An intelligent water service system and an intelligent control method applied thereto

Abstract

The present application provides a smart water system and an intelligent control method for its application, which belongs to the field of smart water services and is used to solve the problem in related technologies that the aeration air volume of the aerator in the aerobic tank during sewage treatment is difficult to accurately control. In this method and system, a pre-constructed aeration scheme analysis model can be used to intelligently analyze the adjustment amount of the aeration air volume and the application time of the adjusted aeration air volume based on the water volume, water temperature, dissolved oxygen content, internal return flow, external return flow, water quality indicators and aeration air volume of the aerobic tank within a preset number of unit time periods before the current moment, so that the adjusted aeration air volume meets the actual needs of sewage treatment, and there is no need to judge in real time whether the aeration air volume needs to be adjusted. The precise control of the aeration air volume in the aerobic tank during sewage treatment is achieved, which is conducive to ensuring that the treated water quality meets the standards.

Description

Technical Field

[0001] The present application relates to the field of smart water services, and in particular to a smart water system and an intelligent control method for its application. Background Art

[0002] Wastewater treatment involves a series of processes, including aeration, denitrification, ozone, and coagulation. To ensure that treated water meets quality standards, the input of resources such as oxygen, carbon sources, ozone, and flocculants must be continuously adjusted based on actual conditions. This process is typically implemented by smart water management systems, which typically use a series of monitoring devices to determine the status of wastewater treatment and, based on regulatory mechanisms such as feedback control, determine the dosage of various treatment resources. However, actual wastewater treatment conditions are complex, and feedback control and other regulatory mechanisms cannot accurately meet the actual resource requirements of wastewater treatment. Summary of the Invention

[0003] The present application provides an intelligent control method for a smart water system and its application, which can be applied to the smart water system, and is conducive to accurately controlling the aeration air volume of the aerator in the aerobic tank during sewage treatment, so that the aeration air volume meets the actual needs of sewage treatment, so as to ensure that the treated water quality meets the standards.

[0004] In a first aspect, the present application provides an intelligent control method for a smart water system. The method comprises:

[0005] Obtain the water volume, water temperature, dissolved oxygen content, internal return flow, external return flow, water quality indicators, and aeration air volume of the aerobic pool within a preset number of time units before the current moment, wherein the water quality indicators include chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH;

[0006] The water volume, water temperature, dissolved oxygen content, internal recirculation volume, external recirculation volume, water quality index and aeration air volume of the aerobic pool within a preset number of unit time periods before the current moment are input into a pre-constructed aeration scheme analysis model to obtain the adjustment amount of the aeration air volume and the application time of the adjusted aeration air volume. The aeration scheme analysis model includes an input layer, multiple hidden layers and an output layer. The input layer includes input nodes that correspond one to one to the water volume, water temperature, dissolved oxygen content, internal recirculation volume, external recirculation volume, water quality index and aeration air volume of the aerobic pool within a preset number of unit time periods before the current moment. The output layer includes two output nodes for outputting the adjustment amount of the aeration air volume and the application time of the adjusted aeration air volume.

[0007] By adopting the above technical solution, a pre-constructed aeration scheme analysis model can be used to intelligently analyze the adjustment amount of the aeration air volume and the application time of the adjusted aeration air volume based on the water volume, water temperature, dissolved oxygen content, internal return flow, external return flow, water quality indicators and aeration air volume of the aerobic tank within a preset number of unit time periods before the current moment, so that the adjusted aeration air volume can meet the actual needs of sewage treatment, and there is no need to judge in real time whether the aeration air volume needs to be adjusted. This achieves precise control of the aeration air volume in the aerobic tank during the sewage treatment process, which is conducive to ensuring that the treated water quality meets the standards.

[0008] Furthermore, the aeration scheme analysis model also includes a fluctuation value calculation sub-model and a change rate calculation sub-model;

[0009] The fluctuation value calculation submodel and the change rate calculation submodel are both connected to the input nodes of the dissolved oxygen amount of a preset number of unit time lengths in the input layer, so that the fluctuation value calculation submodel outputs the dissolved oxygen amount fluctuation value, and the change rate calculation submodel outputs the dissolved oxygen amount change rate, the dissolved oxygen amount fluctuation value is positively correlated to the standard deviation of the dissolved oxygen amount of the aerobic pool within the preset number of unit time lengths and / or the absolute value of the difference between the dissolved oxygen amounts of the aerobic pools within any two adjacent unit time lengths, and the dissolved oxygen amount change rate is positively correlated to the result of subtracting the dissolved oxygen amount of the aerobic pool in the latter of the two adjacent unit time lengths from the dissolved oxygen amount of the former;

[0010] The hidden layer includes a first special neuron, which is used to input the output of each neuron in the previous hidden layer and the dissolved oxygen fluctuation value and the dissolved oxygen change rate.

[0011] Furthermore, the fluctuation value calculation sub-model includes:

[0012] Assume that the preset number is n, and the dissolved oxygen content of the i-th unit time is , then the average value of dissolved oxygen , standard deviation of dissolved oxygen ;

[0013] The sum of the absolute values of the differences in dissolved oxygen content at adjacent time steps ;

[0014] The dissolved oxygen fluctuation value , where and are the pre-acquired standard deviation fluctuation coefficient and step difference fluctuation coefficient respectively.

[0015] Furthermore, the change rate calculation sub-model includes:

[0016] Assume that the preset number is n, and the dissolved oxygen content of the i-th unit time is , calculate the average value of the difference between adjacent time steps of dissolved oxygen The rate of change of dissolved oxygen is equal to the average value of the difference between adjacent time steps of dissolved oxygen. .

[0017] Furthermore, the aeration scheme analysis model also includes a water quality fluctuation analysis model and a fluctuation trend analysis model;

[0018] The water quality fluctuation analysis model and the fluctuation trend analysis model are both connected to the input nodes of the water quality indicators of a preset number of unit time lengths in the input layer, so that the water quality fluctuation analysis sub-model outputs water quality fluctuation data, and the fluctuation trend analysis sub-model outputs fluctuation trend data, the water quality fluctuation data is positively correlated with the standard deviation of the chemical oxygen demand, and / or biochemical oxygen demand, and / or ammonia nitrogen content, and / or phosphorus content, and / or pH within the preset number of unit time lengths, and the fluctuation trend data is positively correlated with the rate of change of the chemical oxygen demand, and / or biochemical oxygen demand, and / or ammonia nitrogen content, and / or phosphorus content, and / or pH within the preset number of unit time lengths;

[0019] The hidden layer includes a second special neuron, which is used to input the output of each neuron in the previous hidden layer and the water quality fluctuation data and fluctuation trend data.

[0020] Furthermore, the water quality fluctuation analysis sub-model includes:

[0021] Assuming the preset number is n, the chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH value of the i-th unit time are respectively 、 、 、 、 , calculate the average values of chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH, respectively.

[0022] ,

[0023] ,

[0024] ,

[0025] , ,

[0026] Then calculate the standard deviation of chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH respectively.

[0027] ,

[0028] ,

[0029] ,

[0030] ,

[0031] ,

[0032] Post-calculation of water quality fluctuation data .

[0033] Furthermore, the fluctuation trend analysis sub-model includes:

[0034] Assuming the preset number is n, the chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH value of the i-th unit time are respectively 、 、 、 、 ;

[0035] Linear regression model was used for chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH and the least squares method to fit a straight line, where y is the chemical oxygen demand, or biochemical oxygen demand, or ammonia nitrogen content, or phosphorus content, or pH, and x is the time per unit time;

[0036] The fitting lines of chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH are, ,

[0037] ,

[0038] ,

[0039] ,

[0040] ,

[0041] Determine the slopes of the fitted lines for chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH, and calculate the fluctuation trend data based on the slopes.

[0042] ,

[0043] Where, They are chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH fluctuation trend attention coefficient.

[0044] In a second aspect, the present application provides a smart water management system. The system includes a controller configured to control an adjustment amount of aeration air volume in an aerobic tank and a duration of application of the adjusted aeration air volume, the controller being configured to execute any one of the methods described in the first aspect above.

[0045] In summary, this application has at least the following beneficial effects:

[0046] Provided is a smart water system and an intelligent control method for its application, which can intelligently analyze the aeration scheme of the aeration air volume in the aerobic tank based on the parameters in the actual sewage treatment process, which is conducive to ensuring the reliability of the sewage treatment results.

[0047] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0049] Figure 1 A schematic diagram of a smart water system according to an embodiment of the present application is shown;

[0050] Figure 2 A flow chart of an intelligent control method applied to a smart water system in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0051] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0052] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0053] The sewage treatment system generally includes anaerobic tanks, anoxic tanks, aerobic tanks and secondary sedimentation tanks arranged in sequence. The external sewage to be treated continuously flows through the anaerobic tanks, anoxic tanks, aerobic tanks and secondary sedimentation tanks in sequence and is continuously discharged after meeting the standards, forming a continuous sewage treatment system with continuous reactions. Among them, the anaerobic tank is a closed reaction tank, which is used to use anaerobic microorganisms to carry out the first step of sewage treatment; the anoxic tank is located after the anaerobic tank, which is mainly used for denitrification reaction. It has an internal reflux inlet for receiving the mixed liquid containing nitrate nitrogen returned from the aerobic tank; the aerobic tank is after the anoxic tank, and is equipped with an aerator. At the same time, there is an internal reflux outlet to send the mixed liquid containing nitrate nitrogen back to the anoxic tank, and an external reflux outlet to send the activated sludge back to the front-end anaerobic tank or anoxic tank. The aerobic tank is generally equipped with a dissolved oxygen sensor for real-time monitoring of the dissolved oxygen concentration. The aerobic tank is also provided with an external reflux inlet; the secondary sedimentation tank is set after the aerobic tank to achieve mud-water separation. The secondary sedimentation tank is also equipped with an external reflux outlet for returning part of the sludge to the aerobic tank.

[0054] Previous technologies typically use dissolved oxygen sensors to provide feedback and adjust the aeration volume of the aerator, ensuring that the dissolved oxygen concentration in the aerobic tank remains dynamically stable at the target value and ensuring the effectiveness of wastewater treatment. However, complex wastewater treatment conditions can affect the aerator's aeration volume mechanism, making feedback-based regulation alone insufficiently intelligent.

[0055] Therefore, the present application provides an intelligent water system and an intelligent control method for its application, which can take into account the complex situations in various aspects of the sewage treatment process and accurately control the aeration scheme of the aerator to ensure stable and reliable sewage treatment effects.

[0056] In a first aspect, an embodiment of the present application discloses a smart water system.

[0057] Figure 1 A schematic diagram of a smart water system in an embodiment of the present application is shown.

[0058] Reference Figure 1 The smart water system includes a water volume detection module, a water temperature detection module, a dissolved oxygen detection module, an internal return flow detection module, an external return flow detection module, an aeration air volume detection module, a water quality index acquisition module and a controller.

[0059] Among them, the water volume detection module is used to detect the water volume in the aerobic tank, the water temperature detection module is used to detect the water temperature in the aerobic tank, the dissolved oxygen detection module is used to detect the dissolved oxygen content in the aerobic tank, the internal reflux detection module is used to detect the internal reflux volume from the aerobic tank to the anoxic tank, the external reflux volume detection module is used to detect the external reflux volume from the secondary sedimentation tank to the aerobic tank, and the aeration air volume detection module is used to detect the aeration air volume of the aerator. The aforementioned data are all acquired in real time, and the acquired data are all timestamp-bearing. Regarding the aforementioned detection modules, they are all based on corresponding common knowledge, and there is no special improvement in this application, so they will not be elaborated on.

[0060] The water quality index acquisition module is used to obtain the water quality indicators of the input sewage. The water quality indicators include chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH. In the embodiment of the present application, the water quality indicators are estimated by the staff to be treated sewage water quality conditions, and the water quality indicators are entered into the smart water system. That is, the water quality index acquisition module can be specifically manifested as a human-computer interaction facility, such as a touch screen, for the staff to input water quality indicators. The water quality indicators also carry a timestamp. Generally speaking, if the source of the incoming sewage remains unchanged,

[0061] The controller of the smart water system is equipped with an intelligent control method. This method can intelligently analyze the aeration plan of the aerator based on the water volume, water temperature, dissolved oxygen content, internal return flow, external return flow, water quality indicators, and aeration air volume of the aerobic tank. This method can determine whether the resulting aeration plan can accurately meet the requirements of sewage treatment, thereby effectively ensuring that the treated sewage meets the standards. The intelligent control method within the controller is described based on the disclosure of the second aspect of the embodiment of this application.

[0062] In a second aspect, an embodiment of the present application discloses an intelligent control method applied to a smart water system.

[0063] Figure 2 A flow chart of an intelligent control method applied to a smart water system in an embodiment of the present application is shown.

[0064] Reference Figure 2 , the method specifically includes:

[0065] S210: Obtain the water volume, water temperature, dissolved oxygen content, internal return flow, external return flow, water quality index and aeration air volume of the aerobic pool within a preset number of unit time periods before the current moment.

[0066] For the specific content of the water quality indicators, please refer to the disclosure of the first aspect of the embodiment of this application. The process of acquiring water volume, water temperature, dissolved oxygen content, internal recirculation volume, external recirculation volume, and water quality indicators is a real-time data access process. The water volume, water temperature, and dissolved oxygen content of each unit time are all average values within the unit time. The aeration air volume, internal recirculation volume, and external recirculation volume of each unit time are the cumulative values within the unit time. The values of chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH of each water quality indicator in each unit time are the average values of each water quality indicator, where the pH value is specially processed in advance by the pH value, and the neutral value of pH=7 is defined as the zero point. The pH value is equal to the result of subtracting 7 from the actual pH value. Acidity is a negative value and alkalinity is a positive value. The average value is calculated based on the treated pH.

[0067] S220: Input the water volume, water temperature, dissolved oxygen content, internal return flow, external return flow, water quality index and aeration air volume of the aerobic pool within a preset number of unit time periods before the current moment into a pre-constructed aeration scheme analysis model to obtain the adjustment amount of the aeration air volume and the application time of the adjusted aeration air volume.

[0068] The aeration scheme analysis model is a neural network model, which includes an input layer, multiple hidden layers and an output layer. The input layer includes input nodes that correspond one-to-one to the water volume, water temperature, dissolved oxygen content, internal return flow, external return flow, water quality indicators and aeration air volume of the aerobic pool within a preset number of unit time periods before the current moment. The output layer includes two output nodes for outputting the adjustment amount of the aeration air volume and the application time of the adjusted aeration air volume.

[0069] In a specific example, the preset number is n, and the water volume, water temperature, dissolved oxygen content, internal return flow, external return flow, chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH value within the preset number of unit time periods total 10n data. These 10n data are used to input the input nodes of the aeration scheme analysis model, that is, the total number of input nodes in the input layer is 10n.

[0070] There are three hidden layers, and the first hidden layer has 20 neurons, namely , which is used for conventional feature extraction and preliminary information fusion. The content of the j-th neuron in the first hidden layer is ,in, is an activation function, such as the sigmoid function or the ReLU function, Represents the data of the i-th input node, represents the bias of the jth neuron in the second layer, is the calculated weight of the i-th input node to the j-th neuron, is the output of the jth neuron.

[0071] The second hidden layer has 15 neurons, , used for further feature extraction and information fusion, each neuron in the second hidden layer is fully connected to the output of all neurons in the first hidden layer, and the content of the kth neuron in the second hidden layer is ,in, is the activation function, is the output of the jth neuron in the first layer, is the calculation weight of the j-th neuron in the first layer to the k-th neuron in the second layer, is the bias of the kth neuron in the second layer, is the output of the kth neuron in the second layer.

[0072] The third hidden layer has 10 neurons, , used for further feature extraction and information fusion, each neuron in the third hidden layer is fully connected to the output of all neurons in the second hidden layer, and the content of the lth neuron in the third hidden layer is ,in, is the activation function, is the output of the kth neuron in the second layer, is the calculation weight of the k-th neuron in the second layer to the l-th neuron in the third layer, is the bias of the lth neuron in the third layer, is the output of the lth neuron in the third layer.

[0073] The output layer has two output nodes, and the contents of the two output nodes are and ,in, is the adjustment amount of aeration air volume, is the duration of the adjusted aeration air volume, in the previous formula, is the lth neuron pair in the third layer The calculation weight of is the lth neuron pair in the third layer The calculation weight of 、 They are 、 The offset amount.

[0074] For the above-mentioned aeration scheme analysis model, some conventional processing methods of neural network models can also be used, such as data preprocessing to normalize the values of input nodes and map them to For the training method of this model, a multi-objective loss function can be used for training, for example:

[0075] ,

[0076] in, It is based on the loss of dissolved oxygen in the aerobic tank when it deviates from the target range, such as:

[0077] ,

[0078] in, is the number of training samples, It is the predicted dissolved oxygen content of the aerobic tank based on the input sample (calculated by adding the current aeration air volume to the predicted aeration air volume adjustment). and It is the target range of dissolved oxygen in the aerobic pool.

[0079] It is the mean square error loss based on the deviation between the predicted aeration air volume and the actual required air volume:

[0080] ,

[0081] in, It is The actual aeration air volume adjustment of each sample, The model predicts Aeration air volume adjustment for each sample.

[0082] It is the mean square error loss based on the deviation between the predicted aeration time and the actual required time:

[0083] ,

[0084] in, is the actual aeration time of the sth sample, is the aeration time of the sth sample predicted by the model. and is a trade-off coefficient used to adjust the importance of different loss terms.

[0085] Regarding the back propagation and optimization process of the model, you can refer to the following:

[0086] Use stochastic gradient descent (SGD) or other optimization algorithms with adaptive learning rates (such as Adam) to update weights and biases. The Adam optimizer formula described above is used to update weights:

[0087] ,

[0088] Using the above aeration scheme analysis model, we can determine the aeration air volume adjustment amount and the duration of the adjusted aeration air volume, which is conducive to precise control of the aeration air volume. Furthermore, the model is continuously trained and optimized during use, enabling more accurate aeration scheme control.

[0089] In another example, the aeration scheme analysis model may be set to include four hidden layers, with the first layer having 15 neurons, the second layer including 10 neurons, the third layer including 8 neurons, and the fourth layer including 5 neurons.

[0090] The aeration scheme analysis model is further specially designed so that the aeration scheme analysis model also includes a fluctuation value calculation submodel and a change rate calculation submodel. The fluctuation value calculation submodel and the change rate calculation submodel are both connected to the input nodes of the dissolved oxygen amount of a preset number of unit time lengths in the input layer, so that the fluctuation value calculation submodel outputs a dissolved oxygen fluctuation value, and the change rate calculation submodel outputs a dissolved oxygen change rate. The dissolved oxygen fluctuation value is positively correlated to the standard deviation of the dissolved oxygen amount of the aerobic tank within the preset number of unit time lengths and / or the absolute value of the difference between the dissolved oxygen amounts of any two adjacent unit time lengths. The dissolved oxygen change rate is positively correlated to the result of subtracting the dissolved oxygen amount of the previous one from the dissolved oxygen amount of the next one in any two adjacent unit time lengths.

[0091] The hidden layer includes a first special neuron, which is used to input the output of each neuron in the previous hidden layer and the dissolved oxygen fluctuation value and the dissolved oxygen change rate.

[0092] Specifically, the fluctuation value calculation sub-model includes:

[0093] Assume that the preset number is n, and the dissolved oxygen content of the i-th unit time is , then the average value of dissolved oxygen , standard deviation of dissolved oxygen ;

[0094] The sum of the absolute values of the differences in dissolved oxygen content at adjacent time steps ;

[0095] The dissolved oxygen fluctuation value , where and are the pre-acquired standard deviation fluctuation coefficient and step difference fluctuation coefficient respectively.

[0096] The change rate calculation sub-model includes:

[0097] Assume that the preset number is n, and the dissolved oxygen content of the i-th unit time is , calculate the average value of the difference between adjacent time steps of dissolved oxygen The rate of change of dissolved oxygen is equal to the average value of the difference between adjacent time steps of dissolved oxygen. .

[0098] Based on the above, the first special neuron is specifically set in the second layer. The specific content of the first special neuron is:

[0099] ,

[0100] in, is the activation function, is the calculation weight of the dissolved oxygen fluctuation value on the kth first special neuron in the second layer, is the calculation weight of the dissolved oxygen change rate on the kth first special neuron in the second layer.

[0101] In another example, the aeration scheme analysis model may be further designed to further include a water quality fluctuation analysis model and a fluctuation trend analysis model;

[0102] The water quality fluctuation analysis model and the fluctuation trend analysis model are both connected to the input nodes of the water quality indicators of a preset number of unit time lengths in the input layer, so that the water quality fluctuation analysis sub-model outputs water quality fluctuation data, and the fluctuation trend analysis sub-model outputs fluctuation trend data, the water quality fluctuation data is positively correlated with the standard deviation of the chemical oxygen demand, and / or biochemical oxygen demand, and / or ammonia nitrogen content, and / or phosphorus content, and / or pH within the preset number of unit time lengths, and the fluctuation trend data is positively correlated with the rate of change of the chemical oxygen demand, and / or biochemical oxygen demand, and / or ammonia nitrogen content, and / or phosphorus content, and / or pH within the preset number of unit time lengths;

[0103] The hidden layer includes a second special neuron, which is used to input the output of each neuron in the previous hidden layer and the water quality fluctuation data and fluctuation trend data.

[0104] The water quality fluctuation analysis sub-model includes:

[0105] Assuming the preset number is n, the chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH value of the i-th unit time are respectively 、 、 、 、 , calculate the average values of chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH, respectively.

[0106] ,

[0107] ,

[0108] ,

[0109] , ,

[0110] Then calculate the standard deviation of chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH respectively.

[0111] ,

[0112] ,

[0113] ,

[0114] ,

[0115] ,

[0116] Post-calculation of water quality fluctuation data .

[0117] The fluctuation trend analysis sub-model includes:

[0118] Assuming the preset number is n, the chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH value of the i-th unit time are respectively 、 、 、 、 ;

[0119] Linear regression model was used for chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH and the least squares method to fit a straight line, where y is the chemical oxygen demand, or biochemical oxygen demand, or ammonia nitrogen content, or phosphorus content, or pH, and x is the time per unit time;

[0120] The fitting lines of chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH are, ,

[0121] ,

[0122] ,

[0123] ,

[0124] ,

[0125] Determine the slopes of the fitted lines for chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH, and calculate the fluctuation trend data based on the slopes.

[0126] ,

[0127] Where, They are chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH fluctuation trend attention coefficient.

[0128] Based on the above, the second special neuron is specifically set in the second layer. The specific content of the second special neuron is:

[0129] ,

[0130] Where, is the activation function, is the calculation weight of the water quality fluctuation data on the second special neuron in the second layer, is the calculation weight of the fluctuation trend data on the second special neuron in the second layer.

[0131] With the aforementioned supplementation, the model can better predict the fluctuations in dissolved oxygen content, temporal changes, and fluctuations and trend changes in water quality indicators in the aerobic tank, giving the model better performance and facilitating more precise control of the aeration scheme.

[0132] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to the embodiments of this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required for this application.

[0133] To sum up, this application includes at least the following beneficial effects: it provides an intelligent water system and an intelligent control method for its application, which can intelligently analyze the aeration scheme of the aeration air volume in the aerobic tank based on the parameters in the actual sewage treatment process, which is conducive to ensuring the reliability of the sewage treatment results.

[0134] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. An intelligent control method applied to a smart water system, characterized in that: include: Obtain the water volume, water temperature, dissolved oxygen content, internal return flow, external return flow, water quality indicators, and aeration air volume of the aerobic pool within a preset number of time units before the current moment, wherein the water quality indicators include chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH; Input the water volume, water temperature, dissolved oxygen content, internal return flow, external return flow, water quality index and aeration air volume of the aerobic pool within a preset number of unit time periods before the current moment into a pre-constructed aeration scheme analysis model to obtain the adjustment amount of the aeration air volume and the application duration of the adjusted aeration air volume, the aeration scheme analysis model comprising an input layer, multiple hidden layers and an output layer, the input layer comprising input nodes corresponding one-to-one to the water volume, water temperature, dissolved oxygen content, internal return flow, external return flow, water quality index and aeration air volume of the aerobic pool within a preset number of unit time periods before the current moment, and the output layer comprising two output nodes for outputting the adjustment amount of the aeration air volume and the application duration of the adjusted aeration air volume; The aeration scheme analysis model also includes a fluctuation value calculation sub-model and a change rate calculation sub-model; The fluctuation value calculation submodel and the change rate calculation submodel are both connected to the input nodes of the dissolved oxygen amount of a preset number of unit time lengths in the input layer, so that the fluctuation value calculation submodel outputs the dissolved oxygen amount fluctuation value, and the change rate calculation submodel outputs the dissolved oxygen amount change rate, the dissolved oxygen amount fluctuation value is positively correlated to the standard deviation of the dissolved oxygen amount of the aerobic pool within the preset number of unit time lengths and / or the absolute value of the difference between the dissolved oxygen amounts of the aerobic pools within any two adjacent unit time lengths, and the dissolved oxygen amount change rate is positively correlated to the result of subtracting the dissolved oxygen amount of the aerobic pool in the latter of the two adjacent unit time lengths from the dissolved oxygen amount of the former; The hidden layer includes a first special neuron, which is used to input the output of each neuron in the previous hidden layer and the dissolved oxygen fluctuation value and the dissolved oxygen change rate; The aeration scheme analysis model also includes a water quality fluctuation analysis model and a fluctuation trend analysis model; The water quality fluctuation analysis model and the fluctuation trend analysis model are both connected to the input nodes of the water quality indicators of a preset number of unit time lengths in the input layer, so that the water quality fluctuation analysis sub-model outputs water quality fluctuation data, and the fluctuation trend analysis sub-model outputs fluctuation trend data, the water quality fluctuation data is positively correlated with the standard deviation of the chemical oxygen demand, and / or biochemical oxygen demand, and / or ammonia nitrogen content, and / or phosphorus content, and / or pH within the preset number of unit time lengths, and the fluctuation trend data is positively correlated with the rate of change of the chemical oxygen demand, and / or biochemical oxygen demand, and / or ammonia nitrogen content, and / or phosphorus content, and / or pH within the preset number of unit time lengths; The hidden layer includes a second special neuron, which is used to input the output of each neuron in the previous hidden layer and the water quality fluctuation data and fluctuation trend data.

2. The method according to claim 1, characterized in that The fluctuation value calculation sub-model includes: Assume that the preset number is n, and the dissolved oxygen content of the i-th unit time is , then the average value of dissolved oxygen , standard deviation of dissolved oxygen ; The sum of the absolute values of the differences in dissolved oxygen content at adjacent time steps ; The dissolved oxygen fluctuation value , where and are the pre-acquired standard deviation fluctuation coefficient and step difference fluctuation coefficient respectively.

3. The method according to claim 1, characterized in that The change rate calculation sub-model includes: Assume that the preset number is n, and the dissolved oxygen content of the i-th unit time is , calculate the average value of the difference between adjacent time steps of dissolved oxygen The rate of change of dissolved oxygen is equal to the average value of the difference between adjacent time steps of dissolved oxygen. .

4. The method according to claim 1, wherein The water quality fluctuation analysis sub-model includes: Assuming the preset number is n, the chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH value of the i-th unit time are respectively 、 、 、 、 , calculate the average values of chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH, respectively. ， ， ， ， ， Then calculate the standard deviation of chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH respectively. ， ， ， ， ， Post-calculation of water quality fluctuation data .

5. The method according to claim 1, wherein The fluctuation trend analysis sub-model includes: Assuming the preset number is n, the chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH value of the i-th unit time are respectively 、 、 、 、 ; Linear regression model was used for chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH and the least squares method to fit a straight line, where y is the chemical oxygen demand, or biochemical oxygen demand, or ammonia nitrogen content, or phosphorus content, or pH, and x is the time per unit time; The fitting lines of chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH are, , ， ， ， ， Determine the slopes of the fitted lines for chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH, and calculate the fluctuation trend data based on the slopes. ， Where, They are chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen content, phosphorus content, and pH fluctuation trend attention coefficient.

6. A smart water system, characterized in that: The method comprises a controller for controlling the adjustment amount of the aeration air volume in the aerobic tank and the application time of the adjusted aeration air volume, and the controller is configured to execute the method according to any one of claims 1 to 5.

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