New pollutant reduction approach identification method for sludge-based modular anti-clogging constructed wetland

Abstract

The invention relates to the technical field of environmental protection, in particular to a new pollutant reduction approach identification method for a sludge-based modular anti-blocking constructed wetland, which is applied to a control unit of a wetland system, and the wetland system further comprises a plurality of constructed wetland modules connected in series. According to the method for identifying the new pollutant reduction approach of the sludge-based modular anti-blocking constructed wetland provided by the invention, data acquisition is carried out on the layered baffling type novel modular anti-blocking constructed wetland, and the pollutant reduction amount is predicted through the time sequence data processing capability of the prediction model; after a pre-constructed prediction model is optimized in combination with analysis and processing of sampled data, new pollutants are continuously added to obtain a concentration distribution model of the new pollutants, and then new pollutant reduction approach recognition is determined in combination with microbial characteristics of each subarea, so that a wetland system is conveniently controlled and adjusted subsequently, and the pollutant treatment capacity is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a method for identifying the reduction pathway of emerging pollutants in a sludge-based modular anti-clogging constructed wetland. Background Art

[0002] Traditional constructed wetlands usually treat sewage by filling dense stone fillers and then planting plants. However, when treating wastewater containing emerging pollutants (such as microplastics), traditional constructed wetlands have problems such as substrate clogging, low purification efficiency, and difficulties in identifying and quantifying emerging pollutants. In contrast, the new modular anti-clogging constructed wetland adopts a folded flow type (alternate up and down folding flow) and an upper and lower layered design. The upper layer is filled with sludge-based lightweight suspended fillers, the middle layer is filled with lightweight ceramsite, the lower layer is filled with lightweight ceramsite, and the bottom layer is the sediment area. Although this design improves the purification efficiency of the wetland system, it also makes the identification and quantification of emerging pollutants more complex. Therefore, a new identification method is needed to accurately reflect the distribution and reduction law of emerging pollutants in the new modular anti-clogging constructed wetland system. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method for identifying the reduction pathway of emerging pollutants in a sludge-based modular anti-clogging constructed wetland.

[0004] In a first aspect, an embodiment of the present invention provides a method for identifying the reduction pathway of emerging pollutants in a sludge-based modular anti-clogging constructed wetland, which is applied to the control unit of the wetland system. The wetland system further includes: a plurality of serially connected constructed wetland modules, and each constructed wetland module includes: a plurality of partitions arranged in sequence along the height direction; the upper partition is filled with sludge-based lightweight suspended fillers, the middle partition is filled with lightweight ceramsite, the lower partition is filled with lightweight ceramsite, and the bottom partition is the sediment area. The method includes: Adding emerging pollutants to the upstream side of the wetland system according to the specified pollutant concentration and pollutant content. At the same time, inputting the pollutant concentration and pollutant content of the emerging pollutants into a pre-constructed prediction model to output the predicted pollutant content of each partition; Obtaining the sampling data of each partition during the operation of the wetland system, and performing quantitative analysis on the sampling data to determine the emerging pollutant content of each partition; For each partition, calculating the difference between the emerging pollutant content of the partition and the predicted pollutant content to obtain the difference result of the partition; Combining all the difference results to adjust the key parameters of the prediction model; Adding emerging pollutants to the upstream side of the wetland system again, and periodically sampling the emerging pollutants to establish a concentration distribution model of the emerging pollutants; Based on the concentration distribution model of emerging pollutants, and combined with the microbial population quantity and microbial population type in different zones, determine the reduction pathways of emerging pollutants.

[0005] Combined with the first aspect, the steps of adjusting the key parameters of the prediction model by combining all the difference results include: Based on the difference results, identify the influencing factors associated with emerging pollutants; Combine the influencing factors to adjust the key parameters of the pre-constructed prediction model to obtain the first prediction model; Use the first prediction model to simulate and run to obtain simulation results; Compare the simulation results with the actual sampling data, and conduct a sensitivity analysis on the adjusted parameters to determine the first quantity of target key parameters; Adjust the first prediction model based on the target key parameters until a target prediction model that meets the preset requirements is obtained.

[0006] Combined with the first aspect, the steps of determining the reduction pathways of emerging pollutants based on the concentration distribution model of emerging pollutants and combined with the microbial population quantity and microbial population type in different zones include: Based on the concentration distribution model of emerging pollutants, determine the spatial distribution of emerging pollutants; Obtain the microbial population quantity and microbial population type within the partition to obtain the microbial population characteristics; Combine all the target key parameters to determine the reduction pathways of multiple emerging pollutants; According to the spatial distribution of emerging pollutants, microbial population characteristics, and the reduction effects corresponding to all the reduction pathways, determine the priority of each reduction pathway; Combine all the reduction pathways to determine the target reduction pathway.

[0007] Before the step of adding emerging pollutants to the upstream side of the wetland system according to the specified pollutant concentration and pollutant content, and at the same time, inputting the pollutant concentration and pollutant content of the emerging pollutants into the pre-constructed prediction model to output the predicted pollutant content in each partition, it also includes: Obtain the time series data and corresponding operating parameters within the preset historical period of each partition; Preprocess the time series data and operating parameters; Use the preprocessed time series data and operating parameters to train a regression model and output a prediction result; Based on the long short-term memory network (LSTM), optimize the regression model, and use the residuals between the prediction result and the actual pollutant time series data corresponding to the historical period to perform error compensation on the regression model to obtain a trained prediction model.

[0008] The steps for preprocessing time series data and operating parameters in combination with the first aspect include: Clean the time series data for each partition's corresponding time series data. Perform data conversion processing on the cleaned time series data. Normalize the time series data after data conversion.

[0009] After the steps of optimizing the regression model based on the long short-term memory network (LSTM) in combination with the first aspect, it further includes: Add a long short-term memory network (LSTM) between the input layer of the regression model and the transformer to obtain an optimized regression model.

[0010] After the steps of compensating for the error of the regression model using the residuals between the prediction results and the actual pollutant time series data corresponding to the historical period to obtain a trained prediction model in combination with the first aspect, it further includes: Obtain the actual pollutant time series data corresponding to the historical period. Calculate the residuals based on the actual pollutant time series data and the prediction results. Use the residuals to perform error compensation on the regression model to obtain a trained prediction model and output the target prediction results.

[0011] In the second aspect, the present application provides a device for identifying new pollutant reduction pathways in a sludge-based modular anti-clogging constructed wetland, which is applied to the control unit of the wetland system. The wetland system further includes: a plurality of serially connected constructed wetland modules. Each constructed wetland module includes: a plurality of partitions arranged in sequence along the height direction; the upper partition is filled with sludge-based lightweight suspended fillers, the middle partition is filled with lightweight ceramsite, the lower partition is filled with lightweight ceramsite, and the bottom partition is a sediment area. The device further includes: A content prediction module for adding new pollutants to the upstream side of the wetland system according to the specified pollutant concentration and pollutant content, and at the same time, inputting the pollutant concentration and pollutant content of the new pollutants into a pre-constructed prediction model to output the predicted pollutant content of each partition.

[0012] A content determination module for obtaining the sampling data of each partition during the operation of the wetland system and performing quantitative analysis on the sampling data to determine the new pollutant content of each partition.

[0013] A calculation module for calculating the difference between the new pollutant content and the predicted pollutant content of each partition for each partition to obtain the difference result of the partition.

[0014] A parameter adjustment module for adjusting the key parameters of the prediction model in combination with all the difference results.

[0015] A model establishment module, configured to add new pollutants to the upstream side of the wetland system again and sample the new pollutants periodically to establish a concentration distribution model of the new pollutants; A reduction path determination module, configured to determine the reduction path of the new pollutants based on the concentration distribution model of the new pollutants and in combination with the number and type of microbial populations in the partitioned areas.

[0016] 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-mentioned method.

[0017] 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-mentioned method is executed.

[0018] The embodiments of the present invention bring the following beneficial effects: A method for identifying the reduction path of new pollutants in a sludge-based modular anti-clogging constructed wetland provided by the present application is applied to the control unit of the wetland system. The wetland system further includes: a plurality of serially connected constructed wetland modules, and each constructed wetland module includes: a plurality of partitioned areas arranged in sequence along the height direction; the upper partitioned area is filled with sludge-based lightweight suspended fillers, the middle partitioned area is filled with lightweight ceramsite, the lower partitioned area is filled with lightweight ceramsite, and the bottom partitioned area is a sediment area; the method includes: adding new pollutants to the upstream side of the wetland system according to the specified pollutant concentration and pollutant content, and at the same time, inputting the pollutant concentration and pollutant content of the new pollutants into a pre-constructed prediction model to output the predicted pollutant content of each partitioned area; obtaining the sampling data of each partitioned area during the operation of the wetland system and performing quantitative analysis on the sampling data to determine the content of new pollutants in each partitioned area; for each partitioned area, calculating the difference between the content of new pollutants in the partitioned area and the predicted pollutant content to obtain the difference result of the partitioned area; combining all the difference results to adjust the key parameters of the prediction model; adding new pollutants to the upstream side of the wetland system again and sampling the new pollutants periodically to establish a concentration distribution model of the new pollutants; based on the concentration distribution model of the new pollutants and in combination with the number and type of microbial populations in the partitioned areas, determining the reduction path of the new pollutants.

[0019] A method for identifying the reduction path of new pollutants in a sludge-based modular anti-clogging constructed wetland provided by the present application collects data for a new type of modular anti-clogging constructed wetland with a stratified and folded flow type, predicts the pollutant reduction amount through the time-series data processing ability of the prediction model, and then optimizes the pre-constructed prediction model by analyzing and processing the sampling data and continues to add new pollutants to obtain the concentration distribution model of the new pollutants. After that, the reduction path of the new pollutants is determined in combination with the microbial characteristics of each partitioned area, which is convenient for subsequent control and adjustment of the wetland system and improves the pollutant treatment ability.

[0020] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention are realized and attained by the structure particularly pointed out in the description, claims and drawings.

[0021] To make the above objectives, features and advantages of the present invention more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, the detailed description is as follows. Description of the Drawings

[0022] 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 following drawings 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.

[0023] Figure 1 Schematic flow diagram of a method for identifying new pollutant reduction pathways in a sludge-based modular anti-clogging constructed wetland provided by this application; Figure 2 Schematic diagram of a device for identifying new pollutant reduction pathways in a sludge-based modular anti-clogging constructed wetland provided by this application; Figure 3 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0024] Reference Signs: 10 - Content Prediction Module, 20 - Content Determination Module, 30 - Calculation Module, 40 - Parameter Adjustment Module, 50 - Model Establishment Module, 60 - Reduction Path Determination Module; 130 - Processor, 131 - Memory, 132 - Bus, 133 - Communication Interface. Detailed Embodiments

[0025] 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.

[0026] To facilitate the understanding of this embodiment, the following first briefly introduces the technical terms designed in this application.

[0027] The folded-flow constructed wetland (also known as a multi-stage or series constructed wetland) is an ecological engineering system designed for wastewater treatment. It purifies sewage by guiding the water flow through multiple wetland units of different types, and each unit has its specific functions and plant configurations to optimize the pollutant removal effect.

[0028] The sludge-based lightweight suspended filler is a new type of environmentally friendly material made from treated sludge as the main raw material through a specific process. This filler has the characteristics of low density, large specific surface area, high mechanical strength, and good biological affinity, and is widely used in biological membrane reactors (MBBR) and other water treatment technologies in sewage treatment.

[0029] After introducing the technical terms involved in this application, next, a brief introduction to the application scenario and design concept of the embodiments of this application will be given.

[0030] Existing constructed wetlands cannot quickly and effectively identify the law of pollutant reduction, which is not conducive to improving the pollutant treatment efficiency.

[0031] Based on this, the embodiments of this application provide a method for identifying new pollutant reduction pathways in a sludge-based modular anti-clogging constructed wetland.

[0032] Embodiment 1 This application provides a method for identifying new pollutant reduction pathways in a sludge-based modular anti-clogging constructed wetland, which is applied to the control unit of the wetland system. The wetland system further includes: a plurality of serially connected constructed wetland modules. The constructed wetland module includes: a plurality of partitions arranged in sequence along the height direction; the upper partition is filled with sludge-based lightweight suspended filler, the middle partition is filled with lightweight ceramsite, the lower partition is filled with lightweight ceramsite, and the bottom partition is a sediment area.

[0033] Combined with Figure 1 As shown, the method includes: S110, according to the specified pollutant concentration and pollutant content, add the new pollutant to the upstream side of the wetland system. At the same time, input the pollutant concentration and pollutant content of the new pollutant into a pre-constructed prediction model, and output the predicted pollutant content of each partition.

[0034] S120, obtain the sampling data of each partition during the operation of the wetland system, and conduct a quantitative analysis on the sampling data to determine the new pollutant content of each partition.

[0035] S130, for each partition, calculate the difference between the new pollutant content and the predicted pollutant content of the partition to obtain the difference result of the partition.

[0036] S140, combine all the difference results and adjust the key parameters of the prediction model.

[0037] S150. Add new pollutants to the upstream side of the wetland system again, and periodically sample the new pollutants to establish a concentration distribution model of the new pollutants.

[0038] S160. Based on the concentration distribution model of the new pollutants and combined with the number and types of microbial populations in each zone, determine the reduction pathways of the new pollutants.

[0039] A method for identifying the reduction pathways of new pollutants in a sludge-based modular anti-clogging constructed wetland provided by this application adds new pollutants with specified pollutant concentrations and pollutant contents to the upstream side of the wetland system, obtains the new pollutant contents in each zone through sampling, and compares the new pollutant contents with the predicted pollutant contents output by the prediction model to optimize the key parameters of the prediction model. Then, combined with the new pollutant distribution models in each zone, the number and types of microbial populations in each zone, determine the reduction pathways of the new pollutants, so as to facilitate subsequent control and adjustment of the wetland system and improve the pollutant treatment capacity.

[0040] Among them, the design of the folded-flow waterway structure and the layered structure can effectively prevent matrix clogging, improve the reliability and stability of the operation of the wetland system, and is conducive to enhancing the purification efficiency of the wetland system. In this embodiment, the output end of the previous constructed wetland module is connected to the input end of the current constructed wetland module, and the setting directions of the input ends of adjacent constructed wetland modules are opposite, thus forming a folded-flow waterway structure. For example, the input end of the first constructed wetland module is at the top and the output end is at the bottom; correspondingly, the input end of the adjacent second constructed wetland module is at the bottom and the output end is at the top; the third constructed wetland module is the same as the first constructed wetland module, with the input end at the top and the output end at the bottom... In this way, the sewage flows into the first constructed wetland module from above and then flows downward and into the second constructed wetland module along the input end located below, the water level rises until it is output from the output end above to the third constructed wetland... until it is output from the output end of the last constructed wetland module. In order to achieve the folded-flow water path, adjacent constructed wetland modules are separated by partition walls or barriers, forcing the water flow to flow back and forth between these units. Further, flow deflectors can be installed on the partition walls or barriers to help the water flow turn and flow more smoothly.

[0041] In this embodiment, the wetland system is divided into multiple zones along the flow direction according to the folded-flow water flow mode in the front, back, left, and right directions, and each wetland module further includes an upper zone, a middle zone, a lower zone, and a bottom zone separated along the height direction.

[0042] It can be understood that the above data processing process involves a deep learning model. Before actual application, the prediction model (i.e., the hydrodynamic-water quality model of the anti-clogging constructed wetland system) should be constructed, trained, optimized until a model that meets the usage requirements is obtained for application.

[0043] Among them, new pollutants such as microplastics, antibiotics, and PFAS (per- or polyfluoroalkyl substances) are calculated based on the specified pollutant concentration and pollutant content, and sufficient pure water is prepared based on the total volume of the wetland system. Then, the new pollutant solution is prepared, and one or more of the above-mentioned new pollutant solutions are mixed. A flow meter and pump are installed at the entrance of the wetland system to control the water flow rate and flow rate so as to evenly add the new pollutant solution to the wetland system.

[0044] In combination with the first aspect, before step S110, the method further includes: S010, obtaining time series data and corresponding operating parameters within a preset historical period of each partition.

[0045] For each partition (a partition refers to one of the upper partition, middle partition, lower partition and bottom partition), the time series data of the partition in the historical period is obtained, and the operating parameters of the wetland system in the corresponding period are obtained. Among them, the installation method of the wetland system is surface installation or semi-buried installation, and the removal method of the sediment in the bottom sediment area can be emptying or suction, and the sediment removal should be carried out regularly.

[0046] S020, preprocessing the time series data and operating parameters.

[0047] In combination with the first aspect, step S020 specifically includes: S021, cleaning the time series data corresponding to each partition.

[0048] S022, performing data conversion processing on the cleaned time series data.

[0049] S023, standardize the time series data after data conversion.

[0050] It can be understood that step S021 pre-processes the data collected in step S010 to remove abnormal data, check and supplement missing values, and mark abnormal values, thereby improving the quality of the data and enhancing the model performance.

[0051] Step S022 performs format conversion on the cleaned time series data to convert data from different sources into a unified format. Then, step S023 standardizes the converted time series data, which is an important step to ensure that different features have the same scale, which helps to improve the performance and explanatory power of the model.

[0052] S030, using the preprocessed time series data and operating parameters to train a regression model and output a prediction result.

[0053] It is understandable that first, the target variable to be predicted (e.g., water quality index, equipment performance, etc.) and the features used for prediction (e.g., temperature, pH value, timestamp, etc.) are clarified. In this embodiment, the target variable to be predicted is the pollutant content.

[0054] Take the multiple preprocessed time-series data and operating parameters collected as training samples, and divide them into a training set and a test set. Use the training set data to train the selected regression model, make predictions on the test set, and evaluate the model performance. Among them, common evaluation metrics include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²).

[0055] S040, based on the long short-term memory network LSTM, optimize the regression model, and use the residuals between the prediction results and the actual pollutant time-series data corresponding to historical periods to perform error compensation on the regression model to obtain the trained prediction model.

[0056] Combined with the first aspect, the step S040 of optimizing the regression model based on the long short-term memory network LSTM includes: S041, add a long short-term memory network LSTM between the input layer and the transformer of the regression model to obtain the optimized regression model.

[0057] LSTM is a special recurrent neural network (RNN) that can learn long-term dependencies and is very suitable for processing time-series data. Adding the long short-term memory network LSTM to this regression model, LSTM is used to process time-series data, while the transformer in the regression model is used to capture more complex sequence patterns or dependencies between features, thus constructing an optimized hybrid model. This model combines the good processing ability of LSTM for time-series data and the powerful feature learning ability of the transformer, and is suitable for various complex prediction tasks.

[0058] In step S040, using the residuals between the prediction results and the actual pollutant time-series data corresponding to historical periods to perform error compensation on the regression model to obtain the trained prediction model specifically includes: S042, obtain the actual pollutant reduction time-series data corresponding to the historical period.

[0059] S043, calculate the residuals based on the actual pollutant time-series data and the prediction results.

[0060] S044, use the residuals to perform error compensation on the regression model to obtain the trained prediction model and output the target prediction result.

[0061] To utilize the residuals between the prediction results and the actual pollutant time-series data corresponding to historical periods for error compensation of the regression model, which helps improve the accuracy of model prediction. In step S042, obtain the actual pollutant reduction time-series data in the historical period; based on the existing regression model (i.e., a hybrid model with LSTM added between the input layer and the transducer), use the data in the test set to generate prediction values and calculate the differences between these prediction values and the actual observed values, that is, the residuals. Next, analyze the residuals to understand the pattern of prediction errors. For example, methods such as plotting residual plots and autocorrelation plots can be used to check for systematic biases or periodic variations. Then, construct a residual model. Based on the characteristics of the residuals, build a new model to predict these residuals. Commonly used models include linear regression, ARIMA (Autoregressive Integrated Moving Average model), or even another LSTM model. Finally, apply error compensation. Finally, add the residuals predicted by the residual model to the original prediction results to obtain the final prediction values, thereby achieving error compensation and performing model performance evaluation. If inverse normalization of the prediction results is required, repeat the above steps. Use these residuals to adjust and compensate for the errors in the regression model, thus obtaining a more accurate prediction model and using it to output the predicted pollutant content for each partition in the specified period.

[0062] Combined with the first aspect, step S140 includes: S141, based on the difference results, identify the influencing factors associated with the new pollutant.

[0063] S142, adjust the key parameters of the pre-constructed prediction model in combination with the influencing factors to obtain an updated first prediction model.

[0064] S143, use the first prediction model to simulate and run to obtain simulation results.

[0065] S144, compare the simulation results with the actual sampling data and conduct a sensitivity analysis on the adjusted parameters to determine the first quantity of target key parameters.

[0066] S145, adjust the first prediction model based on the target key parameters until a target prediction model that meets the preset requirements is obtained.

[0067] It is understandable that after sampling and quantitative analysis of each partition to determine the content of new pollutants in the partition, calculating the difference between it and the predicted pollutant content output by the prediction model, and combining the difference results of each partition, first identify the influencing factors associated with the new pollutants, and adjust the key parameters pre-configured in the prediction model based on the specific influencing factors to initially update the first prediction model. Then, simulate the operation of the updated first prediction model to output simulation results, compare the simulation results with the actual sampling data, and conduct parameter sensitivity analysis to evaluate the first quantity of target key parameters strongly associated with the new pollutants. Furthermore, based on these target key parameters, perform parameter adjustment on the first prediction model again to optimize the prediction model again and obtain a target prediction model that can meet the purification requirements of the new pollutants.

[0068] Among them, the influencing factors at least include one of hydrodynamic parameters, physical factors, chemical factors, and biological factors; the difference results at least include the concentration difference result of new pollutants, the spatial distribution difference result, and the time series difference result.

[0069] It is understandable that in this embodiment, the hydrodynamic parameters may include flow velocity, flow direction, and turbulence intensity. Among them, the speed and direction of the water flow directly affect the transmission path and diffusion speed of pollutants, and the turbulent motion in the water body can increase the mixing efficiency between pollutants and other substances, thereby affecting its chemical reaction rate and biodegradation process.

[0070] Physical factors refer to physical phenomena that occur during the sewage purification process, such as sedimentation, suspension, adsorption, or desorption. It is understandable that sedimentation and suspension can characterize the vertical migration law of new pollutants in the water body; when the environmental conditions change, these two physical phenomena of adsorption and desorption will cause changes in the content of the new pollutants.

[0071] Chemical factors refer to the change in the form or chemical properties of new pollutants due to chemical reactions, or the influence of environmental pH changes on the existence form and stability of the new pollutants. For example, some heavy metal ions can be converted into more insoluble forms through redox reactions.

[0072] Biological factors refer to the metabolism and decomposition or accumulation and amplification of new pollutants by microorganisms. For example, specific types of microorganisms can metabolize and decompose certain types of pollutants and convert them into harmless compounds. This natural purification mechanism is of great significance for controlling environmental pollution.

[0073] After determining the influencing factors that affect the content of the new pollutant in step S141, in step S142, the parameters of the model are adjusted based on the determined influencing factors, and in step S143, the simulation is run to output the simulation results. Then, in step S144, the simulation results are compared with the data obtained from the current sampling, and the key parameters are evaluated to check whether each influencing parameter really affects the content of the new pollutant, so as to determine the first quantity of target key parameters that can have a substantial impact on the new pollutant. Then, in step S145, the target key parameters of the model are adjusted to obtain a prediction model that can meet the removal of the new pollutant.

[0074] Combined with the first aspect, step S160 includes: S161, based on the concentration distribution model of the new pollutant, determine the spatial distribution of the new pollutant.

[0075] S162, obtain the number and type of microbial populations in the partition to obtain the characteristics of the microbial populations.

[0076] S163, combined with all the target key parameters, determine various reduction paths for the new pollutants.

[0077] S164, according to the spatial distribution of the new pollutant, the characteristics of the microbial populations, and the reduction effects corresponding to all the reduction paths, determine the target reduction path.

[0078] In this embodiment, based on the concentration distribution model of the new pollutant and the characteristics of the microbial populations, the reduction paths can be reasonably selected and optimized, so as to effectively reduce the risk of new pollutants in the environment. Specifically, first, based on the concentration distribution model of the new pollutant, understand the concentration differences of the pollutants in different partitions, identify the high-pollution areas and low-pollution areas, and then, based on time series analysis, analyze the changing trend of the pollutant concentration over time to understand its dynamics (such as seasonal fluctuations, long-term changes, etc.), so as to clarify the influencing factors that have a significant impact on the concentration of the new pollutant. The principle is the same as that of step S141.

[0079] After that, in step S162, obtain the current characteristics of the microbial populations. In step S163, combine the determined target key parameters that can have a substantial impact with the characteristics of the microbial populations to formulate multiple reduction paths. Then, combine the reduction effects that each reduction path can produce to fuse multiple reduction paths to obtain the target reduction path that can meet the requirements for reducing the new pollutant. After determining the pollutant reduction path in step S160, it provides a direction for adjusting the working parameters of the wetland system. For example, the aeration volume is increased. At this time, the working parameters can be adjusted based on the provided direction for adjusting the working parameters, so as to determine the target working parameters, and then control the operation of the wetland system to reduce pollutants and improve the purification efficiency.

[0080] Second aspect, the present application provides a device for identifying the reduction pathways of emerging pollutants in a sludge-based modular anti-clogging constructed wetland, which is applied to the control unit of the wetland system. The wetland system further includes: a plurality of serially connected constructed wetland modules, and each constructed wetland module includes: a plurality of partitions arranged successively in the height direction; the upper partition is filled with sludge-based light suspended fillers, the middle partition is filled with light ceramsite, the lower partition is filled with light ceramsite, and the bottom partition is a sediment area; as Figure 2 shown, the device further includes: a content prediction module 10, a content determination module 20, a calculation module 30, a parameter adjustment module 40, a model establishment module 50, and a reduction pathway determination module 60.

[0081] The content prediction module 10 is used to add emerging pollutants to the upstream side of the wetland system according to the specified pollutant concentration and pollutant content. At the same time, the pollutant concentration and pollutant content of the emerging pollutants are input into a pre-constructed prediction model, and the predicted pollutant content of each partition is output.

[0082] The content determination module 20 is used to obtain the sampling data of each partition during the operation of the wetland system, and perform quantitative analysis on the sampling data to determine the content of emerging pollutants in each partition.

[0083] The calculation module 30 is used to calculate the difference between the content of emerging pollutants in each partition and the predicted pollutant content for each partition, and obtain the difference result of the partition.

[0084] The parameter adjustment module 40 is used to adjust the key parameters of the prediction model in combination with all the difference results.

[0085] The model establishment module 50 is used to add emerging pollutants to the upstream side of the wetland system again, and periodically sample the emerging pollutants to establish a concentration distribution model of the emerging pollutants.

[0086] The reduction pathway determination module 60 is used to determine the reduction pathways of emerging pollutants based on the concentration distribution model of emerging pollutants and in combination with the microbial population quantity and microbial population type in each partition.

[0087] Third aspect, an embodiment of the present application provides an electronic device. In combination with Figure 3 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.

[0088] Further, the electronic device shown in combination with Figure 3 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.

[0089] Among them, the memory 131 may include high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 133 (which can be wired or wireless), 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, 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 Figure 3 , but it does not mean that there is only one bus or one type of bus.

[0090] The processor 130 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit in the hardware of the processor 130 or the instructions in software form. The above-mentioned processor 130 can be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it can 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 can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, 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.

[0091] In a fourth aspect, an embodiment of 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.

[0092] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems and devices described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0093] In addition, in the description of the embodiments of the present invention, unless otherwise clearly defined and limited, the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0094] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this 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 to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0095] 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, and therefore should not be construed as a limitation to the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0096] 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 it. 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 recorded 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 method for identifying the reduction pathway of emerging pollutants in a sludge-based modular anti-clogging constructed wetland, characterized in that, A control unit applied to a wetland system, the wetland system further comprising: a plurality of artificial wetland modules connected in series, each artificial wetland module comprising: a plurality of partitions arranged in sequence along the height direction; the upper partition is filled with sludge-based light suspended fillers, the middle partition is filled with light ceramsite, the lower partition is filled with light ceramsite, and the bottom partition is a sediment area; the method comprises: Adding a new pollutant to the upstream side of the wetland system according to the specified pollutant concentration and pollutant content, and at the same time, inputting the pollutant concentration and pollutant content of the new pollutant into a pre-constructed prediction model to output the predicted pollutant content of each partition; Obtaining the sampling data of each partition during the operation of the wetland system, and performing quantitative analysis on the sampling data to determine the content of the new pollutant in each partition; For each partition, calculating the difference between the content of the new pollutant in the partition and the predicted pollutant content to obtain the difference result of the partition; Combining all the difference results to adjust the key parameters of the prediction model; Adding the new pollutant to the upstream side of the wetland system again, and periodically sampling the new pollutant to establish a concentration distribution model of the new pollutant; Based on the concentration distribution model of the new pollutant, and in combination with the microbial population quantity and microbial population type of the partition, determining the reduction pathway of the new pollutant.

2. The method according to claim 1, characterized in that, The step of combining all the difference results to adjust the key parameters of the prediction model includes: Based on the difference results, identifying the influencing factors associated with the new pollutant; Combining the influencing factors to adjust the key parameters of the pre-constructed prediction model to obtain an updated first prediction model; Simulating the operation using the first prediction model to obtain a simulation result; Comparing the simulation result with the actual sampling data, and performing sensitivity analysis on the adjusted parameters to determine a first quantity of target key parameters; Adjusting the first prediction model based on the target key parameters until a target prediction model that meets the preset requirements is obtained.

3. The method according to claim 2, characterized in that, The step of determining the reduction pathway of the new pollutant based on the concentration distribution model of the new pollutant, and in combination with the microbial population quantity and microbial population type of the partition, includes: Based on the concentration distribution model of the new pollutant, determining the spatial distribution of the new pollutant; Obtaining the microbial population quantity and microbial population type in the partition to obtain the microbial population characteristics; Combining all the target key parameters to determine multiple reduction pathways of the new pollutant; According to the spatial distribution of the new pollutant, the microbial population characteristics, and the reduction effects corresponding to all the reduction pathways, determining the target reduction pathway.

4. The method according to claim 1, wherein Before the step of adding a new pollutant to the upstream side of the wetland system according to the specified pollutant concentration and pollutant content, and at the same time, inputting the pollutant concentration and pollutant content of the new pollutant into a pre-constructed prediction model to output the predicted pollutant content of each partition, further comprising: Obtaining the time-series data and corresponding operating parameters of each partition within a preset historical period; Preprocess the time series data and the operating parameters; Train a regression model using the preprocessed time series data and the operating parameters, and output a prediction result; Based on the long short-term memory network (LSTM), optimize the regression model, and use the residual between the prediction result and the actual pollutant time series data corresponding to the historical period to perform error compensation on the regression model to obtain a trained prediction model.

5. The method according to claim 4, wherein The steps of preprocessing the time series data and the operating parameters include: For the time series data corresponding to each partition, clean the time series data; Perform data conversion processing on the cleaned time series data; Normalize the time series data after data conversion.

6. The method according to claim 4, wherein The steps of optimizing the regression model based on the long short-term memory network (LSTM) include: Add a long short-term memory network (LSTM) between the input layer and the transformer of the regression model to obtain the optimized regression model.

7. The method according to claim 4, wherein The steps of using the residual between the prediction result and the actual pollutant time series data corresponding to the historical period to perform error compensation on the regression model to obtain a trained prediction model further include: Obtain the actual pollutant time series data corresponding to the historical period; Calculate the residual based on the actual pollutant time series data and the prediction result; Use the residual to perform error compensation on the regression model to obtain a trained prediction model and output the target prediction result.

8. An apparatus for identifying the reduction pathways of emerging pollutants in a sludge-based modular anti-clogging constructed wetland, characterized in that, Applied to the control unit of a wetland system, the wetland system further includes: a plurality of serially connected artificial wetland modules, and each artificial wetland module includes: a plurality of partitions arranged in sequence along the height direction; the upper partition is filled with sludge-based lightweight suspended fillers, the middle partition is filled with lightweight ceramsite, the lower partition is filled with lightweight ceramsite, and the bottom partition is a sediment area; the device further includes: A content prediction module, configured to add a new pollutant to the upstream side of the wetland system according to the specified pollutant concentration and pollutant content, and at the same time, input the pollutant concentration and pollutant content of the new pollutant into a pre-constructed prediction model to output the predicted pollutant content of each partition; A content determination module, configured to obtain the sampling data of each partition during the operation of the wetland system, and perform quantitative analysis on the sampling data to determine the new pollutant content of each partition; A calculation module, configured to calculate the difference between the new pollutant content and the predicted pollutant content of each partition for each partition to obtain the difference result of the partition; A parameter adjustment module, configured to adjust the key parameters of the prediction model by combining all the difference results; A model establishment module, configured to add the new pollutant to the upstream side of the wetland system again, and perform periodic sampling of the new pollutant to establish a concentration distribution model of the new pollutant; A reduction path determination module, configured to determine the reduction path of the new pollutant based on the concentration distribution model of the new pollutant and in combination with the microbial population quantity and microbial population type of the partition.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to cause the electronic device to execute the method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, Computer program instructions are stored in the storage medium. When the computer program instructions are read and run by a processor, the method according to any one of claims 1 to 7 is executed.

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