Energy consumption optimization scheduling method and system for single-village water plant combined with big data analysis

Abstract

The present invention relates to the field of water treatment technology, and specifically to a method and system for optimizing energy consumption scheduling of a single-village water plant in combination with big data analysis, including a water quality and energy consumption parameter acquisition module, a data storage and management module, a dynamic water quality treatment demand prediction module, an adaptive energy consumption optimization scheduling module, a real-time scheduling and feedback control module, a water quality and energy consumption trend analysis module, an emergency response module, and a user demand and water quality feedback module. The water quality treatment demand is predicted by a multi-layer perceptron model, and a genetic algorithm and a particle swarm optimization algorithm are combined to generate an optimal energy consumption scheduling scheme to achieve a dynamic balance between water quality treatment and energy consumption optimization. The system dynamically adjusts the water quality treatment standard and energy consumption strategy based on the user's water demand and feedback, ensuring water quality safety while optimizing the water plant's energy consumption. The present invention effectively reduces the waste of energy consumption in the water plant and improves the overall operating efficiency of the water plant.

Description

Technical Field

[0001] The present invention relates to the technical field of water treatment, and in particular to a method and system for optimizing energy consumption of a single-village water plant in combination with big data analysis. Background Art

[0002] With the continuous improvement of rural water supply infrastructure, more and more single-village water plants have been put into operation to ensure the safety and stable supply of water for residents in rural areas. However, in actual operation, problems such as high energy consumption, unstable equipment operation and low water quality treatment efficiency have gradually emerged. In order to achieve efficient operation of water plants, ensure water quality safety and reduce energy consumption, intelligent scheduling and energy consumption optimization of water plant operations have become the focus of current research. The application of big data analysis technology and artificial intelligence algorithms can improve the intelligent scheduling capabilities of water plants through real-time monitoring and data analysis of water quality and energy consumption, and meet the water quality treatment needs under different environmental conditions.

[0003] The existing water plant energy consumption scheduling system still has some technical difficulties in practical application. First, most systems lack real-time correlation analysis between water quality treatment and energy consumption, resulting in energy waste and serious over-operation or no-load operation of equipment. Secondly, the existing system has a slow response speed when dealing with sudden changes in water quality (such as floods, pollutant intrusion, etc.), and lacks energy consumption management during emergency treatment, and cannot take into account both water quality safety and energy consumption optimization. In addition, users' personalized water demand and water quality feedback data are not fully utilized, making it difficult for water plants to dynamically schedule according to users' actual needs, and lacking flexible energy consumption management and water quality adjustment mechanisms in long-term operation. Summary of the invention

[0004] The present invention provides a method and system for optimizing energy consumption of a single-village water plant in combination with big data analysis.

[0005] The energy consumption optimization scheduling method of a single village water plant combined with big data analysis includes the following steps:

[0006] S1, water quality and energy consumption correlation parameter collection and modeling: through the sensor network installed at each processing node of the water plant, the water quality parameters and energy consumption parameters of the water plant are collected in real time. The water quality parameters include turbidity, dissolved oxygen, pH value, residual chlorine, total dissolved solids and heavy metal content. At the same time, the energy consumption parameters of the equipment are obtained. The energy consumption parameters include water pump power, power consumption and equipment operation status. The collected water quality parameters and energy consumption parameters are subjected to multi-dimensional data fusion analysis to generate a water quality-energy consumption correlation model for subsequent optimization scheduling;

[0007] S2, construction of dynamic water quality treatment demand prediction model: based on historical water quality parameter changes, real-time water quality parameters and seasonal external environmental parameters, a multi-layer perceptron model is constructed to predict the water quality treatment demand forecast value in the future period;

[0008] S3, construction of adaptive energy consumption optimization scheduling model: Based on the output results of the multi-layer perceptron model, combined with the water quality-energy consumption association model and the energy consumption characteristics of water plant equipment, the adaptive energy consumption optimization scheduling model is constructed using genetic algorithm and particle swarm optimization algorithm. By balancing the water quality treatment requirements and energy consumption, the optimal energy consumption scheduling plan is generated;

[0009] S4, real-time scheduling and feedback control: According to the scheduling plan generated by the adaptive energy consumption optimization scheduling model, the water pump start and stop, equipment load and water treatment agent dosage parameters are automatically adjusted. The equipment operation status is dynamically adjusted in combination with real-time water quality parameters and energy consumption parameters. The scheduling strategy is adaptively adjusted through real-time monitoring feedback data.

[0010] S5, water quality and energy consumption historical parameter storage and trend analysis: the water quality parameters and energy consumption parameters of each water treatment process are stored on the big data platform, and long-term trend analysis is performed based on historical water quality parameters and energy consumption parameters. Cluster analysis and machine learning algorithms are used to identify periodic changes in water quality treatment and energy consumption, and the adaptive energy consumption optimization scheduling model is regularly adjusted to cope with long-term changes.

[0011] S6, emergency response to sudden water quality changes: Based on the real-time monitoring results of dynamic changes in water quality and combined with trend analysis of historical data, it can quickly identify sudden water pollution incidents and automatically adjust the operating status of equipment through pre-set emergency strategies to optimize energy consumption and ensure water quality safety;

[0012] S7, user water demand and water quality scheduling feedback: By collecting users' water demand data and combining users' water quality feedback data with health monitoring data, the water quality treatment standards and energy consumption scheduling strategies of the water plant are dynamically adjusted.

[0013] Optionally, the S1 includes:

[0014] S11, Installation and configuration of multi-source sensor network: Install various types of sensors at different processing nodes of the water plant to collect water quality parameters and energy consumption parameters in real time;

[0015] S12, data collection and transmission: the water quality parameters and energy consumption parameters collected by each sensor are transmitted to the central control unit via a wireless transmission network or wired transmission;

[0016] S13, data preprocessing: in the central control unit, the transmitted water quality parameters and energy consumption parameters are preprocessed, including outlier detection, noise filtering and missing data filling;

[0017] S14, multi-dimensional data fusion analysis: multi-dimensional data fusion analysis is performed on the pre-processed water quality parameters and energy consumption parameters, and the principal component analysis method is used to reduce the dimension of high-dimensional data to identify the correlation characteristics between water quality parameters and energy consumption parameters;

[0018] S15, construction of water quality-energy consumption correlation model: based on the results of multi-dimensional data fusion analysis, a water quality-energy consumption correlation model was constructed using a linear regression algorithm;

[0019] S16, model verification and optimization, uses historical data to verify the water quality-energy consumption correlation model, compares the energy consumption predicted by the model with the actual collected energy consumption parameters, and calculates the prediction error.

[0020] Optionally, S2 includes:

[0021] S21, data preprocessing: normalize historical water quality parameters, real-time water quality parameters and seasonal external environmental data, and divide them into training sets and test sets;

[0022] S22, construction of multilayer perceptron model: based on the preprocessed data, a multilayer perceptron model including an input layer, a hidden layer and an output layer is constructed. The input layer receives the processed water quality parameters and external environmental parameters, the hidden layer is processed using the ReLU activation function, and the output layer is the predicted value of the water quality treatment demand, and the output is the water quality treatment demand in the future period;

[0023] S23, model training and optimization: train the multilayer perceptron model based on the training set using the back propagation algorithm and gradient descent method, and optimize the model parameters through cross-validation;

[0024] S24, real-time prediction and application: input the real-time monitored water quality parameters and external environmental parameters into the trained multi-layer perceptron model to generate the predicted value of water quality treatment demand in the future period, and adjust the water plant operation strategy based on the model output, including the start and stop time of the water pump, the amount of water treatment agent added, etc.

[0025] Optionally, the S3 includes:

[0026] S31, construction of energy consumption optimization objective function: based on the water quality treatment demand prediction results output by the multi-layer perceptron model, combined with the water quality-energy consumption correlation model and the energy consumption characteristics of the water plant equipment, define the energy consumption optimization objective function;

[0027] S32, genetic algorithm initialization: the genetic algorithm is used as a global optimization algorithm to preliminarily optimize the scheduling scheme;

[0028] S33, crossover and mutation operations of genetic algorithm, generating new scheduling scheme individuals through crossover and mutation operations;

[0029] S34: local search of the particle swarm optimization algorithm, based on the individual scheduling solutions obtained by the genetic algorithm, further local search is performed using the particle swarm optimization algorithm to improve the accuracy of the scheduling solution;

[0030] S35, generation of optimal energy consumption scheduling plan: through the combination of genetic algorithm and particle swarm optimization algorithm, the adaptive energy consumption optimization scheduling model generates the optimal energy consumption scheduling plan, including the start and stop schedule of the water pump, the amount of water treatment agent added in different time periods, etc.

[0031] Optionally, the S4 includes:

[0032] S41, real-time data collection and monitoring: continuously monitor the operation status of the water plant through the sensor network, including water quality parameters and energy consumption parameters;

[0033] S42, automatic execution of the scheduling plan: automatically adjust the operating status of key equipment in the water plant according to the optimal scheduling plan generated by the adaptive energy consumption optimization scheduling model;

[0034] S43, real-time feedback mechanism, real-time monitoring of the operation status of the water plant, combined with water quality parameters and energy consumption parameters, continuous feedback on the start and stop status of the water pump, equipment load and dosage of the reagent, and obtain feedback data;

[0035] S44, adaptive scheduling strategy adjustment: based on feedback data, use adaptive control algorithm to dynamically adjust the scheduling plan;

[0036] S45, linkage control of energy consumption and water quality: by combining energy consumption parameters with water quality parameters, when the water quality changes significantly, the system adjusts the equipment operation according to the adaptive energy consumption optimization model.

[0037] Optionally, the S5 specifically includes:

[0038] S51, data storage and management, regularly storing water quality parameters and energy consumption parameters in the water treatment process in the big data platform;

[0039] S52, historical data extraction and cleaning: regularly extract historical data from the big data platform, including water quality parameters and energy consumption parameters in different time periods;

[0040] S53, cluster analysis: cluster analysis is performed on the extracted historical data to identify the periodic changes between water quality treatment and energy consumption;

[0041] S54, long-term trend analysis: Based on the results of cluster analysis, the time series analysis algorithm is used to make long-term trend predictions for water quality parameters and energy consumption parameters;

[0042] S55, model adjustment and optimization: regularly adjust the parameters in the adaptive energy consumption optimization scheduling model according to the results of cluster analysis and trend analysis.

[0043] Optionally, the S6 specifically includes:

[0044] S61, real-time monitoring of dynamic changes in water quality parameters: real-time monitoring of water quality parameters in the water plant through a sensor network, continuous analysis through a central control unit, detection of water quality changes, and setting thresholds to identify abnormal water quality conditions;

[0045] S62, emergency event identification: Combine real-time monitoring of water quality parameters with trend analysis of historical water quality parameters to quickly identify sudden water pollution events;

[0046] S63, calling a preset emergency strategy library: based on the identification result of the emergency event, calling a corresponding emergency strategy from a preset emergency strategy library.

[0047] Optionally, S6 further includes:

[0048] S64, automatically adjust the operating status of equipment: automatically adjust the operating status of water plant equipment according to emergency strategies;

[0049] S65, Energy consumption optimization and emergency response balance: During the emergency response process, based on the prediction results of the adaptive energy consumption optimization scheduling model, ensure that the water plant equipment has the optimal energy consumption during the emergency treatment period;

[0050] S66, status recovery after the event: After the emergency event is handled, the system automatically returns to normal operation mode, gradually reduces the water pump load and the amount of chemical dosage, and continuously monitors the current water quality and energy consumption status.

[0051] Optionally, the S7 includes:

[0052] S71, user water demand data collection: collect user water demand data in real time through sensors installed on user terminals, and transmit the water demand data to the central control unit of the water plant through the Internet of Things platform;

[0053] S72, user water quality feedback data collection: Through the user-side feedback mechanism, collect user feedback data on water quality, including satisfaction with water quality, odor and taste evaluation. The system is connected with community health monitoring data to collect health data related to drinking water quality;

[0054] S73, Data Fusion and Analysis: Combine users’ water demand data with water quality feedback data, use big data analysis technology to perform data fusion, and analyze the changing trends of users’ water use behavior and water quality demand;

[0055] S74, dynamically adjust water quality treatment standards: dynamically adjust the water quality treatment standards of the water plant according to the data analysis results;

[0056] S75, energy consumption scheduling strategy optimization: adjust the energy consumption scheduling strategy based on the user's water demand changes and health data.

[0057] The energy consumption optimization scheduling system for a single village water plant combined with big data analysis is used to implement the above-mentioned energy consumption optimization scheduling method for a single village water plant combined with big data analysis, and includes the following modules:

[0058] Water quality and energy consumption parameter collection module: collects water quality parameters and energy consumption parameters in real time through the sensor network installed at each processing node of the water plant;

[0059] Data storage and management module: store the collected water quality parameters and energy consumption parameters on the big data platform;

[0060] Dynamic water treatment demand prediction module: Based on historical water quality parameters, real-time water quality parameters and external environmental data, it uses big data analysis technology and multi-layer perceptron model to predict future water quality treatment needs and provide scheduling basis;

[0061] Adaptive energy consumption optimization scheduling module: Combining the water quality treatment demand prediction results with the water quality-energy consumption correlation model, using genetic algorithm and particle swarm optimization algorithm to build an adaptive energy consumption optimization scheduling model to generate the optimal energy consumption scheduling plan;

[0062] Real-time scheduling and feedback control module: According to the energy consumption scheduling plan, the operating status of the water plant equipment is adjusted in real time, and the scheduling strategy is adaptively adjusted based on the feedback data;

[0063] Water quality and energy consumption trend analysis module: Combine historical water quality parameters and energy consumption data, perform long-term trend analysis through cluster analysis and machine learning algorithms, and regularly optimize the adaptive energy consumption scheduling model to cope with periodic changes in water quality and energy consumption;

[0064] Emergency response module: Based on real-time monitoring of water quality parameters and trend analysis of historical data, it identifies sudden water pollution incidents, calls preset emergency strategies, and automatically adjusts the operating status of equipment;

[0065] User demand and water quality feedback module: collects user water demand data and water quality feedback data, combines it with health monitoring data, and dynamically adjusts water quality treatment standards and energy consumption scheduling strategies to ensure user water safety while optimizing water plant energy consumption.

[0066] Beneficial effects of the present invention:

[0067] The present invention, by combining big data analysis technology, the energy consumption optimization scheduling system of a single village water plant can accurately collect and monitor water quality parameters and energy consumption parameters in real time. Based on the dynamic water quality treatment demand prediction of the multi-layer perceptron model, the system can adjust the start and stop of water pumps, equipment load and agent delivery in real time according to the water quality changes, ensuring that the optimal energy consumption scheduling is achieved while meeting the water quality treatment requirements. In addition, the combination of genetic algorithm and particle swarm optimization algorithm realizes the global optimization of energy consumption scheduling scheme, ensures that the water plant equipment operates efficiently under different load conditions, reduces energy waste, and greatly improves the overall operation efficiency of the water plant.

[0068] The present invention, through real-time monitoring of water quality parameters, combined with trend analysis of historical data, can quickly identify sudden water pollution events (such as floods or excessive pollutants), and timely call the preset emergency strategy library to automatically adjust the equipment operation status to ensure the rapid improvement of water quality treatment capacity. It can not only ensure water quality safety in emergencies, but also avoid unnecessary energy waste through automatic balancing of the adaptive energy consumption scheduling model during the emergency treatment process, and ensure energy efficiency optimization in emergencies. This dual guarantee of rapid response and energy consumption optimization has greatly improved the reliability and safety of the water plant in responding to emergencies.

[0069] In the present invention, the system can dynamically collect users' water demand data and water quality feedback information, and combine with health monitoring data to flexibly adjust water quality treatment standards and energy consumption scheduling strategies. By predicting the trend of users' water demand, the system can foresee future peak and low-peak water use periods, optimize the start and stop of water pumps and equipment load in advance, and reduce energy waste. At the same time, the correlation analysis of user feedback data and water quality data ensures personalized adjustment of the water quality treatment process, improves user satisfaction, and realizes the organic combination of water quality safety and energy efficiency optimization, so that the water plant can maintain an efficient operation state for a long time and better meet the actual water needs of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0071] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;

[0072] Figure 2 Schematic diagram of a system flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0074] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0075] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0076] like Figure 1 As shown in the figure, the energy consumption optimization scheduling method of a single village water plant combined with big data analysis includes the following steps:

[0077] S1, water quality and energy consumption correlation parameter collection and modeling: through the sensor network installed at each processing node of the water plant, the water quality parameters and energy consumption parameters of the water plant are collected in real time. The water quality parameters include turbidity, dissolved oxygen, pH value, residual chlorine, total dissolved solids and heavy metal content. At the same time, the energy consumption parameters of the equipment are obtained. The energy consumption parameters include water pump power, power consumption and equipment operation status. The collected water quality parameters and energy consumption parameters are analyzed by multi-dimensional data fusion to generate a water quality-energy consumption correlation model for subsequent optimization scheduling;

[0078] S2, construction of dynamic water quality treatment demand prediction model: based on historical water quality parameter changes, real-time water quality parameters and seasonal external environmental parameters, a multi-layer perceptron model is constructed to predict the water quality treatment demand forecast value in the future period;

[0079] S3, construction of adaptive energy consumption optimization scheduling model: Based on the output results of the multi-layer perceptron model, combined with the water quality-energy consumption association model and the energy consumption characteristics of water plant equipment, the adaptive energy consumption optimization scheduling model is constructed using genetic algorithm and particle swarm optimization algorithm. By balancing the water quality treatment requirements and energy consumption, the optimal energy consumption scheduling plan is generated;

[0080] S4, real-time scheduling and feedback control: According to the scheduling plan generated by the adaptive energy consumption optimization scheduling model, the water pump start and stop, equipment load and water treatment agent dosage parameters are automatically adjusted. The equipment operation status is dynamically adjusted by combining real-time water quality parameters and energy consumption parameters. Through real-time monitoring feedback data, the scheduling strategy is adaptively adjusted to ensure the optimal energy consumption of the water plant when the water quality treatment demand changes;

[0081] S5, water quality and energy consumption historical parameter storage and trend analysis: the water quality parameters and energy consumption parameters of each water treatment process are stored on the big data platform, and long-term trend analysis is performed based on historical water quality parameters and energy consumption parameters. Cluster analysis and machine learning algorithms are used to identify periodic changes in water quality treatment and energy consumption, and the adaptive energy consumption optimization scheduling model is regularly adjusted to cope with long-term changes.

[0082] S6, emergency response to sudden water quality changes: based on real-time monitoring results of dynamic changes in water quality, combined with trend analysis of historical data, it can quickly identify sudden water pollution events (such as floods or other abnormal water quality changes), and automatically adjust the operating status of equipment through pre-set emergency strategies, optimize energy consumption, and ensure water quality safety;

[0083] S7, user water demand and water quality scheduling feedback: By collecting user water demand data and combining user water quality feedback data with health monitoring data, the water quality treatment standards and energy consumption scheduling strategies of the water plant are dynamically adjusted to ensure user water safety while optimizing water plant energy consumption.

[0084] S1 includes:

[0085] S11, installation and configuration of multi-source sensor network: install various types of sensors at different processing nodes of the water plant to collect water quality parameters and energy consumption parameters in real time. The installation locations of sensors include water source inlet, water purification unit, water storage tank and water outlet to ensure comprehensive monitoring of the entire water treatment process;

[0086] S12, data collection and transmission: the water quality parameters and energy consumption parameters collected by each sensor are transmitted to the central control unit through a wireless transmission network or wired transmission, and synchronized according to the transmission frequency of sensors at different nodes to ensure the consistency of all parameters in the same time period;

[0087] S13, data preprocessing: in the central control unit, the transmitted water quality parameters and energy consumption parameters are preprocessed, including outlier detection, noise filtering and missing data filling. Outlier detection uses statistical analysis methods to identify data points that deviate from the normal range. Noise filtering uses median filtering methods. Missing data are filled according to the interpolation algorithm of time series analysis.

[0088] S14, multi-dimensional data fusion analysis: The pre-processed water quality parameters and energy consumption parameters are subjected to multi-dimensional data fusion analysis, and the principal component analysis method is used to reduce the dimension of high-dimensional data and identify the correlation characteristics between water quality parameters and energy consumption parameters. The data fusion analysis is expressed as:

[0089] ;

[0090] in, is the principal component after dimensionality reduction, is the original matrix of water quality parameters and energy consumption parameters after preprocessing, is the weight matrix of the principal components;

[0091] By interpreting the results of principal component analysis, the key correlation between water quality changes and energy consumption is determined;

[0092] S15, Construction of water quality-energy consumption correlation model: Based on the results of multi-dimensional data fusion analysis, a water quality-energy consumption correlation model is constructed using a linear regression algorithm. The mathematical expression is:

[0093] ;

[0094] in, Indicates time The energy consumption of the equipment, Indicates time Water quality parameters (such as turbidity, dissolved oxygen, etc.), is a constant term, The regression coefficient of each water quality parameter is optimized by the least square method so that the model can accurately predict the change of energy consumption;

[0095] S16, model verification and optimization, use historical data to verify the water quality-energy consumption correlation model, compare the energy consumption predicted by the model with the actual collected energy consumption parameters, and calculate the prediction error. The error calculation formula is:

[0096] ;

[0097] in, is the energy consumption value predicted by the model, is the actual energy consumption value, is the number of time points. By adjusting the model parameters, the mean square error is reduced to improve the prediction accuracy of the model;

[0098] Through data collection, data preprocessing, fusion analysis and correlation model construction of multi-source sensors, a comprehensive correlation between water quality changes and energy consumption is provided, providing basic data and accurate models for subsequent optimization scheduling.

[0099] S2 includes:

[0100] S21, data preprocessing: normalize historical water quality parameters, real-time water quality parameters and seasonal external environmental data, and divide them into training sets and test sets;

[0101] After normalization, ensure that data of different dimensions have the same numerical range to avoid the impact of data scale differences on the results during model training. Then divide the normalized data into training set and test set, usually in a ratio of 8:2;

[0102] S22, construction of multilayer perceptron model: based on the preprocessed data, a multilayer perceptron model including an input layer, a hidden layer and an output layer is constructed. The input layer receives the processed water quality parameters and external environmental parameters, the hidden layer is processed using the ReLU activation function, and the output layer is the predicted value of the water quality treatment demand, and the output is the water quality treatment demand in the future period;

[0103] S23, model training and optimization: Based on the training set, the multi-layer perceptron model is trained using the back propagation algorithm and gradient descent method, and the model parameters are optimized through cross-validation to ensure that the model achieves high prediction accuracy on the test set;

[0104] S24, real-time prediction and application: input the real-time monitored water quality parameters and external environmental parameters into the trained multi-layer perceptron model to generate the predicted value of water quality treatment demand in the future period, and adjust the water plant operation strategy based on the model output, including the start and stop time of the water pump, the amount of water treatment agent added, etc.;

[0105] Through data preprocessing, construction, training and optimization of multi-layer perceptron models, and real-time prediction applications, big data analysis technology is used to accurately predict water quality treatment needs, thereby supporting dynamic optimization and scheduling of water plant operation strategies and achieving energy consumption optimization.

[0106] S3 includes:

[0107] S31, construction of energy consumption optimization objective function: Based on the water quality treatment demand prediction results output by the multi-layer perceptron model, combined with the water quality-energy consumption association model and the energy consumption characteristics of the water plant equipment, the energy consumption optimization objective function is defined. The core of the objective function is to balance the water quality treatment demand and energy consumption, which is expressed as:

[0108] ;

[0109] in, Indicates time Energy consumption, The deviation between the predicted water quality treatment demand and the actual demand, It is a trade-off coefficient used to balance the error between energy consumption and water quality treatment requirements. The minimization process of the objective function is the scheduling optimization process. The ultimate goal is to minimize energy consumption while meeting water quality requirements.

[0110] S32, genetic algorithm initialization: The genetic algorithm is used as a global optimization algorithm to preliminarily optimize the scheduling scheme. The initialization process includes:

[0111] S321, population initialization: randomly generate multiple scheduling strategies, each individual in the population represents a different water pump start-stop plan and water treatment agent placement plan;

[0112] S322, fitness function calculation: the fitness value of each individual is calculated according to the energy consumption optimization objective function. The lower the fitness value, the better the scheduling scheme.

[0113] S323, selection operation: select individuals with better performance according to fitness values ​​to enter the next round of operation;

[0114] S33, the crossover and mutation operation of the genetic algorithm, generates new scheduling scheme individuals through crossover and mutation operations, including:

[0115] Crossover operation: select two individuals (scheduling schemes) for crossover, exchange some of their genes (such as water pump start and stop time or drug dosage) to generate new individuals, and enhance the diversity of solutions;

[0116] Mutation operation: Randomly mutate the genes of some individuals, such as changing the state of the water pump or the amount of medicine at a certain moment, to avoid falling into the local optimum;

[0117] S34: local search of the particle swarm optimization algorithm. Based on the individual scheduling solutions obtained by the genetic algorithm, the particle swarm optimization algorithm is used to further perform local search to improve the accuracy of the scheduling solution, specifically including:

[0118] S341, particle position update: Each particle represents a scheduling strategy, and the particle's speed and position are updated according to the following formula:

[0119] ;

[0120] ;

[0121] in, For particles At the moment speed, For particles location, For particles The individual best position of is the global best position, is the inertia weight, is the learning factor, is a random number. Through continuous iteration, the particle gradually approaches the global optimal solution.

[0122] S35, optimal energy consumption scheduling scheme generation: through the combination of genetic algorithm and particle swarm optimization algorithm, the adaptive energy consumption optimization scheduling model generates the optimal energy consumption scheduling scheme, including the start and stop schedule of the water pump, the amount of water treatment chemicals added in different time periods, etc., to ensure that the energy consumption is minimized while meeting the water quality requirements;

[0123] By combining genetic algorithm and particle swarm optimization algorithm, an adaptive energy consumption optimization scheduling model was constructed, which achieved a dynamic balance between water quality treatment demand and energy consumption, and generated the optimal scheduling plan to improve the energy efficiency of the water plant.

[0124] S4 includes:

[0125] S41, real-time data collection and monitoring: continuously monitor the operation status of the water plant through the sensor network, including water quality parameters and energy consumption parameters;

[0126] S42, automatic execution of the dispatching plan: According to the optimal dispatching plan generated by the adaptive energy consumption optimization dispatching model, the operating status of the key equipment of the water plant is automatically adjusted, including:

[0127] (1) Pump start and stop control: Control the start and stop of the pump according to the start and stop schedule in the scheduling plan to ensure that the pump operates with the lowest energy consumption while meeting the water supply demand;

[0128] (2) Equipment load adjustment: Dynamically adjust the load of water pumps and other equipment according to real-time water quality treatment requirements to avoid equipment overload or no-load operation;

[0129] (3) Water treatment agent delivery: Automatically adjust the delivery amount of water treatment agents (such as disinfectants, flocculants, etc.) based on the real-time monitoring results of water quality parameters to ensure the accuracy and effectiveness of delivery under different water quality conditions;

[0130] S43, real-time feedback mechanism, real-time monitoring of the water plant's operating status, combined with water quality parameters and energy consumption parameters, continuous feedback on the pump start and stop status, equipment load and dosage of chemicals, and obtain feedback data, which includes:

[0131] (1) Equipment operating status data: operating frequency, operating time, number of starts, etc. of the water pump;

[0132] (2) Energy consumption parameters: power consumption and actual energy consumption of the equipment;

[0133] (3) Water quality parameters: water quality data of purified water to ensure that water quality standards are met;

[0134] S44, adaptive scheduling strategy adjustment: Based on feedback data, the scheduling plan is dynamically adjusted using an adaptive control algorithm to ensure that the energy consumption optimization plan is automatically adjusted when the water quality treatment demand changes. The adaptive scheduling control is expressed as:

[0135] ;

[0136] in, for, for, , , They are proportional, integral and differential coefficients respectively. Through the PID control mechanism, the operating parameters of the equipment are adjusted according to the error to achieve adaptive scheduling;

[0137] S45, linkage control of energy consumption and water quality: By combining energy consumption parameters with water quality parameters, when water quality changes significantly (such as sudden pollution or water quality deterioration), the system quickly adjusts equipment operation according to the adaptive energy consumption optimization model to ensure that water quality treatment capacity is improved without significantly increasing energy consumption;

[0138] Through real-time data collection, automatic execution of scheduling plans, feedback control and adaptive adjustment, the water plant achieves optimal energy consumption control when water quality treatment needs change, ensuring that the system flexibly responds to environmental changes while maintaining efficient control of energy consumption.

[0139] S5 specifically includes:

[0140] S51, data storage and management, the water quality parameters and energy consumption parameters in the water treatment process are regularly stored in the big data platform. The water quality parameters include turbidity, dissolved oxygen, pH value, residual chlorine, total dissolved solids and heavy metal content. The energy consumption parameters include water pump power, power consumption and equipment operation status. The stored data are marked according to the timestamp to ensure the traceability of the data. The data compression algorithm is applied during the storage process to optimize the storage space;

[0141] S52, historical data extraction and cleaning: Historical data are regularly extracted from the big data platform, including water quality parameters and energy consumption parameters in different time periods. The extracted historical data undergoes a data cleaning process to remove abnormal data points and use time series interpolation to fill in missing data. The cleaned data can serve as the basis for subsequent trend analysis and model optimization;

[0142] S53, cluster analysis: cluster analysis is performed on the extracted historical data to identify the periodic changes between water quality treatment and energy consumption. The clustering algorithm used is the K-means algorithm. The steps are as follows:

[0143] S531, feature extraction: extract key features based on historical water quality parameters and energy consumption parameters, such as pump operation time, daily average power consumption, water quality fluctuation range, etc.;

[0144] S532, K-means algorithm: The extracted features are input into the K-means clustering algorithm, and clustering is performed by minimizing the following objective function:

[0145] ;

[0146] in, is the number of clusters, is the data point, For the The algorithm gradually divides the historical data into multiple clusters with similar characteristics through multiple rounds of iterations, and identifies the periodic change law between water quality and energy consumption in different time periods;

[0147] S54, long-term trend analysis: Based on the results of cluster analysis, the time series analysis algorithm is used to make long-term trend predictions for water quality parameters and energy consumption parameters;

[0148] S55, Model Adjustment and Optimization: Based on the results of cluster analysis and trend analysis, regularly adjust the parameters in the adaptive energy consumption optimization scheduling model, and use machine learning algorithms to continuously optimize the adaptive energy consumption optimization scheduling model to ensure that it can dynamically adapt to new conditions when water quality parameters and energy consumption parameters undergo long-term changes. The optimization process includes retraining the scheduling model and adjusting the optimization objectives and parameter weights based on the latest historical data;

[0149] Through the storage, cluster analysis, long-term trend analysis and model optimization of water quality and energy consumption parameters, accurate identification and prediction of periodic changes in water quality and energy consumption are achieved, ensuring that the adaptive energy consumption optimization scheduling model can dynamically adapt to long-term changing trends and improve the operating efficiency and energy-saving effects of the water plant.

[0150] S6 specifically includes:

[0151] S61, real-time monitoring of dynamic changes in water quality parameters: real-time monitoring of water quality parameters in the water plant through a sensor network, continuous analysis through a central control unit, detection of significant changes in water quality, and setting thresholds to identify abnormal water quality conditions. The thresholds include comparisons with historical water quality parameters and industry standards to ensure monitoring accuracy;

[0152] S62, emergency event identification: Combine the real-time monitoring of water quality parameters with the trend analysis of historical water quality parameters to quickly identify sudden water pollution events, such as floods, sediment intrusion or other abnormal water quality changes. The identification process of abnormal events is completed through the anomaly detection algorithm, which is expressed as:

[0153] ;

[0154] in, For the moment The outliers of The water quality parameters currently monitored are: is the mean of historical data, is the standard deviation, when When the preset threshold is exceeded, the system identifies it as a sudden water quality incident and triggers the emergency response process;

[0155] S63, calling a preset emergency strategy library: Based on the identification result of the emergency event, calling a corresponding emergency strategy from a preset emergency strategy library, the emergency strategy library contains response strategies for different types of emergencies, such as:

[0156] (1) When floods invade: immediately increase the amount of water treatment chemicals added to enhance filtration and purification efforts;

[0157] (2) When the heavy metal content exceeds the standard: activate special chemical treatment processes and use adsorption materials or special agents to remove harmful metal elements in the water.

[0158] The S6 also includes:

[0159] S64, automatic adjustment of equipment operating status: Automatically adjust the operating status of water plant equipment according to emergency strategies, including:

[0160] Adjust the water pump operation mode: change the start and stop time of the water pump or increase the pumping frequency to ensure the rapid treatment of contaminated water sources;

[0161] Adjustment of reagent dosage: Increase or decrease the dosage of water purification reagents in real time to ensure that water quality returns to a safe range as soon as possible;

[0162] Water treatment equipment load adjustment: adjust the load of water treatment equipment according to real-time water quality parameters and event severity to avoid equipment overload or overload operation;

[0163] S65, energy consumption optimization and emergency response balance: During the emergency response process, based on the prediction results of the adaptive energy consumption optimization scheduling model, the water plant equipment is ensured to have the optimal energy consumption during the emergency treatment period. By setting the energy consumption limit in the emergency strategy library, the system will automatically balance the effectiveness of emergency treatment and energy consumption to avoid unnecessary energy waste;

[0164] S66, status recovery after the event: After the emergency event is handled, the system automatically returns to normal operation mode, gradually reduces the water pump load and the amount of chemical injection, and continuously monitors the current water quality and energy consumption status to ensure that the water quality has returned to a safe level;

[0165] Through real-time monitoring, rapid identification of sudden water quality events, calling into preset emergency strategies and automatic adjustment of equipment, the water quality safety of the water plant is ensured during emergencies, while energy consumption is optimized, achieving a dynamic balance between emergency handling and energy consumption optimization.

[0166] S7 includes:

[0167] S71, user water demand data collection: through sensors installed on user terminals (such as smart water meters and smart home systems), users’ water demand data is collected in real time. The water demand data includes the user’s daily water consumption, peak water consumption period, and water consumption change trend. The water demand data is transmitted to the central control unit of the water plant through the Internet of Things platform for dynamic monitoring and analysis;

[0168] S72, user water quality feedback data collection: Through the user-side feedback mechanism (such as smart terminals or mobile applications), collect user feedback data on water quality. The feedback data includes satisfaction with water quality, odor and taste evaluation. The system is connected with community health monitoring data (such as hospitals and health management systems) to collect health data related to drinking water quality, such as users' health status after drinking water, allergic reactions, etc.;

[0169] S73, Data Fusion and Analysis: Combine the user’s water demand data with the water quality feedback data, use big data analysis technology to perform data fusion, and analyze the changing trends of user water use behavior and water quality demand. The analysis process includes:

[0170] Water demand analysis: Based on historical water demand data, use the pre-trained time series analysis model to predict users' future water demand trends;

[0171] Water quality feedback analysis: Based on user feedback data and combined with water quality parameters (such as turbidity, residual chlorine, etc.) monitored in real time by the water plant, a correlation model between feedback evaluation and water quality parameters is established to analyze the relationship between user subjective feelings and water quality changes;

[0172] S74, Dynamically adjust water quality treatment standards: According to the data analysis results, dynamically adjust the water quality treatment standards of the water plant, and automatically adjust the water treatment process for the dissatisfied parts of user feedback, such as improving the accuracy of water treatment agent delivery or increasing the filtration frequency. The adjustment strategy is optimized through the following formula:

[0173] ;

[0174] in, For the moment Water treatment standards, As the basic water quality treatment standard, is the weight coefficient, The impact factor of user feedback;

[0175] S75, energy consumption scheduling strategy optimization: Combine the user's water demand changes and health data to adjust the energy consumption scheduling strategy, use the adaptive energy consumption optimization scheduling model, balance the relationship between the water quality treatment standard and energy consumption of the water plant, and ensure that while meeting the user's water demand, energy consumption is minimized as much as possible. For example, during the user's water use peak period, the operating frequency of the water pump is increased, and during the off-peak period, the equipment load is reduced to save energy.

[0176] Through the real-time collection, analysis and integration of user water demand and water quality feedback data, the water quality treatment standards and energy consumption scheduling strategies of the water plant are dynamically adjusted to ensure the safety of user water use while optimizing the energy consumption management of the water plant.

[0177] like Figure 2 As shown, the energy consumption optimization scheduling system for a single-village water plant combined with big data analysis is used to implement the above-mentioned energy consumption optimization scheduling method for a single-village water plant combined with big data analysis, and includes the following modules:

[0178] Water quality and energy consumption parameter collection module: collects water quality parameters and energy consumption parameters in real time through the sensor network installed at each processing node of the water plant;

[0179] Data storage and management module: store the collected water quality parameters and energy consumption parameters on the big data platform;

[0180] Dynamic water treatment demand prediction module: Based on historical water quality parameters, real-time water quality parameters and external environmental data, it uses big data analysis technology and multi-layer perceptron model to predict future water quality treatment needs and provide scheduling basis;

[0181] Adaptive energy consumption optimization scheduling module: Combining the water quality treatment demand prediction results with the water quality-energy consumption correlation model, using genetic algorithm and particle swarm optimization algorithm to build an adaptive energy consumption optimization scheduling model to generate the optimal energy consumption scheduling plan;

[0182] Real-time scheduling and feedback control module: According to the energy consumption scheduling plan, the operating status of the water plant equipment is adjusted in real time, and the scheduling strategy is adaptively adjusted based on the feedback data;

[0183] Water quality and energy consumption trend analysis module: Combine historical water quality parameters and energy consumption data, perform long-term trend analysis through cluster analysis and machine learning algorithms, and regularly optimize the adaptive energy consumption scheduling model to cope with periodic changes in water quality and energy consumption;

[0184] Emergency response module: Based on real-time monitoring of water quality parameters and trend analysis of historical data, it identifies sudden water pollution incidents, calls preset emergency strategies, and automatically adjusts the operating status of equipment;

[0185] User demand and water quality feedback module: collects user water demand data and water quality feedback data, combines it with health monitoring data, and dynamically adjusts water quality treatment standards and energy consumption scheduling strategies to ensure user water safety while optimizing water plant energy consumption.

[0186] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0187] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing energy consumption of a single village water plant combined with big data analysis, characterized in that: The following steps are involved: S1, water quality and energy consumption correlation parameter collection and modeling: through the sensor network installed at each processing node of the water plant, the water quality parameters and energy consumption parameters of the water plant are collected in real time. The water quality parameters include turbidity, dissolved oxygen, pH value, residual chlorine, total dissolved solids and heavy metal content. At the same time, the energy consumption parameters of the equipment are obtained. The energy consumption parameters include water pump power, power consumption and equipment operation status. The collected water quality parameters and energy consumption parameters are subjected to multi-dimensional data fusion analysis to generate a water quality-energy consumption correlation model; S2, construction of dynamic water quality treatment demand prediction model: based on historical water quality parameter changes, real-time water quality parameters and seasonal external environmental parameters, a multi-layer perceptron model is constructed to predict the water quality treatment demand forecast value in the future period; S3, construction of adaptive energy consumption optimization scheduling model: Based on the output results of the multi-layer perceptron model, combined with the water quality-energy consumption association model and the energy consumption characteristics of water plant equipment, the adaptive energy consumption optimization scheduling model is constructed using genetic algorithm and particle swarm optimization algorithm. By balancing the water quality treatment requirements and energy consumption, the optimal energy consumption scheduling plan is generated; S4, real-time scheduling and feedback control: According to the scheduling plan generated by the adaptive energy consumption optimization scheduling model, the water pump start and stop, equipment load and water treatment agent dosage parameters are automatically adjusted. The equipment operation status is dynamically adjusted in combination with real-time water quality parameters and energy consumption parameters. The scheduling strategy is adaptively adjusted through real-time monitoring feedback data. S5, water quality and energy consumption historical parameter storage and trend analysis: the water quality parameters and energy consumption parameters of each water treatment process are stored on the big data platform, and long-term trend analysis is performed based on historical water quality parameters and energy consumption parameters. Cluster analysis and machine learning algorithms are used to identify periodic changes in water quality treatment and energy consumption, and the adaptive energy consumption optimization scheduling model is regularly adjusted to cope with long-term changes. S6, emergency response to sudden water quality changes: Based on the real-time monitoring results of dynamic changes in water quality and combined with trend analysis of historical data, it identifies sudden water pollution incidents and automatically adjusts the operating status of the equipment through emergency strategies set in advance; S7, user water demand and water quality scheduling feedback: By collecting users' water demand data and combining users' water quality feedback data with health monitoring data, the water quality treatment standards and energy consumption scheduling strategies of the water plant are dynamically adjusted.

2. The method for optimizing energy consumption of a single-village water plant combined with big data analysis according to claim 1 is characterized in that: The S1 includes: S11, Installation and configuration of multi-source sensor network: Install various types of sensors at different processing nodes of the water plant to collect water quality parameters and energy consumption parameters in real time; S12, data collection and transmission: the water quality parameters and energy consumption parameters collected by each sensor are transmitted to the central control unit via a wireless transmission network or wired transmission; S13, data preprocessing: in the central control unit, the transmitted water quality parameters and energy consumption parameters are preprocessed, including outlier detection, noise filtering and missing data filling; S14, multi-dimensional data fusion analysis: multi-dimensional data fusion analysis is performed on the pre-processed water quality parameters and energy consumption parameters, and the principal component analysis method is used to reduce the dimension of high-dimensional data to identify the correlation characteristics between water quality parameters and energy consumption parameters; S15, construction of water quality-energy consumption correlation model: based on the results of multi-dimensional data fusion analysis, a water quality-energy consumption correlation model was constructed using a linear regression algorithm; S16, model verification and optimization, uses historical data to verify the water quality-energy consumption correlation model, compares the energy consumption predicted by the model with the actual collected energy consumption parameters, and calculates the prediction error.

3. The method for optimizing energy consumption of a single-village water plant combined with big data analysis according to claim 2 is characterized in that: The S2 includes: S21, data preprocessing: normalize historical water quality parameters, real-time water quality parameters and seasonal external environmental data, and divide them into training sets and test sets; S22, Construction of Multilayer Perceptron Model: Based on the preprocessed data, a multilayer perceptron model including input layer, hidden layer and output layer is constructed; S23, model training and optimization: train the multilayer perceptron model based on the training set using the back propagation algorithm and gradient descent method, and optimize the model parameters through cross-validation; S24, real-time prediction and application: input the real-time monitored water quality parameters and external environmental parameters into the trained multi-layer perceptron model to generate the predicted value of water quality treatment demand in the future period, and adjust the water plant operation strategy based on the model output.

4. The method for optimizing energy consumption of a single-village water plant combined with big data analysis according to claim 3 is characterized in that: The S3 includes: S31, construction of energy consumption optimization objective function: based on the water quality treatment demand prediction results output by the multi-layer perceptron model, combined with the water quality-energy consumption correlation model and the energy consumption characteristics of the water plant equipment, define the energy consumption optimization objective function; S32, genetic algorithm initialization: the genetic algorithm is used as a global optimization algorithm to preliminarily optimize the scheduling scheme; S33, crossover and mutation operations of genetic algorithm, generating new scheduling scheme individuals through crossover and mutation operations; S34: local search of the particle swarm optimization algorithm, based on the individual scheduling schemes obtained by the genetic algorithm, a particle swarm optimization algorithm is used to perform local search to improve the accuracy of the scheduling scheme; S35, optimal energy consumption scheduling scheme generation: through the combination of genetic algorithm and particle swarm optimization algorithm, the adaptive energy consumption optimization scheduling model generates the optimal energy consumption scheduling scheme.

5. The method for optimizing energy consumption of a single-village water plant combined with big data analysis according to claim 4 is characterized in that: The S4 includes: S41, real-time data collection and monitoring: continuously monitor the operation status of the water plant through the sensor network, including water quality parameters and energy consumption parameters; S42, automatic execution of the scheduling plan: automatically adjusting the operating status of the water plant equipment according to the optimal scheduling plan generated by the adaptive energy consumption optimization scheduling model; S43, real-time feedback mechanism, real-time monitoring of the operation status of the water plant, combined with water quality parameters and energy consumption parameters, continuous feedback on the start and stop status of the water pump, equipment load and dosage of the reagent, and obtain feedback data; S44, adaptive scheduling strategy adjustment: based on feedback data, use adaptive control algorithm to dynamically adjust the scheduling plan; S45, linkage control of energy consumption and water quality: by combining energy consumption parameters with water quality parameters, when the water quality changes significantly, the system adjusts the equipment operation according to the adaptive energy consumption optimization model.

6. The method for optimizing energy consumption of a single-village water plant combined with big data analysis according to claim 5 is characterized in that: The S5 specifically includes: S51, data storage and management, regularly storing water quality parameters and energy consumption parameters in the water treatment process in the big data platform; S52, historical data extraction and cleaning: regularly extract historical data from the big data platform, including water quality parameters and energy consumption parameters in different time periods; S53, cluster analysis: cluster analysis is performed on the extracted historical data to identify the periodic changes between water quality treatment and energy consumption; S54, long-term trend analysis: Based on the results of cluster analysis, the time series analysis algorithm is used to make long-term trend predictions for water quality parameters and energy consumption parameters; S55, model adjustment and optimization: regularly adjust the parameters in the adaptive energy consumption optimization scheduling model according to the results of cluster analysis and trend analysis.

7. The method for optimizing energy consumption of a single village water plant combined with big data analysis according to claim 6 is characterized in that: The S6 specifically includes: S61, real-time monitoring of dynamic changes in water quality parameters: real-time monitoring of water quality parameters in the water plant through a sensor network, continuous analysis through a central control unit, detection of water quality changes, and setting thresholds to identify abnormal water quality conditions; S62, emergency event identification: combining real-time monitoring of water quality parameters with trend analysis of historical water quality parameters to identify sudden water pollution events; S63, calling a preset emergency strategy library: based on the identification result of the emergency event, calling a corresponding emergency strategy from a preset emergency strategy library.

8. The method for optimizing energy consumption of a single-village water plant combined with big data analysis according to claim 7 is characterized in that: The S6 further includes: S64, automatically adjust the operating status of equipment: automatically adjust the operating status of water plant equipment according to emergency strategies; S65, Balance between energy consumption optimization and emergency response: During the emergency response process, based on the prediction results of the adaptive energy consumption optimization scheduling model, ensure that the water plant equipment consumes the optimal amount of energy during the emergency treatment; S66, status recovery after the event: After the emergency event is handled, the system automatically returns to normal operation mode, gradually reduces the water pump load and the amount of chemical dosage, and continuously monitors the current water quality and energy consumption status.

9. The method for optimizing energy consumption of a single village water plant combined with big data analysis according to claim 8 is characterized in that: The S7 includes: S71, user water demand data collection: collect user water demand data in real time through sensors installed on user terminals, and transmit the water demand data to the central control unit of the water plant through the Internet of Things platform; S72, user water quality feedback data collection: Through the user-side feedback mechanism, collect user feedback data on water quality, including satisfaction with water quality, odor and taste evaluation. The system is connected with community health monitoring data to collect health data related to drinking water quality; S73, Data Fusion and Analysis: Combine users’ water demand data with water quality feedback data, use big data analysis technology to perform data fusion, and analyze the changing trends of users’ water use behavior and water quality demand; S74, dynamically adjust water quality treatment standards: dynamically adjust the water quality treatment standards of the water plant according to the data analysis results; S75, energy consumption scheduling strategy optimization: adjust the energy consumption scheduling strategy based on the user's water demand changes and health data.

10. A single-village water plant energy consumption optimization scheduling system combined with big data analysis, used to implement the single-village water plant energy consumption optimization scheduling method combined with big data analysis as described in any one of claims 1 to 9, characterized in that: Includes the following modules: Water quality and energy consumption parameter collection module: collects water quality parameters and energy consumption parameters in real time through the sensor network installed at each processing node of the water plant; Data storage and management module: store the collected water quality parameters and energy consumption parameters on the big data platform; Dynamic water quality treatment demand prediction module: Based on historical water quality parameters, real-time water quality parameters and external environmental data, it uses big data analysis technology and multi-layer perceptron model to predict future water quality treatment needs and provide scheduling basis; Adaptive energy consumption optimization scheduling module: Combining the water quality treatment demand prediction results with the water quality-energy consumption correlation model, using genetic algorithm and particle swarm optimization algorithm to build an adaptive energy consumption optimization scheduling model to generate the optimal energy consumption scheduling plan; Real-time scheduling and feedback control module: According to the energy consumption scheduling plan, the operating status of the water plant equipment is adjusted in real time, and the scheduling strategy is adaptively adjusted based on the feedback data; Water quality and energy consumption trend analysis module: Combine historical water quality parameters and energy consumption data, perform long-term trend analysis through cluster analysis and machine learning algorithms, and regularly optimize the adaptive energy consumption scheduling model to cope with periodic changes in water quality and energy consumption; Emergency response module: Based on real-time monitoring of water quality parameters and trend analysis of historical data, it identifies sudden water pollution incidents, calls preset emergency strategies, and automatically adjusts the operating status of equipment; User demand and water quality feedback module: collects user water demand data and water quality feedback data, combines it with health monitoring data, and dynamically adjusts water quality treatment standards and energy consumption scheduling strategies to ensure user water safety while optimizing water plant energy consumption.

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