A rural sewage treatment result prediction method and system

Abstract

The present application relates to sewage treatment and intelligent detection technical field, specifically to a kind of rural sewage treatment result prediction method and system, the above-mentioned method includes: rural sewage treatment data set is collected by rural sewage treatment station;Establish sewage treatment data processing mechanism, utilize sewage treatment data processing mechanism to rural sewage treatment data set is handled, and obtain target prediction dataset;Rural sewage treatment result prediction analysis system is constructed, and the evaluation prediction result of prediction analysis system is obtained in combination with target prediction dataset and prediction analysis system;Establish sewage treatment index correlation analysis model, and the rural sewage treatment result is evaluated and analyzed in combination with sewage treatment index correlation analysis model, evaluation prediction result and target prediction dataset.This application is based on data processing model, prediction analysis system and correlation analysis model, and the rural sewage treatment result is evaluated and analyzed comprehensively, improves detection efficiency and result accuracy.

Description

Technical Field

[0001] This invention relates to the fields of wastewater treatment and intelligent detection technology, specifically to a method and system for predicting rural wastewater treatment results. Background Technology

[0002] With the improvement of rural living standards and the increase in water consumption, the amount of rural domestic sewage discharge is also increasing. If the sewage is not properly treated, it will cause serious pollution to the rural environment and affect the quality of life and health of farmers. Therefore, effective detection of rural sewage treatment results is an urgent problem to be solved.

[0003] Traditional methods for predicting rural wastewater treatment outcomes have significant limitations. They often focus on monitoring a few water quality indicators, neglecting the impact of other factors on treatment effectiveness. Furthermore, existing methods frequently employ simple statistical models or empirical formulas to process and analyze data, making it difficult to handle the complexity, real-time nature, and nonlinear characteristics of the wastewater treatment process, resulting in insufficient accuracy and reliability of the predictions. On the other hand, existing methods lack a unified prediction and evaluation system and correlation analysis mechanism, failing to comprehensively integrate and consider the impact of multiple factors on wastewater treatment outcomes, thus compromising the comprehensiveness and accuracy of the prediction results.

[0004] To overcome the aforementioned challenges and problems, it is urgent to introduce intelligent detection models and evaluation systems to provide technical support for the field of rural sewage treatment detection, provide a more scientific and reliable basis for decision-making in rural sewage treatment, and contribute to the continuous improvement of the rural ecological environment and the effective enhancement of the quality of life. Summary of the Invention

[0005] To address the shortcomings of existing methods and the needs of practical applications, and in order to scientifically and accurately evaluate rural wastewater treatment outcomes, it is necessary to construct corresponding data processing models, predictive analysis frameworks, and correlation analysis models. These related models work together to predict and analyze rural wastewater treatment outcomes, contributing to a more comprehensive understanding of the wastewater treatment process and enabling precise detection of wastewater treatment results, thereby allowing for the formulation of more scientific and reasonable wastewater treatment strategies. On one hand, this invention provides a method for predicting rural wastewater treatment outcomes. The method includes: collecting a rural wastewater treatment data set through rural wastewater treatment plants; establishing a wastewater treatment data processing mechanism to process the rural wastewater treatment data set and obtain a target prediction dataset for the rural wastewater treatment plants; constructing a rural wastewater treatment outcome prediction analysis system, combining the target prediction dataset and the rural wastewater treatment outcome prediction analysis system to obtain an evaluation prediction result; and establishing a wastewater treatment index correlation analysis model, combining the wastewater treatment index correlation analysis model, the evaluation prediction result, and the target prediction dataset to conduct a comprehensive evaluation and analysis of the rural wastewater treatment outcomes. This invention provides a comprehensive evaluation and analysis of rural sewage treatment results. The data processing mechanism can improve the accuracy of system data, and the correlation analysis helps to enhance the scientific nature and effectiveness of decision-making. This provides strong technical support and decision-making basis for rural sewage treatment work, and promotes the continuous improvement and sustainable development of the rural ecological environment.

[0006] Optionally, the collection of rural sewage treatment data sets through rural sewage treatment stations includes: obtaining historical information on rural sewage treatment based on the rural sewage treatment stations; collecting real-time monitoring data on rural sewage treatment through the rural sewage treatment stations; and combining the historical information and the real-time monitoring data to obtain a rural sewage treatment data set. This invention, by collecting historical information and real-time monitoring data through rural sewage treatment stations, can improve the comprehensiveness and effectiveness of the data set.

[0007] Optionally, establishing a wastewater treatment data processing mechanism includes: setting a data encoding model, a data decoding model, and a data error analysis model based on the time series characteristics of the data; and combining the data encoding model, the data decoding model, and the data error analysis model to establish a wastewater treatment data processing mechanism. The wastewater treatment data processing mechanism established by this invention makes the data processing flow more standardized and regulated.

[0008] Optionally, the data encoding model satisfies the following relationship:

[0009]

[0010] in, This represents the encoding results of different time series data sets. This represents a non-linear change function in the encoding process. Represents the weights of the encoding model. express Time series datasets Indicates encoding bias;

[0011] The data decoding model satisfies the following relationship:

[0012]

[0013] in, This represents the decoding results of different time series data sets. This represents the constant corresponding to the decoding model. This represents a nonlinear change function in the decoding process. Indicates the weights of the decoding model. This represents the encoding results of different time series data sets. Indicates decoding bias;

[0014] The data error analysis model satisfies the following relationship:

[0015] ,

[0016] in, The error coefficients represent different time series datasets. express The amount of data, The number of time series data representing wastewater treatment information. express The feature information matrix, express The corresponding coefficient of change, express Time series datasets This represents the decoding results of different time series data sets.

[0017] The data encoding model, data decoding model, and data error analysis model of this invention can significantly improve data processing capabilities, optimize processing efficiency, and promote innovation and improvement in data processing technology.

[0018] Optionally, the step of processing the rural sewage treatment dataset using the sewage treatment data processing mechanism to obtain the target prediction dataset for rural sewage treatment plants includes: processing the rural sewage treatment dataset using the data encoding model to obtain encoding results for different time series datasets; decoding the encoding results for different time series datasets using the data decoding model to obtain decoding results for different time series datasets; performing error analysis on the different time series datasets and decoding results based on the data error analysis model to obtain anomaly determination results for different time series datasets; and optimizing the different time series datasets based on the anomaly determination results to obtain the target prediction dataset for rural sewage treatment plants. The data encoding and decoding processes of this invention employ different algorithm models, enabling rapid processing of large amounts of data, helping to shorten data processing time and improve work efficiency. Furthermore, analyzing the target prediction dataset can identify bottlenecks and problems in the sewage treatment process, facilitating the development of targeted sewage treatment strategies.

[0019] Optionally, the construction of the rural sewage treatment outcome prediction and analysis system includes: setting a reference index layer for the rural sewage treatment outcome prediction and analysis system based on the historical information of rural sewage treatment. The reference index layer includes a water quality reference index layer, a treatment efficiency reference index layer, and a sewage process performance reference index layer. This invention sets multiple reference index layers, which can analyze rural sewage treatment results from different dimensions, thereby comprehensively reflecting the actual situation of rural sewage treatment.

[0020] Optionally, the evaluation and prediction results of the prediction and analysis system obtained by combining the target prediction dataset and the rural wastewater treatment result prediction and analysis system include: obtaining BOD analysis results, COD analysis results, suspended solids analysis results, and total phosphorus analysis results based on the water quality reference index layer; obtaining pollutant removal rate analysis results and wastewater treatment volume analysis results based on the treatment efficiency reference index layer; and obtaining sludge analysis results and sludge moisture content analysis results based on the wastewater process performance reference index layer. The prediction and analysis system of this invention can provide environmental regulatory departments with real-time and accurate wastewater treatment data and information, which helps in the real-time monitoring and analysis of changes in water quality indicators.

[0021] Optionally, the BOD analysis results satisfy the following relationship:

[0022]

[0023] in, This indicates the biochemical oxygen demand (BOD) in a wastewater sample over 15 days. This indicates the initial dissolved oxygen in the diluted wastewater sample. This indicates the dissolved oxygen level of the diluted wastewater sample after 15 days of incubation in a constant temperature incubator. Indicates the dilution ratio of the wastewater sample;

[0024] The COD analysis results satisfy the following relationship:

[0025]

[0026] in, This indicates the chemical oxygen demand (COD) in a wastewater sample. The conversion factor representing the number of moles of oxygen required to oxidize 1 mol of organic matter to the number of moles of KMnO4. Indicates the molar mass of an oxygen atom. This represents the difference in KMnO4 dosage before and after titration of the wastewater sample. This indicates the standard concentration of KMnO4. This indicates the volume of the wastewater sample after adding distilled water;

[0027] The suspended solids analysis results satisfy the following relationship:

[0028]

[0029] in, This indicates the calculation results of suspended solids in the wastewater sample. This indicates the mass of suspended solids in a wastewater sample. Indicates the flow rate of the wastewater sample;

[0030] The total phosphorus analysis results satisfy the following relationship:

[0031]

[0032] in, This indicates the total phosphorus concentration in the wastewater sample. This indicates the measured value of total phosphorus in a wastewater sample. This indicates the dilution factor of the wastewater sample. This indicates the influence index of the measurement of total phosphorus content;

[0033] The pollutant removal rate analysis results satisfy the following relationship:

[0034]

[0035] in, Indicates the removal rate of pollutant concentration in a wastewater sample. This indicates the initial pollutant concentration in the wastewater sample. Indicates the initial volume of the wastewater sample. This represents the concentration of pollutants in the wastewater after treatment on day n. This represents the volume of the wastewater sample after treatment on day n.

[0036] The sludge analysis results satisfy the following relationship:

[0037]

[0038] in, This indicates the sedimentation volume of sludge in a wastewater sample. This indicates the sedimentation efficiency of the sedimentation tank. Indicates the flow rate of the wastewater sample. This indicates the concentration of suspended solids in the wastewater sample before it enters the sedimentation tank. This indicates the moisture content of the sludge in the wastewater sample. This indicates the initial sludge concentration in the sedimentation tank. This represents the influencing parameters corresponding to the number of wastewater treatment inlets in the wastewater treatment system. This parameter represents the impact of sludge discharge interval time in sedimentation tanks during wastewater treatment.

[0039] The sludge moisture content analysis results satisfy the following relationship:

[0040]

[0041] in, This indicates the moisture content of the sludge in the wastewater sample. This indicates the total mass of sludge in the wastewater sample. This indicates the mass of solid matter in sludge after water has been removed.

[0042] The present invention uses different calculation formulas for different reference indicators, which can significantly improve the accuracy of rural sewage treatment prediction results and comprehensive evaluation results.

[0043] Optionally, the step of establishing a correlation analysis model for wastewater treatment indicators and combining the correlation analysis model, the assessment and prediction results, and the target prediction dataset to conduct a comprehensive evaluation and analysis of rural wastewater treatment results includes: establishing a correlation analysis model for wastewater treatment indicators based on rural wastewater treatment standards; using the correlation analysis model to obtain the correlation coefficients of different reference indicators in the rural wastewater treatment result prediction and analysis system; and combining the correlation coefficients, the assessment and prediction results, and the target prediction dataset to conduct a comprehensive evaluation and analysis of rural wastewater treatment results.

[0044] The correlation analysis model for wastewater treatment indicators satisfies the following relationship:

[0045]

[0046] in, This represents the correlation coefficient of different reference indicators in the rural sewage treatment outcome prediction and analysis system. This represents the average measurement value of different reference indicators. express The corresponding weighting coefficients, These represent reference values ​​for different reference indicators.

[0047] The correlation analysis model of this invention provides more scientific predictive information, which helps to more accurately determine the operating status and treatment effect of the sewage treatment system, and is conducive to formulating more reasonable and effective sewage treatment strategies.

[0048] Secondly, to efficiently execute the rural sewage treatment result prediction method provided by this invention, this invention also provides a rural sewage treatment result prediction system. The system includes an input device, a processor, an output device, and a memory, wherein the input device, processor, output device, and memory are interconnected. The memory includes the rural sewage treatment result prediction method as described in the first aspect of this invention. The memory stores a computer program, which includes program instructions, and the processor is configured to call the program instructions. The rural sewage treatment result prediction system provided by this invention has a compact structure, strong applicability, and greatly improves operating efficiency. Attached Figure Description

[0049] Figure 1 This is a flowchart of the rural sewage treatment result prediction method of the present invention;

[0050] Figure 2 This is a flowchart illustrating the wastewater treatment data processing mechanism in the rural wastewater treatment result prediction method of the present invention.

[0051] Figure 3 This is a structural diagram of the rural sewage treatment result prediction system of the present invention. Detailed Implementation

[0052] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0053] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0054] Please see Figure 1 To gain a more comprehensive understanding of the wastewater treatment process and formulate more scientific and rational wastewater treatment strategies, this invention addresses two key aspects: firstly, cleaning, integrating, and processing wastewater treatment data to ensure its accuracy and consistency; secondly, introducing prediction systems and correlation models to gain a deeper understanding of the interactions between different influencing factors in the wastewater treatment process and quantifying the correlations between different indicators, thus providing a scientific basis for optimizing wastewater treatment technologies and formulating wastewater treatment strategies. This invention provides a method for predicting rural wastewater treatment results, comprising the following steps:

[0055] S1. Collect rural sewage treatment data sets through rural sewage treatment plants. The specific setup steps and implementation details are as follows:

[0056] In this embodiment, historical information on rural sewage treatment and real-time monitoring data of rural sewage treatment are obtained based on rural sewage treatment stations; and a rural sewage treatment data set is obtained by combining the historical information on rural sewage treatment and the real-time monitoring data of rural sewage treatment.

[0057] First, historical information and real-time monitoring data on rural wastewater treatment are systematically acquired based on key facilities and equipment in rural wastewater treatment plants. This process not only focuses on the collection of historical data but also emphasizes the monitoring and integration of real-time data to ensure the timeliness and accuracy of the rural wastewater treatment data set.

[0058] The rural sewage treatment dataset includes, but is not limited to, influent water quality parameters: detailed records of various influent water quality indicators, such as chemical oxygen demand (COD), biological oxygen demand (BOD), ammonia nitrogen content, total phosphorus concentration, etc. These indicators can intuitively reflect the original pollution level of rural sewage.

[0059] Effluent water quality parameters: Similarly, the quality of the treated effluent is monitored in all aspects, and the data and changes of key indicators such as COD, BOD, ammonia nitrogen, and total phosphorus are recorded, which helps to evaluate the effectiveness of rural sewage treatment in the future.

[0060] Treatment process parameters: It is also necessary to collect various process parameters during the wastewater treatment process, including but not limited to aeration rate, which affects the dissolved oxygen content in the wastewater; sludge age, which reflects the biological activity of the sludge; and return ratio, which adjusts the circulation ratio of wastewater and sludge. These parameters are helpful for subsequent optimization of wastewater treatment processes and strategies.

[0061] Operating time recording: Accurately recording the operating time of the sewage treatment plant, including but not limited to daily operating time and downtime maintenance time, helps to analyze the relationship between the treatment efficiency and energy consumption of rural sewage treatment plants.

[0062] In this embodiment, in addition to collecting historical data, the sewage treatment data in the rural sewage treatment plant will also be monitored in real time. Online monitoring equipment and sensor devices are installed in the rural sewage treatment plant to collect various data during the sewage treatment process in real time, such as flow rate, pressure, and temperature, and upload them to the data processing center in an instant, thereby realizing the dynamic updating and real-time analysis of the monitoring data of the rural sewage treatment plant.

[0063] In summary, by integrating historical information and real-time monitoring data, a comprehensive, accurate, and timely dataset of rural wastewater treatment data was obtained, providing strong support for subsequent wastewater treatment data analysis, process optimization, and environmental decision-making.

[0064] Furthermore, the method for acquiring the rural sewage treatment data set in this embodiment is merely one optional condition of the present invention. In other embodiments, the method for acquiring the rural sewage treatment data set can be updated based on the actual operating conditions of the rural sewage treatment plant and the predicted demand for sewage treatment results. This ensures that the collected data is more closely aligned with the actual operating conditions of the rural sewage treatment plant, reduces unnecessary data collection work, improves the relevance and effectiveness of the data set, and thus provides more valuable information for subsequent data analysis and effect evaluation.

[0065] S2. Establish a wastewater treatment data processing mechanism, and use the above-mentioned wastewater treatment data processing mechanism to process the rural wastewater treatment dataset and obtain the target prediction dataset for rural wastewater treatment plants. The specific steps and implementation content are as follows:

[0066] The method for predicting rural wastewater treatment outcomes also includes preprocessing the rural wastewater treatment dataset in advance. During the process of obtaining the rural wastewater treatment dataset, a series of preprocessing operations are performed on the collected raw data. These preprocessing operations include, but are not limited to, data cleaning, noise reduction, and normalization. This ensures the quality of the dataset and the accuracy of the computational model.

[0067] The specific details of the above data preprocessing operations are as follows:

[0068] Data cleaning focuses on identifying and correcting errors, missing values, or outliers in rural wastewater treatment datasets. By deleting invalid records, filling in missing data, and correcting obvious errors, the integrity and consistency of rural wastewater treatment datasets can be ensured.

[0069] Data denoising needs to take into account random fluctuations or measurement errors that may exist during data monitoring. Data denoising operations can smooth relevant data and reduce noise interference. In this embodiment, filtering techniques and statistical methods are used to extract the true signal while weakening or eliminating unnecessary noise information.

[0070] Because different water quality parameters and treatment process parameters have drastically different numerical ranges and dimensions, the dataset needs to be normalized. Transforming all data in the rural wastewater treatment dataset to the same scale not only simplifies the model training process but also improves the convergence speed and predictive performance of different models, further ensuring the comparability and consistency of model decisions based on different data characteristics.

[0071] In conclusion, preprocessing rural wastewater treatment datasets, such as cleaning, denoising, and normalizing, can improve the data quality, enhance the model's generalization ability, and ensure the accuracy of prediction results, providing strong data support for optimizing rural wastewater treatment processes and making environmental management decisions.

[0072] To obtain higher-quality rural wastewater treatment data, a data encoding model, a data decoding model, and a data error analysis model were first established based on the time series characteristics of the data. Simultaneously, a wastewater treatment data processing mechanism was established by combining these three models.

[0073] In this embodiment, the rural sewage treatment data follows a certain time pattern. Based on the data collection rules and time interval patterns, a data set based on different time series is constructed. The above data set consists of multiple time series data subsets, which can comprehensively record and analyze various parameters and operating status of sewage treatment.

[0074] The rural sewage treatment dataset in this embodiment needs to satisfy the following relationship;

[0075]

[0076] in, This represents a dataset of rural wastewater treatment data. This represents a time series data set. This represents a data set with two time series characteristics. express Time series datasets, each of the above They all contain monitoring information on wastewater treatment parameters or operating status within a specific time period.

[0077] By integrating different time-series data subsets into a unified dataset based on data collection patterns, centralized data management and analysis can be achieved, ensuring the dataset's integrity and consistency, and facilitating subsequent data processing and mining. Furthermore, the time-series information within the rural wastewater treatment dataset can be utilized more effectively to better understand the actual situation of wastewater treatment, identify potential problems and areas for improvement, and provide a scientific basis for optimizing wastewater treatment solutions.

[0078] In this embodiment, the automatic encoder anomaly detection mechanism was optimized, and the wastewater treatment data processing mechanism in this embodiment was established. Its core is to capture the potential features in the original input data through the encoding process and reconstruct the relevant data using the decoding model.

[0079] A data encoding model is established to encode the above time series data set. The above data encoding model satisfies the following relationship:

[0080]

[0081] in, This represents the encoding results of different time series data sets. This represents a non-linear change function in the encoding process. Represents the weights of the encoding model. express Time series datasets Indicates encoding bias;

[0082] The encoding results of different time series datasets are encoding vectors output by the data encoding model, which capture the key information and features in different time series datasets.

[0083] The nonlinear transformation function in the encoding process can learn the nonlinear relationships in the data, mapping the input data to a higher or lower dimensional space to capture the complex features in the data.

[0084] The weights of the encoding model are parameters learned by the model during training. They determine the importance of each feature in the input data and can linearly transform the input data to the latent feature space. These weights can be optimized using the backpropagation algorithm to minimize reconstruction error or other losses.

[0085] Data sets from different time series serve as input data for the nonlinear variation function of the data encoding model process. Data sets from different time series can be a multidimensional vector, where each dimension represents a different feature or variable.

[0086] Encoding bias is a learnable parameter that allows the model to fine-tune the output of each neuron. During the encoding process, the bias term can adjust the baseline level of the encoding results, thereby increasing the flexibility of the model.

[0087] In summary, the parameters mentioned above together constitute the data encoding model. Through nonlinear transformation and linear combination, the input data is mapped to the latent feature space, and the corresponding encoding results are generated. The above encoding process can capture key information and features in the data, providing information support for subsequent wastewater data analysis, anomaly detection, and result prediction.

[0088] The wastewater treatment data processing mechanism can learn key features in the data through the coding process, and reconstruct the original data using these features in the decoding stage. If the reconstructed data is significantly different from the original data, it indicates that there is an anomaly in the input data.

[0089] To reconstruct different subsets of time series data, the data decoding model satisfies the following relationship:

[0090]

[0091] in, This represents the decoding results of different time series data sets. This represents the constant corresponding to the decoding model. This represents a nonlinear change function in the decoding process. Indicates the weights of the decoding model. This represents the encoding results of different time series data sets. Indicates decoding bias;

[0092] The decoding result of different time series datasets is a representation of the original data or approximately the original data reconstructed from the encoded data by the data decoding model. The decoding process can restore the structure and information of the input data so that it can be compared with the original data or used for other data analysis tasks.

[0093] The constant corresponding to the decoding model is a learnable parameter or a fixed scaling factor, which can be used to adjust the amplitude or range of the decoding output, thereby ensuring that the decoded data is consistent with the original data in scale.

[0094] The nonlinear transformation function in the decoding process is similar to that in the encoding process, which helps to map the encoded data in the latent feature space back to the original data space.

[0095] The weights of the decoding model are parameters learned during the training process of the data decoding model, which determine the importance of each feature in the encoded data during the decoding process.

[0096] Decoding bias and encoding bias are also learnable parameters that allow the model to fine-tune each neuron of the decoding output. The bias term can adjust the baseline level of the decoding result, thereby increasing the flexibility and accuracy of the model.

[0097] The data decoding model maps encoded data to the original data space and generates corresponding decoding results. This is a key step in the wastewater treatment data processing mechanism, which can reconstruct input data from the latent feature space, thereby supporting tasks such as data analysis, anomaly detection, or prediction.

[0098] Based on the aforementioned data encoding and processing model, different time series data sets are received, and corresponding encoded information is generated accordingly. Subsequently, the relevant encoded information is fed into the data decoding model to obtain the decoding result. In order to effectively evaluate the accuracy of the decoding results of different time series data and further understand the performance differences of the model on different time series, a data error prediction model is constructed in this embodiment. Based on the above model, the error coefficients corresponding to different time series information are analyzed and predicted.

[0099] The above data error analysis model satisfies the following relationship:

[0100]

[0101] in, The error coefficients represent different time series datasets. express The amount of data, The number of time series data representing wastewater treatment information. express The feature information matrix, express The corresponding coefficient of change, express Time series datasets This represents the decoding results of different time series data sets.

[0102] The error coefficient of different time series datasets is a quantitative indicator that can measure the degree of difference or error between the decoded result and the original data. The smaller the error coefficient, the higher the accuracy of the decoded result.

[0103] The number of data points in a time series dataset can be normalized when calculating error coefficients to ensure comparability of time series of different lengths in error assessment.

[0104] The number of time series data related to wastewater treatment, i.e., the total number of time series data, is i.

[0105] The feature information matrix of different time series datasets can be a two-dimensional array or matrix, which contains the feature information of the time series dataset. The relevant features can be statistics of the time series data, transformed values, or other quantities that can describe the characteristics of the data.

[0106] The coefficient of variation corresponding to the feature information matrix is ​​a scalar value that can be used to measure the degree of change of the feature values ​​in the feature information matrix. The aforementioned coefficient of variation reflects the volatility, complexity, or other attributes related to feature changes in time series data.

[0107] The aforementioned data error analysis model can receive the decoding results and the original time series dataset as input. Through calculation and analysis, it outputs the error coefficients corresponding to different time series data. These coefficients quantify the differences or errors between the original data and the decoding results, thus providing technical support for the optimized processing of rural sewage treatment datasets.

[0108] Then, the rural sewage treatment dataset is processed using a sewage treatment data processing mechanism to obtain a target prediction dataset for rural sewage treatment plants.

[0109] The rural sewage treatment dataset is processed using a data encoding model to obtain encoding results for different time series datasets. A data decoding model is then used to decode these encoding results, yielding different decoding results. An error analysis model is used to perform error analysis on the different time series datasets and decoding results, resulting in anomaly detection results for different time series subsets. Based on these anomaly detection results, the different time series subsets are optimized to obtain the target prediction dataset for the rural sewage treatment plant.

[0110] In an optional embodiment, firstly, the rural sewage treatment dataset is processed using a data coding model, thereby converting the raw data into a coding form more suitable for subsequent analysis and processing, thus obtaining coding results for different time series datasets.

[0111] Then, the encoding results of different time series data sets are decoded using a data decoding model. The above decoding process is to convert the encoded data back to the original data space or an approximate original data space, thereby obtaining the decoding results of different time series data sets.

[0112] To assess the accuracy of the decoding results, error analysis and anomaly detection are then performed. Based on the data error analysis model, the decoding results are compared with the original time series data set. If the error is large or exceeds the preset threshold, the subset of time series data can be determined to be abnormal. The above threshold can be set and adjusted based on the historical information of rural sewage treatment plants.

[0113] Finally, data optimization and target prediction dataset acquisition are performed. Based on the anomaly detection results of the time series dataset, different time series data sets are optimized to eliminate or reduce the impact of outliers. The optimized dataset will serve as the target prediction dataset for rural wastewater treatment plants.

[0114] For the specific steps of the wastewater treatment data processing mechanism, please refer to [link / reference]. Figure 2 ,in This represents a dataset of rural wastewater treatment data. This represents the encoding results of different time series data sets. This represents the decoding results of different time series data sets. The error coefficients represent different time series datasets. This represents the target monitoring data for rural sewage treatment.

[0115] Furthermore, the target prediction dataset for rural sewage treatment plants in this embodiment satisfies the following relationship:

[0116]

[0117] in, This represents the target monitoring data for rural wastewater treatment. Indicates the optimized Data subset Indicates the optimized Data subset Indicates the optimized Data subset.

[0118] In summary, the data encoding and decoding processes in the wastewater treatment data processing mechanism can more effectively extract and process key information from wastewater treatment data; error analysis and anomaly detection steps can identify and eliminate outliers in the data, thereby improving the overall accuracy of wastewater treatment data and obtaining accurate and reliable rural wastewater treatment target prediction datasets and monitoring data. The above processing mechanism provides a clear and intuitive method for anomaly detection and optimization.

[0119] Furthermore, the method for obtaining the rural wastewater treatment target prediction dataset in this embodiment is merely one optional condition of the present invention. In other embodiments, the method for obtaining the wastewater treatment dataset can be optimized according to the actual situation of rural wastewater treatment data and the needs of wastewater treatment prediction. Different rural areas face different wastewater treatment challenges, such as differences in water quality and changes in treatment scale. By optimizing the dataset acquisition method, it is possible to better adapt to different conditions and ensure the scientific validity and practicality of the target prediction dataset.

[0120] S3. Construct a rural sewage treatment outcome prediction and analysis system. Combine the above-mentioned target prediction dataset with the rural sewage treatment outcome prediction and analysis system to obtain the evaluation and prediction results of the prediction and analysis system. The specific implementation content is as follows:

[0121] First, a reference index layer for predicting and analyzing rural sewage treatment results is set based on historical information on rural sewage treatment. In this embodiment, the reference index layer mainly includes a water quality reference index layer, a treatment efficiency reference index layer, and a sewage process performance reference index layer.

[0122] In order to accurately predict and evaluate the results of rural sewage treatment, it is necessary to first set up a predictive analysis system based on historical information and prediction needs. Selecting an appropriate reference index layer is beneficial to constructing a predictive analysis system for rural sewage treatment results, which is directly related to the accuracy and reliability of the prediction results.

[0123] The reference index layer of the rural sewage treatment result prediction and analysis system in the embodiment mainly includes three layers: water quality reference index layer, treatment efficiency reference index layer, and sewage process performance reference index layer.

[0124] The water quality reference index layer mainly focuses on various water quality indicators of wastewater, such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), suspended solids (SS), and ammonia nitrogen (NH3-N). These indicators can directly reflect the degree of pollution of wastewater and the improvement of water quality after treatment, and are the key basis for evaluating the effect of wastewater treatment.

[0125] The treatment efficiency reference index layer is an important indicator for measuring the performance of a wastewater treatment system. It is necessary to pay attention to the removal rate, treatment speed, and energy consumption during the wastewater treatment process in order to comprehensively evaluate the effectiveness of the wastewater treatment system. The above indicators help to understand the operating status of the wastewater treatment system, identify potential problems in a timely manner, and make optimizations and adjustments.

[0126] The wastewater treatment process performance reference index layer is a key indicator reflecting the stability and applicability of wastewater treatment processes. Further attention needs to be paid to the stability, flexibility, and adaptability of wastewater processes to different water quality conditions. The above indicators help to evaluate the merits of existing processes and provide guidance for the optimization and improvement of wastewater treatment strategies.

[0127] When selecting and extracting reference indicators for different reference indicator layers, it is necessary to refer to professional knowledge and experience in wastewater treatment, as well as rural wastewater treatment datasets. Simultaneously, statistical methods or machine learning algorithms are used to screen and optimize the reference indicators to ensure that the selected indicators can truly and accurately reflect the wastewater treatment situation. This process not only improves the performance of the prediction and evaluation system but also lays the foundation for subsequent data processing and analysis.

[0128] Please refer to Table 1 for information on the rural sewage treatment result prediction and analysis system of this embodiment.

[0129] Table 1. Rural Wastewater Treatment Result Prediction and Analysis System

[0130]

[0131] In conclusion, scientifically and rationally setting reference indicator layers to construct a predictive analysis system for rural sewage treatment results can more accurately predict and evaluate sewage treatment effects, providing strong support for rural sewage treatment.

[0132] Then, based on the water quality reference index layer, BOD analysis results, COD analysis results, suspended solids analysis results, and total phosphorus analysis results can be obtained; based on the treatment efficiency reference index layer, pollutant removal rate analysis results and wastewater treatment volume analysis results can be obtained; based on the wastewater process performance reference index layer, sludge analysis results and sludge moisture content analysis results can be obtained.

[0133] In an optional embodiment, BOD analysis results, COD analysis results, suspended solids analysis results, and total phosphorus analysis results are obtained based on a water quality reference index layer.

[0134] BOD (Biochemical Oxygen Demand) in the water quality reference layer:

[0135] BOD refers to the dissolved oxygen concentration required by microorganisms in aerobic environments to decompose organic matter. This process primarily reflects biochemical transformation. BOD is an important benchmark for measuring the content of biodegradable organic matter in wastewater and is crucial for evaluating the effectiveness of wastewater treatment systems.

[0136] In this embodiment, the time for microorganisms in water to decompose organic matter is set to 15 days, and therefore the BOD analysis results need to meet the following relationship:

[0137]

[0138] in, This indicates the biochemical oxygen demand (BOD) in a wastewater sample over 15 days. This indicates the initial dissolved oxygen in the diluted wastewater sample. This indicates the dissolved oxygen level of the diluted wastewater sample after 15 days of incubation in a constant temperature incubator. Indicates the dilution ratio of the wastewater sample;

[0139] Biochemical oxygen demand (BOD) in wastewater samples over 15 days, which is the concentration of dissolved oxygen consumed by microorganisms in the water to decompose organic matter within 15 days under aerobic conditions, is an important indicator for measuring the content of biodegradable organic matter in wastewater.

[0140] The initial dissolved oxygen of the diluted wastewater sample, that is, the dissolved oxygen concentration in the wastewater sample before the start of the BOD analysis experiment, is also the starting point for BOD measurement.

[0141] Dissolved oxygen levels in diluted wastewater samples after 15 days of incubation in a constant-temperature incubator, i.e., the dissolved oxygen concentration in the water sample after the BOD analysis experiment, are compared with... and The difference can be used to calculate the amount of dissolved oxygen consumed by microorganisms over 15 days.

[0142] The wastewater sample dilution ratio refers to the degree to which the original wastewater sample is diluted. The selection of the dilution ratio is crucial for BOD measurement, ensuring that the organic matter in the water sample undergoes sufficient oxidative decomposition during the measurement process. Furthermore, the wastewater sample dilution ratio must satisfy the following relationship:

[0143]

[0144] in, Indicates the dilution ratio of the wastewater sample. Indicates the initial volume of the wastewater sample. This indicates the final volume of the diluted wastewater sample.

[0145] Simultaneously under different circumstances The calculation formula is as follows:

[0146]

[0147] in, express load, Indicates the flow rate of the wastewater sample. This indicates the BOD concentration of the wastewater sample.

[0148] COD (Chemical Oxygen Demand) in the water quality reference layer:

[0149] The example is based on the analysis results of COD (chemical oxygen demand) determined by the potassium permanganate (KMnO4) method. The COD value reflects the amount of chemical oxygen demand in the wastewater sample and can comprehensively reflect the amount of all organic matter in the water. It is an important indicator for water quality monitoring.

[0150] The above COD analysis results satisfy the following relationship:

[0151]

[0152] in, This indicates the chemical oxygen demand (COD) in a wastewater sample. The conversion factor representing the number of moles of oxygen required to oxidize 1 mol of organic matter to the number of moles of KMnO4. Indicates the molar mass of an oxygen atom. This represents the difference in KMnO4 dosage before and after titration of the wastewater sample. This indicates the standard concentration of KMnO4. This indicates the volume of the wastewater sample after adding distilled water;

[0153] The COD analysis formula comprehensively considers factors such as the dosage and concentration of potassium permanganate, as well as the sample volume, to accurately calculate the chemical oxygen demand in wastewater samples. It also demonstrates the importance of COD as a water quality monitoring indicator, comprehensively reflecting the quantity of all organic matter in water.

[0154] The amount of oxygen consumed by oxidizing all oxidizable substances with a chemical oxidant (potassium permanganate KMnO4 in this example).

[0155] The conversion factor represents the ratio between the number of moles of oxygen required to oxidize 1 mol of organic matter and the number of moles of potassium permanganate (KMnO4) consumed. Since different organic compounds are oxidized by potassium permanganate at different efficiencies, a corresponding factor is needed to convert them, which can more accurately reflect the amount of oxygen consumed by the organic matter.

[0156] The molar mass of an oxygen atom is the mass of one mole of oxygen atoms. Converting the number of moles of oxygen consumed into mass is helpful in obtaining the actual value of chemical oxygen demand.

[0157] The change in the amount of potassium permanganate (KMnO4) used during the titration of wastewater samples is usually expressed in moles or milliliters. It further reflects the degree of oxidation of organic matter in the wastewater sample and is one of the key parameters for calculating COD.

[0158] The concentration of potassium permanganate solution can be used to calculate the number of moles of potassium permanganate consumed during the titration process.

[0159] The total volume of the wastewater sample after adding distilled water can be converted into the chemical oxygen demand per unit volume by consuming the number of moles of potassium permanganate, thus obtaining the COD concentration value.

[0160] The above formulas and their parameters together constitute the analytical function for determining COD based on the potassium permanganate method. By accurately measuring and calculating the relevant parameters, the chemical oxygen demand in wastewater samples can be obtained, thereby assessing the degree of organic pollution in water bodies.

[0161] Suspended solids in the water quality reference layer:

[0162] Suspended solids refer to particulate matter that cannot pass through a 2mm sieve and is further retained on filter paper with a 1μm pore size. In wastewater treatment, these substances are the target for removal, as the level of suspended solids directly affects the clarity and overall water quality. Excessive suspended solids content reduces water transparency, impacting the aesthetics and usability of the water body. Furthermore, suspended solids may carry large amounts of harmful substances, posing a threat to aquatic life and human health. Therefore, effective removal and accurate measurement of suspended solids are crucial in wastewater treatment.

[0163] The formula for calculating suspended solids can be used to quantify the content of suspended solids in rural sewage. The above analysis results of suspended solids satisfy the following relationship:

[0164]

[0165] in, This indicates the calculation results of suspended solids in the wastewater sample. This indicates the mass of suspended solids in a wastewater sample. Indicates the flow rate of the wastewater sample;

[0166] The calculation result of suspended solids in sewage samples refers to the concentration of suspended solids in the sewage samples, which reflects the content of suspended solids in the sewage samples and is one of the important indicators for evaluating the degree of water pollution in rural areas.

[0167] In this embodiment, a filtration method is used to measure the mass of suspended solids in a wastewater sample. A certain volume of wastewater sample is filtered through filter paper (1 μm pore size), and then the solid matter on the filter paper is dried and its mass is measured. .

[0168] The flow rate of wastewater samples is measured using equipment such as flow meters. The selection of the flow meter should be adjusted and determined based on the size of the wastewater flow, the measurement accuracy, and the experimental requirements.

[0169] Total phosphorus in the water quality reference layer:

[0170] The formula for calculating total phosphorus is used in wastewater treatment and water quality monitoring to quantify the total phosphorus content in water samples. The above total phosphorus analysis results satisfy the following relationship:

[0171]

[0172] in, This indicates the total phosphorus concentration in the wastewater sample. This indicates the measured value of total phosphorus in a wastewater sample. This indicates the dilution factor of the wastewater sample. This indicates the influence index of the measurement of total phosphorus content;

[0173] The total phosphorus concentration in a wastewater sample represents the mass of total phosphorus per unit volume of wastewater.

[0174] The total phosphorus content in wastewater samples can be measured by absorbance values ​​obtained through chemical analysis methods such as spectrophotometry. These absorbance values ​​are directly proportional to the total phosphorus content in the wastewater sample.

[0175] The dilution factor of a wastewater sample is the number of times the original wastewater sample is diluted before measurement. Because the total phosphorus content in wastewater samples is high, direct measurement would exceed the instrument's measurement range or affect the accuracy of the measurement. Therefore, dilution is necessary to reduce the concentration and bring it within the instrument's normal measurement range.

[0176] The total phosphorus content influence index reflects the degree of influence of various factors on the results during the measurement process. The above parameter is a comprehensive factor, including but not limited to instrument error, reagent purity, and operating conditions. In practical applications, the value of the influence index needs to be defined and calibrated according to experimental conditions and analytical methods.

[0177] In this embodiment, the BOD analysis results, COD analysis results, suspended solids analysis results, and total phosphorus analysis results were obtained based on the analysis functions of different reference indicators in the water quality reference indicator layer.

[0178] BOD analysis results can assess the biodegradability of wastewater. As a benchmark for measuring the content of biodegradable organic matter in wastewater, BOD helps to understand the biological treatment potential of wastewater; it guides wastewater treatment processes, and by analyzing BOD, the operating parameters of wastewater treatment systems can be effectively analyzed, which helps to improve the treatment efficiency of rural wastewater treatment outcome prediction methods; in terms of environmental monitoring and protection, BOD data can be used to assess the self-purification capacity of water bodies and the health status of ecosystems.

[0179] COD analysis results are helpful for the rapid assessment of organic pollution. As an important indicator for water quality monitoring, COD can quickly reflect the total amount of organic matter in water bodies. It is also beneficial for process control and optimization. In the process of sewage treatment, COD data can be used to adjust the dosage of chemical oxidants, further ensuring the treatment effect of rural sewage treatment outcome prediction methods. Furthermore, it helps in the tracking and control of pollution sources. By analyzing COD data from different regions, it is helpful to identify organic pollution sources.

[0180] The results of suspended solids analysis can improve water transparency, as a reduction in suspended solids content helps improve the transparency and aesthetics of water bodies; it is beneficial for the protection of aquatic life, as reducing suspended solids can mitigate the negative impact on aquatic organisms and maintain ecological balance; it can also optimize filtration technology, as analyzing suspended solids data can optimize filtration processes in wastewater treatment and improve the quality of the final effluent.

[0181] Total phosphorus analysis results can help prevent eutrophication of water bodies, as total phosphorus is one of the key factors causing eutrophication. Controlling total phosphorus content helps prevent water quality deterioration. It also helps guide the use of phosphate fertilizers. In agriculture and horticulture, total phosphorus data can be used to guide the rational use of phosphate fertilizers and reduce phosphorus loss. Furthermore, it is beneficial for environmental policy formulation, as total phosphorus data provides a scientific basis for the formulation and implementation of environmental protection policies.

[0182] In summary, the analysis results of BOD, COD, suspended solids, and total phosphorus in the water quality reference index layer not only help to understand the pollution status of water bodies, but also provide data support for the optimization of wastewater treatment technologies, the formulation of environmental policies, and ecological protection plans, jointly promoting the sustainable use of water resources and the achievement of environmental protection goals.

[0183] In an optional embodiment, pollutant removal rate analysis results and wastewater treatment volume analysis results are obtained based on the treatment efficiency reference index layer.

[0184] Analysis results of pollutant removal rate in the treatment efficiency reference index layer:

[0185] By analyzing the difference in pollutant concentrations before and after wastewater sample treatment, and the ratio of wastewater sample concentrations, the proportion or efficiency of pollutant removal during wastewater treatment is further analyzed. The above pollutant removal rate analysis results satisfy the following relationship:

[0186]

[0187] in, Indicates the removal rate of pollutant concentration in a wastewater sample. This indicates the initial pollutant concentration in the wastewater sample. Indicates the initial volume of the wastewater sample. This represents the concentration of pollutants in the wastewater after treatment on day n. This represents the volume of the wastewater sample after treatment on day n.

[0188] The removal rate of pollutant concentration in a wastewater sample indicates the proportion or efficiency of pollutants removed during wastewater treatment, thus reflecting the purification capacity of the wastewater treatment process for specific pollutants.

[0189] The initial pollutant concentration of a wastewater sample refers to the concentration of pollutants in the wastewater sample before treatment, further providing a benchmark for the degree of wastewater pollution before treatment.

[0190] The initial volume of the wastewater sample is the volume of the wastewater sample before treatment, reflecting the volume of the wastewater sample before treatment.

[0191] The concentration of pollutants in wastewater after n days of treatment refers to the concentration of pollutants in the wastewater sample after n days of treatment, indicating the residual amount of pollutants in the treated wastewater sample.

[0192] The volume of the wastewater sample on day n after treatment refers to the volume of the wastewater sample after n days of treatment. Since the volume of the wastewater sample may change due to water evaporation, sludge production, etc. during the treatment process, it is necessary to further consider the volume change of the wastewater sample.

[0193] Analysis results of wastewater treatment volume in the treatment efficiency reference index layer:

[0194] The rural sewage treatment data set based on rural sewage treatment stations provides daily / monthly / annual average treatment volume data for these stations. In addition to directly analyzing the treatment volume at a specific point in time, it can also calculate the daily, monthly, or annual average treatment volume, thereby reflecting the treatment capacity of sewage treatment facilities at different time scales.

[0195] It can also analyze the changing trends of treatment capacity at rural wastewater treatment plants. By plotting a curve showing the change in treatment capacity over time, the trend can be visually displayed, including increases, decreases, or stabilization. Comparing the actual treatment capacity with the preset capacity allows for an assessment of whether the wastewater treatment facilities are operating efficiently enough to meet design requirements, and whether there is overloading or idle capacity. This provides a more comprehensive understanding of the wastewater treatment volume analysis results, as well as the performance and improvement potential of the wastewater treatment facilities in different aspects, helping rural wastewater treatment plants to develop more reasonable wastewater treatment strategies and optimization plans.

[0196] In an optional embodiment, sludge analysis results and sludge moisture content analysis results are obtained based on a wastewater process performance reference index layer.

[0197] Sludge analysis results in the wastewater process performance reference layer:

[0198] Sludge production refers to the total amount of solid waste generated during wastewater treatment due to physical, chemical, or biological processes. This solid waste mainly includes sludge composed of suspended solids, colloids, organic matter, inorganic matter, and microorganisms.

[0199] Sludge production not only directly reflects the operational efficiency of wastewater treatment processes, such as the removal effect of suspended solids during treatment, but is also an important basis for assessing sludge treatment costs, including the expenses for sludge collection, transportation, treatment, and disposal. At the same time, the amount of sludge produced also indirectly affects decisions regarding the operation and management of wastewater treatment plants, equipment maintenance, and environmental protection.

[0200] The above sludge analysis results satisfy the following relationship:

[0201]

[0202] in, This indicates the sedimentation volume of sludge in a wastewater sample. This indicates the sedimentation efficiency of the sedimentation tank. Indicates the flow rate of the wastewater sample. This indicates the concentration of suspended solids in the wastewater sample before it enters the sedimentation tank. This indicates the moisture content of the sludge in the wastewater sample. This indicates the initial sludge concentration in the sedimentation tank. This represents the influencing parameters corresponding to the number of wastewater treatment inlets in the wastewater treatment system. This parameter represents the impact of sludge discharge interval time in sedimentation tanks during wastewater treatment.

[0203] The sedimentation volume of sludge in a wastewater sample represents the volume occupied by sludge after a certain period of sedimentation in a sedimentation tank. It is an important indicator for evaluating sludge production and sedimentation tank efficiency.

[0204] The sedimentation efficiency of a sedimentation tank is a dimensionless parameter that describes the tank's ability to remove suspended solids. In wastewater treatment, a higher sedimentation efficiency indicates a better effect of the sedimentation tank in removing suspended solids.

[0205] Wastewater sample flow rate indicates the amount of wastewater flowing into the sedimentation tank per unit time. The larger the flow rate, the more wastewater the sedimentation tank treats, and the corresponding amount of sludge produced also increases.

[0206] The suspended solids concentration of a wastewater sample entering a sedimentation tank represents the ratio of the mass or volume of suspended solids in the wastewater to the total volume of the wastewater. The higher the suspended solids concentration, the more solid particles the wastewater contains, and the greater the amount of sludge produced.

[0207] The moisture content of sludge in a wastewater sample is a percentage value, representing the proportion of water in the sludge. A higher moisture content indicates more water in the sludge, and vice versa, a lower moisture content indicates less dry matter in the sludge.

[0208] The initial sludge concentration in a sedimentation tank represents the ratio of the mass or volume of dry matter in the sludge to the total volume of the sludge before the sedimentation tank begins operation. The initial sludge concentration affects the settling rate of the sludge during the sedimentation process and the treatment capacity of the sedimentation tank.

[0209] The influencing parameters corresponding to the number of wastewater treatment inlets in a wastewater treatment system refer to parameters related to the design and operation of the wastewater treatment system. They can be used to describe the impact of the number of wastewater treatment inlets on sludge production, involving factors such as wastewater diversion, merging, and treatment processes.

[0210] The parameters affecting the sludge discharge interval in wastewater treatment sedimentation tanks refer to parameters related to the operation of sedimentation tanks. They further describe the impact of the sludge discharge interval on sludge production. The longer the sludge discharge interval, the more sludge accumulates in the sedimentation tank. However, an excessively long interval can lead to sludge thickening and compaction, affecting the sedimentation effect.

[0211] The above parameters are used to comprehensively evaluate the generation and treatment of sludge in wastewater treatment processes. However, in practical applications, the formulas and parameters need to be adjusted and optimized based on factors such as the wastewater treatment process, equipment, and water quality.

[0212] Analysis results of sludge moisture content in the wastewater process performance reference layer:

[0213] Sludge moisture content refers to the proportion of water in sludge. Based on this, it can reflect the humidity or dryness of sludge, and can be further analyzed to determine the proportion of water remaining in the total mass of sludge after removing solids, i.e., the mass after drying.

[0214] The moisture content of sludge not only affects its volume, weight, and transportation costs, but also influences its subsequent treatment and disposal methods. Sludge with high moisture content is bulky and heavy, increasing the difficulty of transportation and treatment; simultaneously, high moisture content is detrimental to sludge stabilization and resource utilization. Therefore, reducing sludge moisture content is a crucial step in the sludge treatment and disposal process.

[0215] In an optional embodiment, a certain amount of wastewater sample is taken and the total mass of sludge in it is measured using a weighing device. The sludge is then placed in an oven at a set temperature of 105°C for drying until the sludge sample reaches a constant weight. The dried sludge sample is then removed and its mass, i.e., dry matter mass, is measured again using a weighing device. This allows us to obtain the analysis results of sludge moisture content in wastewater samples.

[0216] The sludge moisture content analysis results in the examples satisfy the following relationship:

[0217]

[0218] in, This indicates the moisture content of the sludge in the wastewater sample. This indicates the total mass of sludge in the wastewater sample. Indicates the mass of solid matter in sludge after water removal.

[0219] The moisture content of sludge in a wastewater sample indicates the proportion of water contained in the sludge. It is used to quantify the humidity or dryness of the sludge. The higher the moisture content, the more water is contained in the sludge; conversely, the lower the moisture content, the drier the sludge.

[0220] The total mass of sludge in a wastewater sample refers to the mass of the sludge before any treatment or drying. It mainly includes all components of the sludge, namely water and solid matter. The above parameters are the basis for calculating the moisture content and can be used to determine the initial mass of the sludge.

[0221] The mass of solid matter in sludge after water removal is the mass obtained by drying methods such as oven drying and vacuum drying to remove water from the sludge. The above parameters directly reflect the mass of water-insoluble solid components in sludge and are key data for calculating moisture content.

[0222] In this embodiment, the rural sewage treatment results of the analysis and prediction system are designed with a multi-dimensional reference index layer, which mainly covers three major reference index layers: water quality, treatment efficiency, and sewage process performance. This allows for a comprehensive and multi-faceted evaluation of the effectiveness and performance of rural sewage treatment.

[0223] First, the water quality reference index layer provides analytical results such as BOD, COD, suspended solids, and total phosphorus, which clearly shows the types and concentrations of pollutants in wastewater, thereby judging the water quality status. These water quality indicators are the basis for evaluating the effectiveness of wastewater treatment and an important basis for formulating subsequent treatment strategies.

[0224] Secondly, the treatment efficiency reference index layer intuitively displays the treatment capacity and efficiency of wastewater treatment facilities by calculating pollutant removal rate and wastewater treatment volume. The relevant indicators help to identify bottlenecks and problems in the treatment process in a timely manner, providing strong support for optimizing treatment processes and improving treatment efficiency.

[0225] Finally, the wastewater process performance reference index layer, through sludge analysis and sludge moisture content analysis, provides an in-depth analysis of the performance and stability of the wastewater treatment process. These indicators not only reflect the generation and treatment of sludge, but also provide important references for the operation and adjustment of the wastewater treatment system.

[0226] In conclusion, selecting reference indicators closely related to wastewater treatment outcomes and constructing a comprehensive predictive analysis system can fully and accurately reflect the effectiveness and performance of rural wastewater treatment. This not only helps to identify and address problems in a timely manner and improve wastewater treatment efficiency and quality, but also provides strong technical support and decision-making basis for rural wastewater management, which is conducive to promoting rural environmental protection and sustainable development.

[0227] Furthermore, the different reference index analysis methods of the analysis system in this embodiment are merely one optional condition of the present invention. In other embodiments, the analysis methods of different reference indices can be adjusted according to the prediction requirements and basic conditions of rural sewage treatment results. Rural sewage treatment is complex and varied, and the composition, concentration, and treatment requirements of sewage vary in different regions and time periods. Adjusting the analysis methods of different reference indices according to the actual situation can ensure that the analysis system can flexibly adapt to various situations and improve the accuracy and practicality of the analysis results.

[0228] S4. Establish a correlation analysis model for wastewater treatment indicators. Combine the above correlation analysis model, assessment and prediction results, and target prediction dataset to conduct a comprehensive evaluation and analysis of rural wastewater treatment results. Specific implementation details are as follows:

[0229] First, a correlation analysis model for wastewater treatment indicators was established based on rural wastewater treatment standards; then, the correlation coefficients of different reference indicators in the rural wastewater treatment outcome prediction and analysis system were obtained using the correlation analysis model.

[0230] The example introduces rural sewage treatment standards to construct a correlation analysis model for sewage treatment indicators. The correlation analysis model is used to explore the intrinsic relationship and influence coefficient between different sewage treatment indicators, as well as the correlation between different reference indicators and rural sewage treatment results.

[0231] The model uses the Pearson correlation coefficient as a statistical tool to quantify the correlation strength between different water quality reference indicators and rural sewage treatment standards. The correlation coefficient can intuitively reveal the degree and direction of the linear relationship between the indicators, providing a numerical reference for positive or negative correlation.

[0232] The correlation analysis model for the above wastewater treatment indicators satisfies the following relationship:

[0233]

[0234] in, This represents the correlation coefficient of different reference indicators in the rural sewage treatment outcome prediction and analysis system. This represents the average measurement value of different reference indicators. express The corresponding weighting coefficients, These represent reference values ​​for different reference indicators.

[0235] The correlation coefficient between different reference indicators is a quantitative indicator that can measure the strength and direction of the linear relationship between two or more indicators. It helps to intuitively understand the relationship between indicators and to identify the degree and role of the reference indicators in the wastewater treatment process.

[0236] The measured average reflects the average state or level of a certain indicator over multiple measurements. As a key input to the model, the accuracy of the measured average directly affects the calculation result of the correlation coefficient.

[0237] The weighting coefficients corresponding to different reference indicators are different in terms of their influence on the wastewater treatment results in the correlation analysis. The correlation weighting coefficients enable the model to reflect the actual situation more flexibly. The model can weight the indicators to more accurately assess the correlation between different reference indicators.

[0238] The reference values ​​for different reference indicators are mainly set based on historical data, industry standards, or expert opinions. They can be used as a benchmark for evaluating current measurements. The reference values ​​provide a benchmark point for comparison for the model and can quantify the difference between the measurement average and the reference values.

[0239] Correlation coefficients can quantify the degree and direction of linear relationships between indicators, thereby more accurately assessing the correlation between different reference indicators and reducing the influence of subjectivity. Compared with expert opinions or experience-based judgment methods, the correlation analysis model for wastewater treatment indicators can reduce subjectivity and uncertainty in the analysis process, providing support for subsequent wastewater treatment decisions and making wastewater treatment schemes more scientific and reasonable. This is of great importance for promoting the scientific, standardized, and efficient development of rural wastewater treatment.

[0240] Then, the results of rural sewage treatment are comprehensively evaluated and analyzed by combining the correlation coefficient, the assessment and prediction results, and the target prediction data set.

[0241] In this embodiment, the correlation coefficients of different reference indicators were analyzed based on the above-mentioned wastewater treatment index correlation analysis model. These coefficients collectively reflect the degree and direction of the linear relationship between different indicators, thereby providing an intuitive understanding of the relationship between different indicators. The correlation analysis results are summarized in Table 2. Table 2 lists the correlation information of each reference indicator in the prediction analysis system. The correlation coefficients of each indicator in the water quality reference indicator layer (BOD, COD, suspended solids, total phosphorus), the treatment efficiency reference indicator layer (pollutant removal rate, wastewater treatment volume), and the wastewater process performance reference indicator layer (wastewater sludge, sludge moisture content) were analyzed.

[0242] Please refer to Table 2 for the information on the correlation coefficients of different reference indicators in this embodiment. In the comprehensive evaluation and analysis process, the correlation coefficients of different reference indicators reflect the degree and direction of the linear relationship between the indicators, and provide an intuitive understanding of the relationship between the indicators.

[0243] Table 2. Correlation information of various reference indicators in the predictive analysis system.

[0244]

[0245] Based on the above implementation steps and related mathematical models, the prediction and evaluation analysis process for rural sewage treatment results is as follows:

[0246] First, rural sewage treatment data was systematically collected through rural sewage treatment plants. This data formed the basis for the analysis of the treatment results. In order to effectively process the rural sewage treatment data set, a sewage treatment data processing mechanism was established. Using the above mechanism, the rural sewage treatment data set was processed. Through a series of data cleaning, transformation, analysis and iterative optimization steps, the target prediction dataset of the rural sewage treatment plants was finally obtained.

[0247] Next, a predictive analysis system for rural sewage treatment results was constructed. This system combines the target prediction dataset and, through the prediction algorithms and models of the examples, can obtain the evaluation prediction results of the predictive analysis system. These results provide preliminary judgment information on the effectiveness of rural sewage treatment.

[0248] To gain a deeper understanding of the outcomes of rural wastewater treatment, a correlation analysis model for wastewater treatment indicators was established. This model combines the inherent relationships between wastewater treatment indicators with the correlations between different reference indicators and rural wastewater treatment outcomes. By using this model in conjunction with the assessment and prediction results and the target prediction dataset, a comprehensive evaluation and analysis of rural wastewater treatment results is conducted.

[0249] Through a series of steps and analyses, not only can predictive results for rural wastewater treatment be obtained, but also the interaction and influence between different indicators can be deeply understood, providing strong support for the optimization and improvement of rural wastewater treatment. This not only improves the accuracy and comprehensiveness of the analysis results, but also provides a reference for wastewater treatment work.

[0250] Please see Figure 3 In an optional embodiment, to efficiently execute the rural sewage treatment result prediction method provided by the present invention, the present invention also provides a rural sewage treatment result prediction system. The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to call the program instructions to execute the specific steps of the rural sewage treatment result prediction method and related embodiments provided by the present invention. The rural sewage treatment result prediction system of the present invention is structurally complete and objectively stable.

[0251] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for predicting the outcome of rural sewage treatment, characterized in that, The method includes: Collect rural sewage treatment data sets through rural sewage treatment plants; A wastewater treatment data processing mechanism is established, and the rural wastewater treatment dataset is processed using the wastewater treatment data processing mechanism to obtain the target prediction dataset for rural wastewater treatment stations. A rural sewage treatment outcome prediction and analysis system is constructed, and the evaluation and prediction results of the prediction and analysis system are obtained by combining the target prediction dataset and the rural sewage treatment outcome prediction and analysis system. A correlation analysis model for wastewater treatment indicators is established, and the results of the assessment and prediction are combined with the correlation analysis model for wastewater treatment indicators, the assessment and prediction results and the target prediction dataset to conduct a comprehensive evaluation and analysis of rural wastewater treatment results. The established wastewater treatment data processing mechanism includes: Based on the characteristics of the data time series, a data encoding model, a data decoding model, and a data error analysis model are set up; A wastewater treatment data processing mechanism is established by combining the data encoding model, the data decoding model, and the data error analysis model. The data encoding model satisfies the following relationship: ， in, This represents the encoding results of different time series data sets. This represents a non-linear change function in the encoding process. Represents the weights of the encoding model. express Time series datasets Indicates encoding bias; The data decoding model satisfies the following relationship: ， in, This represents the decoding results of different time series data sets. This represents the constant corresponding to the decoding model. This represents the nonlinear change function of the decoding process. Indicates the weights of the decoding model. This represents the encoding results of different time series data sets. Indicates decoding bias; The data error analysis model satisfies the following relationship: ， in, The error coefficients represent different time series datasets. express The amount of data, The number of time series data representing wastewater treatment information. express The feature information matrix, express The corresponding coefficient of change, express Time series datasets This represents the decoding results of different time series data sets; The step of establishing a correlation analysis model for wastewater treatment indicators, and combining the correlation analysis model for wastewater treatment indicators, the assessment and prediction results, and the target prediction dataset to conduct a comprehensive evaluation and analysis of rural wastewater treatment results includes: Establish a correlation analysis model for wastewater treatment indicators based on rural wastewater treatment standards; The correlation coefficients of different reference indicators in the rural sewage treatment result prediction and analysis system were obtained using the aforementioned sewage treatment indicator correlation analysis model. The rural sewage treatment results are comprehensively evaluated and analyzed by combining the correlation coefficient, the assessment and prediction results, and the target prediction data set. The correlation analysis model for wastewater treatment indicators satisfies the following relationship: ， in, This represents the correlation coefficient of different reference indicators in the rural sewage treatment outcome prediction and analysis system. This represents the average measurement value of different reference indicators. express The corresponding weighting coefficients, These represent reference values ​​for different reference indicators.

2. The method for predicting rural sewage treatment results according to claim 1, characterized in that, The rural sewage treatment data set collected through rural sewage treatment plants includes: Historical information on rural sewage treatment was obtained based on rural sewage treatment plants; Real-time monitoring data of rural sewage treatment is collected through rural sewage treatment plants; A rural sewage treatment data set is obtained by combining the historical information on rural sewage treatment and the real-time monitoring data of rural sewage treatment.

3. The method for predicting rural sewage treatment results according to claim 1, characterized in that, The process of using the aforementioned wastewater treatment data processing mechanism to process the rural wastewater treatment dataset and obtain the target prediction dataset for rural wastewater treatment plants includes: The data encoding model is used to process the rural sewage treatment dataset to obtain encoding results for different time series datasets; The data decoding model is used to decode the encoding results of different time series data sets to obtain the decoding results of different time series data sets; Based on the data error analysis model, error analysis is performed on the data sets and decoding results of different time series to obtain the anomaly judgment results of different time series data sets; Based on the anomaly detection results, different time series data sets are optimized to obtain the target prediction dataset for rural sewage treatment plants.

4. The method for predicting rural sewage treatment results according to claim 2, characterized in that, The construction of the rural sewage treatment outcome prediction and analysis system includes: Based on the historical information on rural sewage treatment, a reference index layer is set for the prediction and analysis system of rural sewage treatment results. The reference index layer includes a water quality reference index layer, a treatment efficiency reference index layer, and a sewage process performance reference index layer.

5. The method for predicting rural sewage treatment results according to claim 4, characterized in that, The evaluation and prediction results of the prediction analysis system obtained by combining the target prediction dataset and the rural sewage treatment result prediction analysis system include: Based on the aforementioned water quality reference index layer, the BOD analysis results, COD analysis results, suspended solids analysis results, and total phosphorus analysis results were obtained. The pollutant removal rate analysis results and wastewater treatment volume analysis results are obtained based on the aforementioned treatment efficiency reference index layer; Based on the aforementioned wastewater process performance reference index layer, sludge analysis results and sludge moisture content analysis results were obtained.

6. The method for predicting rural sewage treatment results according to claim 5, characterized in that, The BOD analysis results satisfy the following relationship: ， in, This indicates the biochemical oxygen demand (BOD) in a wastewater sample over 15 days. This indicates the initial dissolved oxygen in the diluted wastewater sample. This indicates the dissolved oxygen level of the diluted wastewater sample after 15 days of incubation in a constant temperature incubator. Indicates the dilution ratio of the wastewater sample; The COD analysis results satisfy the following relationship: ， in, This indicates the chemical oxygen demand (COD) in a wastewater sample. The conversion factor representing the number of moles of oxygen required to oxidize 1 mol of organic matter to the number of moles of KMnO4. Indicates the molar mass of an oxygen atom. This represents the difference in KMnO4 dosage before and after titration of the wastewater sample. This indicates the standard concentration of KMnO4. This indicates the volume of the wastewater sample after adding distilled water; The suspended solids analysis results satisfy the following relationship: ， in, This indicates the calculation results of suspended solids in the wastewater sample. This indicates the mass of suspended solids in a wastewater sample. Indicates the flow rate of the wastewater sample; The total phosphorus analysis results satisfy the following relationship: ， in, This indicates the total phosphorus concentration in the wastewater sample. This indicates the measured value of total phosphorus in a wastewater sample. This indicates the dilution factor of the wastewater sample. This indicates the influence index of the measurement of total phosphorus content; The pollutant removal rate analysis results satisfy the following relationship: ， in, Indicates the removal rate of pollutant concentration in a wastewater sample. This indicates the initial pollutant concentration in the wastewater sample. Indicates the initial volume of the wastewater sample. This represents the concentration of pollutants in the wastewater after treatment on day n. This represents the volume of the wastewater sample after treatment on day n. The sludge analysis results satisfy the following relationship: ， in, This indicates the sedimentation volume of sludge in a wastewater sample. This indicates the sedimentation efficiency of the sedimentation tank. Indicates the flow rate of the wastewater sample. This indicates the concentration of suspended solids in the wastewater sample before it enters the sedimentation tank. This indicates the moisture content of the sludge in the wastewater sample. This indicates the initial sludge concentration in the sedimentation tank. This represents the influencing parameters corresponding to the number of wastewater treatment inlets in the wastewater treatment system. This parameter represents the impact of sludge discharge interval time in sedimentation tanks during wastewater treatment. The sludge moisture content analysis results satisfy the following relationship: ， in, This indicates the moisture content of the sludge in the wastewater sample. This indicates the total mass of sludge in the wastewater sample. This indicates the mass of solid matter in sludge after water has been removed.

7. A rural sewage treatment outcome prediction system, characterized in that, The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the rural sewage treatment result prediction method as described in any one of claims 1-6.

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