Sewage treatment equipment monitoring system and method based on Internet of Things

Abstract

The invention discloses a sewage treatment equipment monitoring system and method based on the Internet of Things, and belongs to the technical field of sewage treatment equipment monitoring, the system is used for monitoring the disinfection process of sewage treatment in real time, and the system specifically comprises an ultraviolet disinfection module used for monitoring the disinfection process of ultraviolet in real time; the thermal disinfection module is used for monitoring and controlling the temperature in a disinfection area in real time; the evaluation and prediction module predicts the residual microbial biomass through a model based on the disinfection effects of the ultraviolet disinfection module and the thermal disinfection module; the disinfectant adding module is used for dynamically adjusting the adding amount of the disinfectant according to the result of the disinfection prediction module; and the disinfectant residual quantity module predicts the residual quantity of the residual disinfectant in the sewage after the disinfectant is added according to the adding quantity of the disinfectant.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment equipment monitoring, and specifically to an Internet of Things-based sewage treatment equipment monitoring system and method. Background Technique

[0002] With the rapid advancement of the global urbanization process and the continuous deepening of industrialization, sewage treatment has become an important task in the field of environmental protection. Traditional sewage treatment methods face many challenges, including low treatment efficiency, high energy consumption, poor adaptability to water quality changes, and lack of effective monitoring and control means. These problems not only limit the operating efficiency of sewage treatment plants but also may pose potential threats to the environment and public health.

[0003] In the process of sewage treatment, the disinfection link is a key step to ensure the safety of the effluent water quality. Traditional disinfection methods such as chlorine disinfection and ozone disinfection, although having certain bactericidal effects, also have many limitations, such as the possible generation of harmful by-products, complex operation, and high cost. Therefore, ultraviolet disinfection and thermal disinfection, as two more environmentally friendly and efficient disinfection methods, have gradually been widely used in the field of sewage treatment. However, even with advanced disinfection technologies, traditional sewage treatment systems still face problems of incomplete monitoring and inaccurate control. Especially in terms of disinfectant dosing, traditional practices often rely on manual experience or fixed ratios for dosing, and it is difficult to make dynamic adjustments according to real-time water quality changes. This results in either excessive disinfectant dosing, causing waste of resources and environmental pollution, or insufficient dosing, affecting the disinfection effect and making the effluent water quality fail to meet the standards.

[0004] In addition, the residual amount of disinfectant in the sewage treatment process is also an issue that needs to be closely monitored. Excessive residual amounts may not only have a negative impact on the environment and ecosystem but also pose a potential threat to human health. Therefore, how to accurately predict and control the residual amount of disinfectant has become a technical problem urgently to be solved in the field of sewage treatment. Summary of the Invention

[0005] The purpose of the present invention is to provide an Internet of Things-based sewage treatment equipment monitoring system and method to solve the problems raised in the above background technique.

[0006] To solve the above technical problems, the present invention provides the following technical solution: An Internet of Things-based sewage treatment equipment monitoring system, the system includes an ultraviolet disinfection module, a thermal disinfection module, an evaluation and prediction module, a disinfectant dosing module, and a disinfectant residual amount module;

[0007] The ultraviolet disinfection module is used to monitor the ultraviolet disinfection process in real time;

[0008] The thermal disinfection module is used to monitor and control the temperature in the disinfection area in real time;

[0009] The evaluation and prediction module predicts the remaining microbial quantity through a model based on the disinfection effects of the ultraviolet disinfection module and the thermal disinfection module;

[0010] The disinfectant dosing module is used to dynamically adjust the dosing amount of the disinfectant according to the result of the disinfection prediction module;

[0011] The disinfectant residue amount module predicts the residual amount of the disinfectant in the sewage after the disinfectant is dosed according to the dosing amount of the disinfectant.

[0012] An ultraviolet radiation intensity sensor, a timer, a heater, a temperature sensor, a disinfectant concentration sensor, a water quality parameter sensor, a flow meter, a residual chlorine sensor, and an equipment status sensor are installed on the sewage treatment equipment;

[0013] Further, the ultraviolet disinfection module includes an ultraviolet control unit and a radiation intensity monitoring unit; the ultraviolet control unit collects the radiation intensity and irradiation time of the ultraviolet lamp tubes in real time through the ultraviolet radiation intensity sensor and the timer, monitors the water quality status in real time through the water quality parameter sensor, and automatically turns on the ultraviolet disinfection and sets the disinfection time according to the preset water quality monitoring result when disinfection is required. When the ultraviolet irradiation duration reaches the set value, the ultraviolet lamp tubes are automatically turned off;

[0014] The radiation intensity monitoring unit sets a radiation intensity threshold. When the monitored radiation intensity is lower than the set threshold, the system automatically adjusts the output power of the lamp tubes; if it cannot be restored through automatic adjustment, a warning prompt is issued to remind to replace the ultraviolet lamp tubes.

[0015] In the above technical solution, by monitoring the ultraviolet disinfection process in real time, the effectiveness and stability of ultraviolet disinfection are ensured. By automatically adjusting the output power of the ultraviolet lamp tubes and issuing a warning when the radiation intensity is insufficient, the problem of incomplete disinfection caused by insufficient ultraviolet intensity is effectively avoided, and the disinfection efficiency and reliability are improved.

[0016] Further, the thermal disinfection module includes a temperature control unit and a temperature monitoring unit; the temperature control unit monitors the temperature in the disinfection area in real time through the temperature sensor and records the disinfection time, and gradually raises the temperature of the disinfection area to the set value by controlling the power of the heater. When the temperature reaches the set value, the power of the heater is adjusted to maintain the temperature stability;

[0017] The temperature monitoring unit monitors and displays the equipment temperature in real time during the disinfection time, sets a temperature threshold. When the disinfection time reaches the set value and the temperature in the disinfection area continues to remain within the threshold, the thermal disinfection module will automatically stop heating and send a signal indicating that disinfection is completed; once the temperature exceeds the set threshold, the system will immediately stop heating and issue a warning prompt.

[0018] In the above technical solution, by monitoring and controlling the temperature in the disinfection area in real time, precise control of the thermal disinfection process is achieved. By automatically adjusting the power of the heater, it is ensured that the temperature in the disinfection area reaches and remains at the set value, thus guaranteeing the effect of thermal disinfection; at the same time, when the temperature exceeds the set threshold, the system will automatically stop heating and issue a warning, avoiding adverse effects on the equipment or water quality caused by excessive temperature.

[0019] Furthermore, the evaluation and prediction module includes a remaining microorganism prediction unit and a monitoring and warning unit; the remaining microorganism prediction unit predicts the remaining microorganism quantity based on the water quality conditions after the ultraviolet disinfection module and the thermal disinfection module; a biochemical oxygen demand prediction model based on case-based reasoning and support vector regression machine is used for predicting the remaining microorganisms; real-time data of each sensor in the sewage treatment equipment is obtained and preprocessed; case-based reasoning technology is adopted to construct a historical case library according to the historical water quality data of the sewage treatment equipment after applying the ultraviolet disinfection module and the thermal disinfection module and the water quality data under the experimental conditions of sewage treatment collected by various scientific research institutions; according to the current water quality conditions, matching is carried out with similar cases in the historical case library, and the treatment effects of the most relevant historical cases are retrieved by calculating the similarity, and the biochemical oxygen demand BOD and the remaining microorganism quantity R therein are used as the initial prediction values and input into the support vector regression machine model for training; the current real-time data is input into the trained model to predict the biochemical oxygen demand and the remaining microorganism quantity; at the same time, the prediction results of case-based reasoning and the prediction results of the support vector machine model are weighted and fused to obtain the final prediction result, and the biochemical oxygen demand BOD and the remaining microorganism quantity R are output.

[0020] The monitoring and warning unit sets warning thresholds for the biochemical oxygen demand and the remaining microorganism quantity according to the prediction results. If it is predicted that the biochemical oxygen demand BOD and the remaining microorganism quantity R exceed the standard, a warning prompt will be issued.

[0021] In the above technical solution, based on the effects of ultraviolet disinfection and thermal disinfection, the remaining microbial quantity is predicted through a model, providing a scientific basis for subsequent disinfectant dosing. Moreover, by using a prediction method that combines case-based reasoning and support vector regression machine, the complex relationships between water quality parameters, biochemical oxygen demand, and the remaining microbial quantity can be accurately captured, improving the accuracy and reliability of the prediction. Meanwhile, the monitoring and early warning unit can issue early warning prompts in a timely manner according to the prediction results, helping operators take measures promptly to ensure that the effluent water quality meets the standards.

[0022] Furthermore, the disinfectant dosing module dynamically adjusts the disinfectant dosing amount by real-time monitoring of the residual chlorine in the sewage and the sewage flow rate, combined with the remaining microbial quantity R and the sewage volume, according to the formula:

[0023]

[0024] where Q represents the disinfectant dosing amount, Q t represents the set target residual chlorine concentration, Cl represents the residual chlorine concentration in the sewage real-time monitored by the residual chlorine sensor, Q f represents the sewage flow rate, V represents the total volume of the current sewage, m is the set safety threshold of the remaining microbial quantity; α is an adjustment coefficient that adjusts the disinfectant dosing amount according to the degree of the remaining biomass exceeding the safety threshold; f represents the disinfectant efficiency factor, which is determined by the type of disinfectant, sewage temperature, and pH value, and f ∈ (0, 1).

[0025] In the above technical solution, according to the results of the disinfection prediction module and the real-time monitored sewage parameters, the dosing amount of the disinfectant is dynamically adjusted, realizing the precise dosing of the disinfectant. By comprehensively considering factors such as the target residual chlorine concentration, the real-time monitored residual chlorine concentration, sewage flow rate, sewage volume, and the remaining microbial quantity, a reasonable disinfectant dosing amount is calculated, avoiding the problems of excessive dosing or insufficient dosing of the disinfectant, improving the disinfection effect, reducing the use cost of the disinfectant, and reducing environmental pollution.

[0026] Furthermore, the disinfectant residue module includes a residue prediction unit and a safety assessment unit; the residue prediction unit predicts the remaining disinfectant residue in the sewage after the disinfectant is dosed according to the dosing amount of the disinfectant, according to the formula:

[0027]

[0028] where P represents the disinfectant residue, λ represents the decay rate of the disinfectant, and t represents the disinfection time during the sewage disinfection process;

[0029] The safety assessment unit sets a safety threshold for the residual amount of disinfectant, determines whether the residual amount of disinfectant meets the standard, and if not, generates a corresponding safety report including the specific value of the residual amount of disinfectant and the over-standard situation, and reminds the operator to take corresponding measures.

[0030] In the above technical solution, by predicting the remaining residual amount of disinfectant in the sewage after the addition of disinfectant and evaluating its safety, the safety of the effluent quality is ensured; according to factors such as the dosage of disinfectant, attenuation rate, and disinfection time, the residual amount of disinfectant is accurately predicted; at the same time, the safety assessment unit can judge whether the residual amount of disinfectant meets the standard according to the set standard and generate a corresponding safety report, which helps the operator to timely understand the residual situation of disinfectant and take corresponding measures to ensure the safety of the effluent quality.

[0031] A monitoring method for sewage treatment equipment based on the Internet of Things, the method includes the following steps:

[0032] Step S100: Real-time monitor the ultraviolet disinfection process of the sewage treatment equipment;

[0033] Step S200: Real-time monitor the thermal disinfection process of the sewage treatment equipment;

[0034] Step S300: Based on the water quality treatment effects of ultraviolet disinfection and thermal disinfection, predict the remaining microbial quantity through a model;

[0035] Step S400: Dynamically adjust the dosage of disinfectant according to the remaining microbial quantity predicted by disinfection;

[0036] Step S500: Predict the remaining residual amount of disinfectant in the sewage after the addition of disinfectant according to the dosage of disinfectant.

[0037] In step S100, an ultraviolet radiation intensity sensor, a timer, a heater, a temperature sensor, a disinfectant concentration sensor, a water quality parameter sensor, a residual chlorine sensor, and an equipment status sensor are installed on the sewage treatment equipment; the radiation intensity and irradiation time of the ultraviolet lamp tube are collected in real time through the ultraviolet radiation intensity sensor and the timer, the water quality status is monitored in real time through the water quality parameter sensor, and when disinfection is required according to the preset water quality monitoring results, the ultraviolet disinfection is automatically turned on and the disinfection time is set, and the ultraviolet lamp tube is automatically turned off when the ultraviolet irradiation duration reaches the set value; a radiation intensity threshold is set, and when the monitored radiation intensity is lower than the set threshold, the system automatically adjusts the output power of the lamp tube; if it cannot be restored through automatic adjustment, a warning prompt is issued to remind to replace the ultraviolet lamp tube.

[0038] In step S200, the temperature in the disinfection area is monitored in real time by a temperature sensor and the disinfection time is recorded. By controlling the power of the heater, the temperature in the disinfection area is gradually increased to the set value. When the temperature reaches the set value, the power of the heater is adjusted to maintain a stable temperature. During the disinfection time, the temperature of the device is monitored and displayed in real time, and a temperature threshold is set. When the disinfection time reaches the set value and the temperature in the disinfection area continuously remains within the threshold, the thermal disinfection module will automatically stop heating and send a signal indicating that disinfection is completed. Once the temperature exceeds the set threshold, the system will immediately stop heating and issue a warning prompt.

[0039] Step S300 predicts the remaining microbial quantity based on the water quality conditions processed by the ultraviolet disinfection module and the thermal disinfection module. A biochemical oxygen demand prediction model based on case-based reasoning and support vector regression machine is used for predicting the remaining microorganisms. Real-time data of each sensor in the sewage treatment equipment are obtained and preprocessed. Case-based reasoning technology is adopted to construct a historical case library according to the historical water quality data of the sewage treatment equipment after applying the ultraviolet disinfection module and the thermal disinfection module, as well as the water quality data under the experimental conditions of sewage treatment collected by various scientific research institutions. According to the current water quality conditions, a match is made with the similar cases in the historical case library, and the treatment effects of the most relevant historical cases are retrieved by calculating the similarity, and the biochemical oxygen demand BOD and the remaining microbial quantity R therein are used as the initial prediction values and input into the support vector regression machine model for training. The current real-time data are input into the trained model to predict the biochemical oxygen demand and the remaining microbial quantity. At the same time, the prediction results of case-based reasoning and the prediction results of the support vector machine model are weighted and fused to obtain the final prediction result, and the biochemical oxygen demand BOD and the remaining microbial quantity R are output. Warning thresholds for the biochemical oxygen demand and the remaining microbial quantity are set. If it is predicted that the biochemical oxygen demand BOD and the remaining microbial quantity R exceed the standard, a warning prompt is issued.

[0040] In step S400, the chlorine dosage is dynamically adjusted in real time by monitoring the residual chlorine in the sewage and the sewage flow rate in combination with the remaining microbial quantity R and the sewage volume. According to the formula:

[0041]

[0042] where Q represents the chlorine dosage, Q t represents the set target residual chlorine concentration, Cl represents the residual chlorine concentration in the sewage monitored in real time by the residual chlorine sensor, Q f represents the sewage flow rate, V represents the total volume of the current sewage, m is the set safety threshold of the remaining microbial quantity; α is an adjustment coefficient for adjusting the chlorine dosage according to the degree of the remaining biomass exceeding the safety threshold; f represents the disinfectant efficiency factor, which is determined by the type of disinfectant, the sewage temperature, and the pH value, and f ∈ (0, 1).

[0043] Step S500 predicts the remaining amount of disinfectant residue in the sewage after the addition of the disinfectant according to the dosage of the disinfectant, according to the formula:

[0044]

[0045] where P represents the disinfectant residue amount, λ represents the decay rate of the disinfectant, and t represents the disinfection time during the sewage disinfection process; set a safety threshold for the disinfectant residue amount, judge whether the disinfectant residue amount meets the standard, if it does not meet the standard, generate a corresponding safety report including the specific value of the disinfectant residue amount and the over-standard situation, and remind the operator to take corresponding measures.

[0046] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0047] The present invention uses various sensors such as an ultraviolet radiation intensity sensor and a temperature sensor to monitor the ultraviolet disinfection and thermal disinfection processes in real time, ensuring the accuracy and stability of the disinfection effect, and sending out early warning prompts in a timely manner according to the preset early warning threshold, helping the operator to quickly respond and handle abnormal situations, which helps to reduce the failure downtime and improve the operation reliability and stability of the sewage treatment equipment;

[0048] The present invention predicts the remaining microbial amount through a biochemical oxygen demand prediction model based on case-based reasoning and support vector regression machine, and dynamically adjusts the dosage of the disinfectant in combination with the real-time monitored residual chlorine, flow rate, etc. of the sewage, avoiding problems such as resource waste or poor disinfection effect caused by excessive or insufficient dosing, and improving the disinfection efficiency and economy; at the same time, by predicting the residual amount after the addition of the disinfectant and conducting a safety assessment, it prevents potential hazards caused by disinfectant residues to the environment and human health;

[0049] The present invention integrates multiple functional modules such as ultraviolet disinfection, thermal disinfection, evaluation and prediction, disinfectant dosing, and residual amount prediction, realizing comprehensive monitoring and control of the sewage treatment process, and improving the overall effect and economy of sewage treatment. Description of the Drawings

[0050] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0051] Figure 1 is a system module diagram of a sewage treatment equipment monitoring system based on the Internet of Things;

[0052] Figure 2 is a method flow diagram of a sewage treatment equipment monitoring method based on the Internet of Things. Detailed Embodiments

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] Please refer to Figure 1 - Figure 2 , the present invention provides a technical solution:

[0055] An Internet of Things-based sewage treatment equipment monitoring system, the system includes an ultraviolet disinfection module, a thermal disinfection module, an evaluation and prediction module, a disinfectant dosing module, and a disinfectant residue module;

[0056] The ultraviolet disinfection module is used to monitor the ultraviolet disinfection process in real time;

[0057] The thermal disinfection module is used to monitor and control the temperature in the disinfection area in real time;

[0058] The evaluation and prediction module predicts the remaining microbial quantity through a model based on the disinfection effects of the ultraviolet disinfection module and the thermal disinfection module;

[0059] The disinfectant dosing module is used to dynamically adjust the dosing amount of the disinfectant according to the result of the disinfection prediction module;

[0060] The disinfectant residue module predicts the residual amount of the disinfectant in the sewage after the disinfectant is dosed according to the dosing amount of the disinfectant.

[0061] Install an ultraviolet radiation intensity sensor, a timer, a heater, a temperature sensor, a disinfectant concentration sensor, a water quality parameter sensor, a flow meter, a residual chlorine sensor, and an equipment status sensor on the sewage treatment equipment;

[0062] The ultraviolet disinfection module includes an ultraviolet control unit and a radiation intensity monitoring unit; the ultraviolet control unit collects the radiation intensity and irradiation time of the ultraviolet lamp tube in real time through the ultraviolet radiation intensity sensor and the timer, monitors the water quality status in real time through the water quality parameter sensor, and automatically turns on the ultraviolet disinfection and sets the disinfection time when disinfection is required according to the preset water quality monitoring result. When the ultraviolet irradiation duration reaches the set value, the ultraviolet lamp tube is automatically turned off;

[0063] The radiation intensity monitoring unit sets a radiation intensity threshold. When the monitored radiation intensity is lower than the set threshold, the system automatically adjusts the output power of the lamp tube; if it cannot be restored through automatic adjustment, a warning prompt is issued to remind to replace the ultraviolet lamp tube.

[0064] In the above technical solution, by monitoring the ultraviolet disinfection process in real time, the effectiveness and stability of ultraviolet disinfection are ensured. By automatically adjusting the output power of the ultraviolet lamp tube and giving an early warning when the radiation intensity is insufficient, the problem of incomplete disinfection caused by insufficient ultraviolet intensity is effectively avoided, and the disinfection efficiency and reliability are improved.

[0065] The thermal disinfection module includes a temperature control unit and a temperature monitoring unit; the temperature control unit monitors the temperature in the disinfection area in real time through a temperature sensor and records the disinfection time, and gradually raises the temperature in the disinfection area to the set value by controlling the power of the heater. When the temperature reaches the set value, the power of the heater is adjusted to maintain the temperature stable;

[0066] The temperature monitoring unit monitors and displays the equipment temperature in real time during the disinfection time, sets a temperature threshold. When the disinfection time reaches the set value and the temperature in the disinfection area continuously remains within the threshold, the thermal disinfection module will automatically stop heating and send a signal indicating that the disinfection is completed; once the temperature exceeds the set threshold, the system will immediately stop heating and give an early warning prompt.

[0067] In the above technical solution, by monitoring and controlling the temperature in the disinfection area in real time, precise control of the thermal disinfection process is achieved. By automatically adjusting the power of the heater, it is ensured that the temperature in the disinfection area reaches and maintains the set value, thus guaranteeing the effect of thermal disinfection; at the same time, when the temperature exceeds the set threshold, the system will automatically stop heating and give an early warning, avoiding adverse effects on the equipment or water quality caused by too high temperature.

[0068] The evaluation and prediction module includes a remaining microorganism prediction unit and a monitoring and early warning unit; the remaining microorganism prediction unit predicts the amount of remaining microorganisms based on the water quality conditions after the ultraviolet disinfection module and the thermal disinfection module; a biochemical oxygen demand prediction model based on case-based reasoning and support vector regression machine is used for predicting the remaining microorganisms; real-time data of each sensor in the sewage treatment equipment is obtained and preprocessed; case-based reasoning technology is adopted to construct a historical case library according to the historical water quality data of the sewage treatment equipment after applying the ultraviolet disinfection module and the thermal disinfection module and the water quality data under the experimental conditions of sewage treatment collected by various scientific research institutions; according to the current water quality conditions, matching is carried out with similar cases in the historical case library, and the treatment effect of the most relevant historical case is retrieved by calculating the similarity, and the biochemical oxygen demand BOD and the amount of remaining microorganisms R therein are used as the initial prediction values and input into the support vector regression machine model for training; the current real-time data is input into the trained model to predict the biochemical oxygen demand and the amount of remaining microorganisms; at the same time, the prediction results of case-based reasoning and the prediction results of the support vector machine model are weighted and fused to obtain the final prediction result, and the biochemical oxygen demand BOD and the amount of remaining microorganisms R are output;

[0069] According to the prediction results, the monitoring and early warning unit sets the early warning thresholds for biochemical oxygen demand and the remaining microbial quantity. If it is predicted that the biochemical oxygen demand (BOD) and the remaining microbial quantity (R) exceed the standards, an early warning prompt will be issued.

[0070] In the above technical solution, based on the effects of ultraviolet disinfection and thermal disinfection, the remaining microbial quantity is predicted through a model, providing a scientific basis for the subsequent addition of disinfectants. Moreover, by using a prediction method that combines case-based reasoning and support vector regression machine, the complex relationship between water quality parameters, biochemical oxygen demand, and the remaining microbial quantity can be accurately captured, improving the accuracy and reliability of the prediction. At the same time, the monitoring and early warning unit can promptly issue an early warning prompt according to the prediction results, helping operators take timely measures to ensure that the effluent water quality meets the standards.

[0071] The disinfectant dosing module dynamically adjusts the disinfectant dosing amount by real-time monitoring of the residual chlorine in the sewage and the sewage flow rate, combined with the remaining microbial quantity (R) and the sewage volume, according to the formula:

[0072]

[0073] where Q represents the disinfectant dosing amount, Q t represents the set target residual chlorine concentration, Cl represents the residual chlorine concentration in the sewage real-time monitored by the residual chlorine sensor, Q f represents the sewage flow rate, V represents the total volume of the current sewage, m is the set safety threshold of the remaining microbial quantity; α is an adjustment coefficient that adjusts the disinfectant dosing amount according to the degree of the remaining biomass exceeding the safety threshold; f represents the disinfectant efficiency factor, which is determined by the type of disinfectant, sewage temperature, and pH value, and f ∈ (0, 1).

[0074] In the above technical solution, according to the results of the disinfection prediction module and the real-time monitored sewage parameters, the dosing amount of the disinfectant is dynamically adjusted, achieving the precise dosing of the disinfectant. By comprehensively considering factors such as the target residual chlorine concentration, the real-time monitored residual chlorine concentration, sewage flow rate, sewage volume, and the remaining microbial quantity, a reasonable disinfectant dosing amount is calculated, avoiding the problems of excessive dosing or insufficient dosing of the disinfectant, improving the disinfection effect, reducing the use cost of the disinfectant, and reducing environmental pollution.

[0075] The disinfectant residue module includes a residue prediction unit and a safety assessment unit; the residue prediction unit predicts the remaining disinfectant residue in the sewage after the addition of the disinfectant according to the dosing amount of the disinfectant, according to the formula:

[0076]

[0077] Where P represents the residual amount of disinfectant, λ represents the decay rate of the disinfectant, and t represents the disinfection time during the sewage disinfection process;

[0078] The safety assessment unit sets a safety threshold for the residual amount of disinfectant, determines whether the residual amount of disinfectant meets the standard. If it does not meet the standard, it generates a corresponding safety report including the specific value of the residual amount of disinfectant and the over-standard situation, and reminds the operator to take corresponding measures.

[0079] In the above technical solution, by predicting the remaining residual amount of disinfectant in the sewage after the addition of disinfectant and evaluating its safety, the safety of the effluent quality is ensured; according to factors such as the dosage of disinfectant, decay rate, and disinfection time, the residual amount of disinfectant is accurately predicted; at the same time, the safety assessment unit can judge whether the residual amount of disinfectant meets the standard according to the set standard and generate a corresponding safety report, which helps the operator to timely understand the residual situation of the disinfectant and take corresponding measures to ensure the safety of the effluent quality.

[0080] A monitoring method for sewage treatment equipment based on the Internet of Things, the method includes the following steps:

[0081] Step S100: Real-time monitor the ultraviolet disinfection process of the sewage treatment equipment;

[0082] Step S200: Real-time monitor the thermal disinfection process of the sewage treatment equipment;

[0083] Step S300: Based on the water quality treatment effects of ultraviolet disinfection and thermal disinfection, predict the remaining microbial quantity through a model;

[0084] Step S400: Dynamically adjust the dosage of disinfectant according to the predicted remaining microbial quantity of disinfection;

[0085] Step S500: Predict the remaining residual amount of disinfectant in the sewage after the addition of disinfectant according to the dosage of disinfectant.

[0086] In step S100, install an ultraviolet radiation intensity sensor, a timer, a heater, a temperature sensor, a disinfectant concentration sensor, a water quality parameter sensor, a residual chlorine sensor, and an equipment status sensor on the sewage treatment equipment; collect the radiation intensity and irradiation time of the ultraviolet lamp tube in real time through the ultraviolet radiation intensity sensor and the timer, monitor the water quality status in real time through the water quality parameter sensor, and automatically turn on the ultraviolet disinfection and set the disinfection time when disinfection is required according to the preset water quality monitoring results. When the irradiation time of ultraviolet reaches the set value, automatically turn off the ultraviolet lamp tube; set a radiation intensity threshold, and when the monitored radiation intensity is lower than the set threshold, the system automatically adjusts the output power of the lamp tube; if it cannot be restored through automatic adjustment, issue a warning prompt to remind to replace the ultraviolet lamp tube.

[0087] In step S200, the temperature in the disinfection area is monitored in real time by a temperature sensor and the disinfection time is recorded. By controlling the power of the heater, the temperature in the disinfection area is gradually increased to the set value. When the temperature reaches the set value, the power of the heater is adjusted to maintain a stable temperature. During the disinfection time, the device temperature is monitored and displayed in real time. A temperature threshold is set. When the disinfection time reaches the set value and the temperature in the disinfection area continuously remains within the threshold, the thermal disinfection module will automatically stop heating and send a signal indicating that disinfection is completed. Once the temperature exceeds the set threshold, the system will immediately stop heating and issue a warning prompt.

[0088] Step S300 predicts the remaining microbial quantity based on the water quality conditions processed by the ultraviolet disinfection module and the thermal disinfection module. A biochemical oxygen demand prediction model based on case-based reasoning and support vector regression machine is used for predicting the remaining microorganisms. The real-time data of each sensor in the sewage treatment equipment is obtained and preprocessed. Case-based reasoning technology is adopted to construct a historical case library according to the historical water quality data of the sewage treatment equipment after the ultraviolet disinfection module and the thermal disinfection module are applied, as well as the water quality data under the experimental conditions of sewage treatment collected by various scientific research institutions. According to the current water quality conditions, a match is made with the similar cases in the historical case library. The treatment effects of the most relevant historical cases are retrieved by calculating the similarity, and the biochemical oxygen demand BOD and the remaining microbial quantity R therein are used as the initial prediction values and input into the support vector regression machine model for training. The current real-time data is input into the trained model to predict the biochemical oxygen demand and the remaining microbial quantity. At the same time, the prediction results of case-based reasoning and the prediction results of the support vector machine model are weighted and fused to obtain the final prediction result, and the biochemical oxygen demand BOD and the remaining microbial quantity R are output. Warning thresholds for the biochemical oxygen demand and the remaining microbial quantity are set. If it is predicted that the biochemical oxygen demand BOD and the remaining microbial quantity R exceed the standard, a warning prompt will be issued.

[0089] In step S400, the chlorine dosage is dynamically adjusted in real time by monitoring the residual chlorine in the sewage and the sewage flow rate in combination with the remaining microbial quantity R and the sewage volume. According to the formula:

[0090]

[0091] where Q represents the chlorine dosage, Q t represents the set target residual chlorine concentration, Cl represents the residual chlorine concentration in the sewage monitored in real time by the residual chlorine sensor, Q f represents the sewage flow rate, V represents the total volume of the current sewage, m is the set safety threshold of the remaining microbial quantity; α is an adjustment coefficient for adjusting the chlorine dosage according to the degree to which the remaining biomass exceeds the safety threshold; f represents the disinfectant efficiency factor, which is determined by the type of disinfectant, the sewage temperature, and the pH value, and f ∈ (0, 1).

[0092] In step S500, based on the dosage of the disinfectant, the remaining amount of the disinfectant residue in the sewage after the addition of the disinfectant is predicted according to the formula:

[0093]

[0094] where P represents the disinfectant residue amount, λ represents the decay rate of the disinfectant, and t represents the disinfection time during the sewage disinfection process; a safety threshold for the disinfectant residue amount is set, and it is judged whether the disinfectant residue amount meets the standard. If it does not meet the standard, a corresponding safety report including the specific value of the disinfectant residue amount and the over-standard situation is generated, and the operator is reminded to take corresponding measures.

[0095] In this embodiment, when the sewage treatment equipment enters the disinfection process, each sensor starts to collect data, and the ultraviolet radiation intensity threshold is preset to 900 μW / cm2, the temperature setting value is 80 °C, the target residual chlorine concentration is 2 mg / L, and the safety threshold for the remaining microbial amount is 100 CFU / mL in the monitoring system;

[0096] The data collected by the ultraviolet radiation intensity sensor in real time is 950 μW / cm2, the temperature sensor shows that the temperature in the disinfection area is 78 °C, and the water quality parameter sensor detects that the Escherichia coli concentration is 1200 CFU / 100 mL. According to the standard, the Escherichia coli concentration is in an over-standard situation; the ultraviolet control unit automatically turns on the ultraviolet lamp tube and sets the disinfection time to 30 minutes; the radiation intensity monitoring unit continuously monitors the radiation intensity. If it is lower than the set threshold of 900 μW / cm2, the output power of the lamp tube is automatically adjusted; the temperature control unit gradually raises the temperature to the set value of 80 °C by controlling the heater power. After reaching the set temperature, the heater power is adjusted to maintain the temperature stable, and the disinfection time is set to 60 minutes. When the disinfection is completed, the heater automatically stops working and sends a signal indicating that the disinfection is completed.

[0097] In this embodiment, the historical case database contains multiple historical water quality data points. Each data point includes the BOD value after ultraviolet disinfection, the R value after thermal disinfection, and corresponding water quality parameters, such as Escherichia coli concentration, temperature, radiation intensity, etc. According to the current water quality parameters, similar cases are retrieved from the historical case database, the similarity is calculated, and the initial predicted values of BOD and R are extracted from the most relevant historical cases. The initial predicted value of BOD is 15 mg / L, and the initial predicted value of R is 100 CFU / mL. The initial predicted values are input into a support vector machine model for training. The real-time data in the current sewage treatment equipment is input into the trained model, and the predicted value of BOD is output as 12 mg / L, and the predicted value of R is 120 CFU / mL. The prediction results of the two methods are weighted and fused. The weight of the case-based reasoning result is assigned as 0.4, and the weight of the model prediction result is 0.6, and the final predicted values are obtained as BOD = 13.2 mg / L and R = 112 CFU / mL.

[0098] In this embodiment, sodium hypochlorite disinfectant is used, and the residual chlorine concentration Cl monitored in real time by a residual chlorine sensor is 0.5 mg / L; the sewage flow rate Q is monitored in real time by a flow meter f is 100 m3 / h; according to the scale of the sewage treatment equipment, the total volume V of sewage in the current treatment tank is 500 m3; the safety threshold m of the remaining microbial quantity is set to 50 CFU / mL; according to the degree to which the remaining microbial quantity exceeds the safety threshold, the adjustment coefficient α is set to 1.5; the influence f of sodium hypochlorite by temperature and pH value obtained based on experimental data and technical materials provided by the manufacturer is 0.8; according to the formula:

[0099] Q = ((2 - 0.5) / 0.8)*100 / 500*(1 + 1.5*(112 - 50) / 50) = 1.875*0.2*2.86 = 1.0725 mg / h;

[0100] The decay rate of sodium hypochlorite under specific conditions is obtained by experimental determination or referring to relevant literature as λ = 0.1 / h; the disinfection time t is set to 1 h. According to the prediction formula:

[0101] Within 1 hour, the residual amount of sodium hypochlorite disinfectant in the treated sewage is approximately 0.0102 mg / m 3 , and the safety threshold of the disinfectant residue amount is set to 0.01 mg / m 3 -0.1 mg / m 3 , according to the predicted disinfectant residual amount of 0.0102 mg / m 3 , which is within the safe range. Therefore, the calculated disinfectant dosage can be added to the sewage for disinfection.

[0102] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An Internet of Things-based sewage treatment equipment monitoring system, characterized in that: The system includes an ultraviolet disinfection module, a thermal disinfection module, an evaluation and prediction module, a disinfectant dosing module, and a disinfectant residue module; The ultraviolet disinfection module is used to monitor the ultraviolet disinfection process in real time; The thermal disinfection module is used to monitor and control the temperature in the disinfection area in real time; Based on the disinfection effects of the ultraviolet disinfection module and the thermal disinfection module, the evaluation and prediction module predicts the remaining microbial quantity through a model; The disinfectant dosing module is used to dynamically adjust the dosing amount of the disinfectant according to the results of the disinfection prediction module; The disinfectant residue module predicts the residual amount of the disinfectant in the sewage after the disinfectant is dosed according to the dosing amount of the disinfectant.

2. The sewage treatment equipment monitoring system based on the Internet of Things according to claim 1, characterized in that: An ultraviolet radiation intensity sensor, a timer, a heater, a temperature sensor, a disinfectant concentration sensor, a water quality parameter sensor, a flowmeter, a residual chlorine sensor, and a device status sensor are installed on the sewage treatment equipment; The ultraviolet disinfection module includes an ultraviolet control unit and a radiation intensity monitoring unit; The ultraviolet control unit collects the radiation intensity and irradiation time of the ultraviolet lamp in real time through the ultraviolet radiation intensity sensor and the timer, monitors the water quality status in real time through the water quality parameter sensor, automatically turns on the ultraviolet disinfection and sets the disinfection time according to the preset water quality monitoring results when disinfection is required, and automatically turns off the ultraviolet lamp when the ultraviolet irradiation duration reaches the set value; The radiation intensity monitoring unit sets a radiation intensity threshold. When the monitored radiation intensity is lower than the set threshold, the system automatically adjusts the lamp output power; if it cannot be restored through automatic adjustment, a warning prompt is issued to remind to replace the ultraviolet lamp.

3. The monitoring system for sewage treatment equipment based on the Internet of Things according to claim 1, wherein: The thermal disinfection module includes a temperature control unit and a temperature monitoring unit; The temperature control unit monitors the temperature in the disinfection area in real time through the temperature sensor and records the disinfection time, gradually raises the temperature of the disinfection area to the set value by controlling the power of the heater, and adjusts the heater power to maintain the temperature stability when the temperature reaches the set value; The temperature monitoring unit monitors and displays the equipment temperature in real time during the disinfection time, sets a temperature threshold. When the disinfection time reaches the set value and the temperature in the disinfection area continuously remains within the threshold, the thermal disinfection module will automatically stop heating and send a signal indicating that disinfection is completed; once the temperature exceeds the set threshold, the system will immediately stop heating and issue a warning prompt.

4. The monitoring system for sewage treatment equipment based on the Internet of Things according to claim 1, wherein: The evaluation and prediction module includes a remaining microbe prediction unit and a monitoring and warning unit; The remaining microbe prediction unit predicts the remaining microbial quantity based on the water quality conditions after being processed by the ultraviolet disinfection module and the thermal disinfection module; Use a biochemical oxygen demand prediction model based on case-based reasoning and support vector regression machine to predict the remaining microorganisms; obtain and preprocess the real-time data of each sensor in the sewage treatment equipment; adopt case-based reasoning technology to construct a historical case library according to the historical water quality data of the sewage treatment equipment after applying ultraviolet disinfection module and thermal disinfection module, and the water quality data under the experimental conditions of sewage treatment collected by various scientific research institutions; match according to the current water quality conditions and similar cases in the historical case library, retrieve the treatment effects of the most relevant historical cases by calculating the similarity, and use the biochemical oxygen demand BOD and the remaining microorganism quantity R therein as the initial prediction values, and input them into the support vector regression machine model for training; input the current real-time data into the trained model to predict the biochemical oxygen demand and the remaining microorganism quantity; At the same time, the prediction results of case-based reasoning and the prediction results of the support vector machine model are weighted and fused to obtain the final prediction results, and output the biochemical oxygen demand BOD and the remaining microorganism quantity R; The monitoring and warning unit sets the warning thresholds of biochemical oxygen demand and remaining microorganism quantity according to the prediction results. If it is predicted that the biochemical oxygen demand BOD and the remaining microorganism quantity R exceed the standard, a warning prompt will be issued.

5. The monitoring system for sewage treatment equipment based on the Internet of Things according to claim 1, characterized in that: The disinfectant dosing module dynamically adjusts the disinfectant dosing amount in real time by monitoring the residual chlorine in the sewage and the sewage flow rate in combination with the remaining microorganism quantity R and the sewage volume, according to the formula: where Q represents the dosage of disinfectant, Q t represents the set target residual chlorine concentration, Cl represents the residual chlorine concentration in the sewage monitored in real time by the residual chlorine sensor, Q f represents the sewage flow rate, V represents the total volume of the current sewage, m is the safety threshold of the set residual microbial quantity; α is an adjustment coefficient that adjusts the disinfectant dosage according to the degree to which the residual biomass exceeds the safety threshold; f represents the disinfectant efficiency factor, which is determined by the type of disinfectant, sewage temperature, and pH value.

6. The monitoring system for sewage treatment equipment based on the Internet of Things according to claim 1, wherein: The disinfectant residue module includes a residue prediction unit and a safety assessment unit; the residue prediction unit predicts the remaining disinfectant residue in the sewage after the disinfectant is added according to the dosing amount of the disinfectant, according to the formula: Where P represents the disinfectant residue, λ represents the decay rate of the disinfectant, and t represents the disinfection time of the sewage during the disinfection process; The safety assessment unit judges whether the disinfectant residue meets the standard by setting a safety threshold for the disinfectant residue. If it does not meet the standard, a corresponding safety report will be generated, including the specific value of the disinfectant residue and the exceeding standard situation, and remind the operator to take corresponding measures.

7. An Internet of Things-based sewage treatment equipment monitoring method, characterized in that: The method includes the following steps: Step S100: Monitor the ultraviolet disinfection process of the sewage treatment equipment in real time; Step S200: Monitor the thermal disinfection process of the sewage treatment equipment in real time; Step S300: Predict the remaining microorganism quantity through a model based on the water quality treatment effects of ultraviolet disinfection and thermal disinfection; Step S400: Dynamically adjust the dosing amount of the disinfectant according to the remaining microorganism quantity predicted by disinfection; Step S500: Predict the remaining disinfectant residue in the sewage after the disinfectant is added according to the dosing amount of the disinfectant.

8. A method for monitoring a sewage treatment equipment based on the Internet of Things according to claim 7, characterized in that: In step S100, an ultraviolet radiation intensity sensor, a timer, a heater, a temperature sensor, a disinfectant concentration sensor, a water quality parameter sensor, a residual chlorine sensor, and a device status sensor are installed on the sewage treatment equipment; the radiation intensity and irradiation time of the ultraviolet lamp are collected in real time through the ultraviolet radiation intensity sensor and the timer, the water quality status is monitored in real time through the water quality parameter sensor, and according to the preset water quality monitoring results, when disinfection is required, ultraviolet disinfection is automatically turned on and the disinfection time is set, and when the ultraviolet irradiation duration reaches the set value, the ultraviolet lamp is automatically turned off; a radiation intensity threshold is set, and when the monitored radiation intensity is lower than the set threshold, the system automatically adjusts the lamp output power; if it cannot be restored through automatic adjustment, a warning prompt is issued to remind to replace the ultraviolet lamp. In step S200, the temperature in the disinfection area is monitored in real time through the temperature sensor and the disinfection time is recorded. By controlling the power of the heater, the temperature in the disinfection area is gradually increased to the set value. When the temperature reaches the set value, the heater power is adjusted to maintain the temperature stability; the device temperature is monitored and displayed in real time during the disinfection time. A temperature threshold is set. When the disinfection time reaches the set value and the temperature in the disinfection area continuously remains within the threshold, the thermal disinfection module will automatically stop heating and send a signal indicating that disinfection is completed; once the temperature exceeds the set threshold, the system will immediately stop heating and issue a warning prompt.

9. A monitoring method for sewage treatment equipment based on the Internet of Things according to claim 7, characterized in that: In step S300, the remaining microbial quantity is predicted based on the water quality conditions after being processed by the ultraviolet disinfection module and the thermal disinfection module. A biochemical oxygen demand prediction model based on case-based reasoning and support vector regression machine is used for predicting the remaining microorganisms; the real-time data of each sensor in the sewage treatment equipment is obtained and preprocessed; case-based reasoning technology is adopted to construct a historical case library according to the historical water quality data of the sewage treatment equipment after being processed by the ultraviolet disinfection module and the thermal disinfection module and the water quality data under the experimental conditions of sewage treatment collected by various scientific research institutions; according to the current water quality conditions, a match is made with the similar cases in the historical case library, and the treatment effects of the most relevant historical cases are retrieved by calculating the similarity, and the biochemical oxygen demand BOD and the remaining microbial quantity R therein are used as the initial prediction values and input into the support vector regression machine model for training; the current real-time data is input into the trained model to predict the biochemical oxygen demand and the remaining microbial quantity. At the same time, the prediction results of case-based reasoning and the prediction results of the support vector machine model are weighted and fused to obtain the final prediction result, and the biochemical oxygen demand BOD and the remaining microbial quantity R are output. Warning thresholds for the biochemical oxygen demand and the remaining microbial quantity are set. If it is predicted that the biochemical oxygen demand BOD and the remaining microbial quantity R exceed the standard, a warning prompt is issued.

10. A monitoring method for sewage treatment equipment based on the Internet of Things according to claim 7, characterized in that: In step S400, the disinfectant dosage is dynamically adjusted by monitoring the residual chlorine in the sewage and the sewage flow rate in real time in combination with the remaining microbial quantity R and the sewage volume. According to the formula: where Q represents the dosage of disinfectant, Q t represents the set target residual chlorine concentration, Cl represents the residual chlorine concentration in the sewage monitored in real time by the residual chlorine sensor, Q f represents the sewage flow rate, V represents the total volume of the current sewage, m is the safety threshold of the set residual microbial mass; α is an adjustment coefficient that adjusts the disinfectant dosage according to the degree of excess of the residual biomass over the safety threshold; f represents the disinfectant efficiency factor, which is determined by the type of disinfectant, sewage temperature, and pH value; In step S500, according to the disinfectant dosage, the remaining disinfectant residue in the sewage after the disinfectant is added is predicted. According to the formula: Where P represents the residual amount of disinfectant, λ represents the decay rate of the disinfectant, and t represents the disinfection time of the sewage during the disinfection process; Set the safety threshold of the residual amount of disinfectant, judge whether the residual amount of disinfectant meets the standard. If it does not meet the standard, generate a corresponding safety report including the specific value of the residual amount of disinfectant and the over-standard situation, and remind the operator to take corresponding measures.

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