A modular constructed wetland optimization method, device, electronic device, and storage medium

Abstract

The present invention relates to the field of purification technologies, and particularly to an optimization method, device, electronic device, and storage medium for a modular artificial wetland, which are applied to a water purification system. The method includes: obtaining the influent water volume, influent water quality, and effluent water quality; and calculating the water quality removal rate according to the influent water quality and the effluent water quality. Through monitoring the water quality removal rate of the water purification system and performing multi-level data processing through a pre-trained adjustment model, the comprehensive index prediction results of various preset indicators are first calculated. When the comprehensive index prediction results do not meet the process requirements, a preliminary adjustment strategy is first determined based on a multi-objective optimization model to determine the target adjustment indicators, and then the preliminary adjustment strategy is further refined based on a strategy evaluation model to determine the target adjustment strategy, thereby realizing intelligent and accurate adjustment of the water purification system to obtain a target adjustment strategy that meets the preset purification requirements.

Description

Technical Field

[0001] The present invention relates to the field of purification technology, and more particularly to an optimization method, device, electronic device, and storage medium for a modular constructed wetland. Background Art

[0002] Constructed wetlands play an important role in sewage treatment and ecological restoration. However, traditional constructed wetlands have clogging problems, which affect the long-term operation efficiency. The present invention optimizes water distribution and reflux through modular design and artificial intelligence technology, improves the anti-clogging ability of the system, and realizes the optimization of water quality, economy, and greenhouse gas emissions. Summary of the Invention

[0003] In view of this, an object of the present invention is to provide an optimization method, device, electronic device, and storage medium for a modular constructed wetland.

[0004] In a first aspect, an embodiment of the present invention provides an optimization method for a modular constructed wetland, which is applied to a water purification system. The method includes:

[0005] Obtaining the influent water volume, influent water quality, and effluent water quality;

[0006] Calculating the water quality removal rate according to the influent water quality and the effluent water quality;

[0007] Inputting the influent water volume and the water quality removal rate into a pre-trained adjustment model, and outputting a target adjustment strategy;

[0008] Optimizing the water purification system according to the target adjustment strategy;

[0009] Wherein, the adjustment model includes a prediction model, a multi-objective optimization model, and a strategy evaluation model connected in sequence.

[0010] Combined with the first aspect, the step of inputting the influent water volume and the water quality removal rate into a pre-trained adjustment model and outputting a target adjustment strategy includes:

[0011] Inputting the influent water volume and the water quality removal rate into the prediction model, and outputting a comprehensive index prediction result of multiple indicators;

[0012] Judging whether the comprehensive index prediction result meets the preset requirements;

[0013] If not, inputting the comprehensive index prediction result into the multi-objective optimization model, and outputting a target adjustment index and an initial adjustment strategy;

[0014] Invoking the strategy evaluation model, and determining the target adjustment strategy according to the strategy evaluation model, the initial adjustment strategy, each reflux pipeline, and each reflux pump. The target adjustment strategy includes the opening and closing angles of the reflux pipeline and the target reflux pump.

[0015] In combination with the first aspect, the steps of inputting the influent water volume and the water quality removal rate into the prediction model and outputting the prediction result of the comprehensive index include:

[0016] Input the influent water volume and the water quality removal rate into the prediction model to obtain multiple prediction results corresponding to multiple indicators;

[0017] Combine the multiple prediction results and the weight coefficients corresponding to each indicator to calculate the prediction result of the comprehensive index.

[0018] In combination with the first aspect, the steps of inputting the prediction result of the comprehensive index into the multi-objective optimization model and outputting the target adjustment index and the initial adjustment strategy include:

[0019] Train the multi-objective optimization model with the prediction result of the comprehensive index, and determine the target adjustment index based on the multi-objective optimization algorithm;

[0020] Based on the target adjustment index, determine the initial adjustment strategy corresponding to the target adjustment index.

[0021] In combination with the first aspect, the steps of calling the policy evaluation model and determining the target adjustment strategy according to the policy evaluation model, the initial adjustment strategy, each return pipeline and each return pump include:

[0022] Use the policy evaluation model to evaluate the comprehensive index corresponding to the opening and closing angles of each return pipeline and each return pump in the initial adjustment strategy, and determine the target adjustment strategy.

[0023] In combination with the first aspect, the steps of calculating the water quality removal rate according to the influent water quality and the effluent water quality include:

[0024] Calculate the difference between the effluent water quality and the influent water quality to obtain the water quality removal rate.

[0025] After the steps of judging whether the prediction result of the comprehensive index meets the preset requirements in combination with the first aspect, it further includes:

[0026] If so, control the water purification system to continue operating with the current control strategy.

[0027] In the second aspect, the present application provides a modular artificial wetland optimization device applied to a water purification system. The device includes:

[0028] An acquisition module for acquiring the influent water volume, the influent water quality and the effluent water quality;

[0029] A calculation module for calculating the water quality removal rate according to the influent water quality and the effluent water quality;

[0030] An output module for inputting the influent water volume and the water quality removal rate into a pre-trained adjustment model and outputting the target adjustment strategy;

[0031] An optimization module for optimizing a water purification system according to a target adjustment strategy;

[0032] Among them, the adjustment model includes a prediction model, a multi-objective optimization model, and a strategy evaluation model connected in sequence.

[0033] In a third aspect, the present application provides an electronic device, which includes a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above method.

[0034] In a fourth aspect, the present application provides a readable storage medium, in which computer program instructions are stored. When the computer program instructions are read and run by a processor, the above method is executed.

[0035] The embodiments of the present invention bring the following beneficial effects: A modular artificial wetland optimization method provided by the present application is applied to a water purification system. The method includes: obtaining the influent water volume, influent water quality, and effluent water quality; calculating the water quality removal rate according to the influent water quality and effluent water quality; inputting the influent water volume and water quality removal rate into a pre-trained adjustment model to output a target adjustment strategy; optimizing the water purification system according to the target adjustment strategy; among them, the adjustment model includes a prediction model, a multi-objective optimization model, and a strategy evaluation model connected in sequence.

[0036] The present application monitors the water quality removal rate of the water purification system, performs multi-level data processing through a pre-trained adjustment model, first calculates the comprehensive index prediction results of various preset indicators, and when the comprehensive index prediction results do not meet the process requirements, first determines the target adjustment indicators based on the multi-objective optimization model to determine the preliminary adjustment strategy, and then further refines the preliminary adjustment strategy based on the strategy evaluation model to determine the target adjustment strategy, thereby realizing intelligent and accurate adjustment of the water purification system to obtain a target adjustment strategy that meets the preset purification requirements.

[0037] Other features and advantages of the present invention will be described in the following specification, and, in part, will become apparent from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification, claims, and drawings.

[0038] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given below, and in conjunction with the accompanying drawings, the detailed description is as follows. Description of the Drawings

[0039] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those skilled in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0040] Figure 1 Flowchart of a modular constructed wetland optimization method provided by an embodiment of the present invention;

[0041] Figure 2 Schematic diagram of a modular constructed wetland optimization device provided by an embodiment of the present invention;

[0042] Figure 3 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0043] Reference numerals:

[0044] 10 - Acquisition module, 20 - Calculation module, 30 - Output module, 40 - Optimization module;

[0045] 130 - Processor, 131 - Memory, 132 - Bus, 133 - Communication interface. Specific embodiments

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention in conjunction with the drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present invention.

[0047] To facilitate the understanding of this embodiment, the application scenarios and design concepts of the embodiments of this application will be briefly introduced below.

[0048] Constructed wetlands play an important role in sewage treatment and ecological restoration. However, traditional constructed wetlands have problems such as clogging, which affects the long-term operation efficiency, and are difficult to manually adjust and have a low degree of intelligence.

[0049] Based on this, the embodiments of the present application provide an optimization method, device, electronic device, and storage medium for a modular artificial wetland. The method is applied to a water purification system, which includes a plurality of purification modules connected in series and in parallel. Along the water flow direction, a reflux pipeline is connected between the series-connected purification modules, and a reflux pump is provided on the reflux pipeline for guiding the water flow in the subsequent purification module to the previous purification module; water quality sensors are provided at the outlets and inlets of each purification module for detecting water quality. It can be understood that according to different factors such as the terrain and floor area of the application scenario, the number and assembly method of the purification modules can be increased or decreased.

[0050] Embodiment 1

[0051] The present application provides an optimization method for a modular artificial wetland, which is applied to a water purification system. As shown in combination with Figure 1 shown, the method includes:

[0052] S110, obtaining the influent water volume, influent water quality, and effluent water quality.

[0053] S120, calculating the water quality removal rate according to the influent water quality and the effluent water quality.

[0054] S130, inputting the influent water volume and the water quality removal rate into a pre-trained adjustment model, and outputting a target adjustment strategy.

[0055] S140, optimizing the water purification system according to the target adjustment strategy.

[0056] Among them, the adjustment model includes a prediction model, a multi-objective optimization model, and a strategy evaluation model connected in sequence.

[0057] In this embodiment, by calculating the water quality removal rate of the water purification system, under the action of the prediction model, the multi-objective optimization model, and the strategy evaluation model in the adjustment model, an adjustment strategy that meets the current water purification requirements is output. Then, according to this adjustment strategy, the operation of the water purification system is controlled to achieve the expected water purification requirements, realizing automatic and precise regulation and improving the purification efficiency.

[0058] Combined with the first aspect, step S120 includes:

[0059] S121, calculating the difference between the effluent water quality and the influent water quality to obtain the water quality removal rate.

[0060] It can be understood that water quality sensors are respectively provided at the inlet end and the outlet end of the water purification system to monitor the water quality of the flowing water; the water purification system includes at least one water purification module. By calculating the difference between the effluent water quality and the influent water quality of the water purification system, the water quality removal rate of the water purification system can be obtained. Similarly, by calculating the difference between the effluent water quality of each water purification module and the influent water quality of the water purification module, the water quality removal rate of the water purification module can be obtained.

[0061] It is understandable that the water quality removal rate can characterize the water purification ability of the water purification system or the water purification module. It is understandable that the higher the water quality removal rate, the stronger the water purification ability and the higher the water purification efficiency.

[0062] In this embodiment, multiple water purification modules are comprehensively connected in series and in parallel in the water purification system. Generally, the number of water purification modules connected in series is generally less than or equal to 3, and the number of parallel connections is not limited.

[0063] Combined with the first aspect, step S130 includes:

[0064] S131, input the influent water volume and water quality removal rate into the prediction model, and output the comprehensive index prediction results of multiple indicators.

[0065] S132, determine whether the comprehensive index prediction results meet the preset requirements.

[0066] If so, execute step S133; if not, execute steps S134 - S135.

[0067] S133, control the water purification system to continue running with the current control strategy.

[0068] If the comprehensive index prediction results under the condition of running with the current control strategy meet the preset requirements, then it can be determined that the current control strategy is feasible and meets the working requirements. At this time, there is no need to adjust, and it can continue to run.

[0069] S134, input the comprehensive index prediction results into the multi-objective optimization model, and output the target adjustment indicators and the initial adjustment strategy.

[0070] S135, call the strategy evaluation model, and determine the target adjustment strategy according to the strategy evaluation model, the initial adjustment strategy, each return pipeline and each return pump; the target adjustment strategy includes the opening and closing angles of the return pipeline and the target return pump.

[0071] If the comprehensive index prediction results under the condition of running with the current control strategy do not meet the preset requirements, then it can be considered that the current control strategy needs to be adjusted to meet the working requirements. At this time, further determine the final adjustment indicators through the multi-objective optimization model and the strategy evaluation model, and the target control strategy formulated based on the adjustment indicators.

[0072] Combined with the first aspect, step S131 specifically includes:

[0073] S1311, input the influent water volume and water quality removal rate into the prediction model, and obtain multiple prediction results corresponding to multiple indicators.

[0074] S1312, combine the multiple prediction results and the weight coefficients corresponding to each indicator, and calculate the comprehensive index prediction results.

[0075] In this implementation, the indicators at least include: water quality indicators, economic indicators, and gas emission flux indicators.

[0076] In this embodiment, the water quality indicators include the COD removal rate, ammonia nitrogen removal rate, and total nitrogen removal rate; the economic indicators include: reflux ratio, aeration, etc.; the gas emission flux indicators include: emissions of methane, nitrous oxide, and carbon dioxide. It can be understood that the above gas emission flux indicators should be as small as possible, and the harm to the environment is also smaller.

[0077] It can be understood that as the water quality removal rate increases, the aeration volume and reflux ratio of the economic indicators increase, and the amount of greenhouse gases generated is relatively large. Therefore, the operation strategy of the water purification system is not the best when the water quality removal rate is the largest. Therefore, it is necessary to comprehensively consider multiple indicators to determine the purification effect. Furthermore, based on the weight coefficients corresponding to each indicator, the purification effect of the water purification system is comprehensively determined.

[0078] Among them, the prediction model is a model for prediction obtained by statistical analysis and machine learning algorithm training based on a large amount of historical data. Common prediction models can be linear regression models, logistic regression models, integrated models including decision trees and random forests, support vector machines, and neural networks integrating deep learning techniques. In this embodiment, the sample data for model training includes the influent volume and water quality removal rate, and the comprehensive index prediction result is output. In this embodiment, there is an interaction relationship between the influent volume and the water quality removal rate. A linear regression model can be used to continuously predict the prediction results corresponding to multiple indicators by fitting the data and making predictions according to the linear equation. Subsequently, based on the influence ratio, the weight coefficient is determined to calculate the comprehensive index prediction result. Among them, the comprehensive index prediction result P = , where is the prediction result of the nth indicator, is the weight coefficient of the nth indicator.

[0079] Combined with the first aspect, step S134 specifically includes:

[0080] S1341, training the multi-objective optimization model through the comprehensive index prediction result, and determining the target adjustment index based on the multi-objective optimization algorithm.

[0081] The multi-objective optimization model is a mathematical model constructed based on multi-objective optimization technology to determine the target adjustment index from multiple adjustment indicators. It can be understood that the adjustment indicator can refer to any one of the above indicators. For example, adjusting the influent water volume to adjust the output water quality removal rate, gas emission flux, etc., or adjusting the reflux ratio to adjust the water quality removal rate, gas emission flux, etc. of the entire constructed wetland.

[0082] S1342. Determine an initial adjustment strategy corresponding to the target adjustment indicator based on the target adjustment indicator.

[0083] In this embodiment, through the data processing ability of the multi-objective optimization model, the most optimal target adjustment indicator is determined. Since adjusting any one indicator will cause changes in other indicators, in order to improve the comprehensive purification efficiency and control accuracy, in this application, any one indicator is adjusted and predicted, and then based on the multi-objective optimization algorithm, an optimal solution that can not only meet the working requirements but also has fast response and ideal purification effect is found.

[0084] In this embodiment, the training process of the multi-objective optimization model is as follows:

[0085] First, obtain a training sample set. The training sample set includes multiple training samples, and each training sample consists of multiple adjustment indicators.

[0086] Specifically, the training sample at least includes water quality indicators, economic indicators, and gas emission fluxes. For example, training sample a: COD removal rate = 80%; ammonia nitrogen removal rate parameter = 95%, total nitrogen removal rate = 92%; reflux ratio = b; aeration = c; CH 4 = d, NO = e, CO 2 = f.

[0087] Then, for each adjustment indicator, obtain other indicators when the adjustment indicator reaches the preset value to obtain a target sample set.

[0088] Specifically, for any one of the above adjustment indicators (for example, increasing the COD removal rate to 90%), adjust the water quality indicator. The adjustment method includes changing the influent water volume, adjusting the influent water quality, and opening one or more of a certain reflux pipeline, so as to obtain other water quality indicators, economic indicators, and gas emission flux indicators after increasing the COD removal rate to 90%; and then obtain a target sample.

[0089] Then, perform linear regression processing on multiple target samples and draw a fitting curve.

[0090] Integrate multiple target samples, find the adjustment rule and then draw a fitting curve.

[0091] Finally, based on the multi-objective optimization algorithm, determine the target adjustment indicator and the target adjustment.

[0092] Combined with the above examples, it can be seen that if the target adjustment index is the gas emission flux index, then the purpose of adjusting the gas emission flux can be achieved by adjusting strategies such as the water inflow, reflux ratio, and aeration volume. Similarly, if the target adjustment index is the economic index, then the optimization purpose can also be achieved by adjusting strategies such as the water inflow, reflux ratio, and aeration volume. In this embodiment, through the multi-objective optimization algorithm, the optimal solution that meets the expected adjustment efficiency is selected as the initial adjustment strategy.

[0093] For example, in this embodiment, the target adjustment index determined by the multi-objective optimization algorithm is the economic index, and the corresponding initial adjustment strategy is to change the reflux ratio and aeration volume.

[0094] Combined with the first aspect, step S135 specifically includes:

[0095] 1351. Use the strategy evaluation model to evaluate the comprehensive index corresponding to the opening and closing angles of each reflux pipeline and each reflux pump in the initial adjustment strategy, and determine the target adjustment strategy.

[0096] It can be understood that on the basis of having determined the target adjustment index and thus obtained the initial adjustment strategy, further precise calculation of the adjustment strategy is performed based on the pre-trained strategy evaluation model to determine which reflux pipeline should be conducted specifically, and which reflux pump corresponding to that reflux pipeline should be opened and closed, that is, to which specific purification module the water flow should return.

[0097] In summary, this application monitors the water quality removal rate of the water purification system, performs multi-level data processing through the pre-trained adjustment model, first calculates the comprehensive index prediction results of the preset indicators, and in the case where the comprehensive index prediction results do not meet the process requirements, first determines the target adjustment index based on the multi-objective optimization model to determine the preliminary adjustment strategy, and then further refines the preliminary adjustment strategy based on the strategy evaluation model, so as to determine the target adjustment strategy, and then realize the intelligent and accurate adjustment of the water purification system to obtain the target adjustment strategy that meets the preset purification requirements.

[0098] Furthermore, the water purification system further includes a voice interaction model; the method further includes:

[0099] Receive the voice or text command information of the user;

[0100] Identify the voice or text command information to generate a control command;

[0101] Based on the control command, correct the target adjustment strategy to obtain the corrected target adjustment strategy.

[0102] In this application, the voice interaction model completed through pre-training identifies the voice and text information input by the user to correct and readjust the target adjustment strategy, so as to facilitate human-computer interaction and the optimization of the water purification system.

[0103] In a second aspect, this application provides a modular artificial wetland optimization device, which is combined with Figure 2 as shown, and is applied to the water purification system; the device includes an acquisition module 10, a calculation module 20, an output module 30, and an optimization module 40.

[0104] The acquisition module 10 is used to acquire the influent water volume, influent water quality, and effluent water quality.

[0105] The calculation module 20 is used to calculate the water quality removal rate according to the influent water quality and the effluent water quality.

[0106] The output module 30 is used to input the influent water volume and the water quality removal rate into the pre-trained adjustment model, and output the target adjustment strategy.

[0107] The optimization module 40 is used to optimize the water purification system according to the target adjustment strategy.

[0108] Among them, the adjustment model includes a prediction model, a multi-objective optimization model, and a strategy evaluation model connected in sequence.

[0109] In a third aspect, an embodiment of this application provides an electronic device, which is combined with Figure 3 as shown. The electronic device includes a memory 131 and a processor 130. The memory 131 is used to store a computer program, and the processor 130 runs the computer program to enable the electronic device to execute the above method.

[0110] Furthermore, the electronic device combined with Figure 3 as shown also includes a bus 132 and a communication interface 133. The processor 130, the communication interface 133, and the memory 131 are connected through the bus 132.

[0111] Among them, the memory 131 may include a high-speed random access memory (RAM, Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 133 (which can be wired or wireless), a communication connection is realized between this system network element and at least one other network element, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used. The bus 132 can be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 3 only a bidirectional arrow is used in

[0112] The processor 130 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method may be completed by the integrated logic circuit of the hardware in the processor 130 or the instructions in the form of software. The above-mentioned processor 130 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention may be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 131, and the processor 130 reads the information in the memory 131 and combines its hardware to complete the steps of the method in the foregoing embodiments.

[0113] In a fourth aspect, an embodiment of the present application provides a readable storage medium. When computer program instructions stored in the readable storage medium are read and run by a processor, the above-mentioned method is executed.

[0114] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems and devices described above may refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0115] In addition, in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0116] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0117] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0118] Finally, it should be noted that the above embodiments are only specific embodiments of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting them. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions described in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A modular artificial wetland optimization method, characterized in that: Applied to a water purification system, the method comprises: Obtain inlet water volume, inlet water quality and outlet water quality; Calculating a water removal rate according to the influent water quality and the effluent water quality; Input the influent water volume and the water quality removal rate into a pre-trained adjustment model, and output a target adjustment strategy; Optimizing the water purification system according to the target adjustment strategy; The adjustment model includes a prediction model, a multi-objective optimization model and a strategy evaluation model connected in sequence; The step of inputting the influent water volume and the water quality removal rate into a pre-trained adjustment model and outputting a target adjustment strategy includes: The influent water volume and the water quality removal rate are input into the prediction model, and the prediction results corresponding to multiple indicators are continuously predicted by fitting the data and predicting according to the linear equation, and the weight coefficient is determined based on the influence ratio to calculate the prediction result of the comprehensive indicator; Determine whether the prediction result of the comprehensive index meets the preset requirements; If so, controlling the water purification system to continue to operate with the current control strategy; If not, the multi-objective optimization model is trained through the comprehensive index prediction result, and the target adjustment index is determined based on the multi-objective optimization algorithm; Based on the target adjustment indicator, determining an initial adjustment strategy corresponding to the target adjustment indicator; The strategy evaluation model is called, and a target adjustment strategy is determined according to the strategy evaluation model, the initial adjustment strategy, each reflux pipeline and each reflux pump. The target adjustment strategy includes the opening and closing angles of the reflux pipeline and the target reflux pump.

2. The method according to claim 1, characterized in that The step of calling the strategy evaluation model and determining the target adjustment strategy according to the strategy evaluation model, the initial adjustment strategy, each reflux pipeline and each reflux pump comprises: The strategy evaluation model is used to evaluate the comprehensive indicators corresponding to the opening and closing angles of each reflux pipeline and each reflux pump in the initial adjustment strategy to determine the target adjustment strategy.

3. The method according to claim 1, characterized in that The step of calculating the water removal rate according to the influent water quality and the effluent water quality comprises: The difference between the outlet water quality and the inlet water quality is calculated to obtain the water quality removal rate.

4. A modular artificial wetland optimization device, characterized in that: Applied to a water purification system, the device comprises: An acquisition module is used to obtain the inlet water volume, inlet water quality and outlet water quality; A calculation module, used for calculating a water removal rate according to the influent water quality and the effluent water quality; An output module, used for inputting the influent water volume and the water quality removal rate into a pre-trained adjustment model, and outputting a target adjustment strategy; An optimization module, for optimizing the water purification system according to the target adjustment strategy; The adjustment model includes a prediction model, a multi-objective optimization model and a strategy evaluation model connected in sequence; The step of inputting the influent water volume and the water quality removal rate into a pre-trained adjustment model and outputting a target adjustment strategy includes: The influent water volume and the water quality removal rate are input into the prediction model, and the prediction results corresponding to multiple indicators are continuously predicted by fitting the data and predicting according to the linear equation, and the weight coefficient is determined based on the influence ratio to calculate the prediction result of the comprehensive indicator; Determine whether the prediction result of the comprehensive index meets the preset requirements; If so, controlling the water purification system to continue to operate with the current control strategy; If not, the multi-objective optimization model is trained through the comprehensive index prediction result, and the target adjustment index is determined based on the multi-objective optimization algorithm; Based on the target adjustment indicator, determining an initial adjustment strategy corresponding to the target adjustment indicator; The strategy evaluation model is called, and a target adjustment strategy is determined according to the strategy evaluation model, the initial adjustment strategy, each reflux pipeline and each reflux pump. The target adjustment strategy includes the opening and closing angles of the reflux pipeline and the target reflux pump.

5. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the method according to any one of claims 1 to 3.

6. A storage medium, characterized in that: The storage medium stores computer program instructions, and when the computer program instructions are read and executed by a processor, the method according to any one of claims 1 to 3 is executed.

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