Rural domestic sewage total collection system based on PVC pipe network

Abstract

The invention relates to a rural domestic sewage full-collection system based on a PVC pipe network, and relates to the field of sewage treatment, the rural domestic sewage full-collection system comprises a pipe network distance acquisition module used for acquiring a plurality of pipe network distances between a plurality of residents and a treatment end in the PVC pipe network; the resident sewage prediction module is used for collecting current real-time time and predicting sewage discharge capacity and sewage parameters of a plurality of residents according to the real-time time; the treatment end sewage prediction module is used for performing sewage transmission change prediction according to real-time time, a plurality of pipe network distances, a plurality of sewage discharge capacities and a plurality of sewage parameters to obtain an average treatment end sewage parameter of sewage transmitted to the treatment end; and the sewage treatment optimization module is used for optimizing treatment parameters of the treatment end according to the average treatment end sewage parameters, obtaining optimal sewage treatment parameters and carrying out domestic sewage collection treatment. The technical problems that in the prior art, the rural domestic sewage collection and treatment effect is poor, and environmental pollution is caused are solved.

Description

Technical Field

[0001] The invention relates to the technical field of sewage treatment, and in particular to a rural domestic sewage collection system based on a PVC pipe network. Background Art

[0002] In the current rural sewage treatment process, due to factors such as dispersed residential distribution, rural domestic sewage has not been fully collected, resulting in some sewage being directly discharged into the surrounding environment, causing water pollution, soil eutrophication and deterioration of groundwater quality.

[0003] In addition, due to the scattered distribution of rural residents, the distance of rural sewage collection and transmission is long, and sewage changes during the transmission process. At present, there is no relevant technology to adjust the sewage treatment strategy in response to this change. Therefore, there are technical problems in the existing technology that the collection and treatment effect of rural domestic sewage is poor and causes environmental pollution. Summary of the invention

[0004] The present invention aims to solve the technical problems of poor rural domestic sewage collection and treatment effect and environmental pollution in the prior art, and provides a rural domestic sewage collection system based on a PVC pipe network.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: The present invention provides a rural domestic sewage collection system based on a PVC pipe network, the system includes a PVC pipe network, the PVC pipe network connects multiple households and a treatment end in a rural area, collects domestic sewage from multiple households and transmits it to the treatment end, and the system also includes: A pipe network distance acquisition module, used to acquire a plurality of pipe network distances between the plurality of households and the processing end in the PVC pipe network; A household sewage prediction module, used to collect the current real-time time and predict the sewage discharge volume and sewage parameters of the plurality of households according to the real-time time; The treatment end sewage prediction module is used to predict the sewage transmission change according to the real-time time, multiple pipe network distances, multiple sewage discharge volumes and multiple sewage parameters, obtain multiple treatment end sewage parameters when the sewage is transmitted to the treatment end, and calculate and obtain the average treatment end sewage parameter; The sewage treatment optimization module is used to optimize the treatment parameters of the treatment end according to the average treatment end sewage parameters, obtain the optimal sewage treatment parameters, and collect and treat domestic sewage.

[0006] The beneficial effects of the present invention are as follows: the present invention connects multiple rural households through a PVC pipe network to achieve full collection of sewage, avoid environmental pollution caused by arbitrary discharge of sewage, and predict the sewage discharge and water quality parameters of each household in different time periods based on real-time data and historical data, thereby improving the intelligent level of sewage collection and management. At the same time, based on time, pipe network distance, sewage discharge and water quality parameters, the sewage parameters at the treatment end after the sewage is transported through a long-distance pipe network are predicted, the changes in pollutants such as dissolved oxygen, nitrogen and phosphorus during the sewage transmission process are analyzed, the average sewage parameters at the treatment end are calculated, and the sewage treatment parameters (such as aeration parameters, etc.) are automatically optimized to ensure that the sewage treatment facilities can adjust the treatment method in real time, improve the treatment efficiency, and reduce energy consumption. In addition, the present invention is aimed at the characteristics of rural decentralized sewage, and through a data-driven prediction method, the changes in sewage parameters caused by the pipe network distance in rural decentralized sewage collection are considered, the sewage treatment process is optimized, the accuracy and stability of the treatment are improved, and finally efficient, energy-saving, and intelligent rural sewage treatment is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 A schematic diagram of the structure of a rural domestic sewage collection system based on a PVC pipe network provided by the present invention; Figure 2 A schematic diagram of a process for obtaining average treatment end sewage parameters in a rural domestic sewage collection system based on a PVC pipe network provided by the present invention.

[0008] In the accompanying drawings, the components represented by the reference numerals are described as follows: Pipeline network distance acquisition module 11, household sewage prediction module 12, treatment end sewage prediction module 13, sewage treatment optimization module 14. DETAILED DESCRIPTION

[0009] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0010] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0011] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0012] Embodiment 1, as Figure 1 As shown, an embodiment of the present invention provides a rural domestic sewage collection system based on a PVC pipe network. The system includes a PVC pipe network, which connects multiple households and treatment ends in a rural area, collects domestic sewage from multiple households and transmits it to the treatment end.

[0013] The PVC pipe network includes PVC pipes and sewage collectors (with filters), which connect multiple households in rural areas and collect domestic sewage generated by each household, including kitchen wastewater, toilet wastewater, laundry wastewater and other domestic sewage. The pipes of each household are connected to the sewage collector. The filter can intercept larger solid debris to prevent pipe blockage, and the filtered sewage is transmitted to the treatment end through the PVC pipe network for centralized sewage treatment. Among them, the rural area is any rural geographical area, such as towns, villages, etc.

[0014] The PVC pipe network includes PVC main pipes, branch pipes, tees, elbows, valves, etc., which connect multiple households in rural areas. The sewage from each household is collected into the main pipe through the branch pipe. Through the slope design, the sewage is transmitted to the sewage treatment end by gravity flow.

[0015] The processing end is used to treat the domestic sewage collected from multiple households, such as decomposing and purifying the organic matter in the sewage through aeration and adding drugs, removing nutrients such as nitrogen and phosphorus in the sewage, completing the sewage treatment, and discharging it after meeting the emission standards to avoid polluting the environment.

[0016] The embodiment of the present application provides a rural domestic sewage collection system based on a PVC pipe network, which further includes: The pipe network distance acquisition module 11 is used to obtain multiple pipe network distances between the multiple households and the processing end in the PVC pipe network.

[0017] In the embodiments of the present application, due to the dispersion of residents in rural areas, the distance between the pipes collecting the sewage from multiple households is relatively long, and the sewage will change during the pipeline transmission process. For example, when the transmission distance is long, the continuous consumption of organic matter and microbial activity will cause the dissolved oxygen in the sewage to decrease, affecting the subsequent sewage treatment.

[0018] Therefore, we first obtain multiple pipe network distances between multiple households and the treatment end in the PVC pipe network to analyze the changes in sewage during its transmission in the pipe network.

[0019] In the embodiment of the present application, the pipe network distance acquisition module 11 is specifically used for: Obtain multiple terminal locations of multiple households in the PVC pipe network; Based on the PVC pipe network, the lengths of the connecting pipes between the multiple terminal positions and the processing end are calculated to obtain multiple pipe network distances.

[0020] In the embodiment of the present application, multiple terminal locations of multiple households in the PVC pipe network are first obtained. The terminal location refers to the sewage discharge point of each household connected to the PVC pipe network, which is usually located at the drain outlet of the residence or the access point outside the courtyard. In order to achieve accurate sewage collection and transmission, the location of each household must first be calibrated. Through GPS positioning technology or spatial data based on geographic information system (GIS), the geographic coordinates of these terminal locations can be accurately obtained to obtain multiple terminal locations.

[0021] Furthermore, based on the network map of the PVC network, the pipelines between each terminal location and the processing end are connected, and according to the scale of the network map, the length of the connecting pipeline between the terminal location and the processing end is measured to obtain the network distance. In this way, the network distances between multiple terminal locations and the processing end are measured. Among them, the network map can be calibrated and constructed through surveying and mapping technologies during the construction of the PVC network, which includes the geographical location information of each pipeline in the PVC network.

[0022] For example, the length of the connecting pipe between the terminal location corresponding to a household and the processing end, that is, the pipe network distance is 1500m.

[0023] Among them, due to the large dispersion of rural households, the distance between sewage collection pipes is long. The longer the pipe network distance, the longer the transmission time during sewage transmission, resulting in greater changes in sewage. Therefore, by accurately calculating the pipe network distance of each household, accurate basic data can be provided for the subsequent optimization of sewage treatment strategies.

[0024] The household sewage prediction module 12 is used to collect the current real time and predict the sewage discharge volume and sewage parameters of the multiple households according to the real time.

[0025] In the embodiments of the present application, sewage discharge in rural areas is time-dependent. For example, more water is used during the busy farming season and less water is used during the slack farming season. This will also affect the parameters in the sewage. For example, the temperature is higher during the busy farming season, and the water body is rich in organic matter, resulting in lower dissolved oxygen in the sewage, while higher dissolved oxygen in the sewage during the slack farming season.

[0026] Therefore, the current real-time time is collected to predict the sewage discharge volume and sewage parameters of multiple households based on time, providing data support for subsequent sewage collection and treatment, so as to optimize the formulation of corresponding sewage treatment strategies.

[0027] In the embodiment of the present application, the household sewage prediction module 12 is specifically used for: Divide the preset time period into multiple time windows, and obtain the time window into which the current moment falls as the real time; Building a rural sewage predictor; The real-time time is input into the rural sewage predictor, and the prediction output obtains multiple sewage discharge volumes and multiple sewage parameters of multiple households.

[0028] In the embodiment of the present application, the preset time period refers to a fixed time period. Activities in rural areas are generally based on an annual cycle. Therefore, the preset time period is preferably one year, which is divided into multiple time windows, such as 12 months, or multiple weeks.

[0029] Further, the time window that the current moment falls into is obtained, for example, March, and the time window that falls into is divided as the current real time. The sewage discharge volume and sewage parameters are predicted based on the time window as a unit.

[0030] Furthermore, a rural sewage predictor is constructed to predict the sewage discharge volume and sewage parameters of multiple households at different times according to time.

[0031] In the embodiment of the present application, “building a rural sewage predictor” includes: According to the sewage discharge monitoring data of the plurality of households in the historical time, collecting a plurality of historical sewage discharge sets and a plurality of historical sewage parameter sets of the plurality of households in the plurality of time windows; A mapping table of the multiple households, multiple time windows, multiple sample sewage discharge sets, and multiple sample sewage parameter sets is constructed to obtain a rural sewage predictor.

[0032] In the embodiment of the present application, based on the sewage discharge monitoring data of multiple households in the historical time, the discharge volume and parameters of the sewage discharged by multiple households in multiple time windows are collected to obtain multiple historical sewage discharge volume sets and multiple historical sewage parameter sets. Among them, the historical sewage parameters include the dissolved oxygen content in the discharged sewage.

[0033] For example, the sewage discharge volume and dissolved oxygen content of multiple households in repeated time windows can be detected through flow meters and sampling in historical time to obtain sewage discharge monitoring data of multiple households in historical time, and then the mean sewage discharge volume and sewage parameters of each household in the same time window (for example, March of each year) are calculated as the historical sewage discharge volume and historical sewage parameters, which are used as sample data for constructing a rural sewage predictor.

[0034] For example, the historical sewage discharge of a household within a certain time window is 450 cubic meters, and the historical sewage parameter is a dissolved oxygen concentration of 4 mg / L.

[0035] Furthermore, after obtaining the sample sewage discharge and sample water quality parameters of multiple households in multiple time windows, a mapping table of multiple households, multiple time windows, multiple sample sewage discharge sets and multiple sample sewage parameter sets is constructed, and the mapping table is used as a rural sewage predictor. In the rural sewage predictor, each household and time window combination corresponds to the sample sewage discharge and sample sewage parameters of the household at that time, thus forming a mapping relationship.

[0036] Exemplarily, some of the data in the mapping table are shown in Table 1.

[0037] Table 1

[0038]

[0039] By building a rural sewage predictor, we can map the corresponding sewage discharge volume and sewage parameters according to the input time window and household, and provide accurate predictions for sewage discharge volume and sewage parameters. For example, if the real time is March and household A is input, the output will obtain the possible sewage discharge volume and sewage parameters of household A within the time window.

[0040] In this way, the current real-time time is input into the rural sewage predictor, which can predict and output multiple sewage discharge volumes and multiple sewage parameters of multiple households in the time window corresponding to the current real-time time, which serves as the data basis for subsequent sewage collection and treatment.

[0041] Among them, by collecting and constructing samples of historical data in multiple time windows, the sewage discharge and sewage parameters of each household can be output, providing effective data support for the dynamic optimization of sewage treatment, and improving treatment efficiency and water quality stability.

[0042] The treatment end sewage prediction module 13 is used to predict sewage transmission changes according to the real-time time, multiple pipe network distances, multiple sewage discharge volumes and multiple sewage parameters, obtain multiple treatment end sewage parameters when sewage is transmitted to the treatment end, and calculate the average treatment end sewage parameter.

[0043] In the embodiments of the present application, due to the dispersed distribution of rural households, the distance of the sewage transmission pipeline is long, resulting in a long sewage transmission time. The sewage will change during the transmission process. For example, microbial activity causes a decrease in dissolved oxygen, which in turn affects subsequent sewage treatment.

[0044] In addition, different temperatures at different times will also cause changes in sewage transmission time. For example, the sewage transmission speed is fast when the temperature is higher and slow when the temperature is lower. Different sewage discharge volumes will also cause the sewage transmission speed to change. Therefore, the embodiment of the present application predicts the changes in sewage parameters during sewage transmission based on real-time time, multiple pipe network distances, multiple sewage discharge volumes and multiple sewage parameters to predict the sewage parameters of each household after the sewage is transmitted to the treatment end, and obtain multiple treatment end sewage parameters.

[0045] Furthermore, the average treatment end sewage parameter is calculated based on multiple treatment end sewage parameters, and the average sewage parameter level after the sewage discharged by multiple households is transmitted to the treatment end is obtained, providing a data basis for the treatment end to treat sewage.

[0046] In the present application embodiment, Figure 2 As shown, the treatment end sewage prediction module 13 is specifically used for: training a treatment end wastewater predictor for predicting treatment end wastewater parameters; The real-time time, the multiple pipe network distances, the multiple sewage discharge volumes and the multiple sewage parameters are respectively input into the treatment end sewage predictor, and the multiple treatment end sewage parameters obtained by transmitting the sewage of the multiple households to the treatment end are output; According to the multiple sewage discharge volumes, the multiple treatment end sewage parameters are weighted and calculated to obtain the average treatment end sewage parameter.

[0047] In an embodiment of the present application, a treatment end sewage predictor for predicting treatment end sewage parameters is first trained to predict sewage parameters of the terminal after the discharged sewage is transmitted through the pipeline network based on real-time time, pipeline network distance, sewage discharge volume and sewage parameters.

[0048] In the embodiment of the present application, “training a treatment end sewage predictor for predicting treatment end sewage parameters” includes: According to the rural sewage collection monitoring data in the historical time, the sample real-time time set, the sample pipe network distance set, the sample sewage discharge set and the sample sewage parameter set are collected, and the sample treatment end sewage parameter set of the sewage transmitted to the treatment end is collected, wherein each sample treatment end sewage parameter includes dissolved oxygen; Using the sample real-time time set, the sample pipe network distance set, the sample sewage discharge volume set and the sample sewage parameter set as input features, using the sample treatment end sewage parameter set as output features, based on machine learning, constructing and training a treatment end sewage predictor; The treatment end sewage predictor is tested and the training is completed after meeting the requirements.

[0049] In the embodiment of the present application, in order to accurately predict the water quality parameters after the sewage is transmitted to the treatment end, especially key indicators such as dissolved oxygen, it is necessary to train the sewage predictor at the treatment end. The predictor uses a machine learning algorithm to establish a mapping relationship between input and output based on historical sewage collection data to improve the accuracy and efficiency of the sewage treatment process.

[0050] In the embodiment of the present application, based on the rural sewage collection monitoring data in the historical period, the real-time time, pipe network distance set, sewage discharge volume and sewage parameters of the sewage collection and treatment of multiple households are collected, and the sample real-time time set, sample pipe network distance set, sample sewage discharge volume set and sample sewage parameter set are collected. And the sample treatment end sewage parameter set is obtained by sampling and testing after the sewage discharged by each household is transmitted to the treatment end, wherein each sample treatment end sewage parameter includes dissolved oxygen concentration.

[0051] For example, after the PVC pipe network is constructed and put into operation, markers (such as fluorescent tracers, rhodamine B, etc.) can be set in the sewage discharged by residents, and then the markers can be collected after the sewage is discharged to the treatment end to determine the source of the sewage and measure the dissolved oxygen concentration in the sewage as the sewage parameter of the sample treatment end. In this way, a set of sewage parameters of the sample treatment end can be collected.

[0052] Furthermore, a treatment-end sewage predictor is constructed and trained based on machine learning, using a sample real-time time set, a sample pipe network distance set, a sample sewage discharge volume set and a sample sewage parameter set as input features, and a sample treatment-end sewage parameter set as output features.

[0053] Exemplarily, based on the feedforward neural network in machine learning, a treatment end sewage predictor is constructed, which includes an input layer, a hidden layer and an output layer. The data dimension of the input layer is 4, corresponding to the real-time time, pipe network distance, sewage discharge volume and sewage parameters in the input features. The data dimension of the output layer is 1, corresponding to the treatment end sewage parameters. The hidden layer contains 2 fully connected layers, each with 16 neurons, and the activation function uses the ReLU activation function. During the training process, the sample real-time time, sample pipe network distance, sample sewage discharge volume and sample sewage parameters are input to obtain the output treatment end sewage parameters. The mean square error loss function is used to calculate the error with the actual sample treatment end sewage parameters as the loss. According to the loss, the network parameters such as the weight of the neuron are adjusted to optimize the network parameters to reduce the loss until the requirements are met. For example, when the loss is less than 0.01, the training is completed.

[0054] Furthermore, the trained treatment-end sewage predictor is tested. For example, the sample real-time time set, the sample pipe network distance set, the sample sewage discharge volume set, the sample sewage parameter set and the sample treatment-end sewage parameter set can be divided, for example, into a training set and a test set in a ratio of 8:2. The test set is used to test the treatment-end sewage predictor, and the loss is calculated. If the loss still meets the requirement, the training is completed and the treatment-end sewage predictor is obtained.

[0055] Based on the trained treatment-end sewage predictor, the current real-time time, multiple pipe network distances, multiple sewage discharge volumes and multiple sewage parameters are respectively used to form multiple input data of multiple households. Each input data includes real-time time, pipe network distance, sewage discharge volume and sewage parameters, which are respectively input into the treatment-end sewage predictor to obtain the predicted output of multiple treatment-end sewage parameters of the multiple households transmitted to the treatment end, specifically the dissolved oxygen concentration in the sewage transmitted to the treatment end.

[0056] Furthermore, the treatment end needs to uniformly treat the sewage discharged by multiple households. Therefore, it is necessary to calculate the sewage parameters of multiple treatment ends of multiple households to obtain the overall sewage parameters.

[0057] Exemplarily, based on multiple sewage discharge volumes, multiple treatment end sewage parameters are weighted and calculated to obtain average treatment end sewage parameters, reflecting the overall sewage parameter level of sewage from multiple households transmitted to the treatment end under the influence of time, pipeline distance, discharge volume and initial sewage parameters.

[0058] For example, the ratio of each sewage discharge volume to the sum of multiple sewage discharge volumes is calculated as the weight, and multiple treatment end sewage parameters are weighted to obtain the average treatment end sewage parameters. For example, if the three sewage discharge volumes are 1500 cubic meters, 1800 cubic meters, and 2000 cubic meters, the weight corresponding to 1500 cubic meters is 1500 / (1500+1800+2000)=0.283.

[0059] The embodiment of the present application can predict the water quality (such as dissolved oxygen) of sewage after it is transmitted to the treatment end by training the sewage predictor at the treatment end according to the real-time time, pipe network distance, sewage discharge volume and initial water quality parameters. This improves the intelligence of sewage treatment, enables the treatment end to adjust the treatment parameters in advance according to the prediction results, and improves the stability and efficiency of sewage treatment.

[0060] The sewage treatment optimization module 14 is used to optimize the treatment parameters of the treatment end according to the average treatment end sewage parameters, obtain the optimal sewage treatment parameters, and collect and treat domestic sewage.

[0061] In an embodiment of the present application, after obtaining the average treatment end sewage parameters predicted for multiple households based on pipe network distance, time, sewage discharge volume and initial sewage parameters, the treatment parameters of sewage treatment at the treatment end are optimized according to the average treatment end sewage parameters to improve the targetedness and treatment effect of rural sewage collection and treatment.

[0062] By optimizing the optimal sewage treatment parameters and collecting and treating domestic sewage, we can carry out targeted sewage treatment based on the distribution characteristics of rural sewage pipe networks, improve the efficiency and quality of sewage treatment, and thus improve the adaptability and treatment effect of rural sewage collection and treatment.

[0063] The sewage treatment optimization module 14 in the embodiment of the present application is specifically used for: Acquire a treatment parameter interval for sewage treatment at a treatment end, and randomly generate a first treatment parameter within the treatment parameter interval, wherein the treatment parameter includes an aeration parameter; According to the first treatment parameter and the average treatment end sewage parameter, a sewage treatment prediction is performed to obtain a first treatment effect parameter; Calculate and set the number of optimized convergences according to the sewage discharge of the plurality of households; Continue to randomly generate and iteratively optimize the treatment parameters until the optimization convergence times are reached, output the treatment parameters with the best treatment effect parameters, obtain the optimal sewage treatment parameters, and collect and treat domestic sewage.

[0064] In the embodiment of the present application, the treatment parameter range for sewage treatment at the treatment end is first obtained, wherein the treatment parameters include the preferred aeration parameters. After sewage discharge has been transmitted for a long time, the internal dissolved oxygen decreases, and aeration is required to increase its dissolved oxygen concentration, thereby improving the treatment efficiency of organic pollutants, thereby improving the sewage treatment efficiency and purification quality.

[0065] For example, the processing parameter interval includes the oxygen supply rate of the aeration equipment, and the interval is specifically [1.0,5.0] / L / min.

[0066] Furthermore, within the treatment parameter interval, a random number generation algorithm is used to randomly generate a first treatment parameter as an initial solution for optimizing the sewage treatment parameters.

[0067] Furthermore, based on the first treatment parameter and the average treatment end sewage parameter, a sewage treatment prediction is performed to obtain a first treatment effect parameter for the current sewage treatment according to the first treatment parameter.

[0068] For example, based on the historical data of sewage treatment, a sample treatment parameter set and a sample average treatment end sewage parameter set can be collected, and the removal rate of organic pollutants in the water after sewage treatment under different sample treatment parameters and sample average treatment end sewage parameters can be collected as treatment effect parameters, and the sample treatment effect parameter set can be obtained. For example, the treatment effect parameter is that the removal rate of organic pollutants in water is 84%.

[0069] Then, the sample treatment parameter set, the sample average treatment end sewage parameter set and the sample treatment effect parameter set are used as training data and test data, and a sewage treatment predictor is constructed based on a feedforward neural network, and training and testing are performed until the requirements are met, such as a loss of less than 0.05, to complete the training of the sewage treatment predictor. The training and testing of the sewage treatment predictor are the same as the training and testing process of the treatment end sewage predictor in the aforementioned content, but the specific training data are different, and the training process is not repeated here.

[0070] The first treatment parameter currently randomly configured and the average treatment end sewage parameter are input into the sewage treatment predictor, and the first treatment effect parameter is obtained by prediction output, so as to complete the evaluation of the first treatment parameter in the optimization. The larger the first treatment effect parameter is, the better the first treatment parameter is.

[0071] Furthermore, the number of optimized convergence times is set according to the sewage discharge volume of multiple households in the current real-time time. The larger the sewage discharge volume, the higher the sewage treatment quality requirement, and a larger number of optimized convergence times needs to be set to improve the optimization quality of sewage treatment parameters and thus improve the sewage treatment quality.

[0072] In the embodiment of the present application, “calculating and setting the number of optimized convergence times according to the sewage discharge of the plurality of households” includes: Calculating the sum of the sewage discharge volumes of the plurality of households to obtain a total sewage discharge volume; The average sewage discharge volume is obtained, the ratio of the total sewage discharge volume to the average sewage discharge volume is calculated, and the preset convergence times are calculated and adjusted to obtain the optimized convergence times.

[0073] In the embodiment of the present application, the sum of the sewage discharge of multiple households is calculated to obtain the total sewage discharge. For example, if the sewage discharge of three households is 1500 cubic meters, 1800 cubic meters and 2000 cubic meters, the total sewage discharge is 5300 cubic meters.

[0074] Furthermore, the average sewage discharge volume is obtained, which is specifically the average of the sum of the sewage discharge volumes discharged by multiple households in different multiple time windows. For example, in three time windows, the total sewage discharge volumes of multiple households are 5,000 cubic meters, 5,500 cubic meters, and 5,200 cubic meters, respectively, and the average sewage discharge volume is 5,233 cubic meters.

[0075] Furthermore, the ratio of the total sewage discharge volume to the average sewage discharge volume is calculated, and the preset convergence times are calculated and adjusted. The preset convergence times are, for example, the preset optimization convergence times for optimizing the processing parameters, for example, 100 times, that is, 100 sewage treatment parameters are randomly generated for optimization.

[0076] The ratio of the total sewage discharge volume to the average sewage discharge volume is used to calculate and adjust the preset convergence times. For example, the ratio is multiplied by the preset convergence times to complete the calculation adjustment and obtain the optimized convergence times. The larger the total sewage discharge volume, the larger the adjusted optimized convergence times, the higher the optimization quality, and the higher the sewage treatment quality.

[0077] For example, the total sewage discharge volume is 5300 cubic meters, and the average sewage discharge volume is 5233 cubic meters. Then, the number of optimized convergence times after calculation adjustment is 5300 / 5233*100 and rounded up to 102.

[0078] Further, the random generation and iterative optimization of the treatment parameters are continued within the treatment parameter interval, for example, the second treatment parameter is continued to be randomly generated, and the sewage treatment prediction is performed in combination with the average treatment end sewage parameter to obtain the second treatment effect parameter. The iterative optimization is performed in this way until the optimization convergence times, for example, 102 times, are reached, and the optimization stops. The treatment parameter with the best treatment effect parameter, that is, the treatment parameter with the largest treatment effect parameter, is output as the optimal sewage treatment parameter, for example, the optimal aeration parameter, and the domestic sewage is collected and treated.

[0079] The embodiment of the present application optimizes sewage treatment parameters based on the sewage parameters at the treatment end under the influence of pipeline transmission distance, time, and discharge, can adapt to the particularity of rural sewage collection, collect and treat sewage, and improve the quality of sewage treatment.

[0080] The rural domestic sewage collection system based on PVC pipe network provided by the embodiment of the present invention has at least the following technical effects: The embodiment of the present invention connects multiple rural households through a PVC pipe network to achieve full collection of sewage, avoid environmental pollution caused by arbitrary discharge of sewage, and predict the sewage discharge and water quality parameters of each household in different time periods based on real-time data and historical data, thereby improving the intelligent level of sewage collection and management. At the same time, based on time, pipe network distance, sewage discharge and water quality parameters, the sewage parameters at the treatment end after the sewage is transported through a long-distance pipe network are predicted, the changes in pollutants such as dissolved oxygen, nitrogen and phosphorus during the sewage transmission process are analyzed, the average sewage parameters at the treatment end are calculated, and the sewage treatment parameters (such as aeration parameters, etc.) are automatically optimized to ensure that the sewage treatment facilities can adjust the treatment method in real time, improve the treatment efficiency, and reduce energy consumption. In addition, the present invention is aimed at the characteristics of rural decentralized sewage, and through a data-driven prediction method, the sewage parameter changes caused by the pipe network distance in rural decentralized sewage collection are considered, the sewage treatment process is optimized, the accuracy and stability of the treatment are improved, and finally efficient, energy-saving, and intelligent rural sewage treatment is achieved.

[0081] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0082] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A rural domestic sewage collection system based on PVC pipe network, characterized in that: The system includes a PVC pipe network, which connects multiple households in a rural area and a treatment end, collects domestic sewage from multiple households and transmits it to the treatment end, and the system also includes: A pipe network distance acquisition module, used to acquire a plurality of pipe network distances between the plurality of households and the processing end in the PVC pipe network; A household sewage prediction module, used to collect the current real-time time and predict the sewage discharge volume and sewage parameters of the plurality of households according to the real-time time; The treatment end sewage prediction module is used to predict the sewage transmission change according to the real-time time, multiple pipe network distances, multiple sewage discharge volumes and multiple sewage parameters, obtain multiple treatment end sewage parameters when the sewage is transmitted to the treatment end, and calculate and obtain the average treatment end sewage parameter; The sewage treatment optimization module is used to optimize the treatment parameters of the treatment end according to the average treatment end sewage parameters, obtain the optimal sewage treatment parameters, and collect and treat domestic sewage.

2. The rural domestic sewage collection system based on PVC pipe network according to claim 1 is characterized in that: The pipe network distance acquisition module is used for: Obtain multiple terminal locations of multiple households in the PVC pipe network; Based on the PVC pipe network, the lengths of the connecting pipes between the multiple terminal positions and the processing end are calculated to obtain multiple pipe network distances.

3. The rural domestic sewage collection system based on PVC pipe network according to claim 1 is characterized in that: The household sewage prediction module is used to: Divide the preset time period into multiple time windows, and obtain the time window into which the current moment falls as the real time; Building a rural sewage predictor; The real-time time is input into the rural sewage predictor, and the prediction output obtains multiple sewage discharge volumes and multiple sewage parameters of multiple households.

4. The rural domestic sewage collection system based on PVC pipe network according to claim 3 is characterized in that: Build a rural sewage predictor, including: According to the sewage discharge monitoring data of the plurality of households in the historical time, collecting a plurality of historical sewage discharge sets and a plurality of historical sewage parameter sets of the plurality of households in the plurality of time windows; A mapping table of the multiple households, multiple time windows, multiple sample sewage discharge sets, and multiple sample sewage parameter sets is constructed to obtain a rural sewage predictor.

5. The rural domestic sewage collection system based on PVC pipe network according to claim 1 is characterized in that: The treatment end sewage prediction module is used for: training a treatment end wastewater predictor for predicting treatment end wastewater parameters; The real-time time, the multiple pipe network distances, the multiple sewage discharge volumes and the multiple sewage parameters are respectively input into the treatment end sewage predictor, and the multiple treatment end sewage parameters obtained by transmitting the sewage of the multiple households to the treatment end are output; According to the multiple sewage discharge volumes, the multiple treatment end sewage parameters are weighted and calculated to obtain the average treatment end sewage parameter.

6. The rural domestic sewage collection system based on PVC pipe network according to claim 5 is characterized in that: Training of end-of-life wastewater predictors for predicting end-of-life wastewater parameters, including: According to the rural sewage collection monitoring data in the historical time, the sample real-time time set, the sample pipe network distance set, the sample sewage discharge set and the sample sewage parameter set are collected, and the sample treatment end sewage parameter set of the sewage transmitted to the treatment end is collected, wherein each sample treatment end sewage parameter includes dissolved oxygen; Using the sample real-time time set, the sample pipe network distance set, the sample sewage discharge volume set and the sample sewage parameter set as input features, using the sample treatment end sewage parameter set as output features, based on machine learning, constructing and training a treatment end sewage predictor; The treatment end sewage predictor is tested and the training is completed after meeting the requirements.

7. The rural domestic sewage collection system based on PVC pipe network according to claim 1 is characterized in that: The sewage treatment optimization module is used to: Acquire a treatment parameter interval for sewage treatment at a treatment end, and randomly generate a first treatment parameter within the treatment parameter interval, wherein the treatment parameter includes an aeration parameter; According to the first treatment parameter and the average treatment end sewage parameter, a sewage treatment prediction is performed to obtain a first treatment effect parameter; Calculate and set the number of optimized convergences according to the sewage discharge of the plurality of households; Continue to randomly generate and iteratively optimize the treatment parameters until the optimization convergence times are reached, output the treatment parameters with the best treatment effect parameters, obtain the optimal sewage treatment parameters, and collect and treat domestic sewage.

8. The rural domestic sewage collection system based on PVC pipe network according to claim 7 is characterized in that: According to the sewage discharge of the plurality of households, the number of optimal convergence times is calculated and set, including: Calculating the sum of the sewage discharge volumes of the plurality of households to obtain a total sewage discharge volume; The average sewage discharge volume is obtained, the ratio of the total sewage discharge volume to the average sewage discharge volume is calculated, and the preset convergence times are calculated and adjusted to obtain the optimized convergence times.

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