of existing sediment monitoring, the present invention discloses a disinfection monitoring system based on centralized water supply.

[0006] The present application provides a disinfection monitoring system based on centralized water supply, including water quality monitoring equipment, data acquisition and storage unit, adaptive sampling unit, water quality data analysis unit, monitoring report generation and management unit and early warning information feedback unit, wherein:

[0007] The water quality monitoring device is used to measure the real-time monitoring data of the collected sample water, and the water quality monitoring device is deployed in a centralized water supply system, wherein the water quality monitoring device includes a sensor, and the monitoring indicators corresponding to the real-time monitoring data include turbidity, dissolved oxygen, heavy metal content, bacteria and microorganism content, and pH value. The water quality monitoring device is electrically connected to the data acquisition and storage unit, and the sample water is taken from a centralized water supply source;

[0008] The data acquisition and storage unit is used to obtain the real-time monitoring data collected by the water quality monitoring equipment and store the data in a database;

[0009] The adaptive sampling unit is used to adjust the sampling method of the sample water using an adaptive sampling algorithm based on the changes in the real-time monitoring data; and perform a sampling operation based on the adjusted sampling method; wherein the sampling method of the sample water includes a sampling position and a sampling frequency, and the adaptive sampling algorithm includes a genetic algorithm and a fuzzy logic control algorithm;

[0010] The water quality data analysis unit is used to establish a prediction model for disinfectant concentration using the real-time monitoring data based on a decision tree model;

[0011] The monitoring report generation and management unit is used to generate a monitoring report on the disinfectant concentration according to the prediction result of the prediction model of the disinfectant concentration, and transmit the monitoring report to the management end, wherein the monitoring report includes the real-time monitoring data of the sample water and the prediction result of the disinfectant concentration in the centralized water supply source;

[0012] The early warning information feedback unit is used to send abnormal water quality early warning information of the centralized water supply source to the management end in real time when the monitoring report indicates that the disinfectant concentration is higher than a preset concentration threshold.

[0013] Furthermore, the monitoring index includes dissolved oxygen and pH value, the adaptive sampling algorithm includes a fuzzy logic control algorithm, and the sampling method of the sample water is adjusted by using the adaptive sampling algorithm based on the change of the real-time monitoring data, including:

[0014] The real-time monitoring data collected at the preset sampling time is used as an input variable of the fuzzy logic control algorithm, and the correlation between the sampling method and the real-time monitoring data is calculated;

[0015] According to the index values ​​of the adjacent sampling points of the pH index, obtaining a first change amount of the pH index within a first time interval corresponding to the adjacent sampling points, and using the first change amount to obtain a first change amount set of the pH index within the first time interval;

[0016] Determine, according to the index values ​​of the adjacent sampling points of the dissolved oxygen index, a second variation of the dissolved oxygen index within a second time interval corresponding to the adjacent sampling points, and obtain a second variation set of the dissolved oxygen index within the second time interval by using the second variation;

[0017] The best sampling scheme is selected with reference to the first set of variation quantities and the second set of variation quantities.

[0018] Furthermore, the real-time monitoring data collected at the preset sampling time is used as an input variable of the fuzzy logic control algorithm to calculate the correlation between the sampling method and the real-time monitoring data, including:

[0019] Using the real-time monitoring data collected at the preset sampling time as an input variable of a fuzzy logic control algorithm, and using the correlation between the sampling method and the real-time monitoring data as an output variable of a fuzzy logic controller;

[0020] Using a membership function to transform the input variable and the output variable from precise quantities to fuzzy semantic quantities;

[0021] The fuzzy semantic quantity of each membership degree is calculated by the fuzzy rules in the fuzzy rule base to obtain the relationship between the input fuzzy variable and the output fuzzy variable;

[0022] The output fuzzy variable is defuzzified to determine the correlation between the sampling method and the real-time monitoring data.

[0023] Furthermore, the decision tree model is used to establish a prediction model for disinfectant concentration using the real-time monitoring data, including:

[0024] Using the real-time monitoring data as training data for a prediction model of disinfectant concentration, and using a random forest algorithm to establish multiple decision trees based on the input training data set;

[0025] The multiple decision trees are integrated to obtain a prediction model for the disinfectant concentration.

[0026] Furthermore, before using the real-time monitoring data as training data for a prediction model of disinfectant concentration and establishing multiple decision trees based on the input training data set using a random forest algorithm,

[0027] The real-time monitoring data is divided into blocks, and then the data in each data block is divided into two categories, the category with a large number of samples is the majority class, and the category with a small number of samples is the minority class, and the number of samples in the majority class and the minority class are calculated respectively;

[0028] Then, random sampling is performed from the samples of the majority class, and the number of samples of the majority class in each sampling is equal to the number of samples of the minority class;

[0029] Combining the samples of the majority class after random sampling with the samples of the minority class respectively to generate a random sampling subset;

[0030] The randomly sampled subset is added to the training data set.

[0031] Furthermore, the sensors include: a temperature sensor, a water flow velocity sensor, a pH sensor, a dissolved oxygen sensor and a microbial sensor.

[0032] Furthermore, the early warning information feedback unit includes a sample water real-time monitoring data recording module, and the sample water real-time monitoring data recording module is used to record the real-time monitoring data of the sample water for real-time viewing and analysis by the management end.

[0033] Furthermore, the centralized water supply source is water obtained after disinfection using a disinfectant, wherein the disinfectant includes one or more of the following disinfectants: peracetic acid, glutaraldehyde, benzalkonium bromide, povidone iodine, chlorocresol, acetic acid and potassium persulfate.

[0034] Furthermore, the disinfection monitoring system based on centralized water supply also includes a water quality monitoring equipment switch unit, which is used to receive the monitoring instructions sent by the management end and control the switch of the water quality monitoring equipment according to the monitoring instructions.

[0035] Based on the embodiments provided in the present application, in combination with water quality monitoring equipment, data acquisition and storage units, adaptive sampling units, water quality data analysis units, monitoring report generation and management units, and early warning information feedback units, adaptive monitoring and prediction of water quality are achieved, the accuracy and efficiency of monitoring are improved, and the safety of drinking water is ensured; through intelligent monitoring, accurate monitoring of disinfectant concentration can ensure the safety of drinking water, while improving the efficiency and convenience of monitoring and reducing the waste of manpower and material resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0037] Figure 1 is a structural diagram of an optional disinfection monitoring system based on centralized water supply according to an embodiment of the present application;

[0038] Figure 2 The present invention is an optional flowchart of adjusting the sampling method of sample water using an adaptive sampling algorithm according to an embodiment of the present application.

[0039] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0041] Alternatively, if Figure 1 As shown, the present application provides a disinfection monitoring system based on centralized water supply, including a water quality monitoring device 101, a data acquisition and storage unit 102, an adaptive sampling unit 103, a water quality data analysis unit 104, a monitoring report generation and management unit 105, and an early warning information feedback unit 106, wherein:

[0042] The water quality monitoring device 101 is used to measure the real-time monitoring data of the collected sample water. The water quality monitoring device 101 is deployed in the centralized water supply system. The water quality monitoring device 101 includes a sensor. The monitoring indicators corresponding to the real-time monitoring data include turbidity, dissolved oxygen, heavy metal content, bacteria and microorganism content, and pH value. The water quality monitoring device 101 is electrically connected to the data acquisition and storage unit 102. The sample water is taken from the centralized water supply source.

[0043] The data acquisition and storage unit 102 is used to obtain the real-time monitoring data collected by the water quality monitoring device 101 and store the data in a database;

[0044] The adaptive sampling unit 103 is used to adjust the sampling method of the sample water by using an adaptive sampling algorithm based on the changes of the real-time monitoring data; and perform the sampling operation based on the adjusted sampling method; wherein the sampling method of the sample water includes a sampling position and a sampling frequency, and the adaptive sampling algorithm includes a genetic algorithm and a fuzzy logic control algorithm;

[0045] A water quality data analysis unit 104, for establishing a prediction model of disinfectant concentration using real-time monitoring data based on a decision tree model;

[0046] A monitoring report generation and management unit 105, for generating a monitoring report of the disinfectant concentration according to the prediction result of the prediction model of the disinfectant concentration, and transmitting the monitoring report to the management end, wherein the monitoring report includes the real-time monitoring data of the sample water and the prediction result of the disinfectant concentration in the centralized water supply source;

[0047] The warning information feedback unit 106 is used to send abnormal water quality warning information of the centralized water supply source to the management end in real time when the monitoring report indicates that the disinfectant concentration is higher than the preset concentration threshold.

[0048] Based on the embodiments provided in the present application, in combination with water quality monitoring equipment, data acquisition and storage units, adaptive sampling units, water quality data analysis units, monitoring report generation and management units, and early warning information feedback units, adaptive monitoring and prediction of water quality are achieved, the accuracy and efficiency of monitoring are improved, and the safety of drinking water is ensured; through intelligent monitoring, accurate monitoring of disinfectant concentration can ensure the safety of drinking water, while improving the efficiency and convenience of monitoring and reducing the waste of manpower and material resources.

[0049] Furthermore, if Figure 2 As shown, the monitoring indicators include dissolved oxygen and pH value. The adaptive sampling algorithm includes a fuzzy logic control algorithm. Based on the changes in real-time monitoring data, the adaptive sampling algorithm is used to adjust the sampling method of the sample water, including:

[0050] S201, using the real-time monitoring data collected at the preset sampling time as the input variable of the fuzzy logic control algorithm, and calculating the correlation between the sampling method and the real-time monitoring data;

[0051] S202, obtaining, according to the index values ​​of the adjacent sampling points of the pH index, a first change amount of the pH index within a first time interval corresponding to the adjacent sampling points, and obtaining a first change amount set of the pH index within the first time interval using the first change amount;

[0052] S203, determining a second variation of the dissolved oxygen index within a second time interval corresponding to the adjacent sampling points according to the index values ​​of the dissolved oxygen index at the adjacent sampling points, and obtaining a second variation set of the dissolved oxygen index within the second time interval using the second variation;

[0053] S204, referring to the first set of variation quantities and the second set of variation quantities, selecting an optimal sampling method.

[0054] Furthermore, the real-time monitoring data collected at the preset sampling time is used as the input variable of the fuzzy logic control algorithm to calculate the correlation between the sampling method and the real-time monitoring data, including:

[0055] The real-time monitoring data collected at the preset sampling time is used as the input variable of the fuzzy logic control algorithm, and the correlation between the sampling method and the real-time monitoring data is used as the output variable of the fuzzy logic controller;

[0056] The membership function is used to transform the input and output variables from precise quantities to fuzzy semantic quantities;

[0057] The fuzzy semantic quantity of each membership degree is calculated by the fuzzy rules in the fuzzy rule base to obtain the relationship between the input fuzzy variable and the output fuzzy variable;

[0058] The output fuzzy variables are defuzzified to determine the correlation between the sampling method and the real-time monitoring data.

[0059] Further, the output fuzzy variable is defuzzified to determine the correlation between the sampling method and the real-time monitoring data, including: evaluating the correlation V between the sampling method and the real-time monitoring data based on the following expression:

[0060]

[0061] Where * is the multiplication sign; t is the total number of fuzzy output results; a i represents the membership degree of the i-th fuzzy output result; b i It is the horizontal coordinate position of the centroid of the corresponding membership function.

[0062] Furthermore, based on the decision tree model, a prediction model for disinfectant concentration is established using real-time monitoring data, including:

[0063] The real-time monitoring data is used as training data for the prediction model of disinfectant concentration, and multiple decision trees are established based on the input training data set using the random forest algorithm;

[0064] Multiple decision trees were integrated to obtain a prediction model for disinfectant concentration.

[0065] Furthermore, before using the real-time monitoring data as the training data for the prediction model of disinfectant concentration and establishing multiple decision trees based on the input training data set using the random forest algorithm,

[0066] The real-time monitoring data is divided into blocks, and then the data in each data block is divided into two categories. The category with a large number of samples is the majority class, and the category with a small number of samples is the minority class. The number of samples in the majority class and the minority class are calculated respectively;

[0067] Then, random sampling is performed from the majority class samples, and the number of majority class samples in each sampling is equal to the number of minority class samples;

[0068] Combine the majority class samples after random sampling with the minority class samples to generate random sampling subsets;

[0069] Add randomly sampled subsets to the training dataset.

[0070] Furthermore, the sensors include: a temperature sensor, a water flow velocity sensor, a pH sensor, a dissolved oxygen sensor and a microbial sensor.

[0071] Furthermore, the early warning information feedback unit includes a sample water real-time monitoring data recording module, which is used to record the real-time monitoring data of the sample water for real-time viewing and analysis by the management end.

[0072] Furthermore, the centralized water supply source is water obtained after disinfection using a disinfectant, wherein the disinfectant includes one or more of the following disinfectants: peracetic acid, glutaraldehyde, benzalkonium bromide, povidone iodine, chlorocresol, acetic acid and potassium persulfate.

[0073] Furthermore, the disinfection monitoring system based on centralized water supply also includes a water quality monitoring equipment switch unit, which is used to receive monitoring instructions sent by the management end and control the switch of the water quality monitoring equipment according to the monitoring instructions.

[0074] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A disinfection monitoring system based on centralized water supply, characterized in that: It includes water quality monitoring equipment, data acquisition and storage unit, adaptive sampling unit, water quality data analysis unit, monitoring report generation and management unit and early warning information feedback unit, among which, The water quality monitoring device is used to measure the real-time monitoring data of the collected sample water, and the water quality monitoring device is deployed in a centralized water supply system, wherein the water quality monitoring device includes a sensor, and the monitoring indicators corresponding to the real-time monitoring data include turbidity, dissolved oxygen, heavy metal content, bacteria and microorganism content, and pH value. The water quality monitoring device is electrically connected to the data acquisition and storage unit, and the sample water is taken from a centralized water supply source; The data acquisition and storage unit is used to obtain the real-time monitoring data collected by the water quality monitoring equipment and store the data in a database; The adaptive sampling unit is used to adjust the sampling method of the sample water using an adaptive sampling algorithm based on the changes in the real-time monitoring data; and perform a sampling operation based on the adjusted sampling method; wherein the sampling method of the sample water includes a sampling position and a sampling frequency, and the adaptive sampling algorithm includes a genetic algorithm and a fuzzy logic control algorithm; The water quality data analysis unit is used to establish a prediction model for disinfectant concentration using the real-time monitoring data based on a decision tree model; The monitoring report generation and management unit is used to generate a monitoring report on the disinfectant concentration according to the prediction result of the prediction model of the disinfectant concentration, and transmit the monitoring report to the management end, wherein the monitoring report includes the real-time monitoring data of the sample water and the prediction result of the disinfectant concentration in the centralized water supply source; The warning information feedback unit is used to send abnormal water quality warning information of the centralized water supply source to the management end in real time when the monitoring report indicates that the disinfectant concentration is higher than the preset concentration threshold; The monitoring index includes dissolved oxygen and pH value, the adaptive sampling algorithm includes a fuzzy logic control algorithm, and the adaptive sampling algorithm is used to adjust the sampling method of the sample water based on the change of the real-time monitoring data, including: The real-time monitoring data collected at the preset sampling time is used as an input variable of the fuzzy logic control algorithm to calculate the correlation between the sampling method and the real-time monitoring data; According to the index values ​​of the adjacent sampling points of the pH index, obtaining the first variation of the pH index within the first time interval corresponding to the adjacent sampling points, and using the first variation to obtain the first variation set of the pH index within the first time interval; Determine, according to the index values ​​of the adjacent sampling points of the dissolved oxygen index, a second variation of the dissolved oxygen index within a second time interval corresponding to the adjacent sampling points, and obtain a second variation set of the dissolved oxygen index within the second time interval using the second variation; Selecting an optimal sampling scheme with reference to the first set of variation quantities and the second set of variation quantities; The real-time monitoring data collected at the preset sampling time is used as an input variable of the fuzzy logic control algorithm to calculate the correlation between the sampling method and the real-time monitoring data, including: Using the real-time monitoring data collected at the preset sampling time as an input variable of a fuzzy logic control algorithm, and using the correlation between the sampling method and the real-time monitoring data as an output variable of a fuzzy logic controller; Using a membership function to transform the input variable and the output variable from precise quantities to fuzzy semantic quantities; The fuzzy semantic quantity of each membership degree is calculated by the fuzzy rules in the fuzzy rule base to obtain the relationship between the input fuzzy variable and the output fuzzy variable; Defuzzifying the output fuzzy variable to determine the correlation between the sampling method and the real-time monitoring data; The defuzzifying the output fuzzy variable to determine the correlation between the sampling mode and the real-time monitoring data includes: evaluating and obtaining the correlation V between the sampling mode and the real-time monitoring data based on the following expression: Where * is the multiplication sign; t is the total number of fuzzy output results; a i represents the membership degree of the i-th fuzzy output result; b i It is the horizontal coordinate position of the centroid of the corresponding membership function.

2. The disinfection monitoring system based on centralized water supply according to claim 1 is characterized in that: The method of establishing a prediction model for disinfectant concentration based on a decision tree model using the real-time monitoring data includes: Using the real-time monitoring data as training data for a prediction model of disinfectant concentration, and using a random forest algorithm to establish multiple decision trees based on the input training data set; The multiple decision trees are integrated to obtain a prediction model for the disinfectant concentration.

3. The disinfection monitoring system based on centralized water supply according to claim 2 is characterized in that: Before using the real-time monitoring data as training data for the prediction model of disinfectant concentration and establishing multiple decision trees based on the input training data set using a random forest algorithm, The real-time monitoring data is divided into blocks, and then the data in each data block is divided into two categories, the category with a large number of samples is the majority class, and the category with a small number of samples is the minority class, and the number of samples in the majority class and the minority class are calculated respectively; Then, random sampling is performed from the samples of the majority class, and the number of samples of the majority class in each sampling is equal to the number of samples of the minority class; Combining the samples of the majority class after random sampling with the samples of the minority class respectively to generate a random sampling subset; The randomly sampled subset is added to the training data set.

4. The disinfection monitoring system based on centralized water supply according to claim 1 is characterized in that: The sensors include: a temperature sensor, a water flow velocity sensor, a pH sensor, a dissolved oxygen sensor and a microbial sensor.

5. The disinfection monitoring system based on centralized water supply according to claim 1 is characterized in that: The early warning information feedback unit includes a sample water real-time monitoring data recording module, and the sample water real-time monitoring data recording module is used to record the real-time monitoring data of the sample water for real-time viewing and analysis by the management end.

6. The disinfection monitoring system based on centralized water supply according to claim 1 is characterized in that: The centralized water supply source is water obtained after disinfection using a disinfectant, wherein the disinfectant includes one or more of the following disinfectants: peracetic acid, glutaraldehyde, benzalkonium bromide, povidone iodine, chlorocresol, acetic acid and potassium persulfate.

7. The disinfection monitoring system based on centralized water supply according to claim 1 is characterized in that: The disinfection monitoring system based on centralized water supply also includes a water quality monitoring equipment switch unit, which is used to receive the monitoring instructions sent by the management end and control the switch of the water quality monitoring equipment according to the monitoring instructions.

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