Methods, devices, equipment, and products for controlling aeration volume in wastewater treatment.

Abstract

This invention discloses an aeration control method, device, equipment, and product for wastewater treatment, comprising: using a neural network model to obtain a predicted aeration efficiency value for the current time; determining the aeration rate required to remove a unit of ammonia nitrogen; and finally calculating the actual aeration rate. The aeration control method, device, equipment, and product disclosed in this invention can rapidly react to actual changes in the aerobic tank area corresponding to the aeration, instantly adjusting the aeration rate in that area. It has the advantages of fast response speed and high precision in aeration rate control, effectively improving the efficiency and quality of wastewater treatment.

Description

Technical Field

[0001] This invention belongs to the field of wastewater treatment, and in particular relates to a method, apparatus, equipment and product for controlling aeration volume in wastewater treatment. Background Technology

[0002] Under current technological conditions, the common method for controlling aeration volume in wastewater treatment is to collect ammonia nitrogen concentration at the inlet or outlet of the aerobic tank and calculate the required aeration volume in the aerobic tank based on the collected ammonia nitrogen concentration. This existing aeration volume control method has the problems of slow reaction speed and low aeration volume control accuracy. Summary of the Invention

[0003] In view of this, the present invention aims to overcome the deficiencies in the prior art and proposes a method, apparatus, equipment and product for controlling aeration volume in wastewater treatment.

[0004] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0005] In a first aspect, the present invention discloses a method for controlling aeration volume in wastewater treatment, comprising:

[0006] Based on historical time-series data of ammonia nitrogen concentration, dissolved oxygen concentration, water temperature, mixed liquor suspended solids concentration, ammonia nitrogen reaction rate, aeration efficiency, and aeration volume in the aerobic tank corresponding to the aeration, a neural network model is used to obtain the predicted aeration efficiency value for the current time.

[0007] Determine the current aeration rate required to remove a unit of ammonia nitrogen. The current aeration rate required to remove a unit of ammonia nitrogen is the theoretical oxygen demand for removing a unit of ammonia nitrogen when the current ammonia nitrogen concentration in the area reaches a set threshold, divided by the predicted aeration efficiency value.

[0008] Calculate the actual aeration rate, which is the product of the current aeration rate required to remove a unit of ammonia nitrogen, the volume of the area, and the current ammonia nitrogen concentration in the area.

[0009] In one embodiment of the present invention, the ammonia nitrogen concentration value of the discharged wastewater at the outlet of the aerobic tank is obtained, and a correction amount is determined based on the comparison result between the discharged wastewater ammonia nitrogen concentration value and a set threshold. The correction amount is then used to correct the actual aeration rate value.

[0010] In one embodiment of the present invention, the method further includes: the neural network model is an LSTM neural network model; the neural network model is trained by acquiring historical time-series data of ammonia nitrogen concentration, dissolved oxygen concentration, water temperature, mixed liquor suspended solids concentration, ammonia nitrogen reaction rate, aeration efficiency, and aeration volume in the region, wherein the ammonia nitrogen reaction rate is the result of dividing the difference between two consecutive ammonia nitrogen concentration values ​​by the time interval between acquisitions.

[0011] In one embodiment of the present invention, when training a neural network model, the parameters of the neural network model are updated using the Adam online learning algorithm for adaptive moment estimation.

[0012] In one embodiment of the invention, the area is located in the middle of the aerobic tank.

[0013] In one embodiment of the present invention, the Elastic Weight Consolidation (EWC) method is used to train a neural network model.

[0014] Secondly, this invention discloses an aeration volume control device for wastewater treatment, the device comprising:

[0015] The module for predicting aeration efficiency is used to obtain the predicted aeration efficiency value for the current time based on historical time-series data of ammonia nitrogen concentration, dissolved oxygen concentration, water temperature, mixed liquor suspended solids concentration, ammonia nitrogen reaction rate, aeration efficiency, and aeration volume in the aerobic tank corresponding to the aeration area, using a neural network model.

[0016] The module for determining the aeration rate value for removing a unit of ammonia nitrogen is used to determine the current aeration rate value required to remove a unit of ammonia nitrogen. The current aeration rate value required to remove a unit of ammonia nitrogen is the theoretical oxygen demand value for removing a unit of ammonia nitrogen when the current ammonia nitrogen concentration in the area reaches a set threshold, divided by the predicted aeration efficiency value.

[0017] The module for calculating the actual aeration volume is used to calculate the actual aeration volume, which is the product of the current aeration volume required to remove a unit of ammonia nitrogen, the volume of the area, and the current ammonia nitrogen concentration in the area.

[0018] Thirdly, the present invention discloses an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the above-described method.

[0019] Fourthly, the present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0020] Fifthly, the present invention discloses a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0021] Compared with the prior art, the present invention has the following advantages:

[0022] This invention discloses an aeration control method, device, equipment, and product for wastewater treatment, comprising: using a neural network model to obtain a predicted aeration efficiency value for the current time; determining the aeration rate required to remove a unit of ammonia nitrogen; and finally calculating the actual aeration rate. The aeration control method, device, equipment, and product disclosed in this invention can rapidly react to actual changes in the aerobic tank area corresponding to the aeration, instantly adjusting the aeration rate in that area. It has the advantages of fast response speed and high precision in aeration rate control, effectively improving the efficiency and quality of wastewater treatment. Attached Figure Description

[0023] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0024] In the attached diagram:

[0025] Figure 1 This is a schematic diagram illustrating an application scenario of an aeration volume control method for wastewater treatment according to an embodiment of the present invention.

[0026] Figure 2 This is a schematic diagram of an aeration control method for wastewater treatment according to an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of an aeration volume control device for sewage treatment according to an embodiment of the present invention;

[0028] Figure 4 This is a schematic diagram of an electronic device for controlling aeration volume in wastewater treatment according to an embodiment of the present invention. Detailed Implementation

[0029] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0030] In the description of this invention, it should be understood that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0031] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0032] In the description of this invention, it should be further noted that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0033] This invention discloses an aeration rate control method, device, equipment, and product for wastewater treatment, and their application scenarios are as follows: Figure 1 As shown, under existing technological conditions, common aeration control methods for wastewater treatment typically involve collecting ammonia nitrogen concentrations at the inlet or outlet of the aerobic tank and calculating the required aeration rate based on these concentrations. This method suffers from slow response times and low accuracy in aeration control. This invention discloses an aeration control method, device, equipment, and product for wastewater treatment. It utilizes a neural network model to obtain a predicted aeration efficiency value for the current time; determines the aeration rate required to remove a unit of ammonia nitrogen; and finally calculates the actual aeration rate. This allows for rapid response based on actual changes in the corresponding aerobic tank area, instantly adjusting the aeration rate in that area to improve aeration effectiveness and quality.

[0034] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0035] In one embodiment of the present invention, such as Figure 2 As shown, a method for controlling aeration volume in wastewater treatment includes:

[0036] Step S101: Based on the historical time series data of ammonia nitrogen concentration, dissolved oxygen concentration, water temperature, mixed liquor suspended solids concentration, ammonia nitrogen reaction rate, aeration efficiency, and aeration volume in the aerobic tank corresponding to the aeration, a neural network model is used to obtain the predicted aeration efficiency value for the current time.

[0037] For example, the area in the aerobic tank corresponding to aeration is a continuous area in the aerobic tank.

[0038] For example, the ammonia nitrogen concentration, dissolved oxygen concentration, water temperature, and mixed liquor suspended solids concentration are all collected by sensors, the aeration efficiency and aeration rate are pre-set, and the ammonia nitrogen reaction rate is the result of dividing the difference between two consecutive ammonia nitrogen concentration values ​​by the time interval between collections.

[0039] For example, the ammonia nitrogen reaction rate is expressed as r(t):

[0040]

[0041] Among them, C NH3-N (t) represents the ammonia nitrogen concentration at time t; Δt represents the data collection time interval;

[0042] Step S102: Determine the current aeration rate required to remove a unit of ammonia nitrogen. The current aeration rate required to remove a unit of ammonia nitrogen is the theoretical oxygen demand for removing a unit of ammonia nitrogen when the current ammonia nitrogen concentration in the area reaches a set threshold, divided by the predicted aeration efficiency value.

[0043] For example, the aeration rate required to remove a unit of ammonia nitrogen can be the aeration rate required to remove one gram of ammonia nitrogen, denoted as q(t), with the following relationship:

[0044]

[0045] Among them, C NH3-N,initial C is the current ammonia nitrogen concentration value in the region. NH3-N,target This is a set threshold; 4.57 is the theoretical oxygen demand coefficient for ammonia nitrogen oxidation; η AE (t) is the predicted aeration efficiency value output by the neural network model, r(t) is the calculated ammonia nitrogen reaction rate value, DO(t) is the dissolved oxygen concentration value, and t represents the corresponding time.

[0046] Step S103: Calculate the actual aeration volume value, where the actual aeration volume value is the product of the current aeration volume value required to remove a unit of ammonia nitrogen, the volume of the area, and the current ammonia nitrogen concentration value of the area.

[0047] For example, the actual aeration rate is expressed as Q. air (t) has the following relationship:

[0048] Q air (t)=q(t)·V·C NH3-N,initial ;

[0049] Where V represents the volume of the region, and t represents the corresponding time.

[0050] For example, V can represent the volume of a corridor in an aerobic tank.

[0051] Based on the previous embodiment, in another embodiment of the present invention, the method further includes: obtaining the ammonia nitrogen concentration value of the discharged wastewater at the outlet of the aerobic tank, determining a correction amount based on the comparison result of the discharged wastewater ammonia nitrogen concentration value and a set threshold, and using the correction amount to correct the actual aeration rate value.

[0052] For example, Δq(t) represents the correction amount, and the following relationship holds:

[0053] Δq(t)=k p ·(C NH3-N,out (t)-C NH3-N,target );

[0054] Where, k p It is the proportionality coefficient, C NH3-N,out (t) represents the ammonia nitrogen concentration in the discharged wastewater, where t represents the corresponding time.

[0055] The actual aeration rate is corrected using a correction factor, and the corrected actual aeration rate is expressed as Q. air,finai (t) has the following relationship:

[0056] Q air,final (t)=Q air (t)+Δq(t);

[0057] Where t represents the corresponding time;

[0058] Furthermore, exemplarily, there is the following modified policy relationship:

[0059] When C NH3-N,out (t)=C NH3-n,target When Δq(t) is 0, no correction is performed;

[0060] When C NH3-N,out (t)>C NH3-N,target When Δq(t) is not 0, a correction is made by increasing the aeration rate.

[0061] When C NH3-N,out (t)<C NH3-N,target When Δq(t) is 0, a correction is made to reduce the aeration rate.

[0062] Based on the previous embodiment, in another embodiment of the present invention, the method further includes: the neural network model is an LSTM neural network model; and the neural network model is trained by acquiring historical time-series data of ammonia nitrogen concentration, dissolved oxygen concentration, water temperature, mixed liquor suspended solids concentration, ammonia nitrogen reaction rate, aeration efficiency, and aeration volume in the region.

[0063] Based on the previous embodiment, in another embodiment of the present invention, when training the neural network model, the parameters of the neural network model are updated using the Adam online learning algorithm for adaptive moment estimation, as shown in the following example:

[0064]

[0065] Where t represents the corresponding time, θ(t) represents the parameters of the neural network model at time t, θ(t+1) represents the updated parameters of the neural network model at time t+1; η(t) is the adaptive learning rate, which decays with the number of iterations; Represents the gradient operator; L represents the mean squared error loss function, η AE-pred (t) represents the predicted value of the neural network model, η AE-truE (t) represents the actual set value;

[0066] Based on the previous embodiment, in another embodiment of the present invention, the Elastic Weight Consolidation (EWC) method is used to train the neural network model.

[0067] Based on the previous embodiment, in another embodiment of the present invention, the area is located in the middle of the aerobic tank.

[0068] In this embodiment, the actual aeration rate value calculated by this method corresponds to the area in the middle of the aerobic tank. When this area is the middle corridor of the aerobic tank, the process of determining the actual aeration rate value of the adjacent corridors in the middle corridor of the plug-flow aerobic tank is as follows:

[0069] Taking the determination of the actual aeration rate of the front corridor connected to the intermediate corridor as an example, the process is as follows:

[0070] The ammonia nitrogen reaction rate value r(t) of the middle corridor is derived to the ammonia nitrogen reaction rate value r(t-τ) of the front corridor, where τ represents the time it takes for the water to flow from the front corridor to the middle corridor.

[0071] Furthermore, according to the gradient decay law of ammonia nitrogen concentration, the ammonia nitrogen concentration decreases gradient along the direction of water flow, which conforms to the first-order reaction kinetics law, and the following relationship exists:

[0072]

[0073] Among them, C NH3-N,frontC represents the ammonia nitrogen concentration value in the front corridor. NH3-N This represents the ammonia nitrogen concentration in the intermediate corridor, where k is the decay rate constant, and L... front The length of the front corridor is represented by v, and the water flow velocity is represented by v.

[0074] Furthermore, the decay law of dissolved oxygen concentration gradient has the following relationship:

[0075] DO front (t)=DO mid (t-τ)-ΔDO;

[0076] Among them, DO front (t) represents the dissolved oxygen concentration value in the anterior corridor; DO mid (t) represents the dissolved oxygen concentration in the middle corridor; τ represents the time it takes for water to flow from the front corridor to the middle corridor; ΔDO represents the decrease in dissolved oxygen concentration from the front corridor to the middle corridor, ranging from 0.5 mg / L to 1 mg / L.

[0077] Furthermore, the aeration efficiency value η of the intermediate corridor is... AE (t), derived as the aeration efficiency value η of the front corridor. AE (t-τ), where τ represents the time it takes for the water to flow from the front corridor to the middle corridor;

[0078] The aeration rate required to remove a unit of ammonia nitrogen in the front corridor is expressed as q. front (t) has the following relationship:

[0079]

[0080] This embodiment can derive the actual aeration value of the front corridor connected to the intermediate corridor based on the calculation of the actual aeration value of the intermediate corridor, so as to realize the use of different aeration values ​​for the intermediate corridor and the front corridor, thereby improving the precise control of the aeration value of different corridors in the aerobic tank.

[0081] like Figure 3 As shown, the present invention also discloses an aeration rate control device for wastewater treatment, comprising:

[0082] The module 301 for predicting aeration efficiency is used to obtain the predicted aeration efficiency value for the current time based on the historical time series data of ammonia nitrogen concentration, dissolved oxygen concentration, water temperature, mixed liquor suspended solids concentration, ammonia nitrogen reaction rate, aeration efficiency, and aeration volume in the aerobic tank corresponding to the aeration area, using a neural network model.

[0083] The module 302 for determining the aeration rate value for removing a unit of ammonia nitrogen is used to determine the current aeration rate value required for removing a unit of ammonia nitrogen. The current aeration rate value required for removing a unit of ammonia nitrogen is the theoretical oxygen demand value for removing a unit of ammonia nitrogen when the current ammonia nitrogen concentration in the area reaches a set threshold, divided by the predicted aeration efficiency value.

[0084] The module 303 for calculating the actual aeration volume is used to calculate the actual aeration volume, which is the product of the current aeration volume required to remove a unit of ammonia nitrogen, the volume of the area, and the current ammonia nitrogen concentration in the area.

[0085] The present invention also discloses an electronic device, such as Figure 4 The diagram shows an embodiment of an electronic device for controlling aeration volume in wastewater treatment.

[0086] The electronic device 40 of this embodiment includes a processor 401, which can perform various appropriate actions and processes according to a program stored in ROM 402 or a program loaded from storage portion 408 into RAM 403. The processor 401 may include, for example, a general-purpose microprocessor, an instruction set processor and / or related chipsets and / or dedicated microprocessors, etc. The processor 401 may also include onboard memory for caching purposes. The processor 401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to embodiments of the present invention.

[0087] RAM 403 stores various programs and data required for the operation of electronic device 40. Processor 401, ROM 402, and RAM 403 are interconnected via bus 404. Processor 401 executes various operations of the method flow according to embodiments of the present invention by executing programs in ROM 402 and / or RAM 403. It should be noted that programs may also be stored in one or more memories other than ROM 402 and RAM 403, and processor 401 may also execute various operations of the method flow according to embodiments of the present invention by executing programs stored in one or more memories.

[0088] According to an embodiment of the present invention, the electronic device 40 may further include an I / O interface 405, which is also connected to the bus 404. The electronic device 40 may also include one or more of the following components connected to the I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube, liquid crystal display, and speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card or modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 4010 is also connected to the I / O interface 405 as needed. A removable medium 4011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 4010 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0089] The present invention also provides a computer-readable storage medium.

[0090] The computer-readable storage medium may be included in the electronic device / apparatus system described in the above embodiments; or it may exist independently and not assembled into the electronic device / apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of the present invention.

[0091] According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0092] Embodiments of the present invention also include a computer program product.

[0093] The computer program product includes a computer program containing program code for performing the methods provided in the embodiments of the present invention. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the methods provided in the embodiments of the present invention.

[0094] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0095] According to embodiments of the present invention, program code for executing the computer programs provided in the embodiments of the present invention can be written using any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages. Programming languages ​​include, but are not limited to, Java, C++, Python, C, or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device.

[0096] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features recited in the various embodiments and / or claims of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not expressly stated in the present invention. In particular, the features described in the various embodiments and / or claims of this invention can be combined and / or combined in various ways without departing from the spirit and teachings of this invention. All such combinations and / or combinations fall within the scope of this invention.

[0097] The embodiments of the present invention have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of the invention is defined by the appended claims and their equivalents. Without departing from the scope of the invention, various substitutions and modifications can be made by those skilled in the art, and all such substitutions and modifications should fall within the scope of the invention.

Claims

1. A method for controlling aeration volume in wastewater treatment, characterized in that, include: Based on historical time-series data of ammonia nitrogen concentration, dissolved oxygen concentration, water temperature, mixed liquor suspended solids concentration, ammonia nitrogen reaction rate, aeration efficiency, and aeration volume in the aerobic tank corresponding to the aeration, a neural network model is used to obtain the predicted aeration efficiency value for the current time. The aeration rate required to remove a unit of ammonia nitrogen is determined, wherein the aeration rate required to remove a unit of ammonia nitrogen is the theoretical oxygen demand for removing a unit of ammonia nitrogen when the current ammonia nitrogen concentration in the area reaches a set threshold, divided by the predicted aeration efficiency value. Calculate the actual aeration rate, wherein the actual aeration rate is the product of the current aeration rate required to remove a unit of ammonia nitrogen, the volume of the area, and the current ammonia nitrogen concentration in the area.

2. The aeration rate control method for wastewater treatment according to claim 1, characterized in that, Obtain the ammonia nitrogen concentration value of the discharged wastewater at the outlet of the aerobic tank, determine the correction amount based on the comparison result between the discharged wastewater ammonia nitrogen concentration value and the set threshold, and use the correction amount to correct the actual aeration rate value.

3. The aeration rate control method for wastewater treatment according to claim 1, characterized in that, The method further includes: the neural network model is an LSTM neural network model; obtaining historical time-series data of the ammonia nitrogen concentration, dissolved oxygen concentration, water temperature, mixed liquor suspended solids concentration, ammonia nitrogen reaction rate, aeration efficiency, and aeration volume in the region to train the neural network model, wherein the ammonia nitrogen reaction rate is the result of dividing the difference between two consecutively collected ammonia nitrogen concentration values ​​by the collection time interval.

4. The aeration rate control method for wastewater treatment according to claim 3, characterized in that, During the training of the neural network model, the parameters of the neural network model are updated using the Adam online learning algorithm for adaptive moment estimation.

5. The aeration rate control method for wastewater treatment according to claim 1, characterized in that, The area is located in the middle of the aerobic pool.

6. The aeration rate control method for wastewater treatment according to claim 3, characterized in that, The neural network model is trained using the Elastic Weight Consolidation (EWC) method.

7. An aeration rate control device for wastewater treatment, characterized in that: The device includes: The module for predicting aeration efficiency is used to obtain the predicted aeration efficiency value for the current time based on historical time-series data of ammonia nitrogen concentration, dissolved oxygen concentration, water temperature, mixed liquor suspended solids concentration, ammonia nitrogen reaction rate, aeration efficiency, and aeration volume in the aerobic tank corresponding to the aeration area, using a neural network model. The module for determining the aeration rate value for removing a unit of ammonia nitrogen is used to determine the current aeration rate value required to remove a unit of ammonia nitrogen. The current aeration rate value required to remove a unit of ammonia nitrogen is the theoretical oxygen demand value for removing a unit of ammonia nitrogen when the current ammonia nitrogen concentration value in the area reaches a set threshold, divided by the predicted aeration efficiency value. The module for calculating the actual aeration volume is used to calculate the actual aeration volume, wherein the actual aeration volume is the product of the current aeration volume required to remove a unit of ammonia nitrogen, the volume of the area, and the current ammonia nitrogen concentration in the area.

8. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method of any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that... When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method described in any one of claims 1 to 6.

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